{"as_of":"2026-08-12T14:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:41a5d36475241d3ff687bbcc2ab4d79def8a8c6e74cf67f45e20c14899f8c1a4","coverage":[{"denominator":260,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T13:41:08.279168Z","state":"measured"},{"denominator":101,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":101,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T12:50:23.886751Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2509.00400","snapshot_observed_at":"2026-08-04T12:50:23.886751Z","title":"Deep learning for personalized binaural audio reproduction,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.01891","last_updated":"2026-05-31T13:26:11Z","snapshot_observed_at":"2026-08-04T12:50:18.502575Z","submitted_at":"2025-10-02T10:59:21Z","title":"HRTFformer: A Spatially-Aware Transformer for Individual HRTF Upsampling in Immersive Audio Rendering","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-04T12:50:23.886751Z"},"links":{"cited_paper":"/paper/2509.00400","citing_paper":"/paper/2510.01891"},"observation_digest":"sha256:a798c4798b310d494b16c786a7d8cddc1f2900f4a26804a49bb4fd06ddd03eb6","observation_id":"03fbbee8-1503-498d-8ff5-5e08afb4d50a","resolution":{"observed_at":"2026-08-04T12:50:23.886751Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2509.00400/citation-record","integrity":"/paper/2509.00400/integrity","json":"/paper/2509.00400/citation-record.json","paper":"/paper/2509.00400"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:56.017162Z","title":null,"venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:56.017162Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:829b316a86b02911c00f0423e71017e9ef8c258a382a2f314a7616183eaa08a3","observation_id":"3f7b4fdc-e0be-42ed-b2c6-48342c49c00e","resolution":{"observed_at":"2026-08-05T13:40:56.017162Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:56.107667Z","title":"Blauert, Spatial hearing: the psychophysics of human sound localization","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:56.107667Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:319387f3d3c9a975150cae37036beb3e57d6ddf1f843984242c23d04bf8a460f","observation_id":"54a7a235-846f-442f-92e5-37dd0df0dca1","resolution":{"observed_at":"2026-08-05T13:40:56.107667Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:56.219733Z","title":"Spatial sound- scapes and virtual worlds: Challenges and opportuni- ties,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:56.219733Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:f9d453892e02f4a1fd9a7bb221da0d4bb801e6a7fe12a3ec364be901c11cc02c","observation_id":"b3e69067-64f3-4786-817b-1a5250716463","resolution":{"observed_at":"2026-08-05T13:40:56.219733Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:56.363803Z","title":"Spatial sound-history, principle, progress and challenge,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:56.363803Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:2d8ce53b2974bbc2cf8f5a2166e061a7209b5fea6e88aff90289d373d2a91154","observation_id":"145f0aae-0342-4a59-8912-2e30fea183d9","resolution":{"observed_at":"2026-08-05T13:40:56.363803Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:56.474921Z","title":"A computationally-efficient and perceptually-plausible al- gorithm for binaural room impulse response simula- tion,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:56.474921Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:7c2e380c9efb026e7e433acc85dbac4ffd031d49048b99399a31a255176ff89d","observation_id":"b7f2ed9d-74f2-4ef9-9052-819169811ece","resolution":{"observed_at":"2026-08-05T13:40:56.474921Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:56.563665Z","title":"Low-order filter approxima- tion of diffraction for virtual acoustics,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:56.563665Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:dc8b7001e03c6cdd5259b56766dcc899cc02e348445ecd74366ab451c785009b","observation_id":"397fd985-f405-47c7-bbcb-a366f369912b","resolution":{"observed_at":"2026-08-05T13:40:56.563665Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:56.664858Z","title":"A filter representation of diffraction at infinite and finite wedges,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:56.664858Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:06291ac2247260b84a1f96565c379efccc0bf2bd753a6790651504c9ab53b56e","observation_id":"3f6b27b2-667a-4eab-965a-b572c020e1f4","resolution":{"observed_at":"2026-08-05T13:40:56.664858Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:56.778243Z","title":"A universal filter approx- imation of edge diffraction for geometrical acoustics,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:56.778243Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:d60456afa788fa5976b972a8eaad166d3f2cebcf8b3247b7d3fb8228ba02e657","observation_id":"b9722643-e10d-49e9-b2e1-faca274b412e","resolution":{"observed_at":"2026-08-05T13:40:56.778243Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:56.896668Z","title":"Machine-learning-based estimation and rendering of scattering in virtual reality,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:56.896668Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:8e6e31b89eb1fa678ca086f607e05140affce92d02c945046b1a18dd9caed3bc","observation_id":"e45ae808-f644-4162-9208-23c24d959a7d","resolution":{"observed_at":"2026-08-05T13:40:56.896668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:57.010679Z","title":"Computationally-efficient simulation of late reverberation for inhomogeneous boundary conditions and coupled rooms,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:57.010679Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:acf3b4980b66527e0f0001f8e7f5b4e5fc15b6958a7a7f742be81aa651821d5b","observation_id":"851841c1-f77d-48ff-aa06-bf36e8524a0f","resolution":{"observed_at":"2026-08-05T13:40:57.010679Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:57.135166Z","title":"Integrating real-time room acoustics simulation into a cad modeling software to enhance the architectural design