{"as_of":"2026-08-09T22:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:dd639444230ccf72841df7de789f2ebf7d65bfcc8db4fd1560dc4ab4318d2b47","coverage":[{"denominator":34,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":34,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-08T14:20:24.828073Z","state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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":"2026-07-08T14:20:24.828073Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-08T14:25:02.442305Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"cited_work":{"arxiv_id":"2607.06179","doi":null,"metadata_source":"pith","pith_arxiv_id":"2607.06179","snapshot_observed_at":"2026-07-08T14:25:02.442305Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","venue":"eess.AS","work_id":"1969c977-b571-4980-8c46-ec7e0b4a1ac4","year":2026},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"cited_paper":"/paper/2607.06179","citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:3ab67987bacb7f09a29326c45cdab230fa939db42b4c8a2045dca1a6946fb5d3","observation_id":"68f5d043-9226-4862-b467-bef6ec5da71e","resolution":{"observed_at":"2026-07-08T14:25:02.443766Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2607.06179/citation-record","integrity":"/paper/2607.06179/integrity","json":"/paper/2607.06179/citation-record.json","paper":"/paper/2607.06179"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"cited_work":{"arxiv_id":"2607.06179","doi":null,"metadata_source":"pith","pith_arxiv_id":"2607.06179","snapshot_observed_at":"2026-07-08T14:25:02.442305Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","venue":"eess.AS","work_id":"1969c977-b571-4980-8c46-ec7e0b4a1ac4","year":2026},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"cited_paper":"/paper/2607.06179","citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:3ab67987bacb7f09a29326c45cdab230fa939db42b4c8a2045dca1a6946fb5d3","observation_id":"68f5d043-9226-4862-b467-bef6ec5da71e","resolution":{"observed_at":"2026-07-08T14:25:02.443766Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.570359Z","title":"As illustrated in Figure 1, the TriA Pipeline consists of four main stages: Standardization, Audio Activity Detection (AAD), Audio Event Detection (AED), and Filtering","venue":null,"work_id":"fb06f8f5-f29e-4f62-9450-70fb09b1ecc4","year":null},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:43d00349fe5cd37f82b67b3d967b63564f2b4ca6c407474d445eb5b56e160800","observation_id":"5ad48bc4-98a2-4a7f-a5d4-5e478551fdc4","resolution":{"observed_at":"2026-07-08T14:25:02.572084Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.591680Z","title":null,"venue":null,"work_id":"b19a1912-a505-4504-91e3-020a99d15cc9","year":null},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:23da16d72d80e9e60f7b478bae4caa05f9a826ba60604eeeb3e64e9be74f7e9a","observation_id":"4860a9ff-b559-41c5-a79e-0f0ead916de6","resolution":{"observed_at":"2026-07-08T14:25:02.593769Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.594691Z","title":"For each task, three experiments are conducted","venue":null,"work_id":"f10ddbae-13fc-45ba-9a91-8639f296d243","year":null},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:25b0ab3880e7556edbba328621fdc1cec68cbb2ba502c6c58142468f2cfba9d6","observation_id":"54f1761a-98de-4a6f-b96b-316cb01fc0f5","resolution":{"observed_at":"2026-07-08T14:25:02.597007Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.603930Z","title":"It efficiently converts audio collected from various streaming platforms into high- quality training data with event