process,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:57.135166Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:6e1ad823ee49bdd8276f6a3bb5a64aa9667fc4fd36053210e6a73ab10dae7e69","observation_id":"ac7921a5-f993-4de7-af22-234ad8d9861d","resolution":{"observed_at":"2026-08-05T13:40:57.135166Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:57.242674Z","title":"A round robin on room acoustical simulation and auralization,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:57.242674Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:47d493fd545a5da6b0b89523501f6b527492c393231907458078b68cd740a16c","observation_id":"396560f6-1b5b-44ef-a217-e67ed0752b23","resolution":{"observed_at":"2026-08-05T13:40:57.242674Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:57.319172Z","title":"Overview of geomet- rical room acoustic modeling techniques,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:57.319172Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:72d7f8284d83b96da7822ed8bf4bd319c386fd8ec43ebca56a26bd73388bc99f","observation_id":"a2334623-6ecb-4268-821d-d347807b6f47","resolution":{"observed_at":"2026-08-05T13:40:57.319172Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:57.427620Z","title":"Interactive simulation and free-field auralization of acoustic space with the rtSOFE,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:57.427620Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:3b4aa01a6a30c16d442eb477b37f1a4fbc47d768d7731e27ff735ce1773ce209","observation_id":"0c8c8549-81e7-48be-b23a-ea0c3e79e607","resolution":{"observed_at":"2026-08-05T13:40:57.427620Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:57.556390Z","title":"Schr ¨oder, Physically based real-time auralization of interactive virtual environments","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:57.556390Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:635ea4b39120658b2f290c21054c2168b8c302abc468d1d8378cd95b73201b39","observation_id":"1daefc9a-f556-4a7b-ab58-b560fd0f567c","resolution":{"observed_at":"2026-08-05T13:40:57.556390Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:57.664752Z","title":"Efficient HRTF-based spatial audio for area and volumetric sources,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:57.664752Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:4a08ef3474832c4cba441ff41cca17ae5f34eac39cdf6171a533b94098df419f","observation_id":"51c206ff-a50f-4725-9e11-eca75eb6d7b6","resolution":{"observed_at":"2026-08-05T13:40:57.664752Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:57.864754Z","title":"Natural listening over head- phones in augmented reality using adaptive filtering techniques,","venue":null,"work_id":null,"year":1988},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:57.864754Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:be14f1adb61731192846766124287947bf694c978205cfcef2a80aa99e235a06","observation_id":"4171baab-bf45-477b-8400-b5f8e9dad31e","resolution":{"observed_at":"2026-08-05T13:40:57.864754Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:57.977113Z","title":"Larsson, A","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:57.977113Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:78378649c2f5b8e54322c3190a1d1b7df0a77b1c169077558dc2c67ced52e259","observation_id":"02bae440-3388-4975-a9c1-ab7577b599d7","resolution":{"observed_at":"2026-08-05T13:40:57.977113Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:58.075444Z","title":"Acoustic control by wave field synthesis,","venue":null,"work_id":null,"year":1993},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:58.075444Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:7ffeede25c027d1d9552c312db495bcce893bd47cfdd0aa68187eae8fe0f888d","observation_id":"3c3fe2ac-e9dc-4828-9adf-814d84f3f751","resolution":{"observed_at":"2026-08-05T13:40:58.075444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:58.215162Z","title":"Further investiga- tions of high-order ambisonics and wavefield synthesis for holophonic sound imaging,","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:58.215162Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:3940666e3fd6465617fd7b5b7a849660773bf5cc8672de2e775fe83531c4c568","observation_id":"b6a396ac-6669-440d-b1d1-b371f358986b","resolution":{"observed_at":"2026-08-05T13:40:58.215162Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:58.356971Z","title":"Rumsey, Spatial audio","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:58.356971Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:a2fc8b2f87e0d420f2001ef42a6739bd656b882cb1b80b13e547c87b7b5d77ce","observation_id":"727029af-2b28-49d1-9a62-04404e1e8423","resolution":{"observed_at":"2026-08-05T13:40:58.356971Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:58.447124Z","title":"Headphone simula- JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2025 18 tion of free-field listening. I: Stimulus synthesis,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:58.447124Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:e28a5590e159ea0d06eafef5e7c40403c9399ba8de7b194dd2e8117fb63d8a51","observation_id":"95c83919-83d5-4c33-93b6-fe015825df41","resolution":{"observed_at":"2026-08-05T13:40:58.447124Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:58.574786Z","title":"Sound localiza- tion by human listeners,","venue":null,"work_id":null,"year":1991},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:58.574786Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:db99def9361790ec8377eb3b7154038a68657f59401196eed3b334c8e29e14c7","observation_id":"314b9648-221c-41c3-b731-9c2bc632e976","resolution":{"observed_at":"2026-08-05T13:40:58.574786Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:58.745044Z","title":"Xie, Head-related transfer function and virtual au- ditory display","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:58.745044Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:8237f038fe56dfce5c841b92298523385d8d3ce610a0a9f2f6126ae06ed49737","observation_id":"f5073a11-3ba2-4dcc-9ab4-2e516001f95e","resolution":{"observed_at":"2026-08-05T13:40:58.745044Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:58.904759Z","title":"Fundamentals of binaural technology,","venue":null,"work_id":null,"year":1992},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:58.904759Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:9eb25bd467524ace22303670a26f6362dcb4dc75f28b17ffac08fe2b067c5b87","observation_id":"2e3ad2b9-7b8f-4674-8a7b-caac099d478d","resolution":{"observed_at":"2026-08-05T13:40:58.904759Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:59.064754Z","title":"Individual differences in external- ear transfer functions reduced by scaling in frequency,","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:59.064754Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:8f6d3adc1a4f313f8ef11b61ae4bc62d19fa4b0895a1bee684f238faf265af87","observation_id":"6189a048-c15b-458e-bee9-4af1de275eae","resolution":{"observed_at":"2026-08-05T13:40:59.064754Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:59.173891Z","title":"Localization using nonindividualized head- related transfer functions,","venue":null,"work_id":null,"year":1993},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:59.173891Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:090c2c785b69ddb8aefd01957a4fb67dde4ff696db93dc4e8f02cbf01db1f762","observation_id":"de54e899-f927-473b-acff-b2f08e51a56c","resolution":{"observed_at":"2026-08-05T13:40:59.173891Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:59.296791Z","title":"Binaural technique: Do we need individual recordings?","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:59.296791Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:be864c508419b21c6fdb8c29c34dddf64672a6e554bba6d1436cbcbc77c27769","observation_id":"42ce8378-f707-4854-b123-bd6a99af1efd","resolution":{"observed_at":"2026-08-05T13:40:59.296791Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:59.440944Z","title":"Personalized HRTF model- ing based on deep neural network using anthropometric measurements and images of the ear,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:59.440944Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:59cd9c291f3e9f1a0b22646f354b51a373fe550c93180aa2c7eb8b7464af9233","observation_id":"d67fafa5-3c02-4fb4-96a7-de5e59072814","resolution":{"observed_at":"2026-08-05T13:40:59.440944Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:59.644757Z","title":"Measurement of head-related transfer functions: A review,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:59.644757Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:6f6cb532fc7f7e548c1677d6ba618b4852c65b6d71ddda0edad0fbddb367b80e","observation_id":"aec51472-c9f0-4b70-88fb-e2b6bd7bd058","resolution":{"observed_at":"2026-08-05T13:40:59.644757Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:59.745595Z","title":"The CIPIC HRTF database,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:59.745595Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:29af275156caea95bd5b97a9df423ff830d08d0e9732135c5a591571ff913228","observation_id":"2db1281e-f606-483b-ac40-ea69f6d46c7d","resolution":{"observed_at":"2026-08-05T13:40:59.745595Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:40:59.891862Z","title":"Boundary element method calculation of individual head-related transfer function. I. Rigid model calculation,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T13:40:59.891862Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:db4ea40b582eb71dda9d1b2a534f9bdf0dc5926b2fa2b45a0e6cff9e06a94193","observation_id":"77dd89b5-64bb-49ad-9f86-18894dd2e5ec","resolution":{"observed_at":"2026-08-05T13:40:59.891862Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:00.036043Z","title":"A cross-evaluated database of measured and simulated HRTFs including 3D head meshes, anthropometric features, and headphone im- pulse responses,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:00.036043Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:ce8b86a2e7e8cc726a396a8c0a3898a085e3044df69abf16ab41023f8b3aaedd","observation_id":"8ef032c0-ac58-4c06-a4e4-bd7020441c54","resolution":{"observed_at":"2026-08-05T13:41:00.036043Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:00.146658Z","title":"Virtual sound source positioning using vector base amplitude panning,","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:00.146658Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:11ff16d708d33648bdcda170dbd01357508450fda2773c92eb58795800b9d073","observation_id":"43522786-1c55-4737-9778-1efcf5ba4050","resolution":{"observed_at":"2026-08-05T13:41:00.146658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:00.256718Z","title":"A new HRTF interpolation approach for nonlinear 3D audio systems,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:00.256718Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:0bf54a74781404eaed1e765bf8dceb677494b33d7047ef0f6123b8b8b0ccc01d","observation_id":"65df5a70-daa9-4e28-82c7-a15d38c57233","resolution":{"observed_at":"2026-08-05T13:41:00.256718Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:00.365538Z","title":"A model of head- related transfer functions based on principal compo- nents analysis and minimum-phase reconstruction,","venue":null,"work_id":null,"year":1992},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:00.365538Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:562ecb2e7469fbe918149d042d5e77fd3299651b70d2008a879e6f10ee13f0c5","observation_id":"3dfa21b4-cc3b-4ef2-9538-9bf0dd84b669","resolution":{"observed_at":"2026-08-05T13:41:00.365538Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:00.465140Z","title":"Implicit HRTF modeling using temporal convolutional networks,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:00.465140Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:c8d3a02fcb648010fbb90398589717a28391690ca0b7539d5dfe0de01fbfb715","observation_id":"96a30fa1-e44e-4a87-9a87-fffe3948b5d9","resolution":{"observed_at":"2026-08-05T13:41:00.465140Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:00.615293Z","title":"Neural synthesis of binaural speech from mono audio,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:00.615293Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:2e75ab71cc0a2ffade97c8e7a93c0b02309a65ae630fe1884058a44d0637863e","observation_id":"ff624e2e-d7e5-46c4-8606-909622efacb8","resolution":{"observed_at":"2026-08-05T13:41:00.615293Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:00.714756Z","title":"A machine learning tutorial for spatial auditory display using head-related transfer functions,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:00.714756Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:f2710b11c4fa2818684380a50392eaf53da96cbb4394900b94157d36758a1a94","observation_id":"008ea331-5799-4056-8117-99bfa66300a3","resolution":{"observed_at":"2026-08-05T13:41:00.714756Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:00.884754Z","title":"A review on head- related transfer function generation for spatial audio,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:00.884754Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:1f32796146d8d1dcfa86bcd1d3a5c9d2baee6983810f740865cd1c3d782f5704","observation_id":"fc61a14f-bbe2-4755-a980-41ea862418f4","resolution":{"observed_at":"2026-08-05T13:41:00.884754Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:00.993204Z","title":"A survey on machine learning techniques for head- related transfer function individualization,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:00.993204Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:3dab6fc0ac464b2a5bc211d68e6483a55c107e873a6f30cd36de023ad7b0c11d","observation_id":"6b799fb6-1e6c-4abb-b074-79bf5d4a146a","resolution":{"observed_at":"2026-08-05T13:41:00.993204Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:01.097344Z","title":"An overview of machine learning and other data- based methods for spatial audio capture, processing, and reproduction,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:01.097344Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:139877640a0fe3039fe80baf0e87cc1c12be7d31af3efbd0b28e3c69fd62fede","observation_id":"0d54eeea-3a0e-4b0b-b821-2a1582301a0f","resolution":{"observed_at":"2026-08-05T13:41:01.097344Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:01.238236Z","title":"Perceptually based head- related transfer function database optimization,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:01.238236Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:1344e86148fa181ba0c972302ee29e3da8999a71e15350d4398c33efd53fef13","observation_id":"e246fd18-77bc-42a2-b6cf-f9a4f1269f54","resolution":{"observed_at":"2026-08-05T13:41:01.238236Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:01.380846Z","title":"Estimation and modeling of pinna-related transfer functions,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:01.380846Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:b973d604bd41a91af0786e34eec9b98b9e7e0f17d2e41ee4bfbc0c803a3142f4","observation_id":"cdaa79dd-04e9-4afb-bd92-78b889d62bc2","resolution":{"observed_at":"2026-08-05T13:41:01.380846Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:01.467478Z","title":"Extracting the frequencies of the pinna spectral notches in measured head related impulse responses,","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:01.467478Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:7f9f77e1712884a8ec63860236665e18de5a7ea9dff46d6df8de7f8d4950b67a","observation_id":"be66b2ba-b7cb-4650-9171-d1ceed062625","resolution":{"observed_at":"2026-08-05T13:41:01.467478Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:01.604756Z","title":"A wide dataset of ear shapes and pinna-related transfer functions generated by random ear drawings,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:01.604756Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:bc0d167b17946a61427deaa89d4a4fb70e2731736f0262b46afba1913f394761","observation_id":"014fbb4c-abb7-4604-bfa1-fb0166a2a62c","resolution":{"observed_at":"2026-08-05T13:41:01.604756Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:01.777395Z","title":"Efficient real spherical harmonic representa- tion of head-related transfer functions,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:01.777395Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:2de68c761567ec231a49d3b6729d6c08dbf25cab6935bc7b34a0f8b81250225b","observation_id":"f3708bb7-ba5b-403e-9aa6-f40a4ad0d1a1","resolution":{"observed_at":"2026-08-05T13:41:01.777395Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:01.831579Z","title":"Autoencoding HRTFs for DNN based HRTF personalization using anthropometric features,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:01.831579Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:500d8ef595c8289c4ade2345c27c05be28e3ff0055af89833b117f7993919058","observation_id":"dbd730d2-29b2-4b5d-ab50-7980bdc48ff7","resolution":{"observed_at":"2026-08-05T13:41:01.831579Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:01.964765Z","title":"Autoencoders, unsupervised learning, and JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2025 19 deep architectures,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:01.964765Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:5d72f2a75c7477e9178d2db27305b992846971358f23390dc9fe0a5cc2f78091","observation_id":"8e185924-b109-4b7f-b701-e2f41a4fcbe9","resolution":{"observed_at":"2026-08-05T13:41:01.964765Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:02.064750Z","title":"HRTF personal- ization based on artificial neural network in individual virtual auditory space,","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:02.064750Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:18b5c04aa074f5db9ae40cad4a2ee51d3a4497b2b71e25ec341eef831c0a730b","observation_id":"35ea7585-ef66-4c8c-b4ab-4c3ce2d5eda7","resolution":{"observed_at":"2026-08-05T13:41:02.064750Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:02.170657Z","title":"Global HRTF personalization using anthropometric measures,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:02.170657Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:37ac34356b0358fbcf0e558a3a782b0558e1d76e0fbe1ba43905d0098d3790d0","observation_id":"a00603c3-55e2-4496-820b-e8750d03cc42","resolution":{"observed_at":"2026-08-05T13:41:02.170657Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:02.305454Z","title":"Modeling of individual HRTFs based on spatial principal com- ponent analysis,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:02.305454Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:6c5d65963ba51db07e30c0448c2e1819d8aed4d1099b09dc8aacc65da38989ab","observation_id":"72bc95c6-5049-4a49-ae0d-ba3a60a9bcf2","resolution":{"observed_at":"2026-08-05T13:41:02.305454Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:02.504754Z","title":"HRTF individualization using deep learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:02.504754Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:887c45e9e601e37d2762f2dff41a656454b349f7fdab762a78bf214c3fb96eef","observation_id":"77894aff-43b0-441c-ac0a-43509e73620f","resolution":{"observed_at":"2026-08-05T13:41:02.504754Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:02.575418Z","title":"An individualization approach for head-related transfer function in arbitrary directions based on deep learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:02.575418Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:6a6b54c6d5c5e642e4a184a8d162af8359c51da94057dd62c62351082ebdbf2a","observation_id":"0b601542-1fb9-4da4-8db1-f3717495ba18","resolution":{"observed_at":"2026-08-05T13:41:02.575418Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:02.704759Z","title":"Modelling individual head-related transfer function (HRTF) based on anthropometric parameters and generic HRTF amplitudes,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:02.704759Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:5678dccd6487679fba467d7066336276e25e21d36dfe1a052550c86c1a7c6cf2","observation_id":"7b3f1a1d-58c7-405a-b5c7-1504392cafe3","resolution":{"observed_at":"2026-08-05T13:41:02.704759Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:02.785526Z","title":"Towards HRTF personalization using denoising diffusion models,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:02.785526Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:9b71bd7875c075058da6115abac3f1b4caa176acfe9d433f1c9e093e398a9489","observation_id":"513805ae-77c7-4971-b904-f0be8700f9e5","resolution":{"observed_at":"2026-08-05T13:41:02.785526Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:02.929711Z","title":"A hybrid approach to struc- tural modeling of individualized HRTFs,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:02.929711Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:432019857bb55fe2092f77c100517333974204cc3cdcf5e9aff287038efc8f45","observation_id":"cc7431d5-7ccd-4be8-9cd3-e5f16b23a543","resolution":{"observed_at":"2026-08-05T13:41:02.929711Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:03.028311Z","title":"Magnitude modeling of personalized HRTF based on ear images and an- thropometric measurements,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:03.028311Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:082e0be81441aa5290a7ddd19567954a941f3888fbe8d3bf22cbdfd62498ff8d","observation_id":"101217c2-0253-42ef-8639-734b7afe0201","resolution":{"observed_at":"2026-08-05T13:41:03.028311Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:03.089810Z","title":"PRTFNet: HRTF individualization for accurate spectral cues using a compact prtf,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:03.089810Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:26e10b709c8df32d61cf9347cbe75cb1ef3773185a01d94b9e49e785c7c66254","observation_id":"66d586e7-f045-4862-8b74-51f3c5d391d7","resolution":{"observed_at":"2026-08-05T13:41:03.089810Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:03.184779Z","title":"A ma- chine learning approach to predicting personalized head related transfer functions and headphone equalization from video capture data,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:03.184779Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:678221b8b6ab003cc866a7d694c38da357694449ea5d976bc5ab60c14df9aa58","observation_id":"e435f39f-9720-470b-ae66-d20ab768a483","resolution":{"observed_at":"2026-08-05T13:41:03.184779Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:03.308124Z","title":"HRTF individualization based on anthropometric mea- surements extracted from 3D head meshes,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:03.308124Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:88766117c070d73527a521e9633976ec6131b794289480a8c1d2fe32bb91fd90","observation_id":"6af59537-4517-47ce-9bd6-d88661f74fcf","resolution":{"observed_at":"2026-08-05T13:41:03.308124Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:03.544875Z","title":"On the predictabil- ity of HRTFs from ear shapes using deep networks,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:03.544875Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:4073a922d25be3ebebb92df956d8b071b167401561908dbf1d31a558c41ea2a4","observation_id":"1495a75a-12ab-4939-a4a0-c6ca9680a715","resolution":{"observed_at":"2026-08-05T13:41:03.544875Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.14352","last_updated":"2025-02-05T02:00:26Z","snapshot_observed_at":"2026-08-09T00:06:49.449318Z","submitted_at":"2022-07-28T19:13:17Z","title":"Predicting Global HRTFs From Scanned Head Geometry Using Deep Learning and Compact Representations","version":2},"cited_work":{"arxiv_id":"2207.14352","doi":null,"metadata_source":"pith","pith_arxiv_id":"2207.14352","snapshot_observed_at":"2026-08-05T13:41:30.664077Z","title":"Predicting Global HRTFs From Scanned Head Geometry Using Deep Learning and Compact Representations","venue":"eess.AS","work_id":"7a4c8dd0-77f2-450e-9898-b1b45480eb2d","year":2022},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:03.716597Z"},"links":{"cited_paper":"/paper/2207.14352","citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:5c4fb3207eca3a8e4fd913abb1778d8c01f79f19a0aa088ad78d1e509d3b3bf1","observation_id":"04c0aeac-690b-4475-996f-fc76de84bbcf","resolution":{"observed_at":"2026-08-05T13:41:30.706892Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:03.826640Z","title":"Efficient prediction of individual head-related transfer functions based on 3D meshes,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:03.826640Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:1c5171dce493c93d68dea43e5dce45cb867f643615c1d42bb450e1ac747165f8","observation_id":"1083147a-bb2b-4db4-8980-983b6d21afdc","resolution":{"observed_at":"2026-08-05T13:41:03.826640Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:03.894745Z","title":"AudioEar: single-view ear reconstruction for personalized spatial audio,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:03.894745Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:56b8d01ed4255add8a46086094cb6f3c71b82b1da9a2307e9792bd8ba7941e07","observation_id":"55975618-9bd1-4c30-84e6-907bc4362304","resolution":{"observed_at":"2026-08-05T13:41:03.894745Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.16410","last_updated":"2024-10-29T08:24:59Z","snapshot_observed_at":"2026-08-11T05:43:19.395867Z","submitted_at":"2024-08-29T10:15:19Z","title":"Denoising of photogrammetric dummy head ear point clouds for individual Head-Related Transfer Functions computation","version":2},"cited_work":{"arxiv_id":"2408.16410","doi":null,"metadata_source":"pith","pith_arxiv_id":"2408.16410","snapshot_observed_at":"2026-08-05T13:41:30.553358Z","title":"Denoising of photogrammetric dummy head ear point clouds for individual Head-Related Transfer Functions computation","venue":"eess.AS","work_id":"3733ddd2-0f8d-4f6c-8318-9c3d6b35940e","year":2024},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:03.975104Z"},"links":{"cited_paper":"/paper/2408.16410","citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:fdcc7cb2d4b169732b7b25170e256a585a6c4e294042002d66318a06974fc8f1","observation_id":"c6efb96a-a33b-49d7-bc5e-65ad63bf83e7","resolution":{"observed_at":"2026-08-05T13:41:30.595191Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:04.055361Z","title":"HRTF estimation in the wild,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:04.055361Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:d2a87a54aa552397774a96fd585b099df9be3cf27639af16208a80a87e3d2c78","observation_id":"b9371105-78e8-4f7f-9ba3-2363469bad0d","resolution":{"observed_at":"2026-08-05T13:41:04.055361Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:04.118174Z","title":"HRTF estimation using a score- based prior,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:04.118174Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:df6f02eaed1333f60c5464d56e4eb1a16a790909e48412f43ac04709cc71fe53","observation_id":"63cb54dd-f6b1-4c05-adec-0ef6f108bd7d","resolution":{"observed_at":"2026-08-05T13:41:04.118174Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:04.184861Z","title":"Recovery of individual head-related transfer functions from a small set of measurements,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:04.184861Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:129020d114aca23476fcfc0658eaa44bccae9cdc1c33200d528feacc5f68ef16","observation_id":"090f783a-9104-4dec-be4f-5f9668581505","resolution":{"observed_at":"2026-08-05T13:41:04.184861Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:04.278233Z","title":"Head-related impulse response interpolation in virtual sound system,","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:04.278233Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:6d6d11177080aeebd1d4f3aad62855dec674e88d73e9af16b15525a544e6c0a8","observation_id":"7ea88518-508b-4870-84e6-aa9423c26af4","resolution":{"observed_at":"2026-08-05T13:41:04.278233Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:04.466766Z","title":"Head-related transfer function interpolation from spa- tially sparse measurements using autoencoder with source position conditioning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:04.466766Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:0803a46594cbb0b47caca7b6610f7f9567ccb92fdb5c9a746c34a723886c5d02","observation_id":"ad84c01e-e0fc-4a4e-838a-d940100ba3b5","resolution":{"observed_at":"2026-08-05T13:41:04.466766Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:04.574838Z","title":"Individualizing head-related transfer functions for bin- aural acoustic applications,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:04.574838Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:4254b3c2fc504b91a596ba687638c26679cabc0a6ba4623a2b9d07d40ebc2837","observation_id":"c557d0f5-3e8a-45ca-8a4b-16cd88269790","resolution":{"observed_at":"2026-08-05T13:41:04.574838Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:04.766837Z","title":"Spatial upsampling of sparse head related transfer functions-a VQ-V AE & Transformer based approach,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:04.766837Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:93578fa5f076bf0ab57c00bf83578f5228495bbdc011388455828d37b1084af4","observation_id":"d47e79c2-db84-4973-a797-7672fadd165b","resolution":{"observed_at":"2026-08-05T13:41:04.766837Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:04.923440Z","title":"Spatial group- ing as a method to improve personalized head-related transfer function prediction,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:04.923440Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:e457d74534ebdf08fee02e0bd759951017cc9de3334a35abc02f9324d0abea65","observation_id":"e64b6fc4-b4de-4e8b-9339-0d87e7873aea","resolution":{"observed_at":"2026-08-05T13:41:04.923440Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:05.041994Z","title":"Deep HRTF encoding & interpolation: Exploring spatial correlations using convolutional neural networks,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:05.041994Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:aa5ed9fd3bf2932ec98c68d0a3b2299789e9f2e75ce5dcaca9b500b2bf64f018","observation_id":"09434aae-9f04-46b5-9016-213f55da9e2b","resolution":{"observed_at":"2026-08-05T13:41:05.041994Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:05.105533Z","title":"Modeling individual head-related transfer functions from sparse measurements using a convolutional neural network,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:05.105533Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:3fbeb9003816dc085927995523dcfa5384f0d21cc6b3c84fadb7a2f8b922fcaa","observation_id":"1cb9df40-238e-4a32-914a-5577a5e23c33","resolution":{"observed_at":"2026-08-05T13:41:05.105533Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.08290","last_updated":"2023-09-15T10:11:37Z","snapshot_observed_at":"2026-08-11T23:40:43.601340Z","submitted_at":"2023-09-15T10:11:37Z","title":"Head-Related Transfer Function Interpolation with a Spherical CNN","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.08290","snapshot_observed_at":"2026-08-05T13:41:05.200710Z","title":"Head-related transfer function inter- polation with a spherical CNN,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:05.200710Z"},"links":{"cited_paper":"/paper/2309.08290","citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:2cc9f74a32c6a15457e6ca06c547c58101527dda61efa8fc311bad2732a52de9","observation_id":"e1e7c63f-bfc6-4998-8c1a-4948daea539d","resolution":{"observed_at":"2026-08-05T13:41:05.200710Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:05.255412Z","title":"HRTF inter- polation using a spherical neural process meta-learner,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:05.255412Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:18baa81c967bb68d5c66a114089fd43db5277f7713516912ddd3f245824208dc","observation_id":"992b0059-390f-40f3-b8ab-a20cd7212c80","resolution":{"observed_at":"2026-08-05T13:41:05.255412Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:05.431482Z","title":"Head-related transfer func- tion upsampling with spatial extrapolation features,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:05.431482Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:6cd130a1a816c800e8b1acf3a3f48233a5e2203ed525a3dbe7433a4472cb33ee","observation_id":"f22e8a24-b9c2-4bfc-b4ee-4a464a2ff864","resolution":{"observed_at":"2026-08-05T13:41:05.431482Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:05.646626Z","title":"HRTF upsampling with a generative adversarial network using a gnomonic equiangular pro- jection,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:05.646626Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:49631f7436dc7bb83830459375e51e94ad9524d2ead10218f0ab38283b71710d","observation_id":"99be31c3-3769-44bb-82c8-36df886b3f77","resolution":{"observed_at":"2026-08-05T13:41:05.646626Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:05.767035Z","title":"HRTF spatial upsampling in the spherical harmonics domain employ- ing a generative adversarial network,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:05.767035Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:1ba7182029c60e6f75d9921918d5be17e0feed528dfff87c5e3006218bf207a9","observation_id":"815f8d7d-0646-49d6-a911-be5481395f54","resolution":{"observed_at":"2026-08-05T13:41:05.767035Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2504.17586","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:30.359460Z","title":"A ma- chine learning approach for denoising and upsampling HRTFs,","venue":null,"work_id":"a5a2aaba-7beb-4719-bc1f-1e1248742515","year":2025},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:05.886579Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:404300626312efb1e745663269f6fc9a85a1dc64c6386dfbff34f687e12f79ac","observation_id":"b1c02cfd-f1ff-4557-9f51-7faa0d0e0889","resolution":{"observed_at":"2026-08-05T13:41:30.470242Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:06.034832Z","title":"Global HRTF in- terpolation via learned affine transformation of hyper- conditioned features,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:06.034832Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:780ba3af7d2b3e48c62731fb410d454ea288b4ff5223a647ff8e20fe33dfe1fd","observation_id":"17becb5a-37c2-489d-b3c1-7ccd068d3b9c","resolution":{"observed_at":"2026-08-05T13:41:06.034832Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:06.144775Z","title":"HRTF Field: unifying measured HRTF magnitude representation with neural fields,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:06.144775Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:f1c8bb2c27190e3e04244ec16576bff1c77277c9f53c4700213f7f59fe13a4ef","observation_id":"8d92e643-164d-44e0-9e69-35e9111081e2","resolution":{"observed_at":"2026-08-05T13:41:06.144775Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.14650","last_updated":"2023-12-10T08:57:49Z","snapshot_observed_at":"2026-08-04T21:48:32.979511Z","submitted_at":"2023-07-27T06:55:10Z","title":"Spatial Upsampling of Head-Related Transfer Functions Using a Physics-Informed Neural Network","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.14650","snapshot_observed_at":"2026-08-05T13:41:06.285386Z","title":"Spatial upsampling of head-related transfer functions using a physics-informed neural network,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:06.285386Z"},"links":{"cited_paper":"/paper/2307.14650","citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:cdc18d99522ce8cf1956ed000c3b9f518c2ff57d56d2519f3252a2f15e4f907e","observation_id":"1e4bcf49-b599-44a7-80e0-4d95ac182eda","resolution":{"observed_at":"2026-08-05T13:41:06.285386Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:06.407347Z","title":"NIIRF: neural IIR filter field for HRTF upsampling and personalization,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:06.407347Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:acee5514fc47d404ad7a78a5e2129a8b353d18012145c09f060b21d8fbd7122a","observation_id":"d2f9238b-c0a0-423c-9214-bc467b4ddcaa","resolution":{"observed_at":"2026-08-05T13:41:06.407347Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:06.545184Z","title":"Neural Steerer: novel steering vector synthesis with a causal neural field over frequency and direction,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:06.545184Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:4f6a5d68e7e98b2457d19b7b29fb5f2d87f222723bd8c11c04342d4088f4aef9","observation_id":"4f3714d5-7a31-46c5-aed5-351746bc354c","resolution":{"observed_at":"2026-08-05T13:41:06.545184Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:06.678377Z","title":"Retrieval-augmented neural field for HRTF upsampling and personalization,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:06.678377Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:b803e1cbbeeb5a680476893a3db57676795db7f26982c535f242c8c0be371f02","observation_id":"30a8a40b-30f9-4841-8135-4ad87e36076e","resolution":{"observed_at":"2026-08-05T13:41:06.678377Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:06.819966Z","title":"BiCG: binaural cue generation from unified HRTF datasets,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:06.819966Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:8231949d8dd14eb9babe7872fda546f3f4d990d9475be785b5cc98e36db1e2f3","observation_id":"631faf8a-1ac1-4127-aef0-b696629d860d","resolution":{"observed_at":"2026-08-05T13:41:06.819966Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:06.969781Z","title":"Neural fields in visual computing and beyond,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:06.969781Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:efbd5de30a20ec6723a61410ea77df6ecf3144034ad126cf1ed4023b74f17ef0","observation_id":"d4a9e2d6-a442-4482-8c42-35204a3555d7","resolution":{"observed_at":"2026-08-05T13:41:06.969781Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:07.125026Z","title":"Implicit neural representations with peri- odic activation functions,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:07.125026Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:ffb1f6df23dc0ff443f4cd6e6963b07befff2bb6ad61039764192f2832da3d2c","observation_id":"b7679fe9-e740-45ce-beb0-fdba8b011323","resolution":{"observed_at":"2026-08-05T13:41:07.125026Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:07.235661Z","title":"Fourier features let networks learn high frequency functions in low dimensional domains,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:07.235661Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:e9b5f4e1918f86da49aadc8009d6c381e3506641df52b8400850aa021ea370d7","observation_id":"d57aec2b-1061-4786-a757-bf3ed001c263","resolution":{"observed_at":"2026-08-05T13:41:07.235661Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:07.391780Z","title":"NeRF: Representing scenes as neural radiance fields for view synthesis,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:07.391780Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:f2bf6604ef0c2fbe9c57b8c7fa34f0e9507d16552d55d3738db216e657b24a09","observation_id":"b7d53257-70da-48f7-86d0-d6945eba7817","resolution":{"observed_at":"2026-08-05T13:41:07.391780Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:07.512543Z","title":"Learning neural acoustic fields,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:07.512543Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:d81e3b66c904a8edda5c7478d7621fbfd785ad9f6fd96ceeb2e316676a888d9d","observation_id":"f8d4645d-4b77-42d6-99ff-6cb67a684e75","resolution":{"observed_at":"2026-08-05T13:41:07.512543Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.15977","last_updated":"2023-09-27T19:50:50Z","snapshot_observed_at":"2026-08-09T00:06:38.134207Z","submitted_at":"2023-09-27T19:50:50Z","title":"Neural Acoustic Context Field: Rendering Realistic Room Impulse Response With Neural Fields","version":1},"cited_work":{"arxiv_id":"2309.15977","doi":null,"metadata_source":"pith","pith_arxiv_id":"2309.15977","snapshot_observed_at":"2026-08-05T13:41:30.065002Z","title":"Neural Acoustic Context Field: Rendering Realistic Room Impulse Response With Neural Fields","venue":"cs.SD","work_id":"35292e29-583d-4bb2-85c3-fa95abc17ded","year":2023},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:07.669586Z"},"links":{"cited_paper":"/paper/2309.15977","citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:034d53bd35e82ab143315a52aadd384f5e6c9a21508b69e7332e4d1adf91bc33","observation_id":"87c9efe2-c5e0-445b-8d83-1549b28b8e5d","resolution":{"observed_at":"2026-08-05T13:41:30.179472Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:07.843638Z","title":null,"venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:07.843638Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:63f7a42d5be61752ff9ef9611d38941256cf6ab92ee095667b58b594e9deae4c","observation_id":"a209d7ec-1174-45e9-a25b-7c317f97c7c0","resolution":{"observed_at":"2026-08-05T13:41:07.843638Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:07.952722Z","title":"Listen HRTF database,","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:07.952722Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:888eea0de24f0b7747808a737869d4d1ef712b8adf02e765b3afb1b2e3252a02","observation_id":"d481487a-457f-4736-880c-887dea0d4c89","resolution":{"observed_at":"2026-08-05T13:41:07.952722Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:08.076739Z","title":"Dataset of head-related transfer functions measured with a circular loudspeaker array,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:08.076739Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:9cdeda4c79fb25ebff7e9c5b36b8ce146a6276e00e6e04bb64a1c0122b36bd0f","observation_id":"eb05873a-a35c-487b-af21-c0577b564257","resolution":{"observed_at":"2026-08-05T13:41:08.076739Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:08.143551Z","title":"Measurement of a head-related transfer function database with high spatial resolution,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:08.143551Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:25ab5ab1722d81a14b42e0a076ff17f403c32bd8b28c12af24b9e7aca513ce3c","observation_id":"9faa9b4e-fe7b-4421-9661-4acb93db6388","resolution":{"observed_at":"2026-08-05T13:41:08.143551Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:41:08.279168Z","title":"Sound localiza- tion in individualized and non-individualized crosstalk cancellation systems,","venue":null,"work_id":null,"year":2055},"citing_paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-05T13:41:08.279168Z"},"links":{"citing_paper":"/paper/2509.00400"},"observation_digest":"sha256:f744ac073df4e7789a37868e4d673c68cffa59d4a9b3a4bb184da03d407d2c25","observation_id":"757108b1-73cd-4aa1-9d69-2ea76915aa9d","resolution":{"observed_at":"2026-08-05T13:41:08.279168Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2509.00400","last_updated":"2025-08-30T07:52:28Z","latest_version":1,"primary_category":"eess.AS","snapshot_observed_at":"2026-08-11T19:10:13.333600Z","submitted_at":"2025-08-30T07:52:28Z","title":"Deep Learning for Personalized Binaural Audio Reproduction"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":96,"verified_exact":4,"verified_fuzzy":0},"total_outbound_references":260},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 100 of 260 outbound references and 1 inbound Pith citation observation for arXiv:2509.00400."}