annotations","venue":null,"work_id":"57d00798-42c3-4a4f-bfcf-c8a568c93dab","year":null},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:9deec5fb08b1bd33ef611157565bedfd6690426e23ca95a02102efd1e5afb319","observation_id":"ca8344e8-25e7-4d86-a44f-caadbaf301d0","resolution":{"observed_at":"2026-07-08T14:25:02.605664Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.577130Z","title":null,"venue":null,"work_id":"70dd361d-c4b4-4058-a8b4-2d0af4f04774","year":null},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:8b816d4d039e85ad147d756489db5314e61dfe2455dab9fc04f71a1de434314b","observation_id":"afff97c6-594e-43d6-8db8-eeef88bd7bc9","resolution":{"observed_at":"2026-07-08T14:25:02.578551Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.575089Z","title":null,"venue":null,"work_id":"a73af56a-55c8-49c5-b1f2-eeda56a82a30","year":null},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:09fcdaf7f69d6d3497e686f041a090e9f07eac72eb29fbf17343ea1c7479a2b8","observation_id":"91324e0d-e8f0-412d-a7fe-eb347db68b17","resolution":{"observed_at":"2026-07-08T14:25:02.576440Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.579498Z","title":"Audio set: An ontology and human-labeled dataset for audio events,","venue":null,"work_id":"94643b32-381a-49df-b94b-4486cd133602","year":2017},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:71269fbd47a89d7ec29ef355e411918a91901db756955e28c957863a95816d44","observation_id":"76d4465c-6044-4018-bbbb-a60201b9a07d","resolution":{"observed_at":"2026-07-08T14:25:02.581413Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.582264Z","title":"Clotho: An audio cap- tioning dataset,","venue":null,"work_id":"643b546b-0307-4845-aa69-d8e46b89d0bc","year":2020},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:1f2831618ba2886542bc7ed7f5a3ee61667f842e5707d4cf516a06a435421c72","observation_id":"7a6c7c52-28e0-4be4-a848-21150da2d042","resolution":{"observed_at":"2026-07-08T14:25:02.584082Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.584831Z","title":"Bioa- coustics data analysis–a taxonomy, survey and open challenges,","venue":null,"work_id":"e606fcb9-ff7a-409b-bebc-7e9776b21194","year":2020},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:b1fa684d4c4d9487ce39e938fbfa62d5f2f8df345b1b409a172f2fd8c73b90e6","observation_id":"86742938-78eb-48a8-b3e7-1e7919dab035","resolution":{"observed_at":"2026-07-08T14:25:02.586402Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.567718Z","title":"Comparison of feature extraction methods for sound-based clas- sification of honey bee activity,","venue":null,"work_id":"d541b536-c410-449b-8eeb-57ceb21161eb","year":2021},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:6bf850d0316aaff76d84baab3cce8800fda1b7a554bef8fd13b81742a56b03e7","observation_id":"bfbcd470-c2c3-48c7-a352-3359e151a0f1","resolution":{"observed_at":"2026-07-08T14:25:02.569233Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.587218Z","title":"Fsd50k: an open dataset of human-labeled sound events,","venue":null,"work_id":"4bea26b8-e0c1-47fc-825e-b7a31c28bd86","year":2021},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:125f2af00d04870bdc7af06b90dc6cd4a5bf7f064ff644e97e22cf5fd23618be","observation_id":"11dccf46-5980-4235-b562-78911a1a2e60","resolution":{"observed_at":"2026-07-08T14:25:02.588697Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"3373.28063","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.439749Z","title":"ESC: Dataset for Environmental Sound Classification","venue":null,"work_id":"f0670cd6-59a8-48d2-94ce-45595e02c599","year":2015},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:88d2a01a23cc0d9c8bc3ce9b1c43fddac30faa4e767ef8c79862b12ec1197737","observation_id":"80a216eb-d8a6-4b0f-9830-64aefc9a0899","resolution":{"observed_at":"2026-07-08T14:25:02.441185Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.633464Z","title":"Few-shot class-incremental audio classification using pseudo-incrementally trained embedding learner and continually updated stochastic classifier,","venue":null,"work_id":"b45f343b-7d9d-42c9-9181-c92af73584c4","year":2025},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:b656f879eecf1b9b51772d4516adab3c662a83f58d85b80547d08936f26145b1","observation_id":"2734aaf1-d67d-4ba5-ad6d-b0e9cf107bb1","resolution":{"observed_at":"2026-07-08T14:25:02.635551Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.628093Z","title":"Benchmark for kitchen20, a daily life dataset for audio-based human action recognition,","venue":null,"work_id":"c0049452-dff6-4dca-a6fe-d9f7c9af39a8","year":2019},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:f7c7f4dccec23f045b96e6a86f86a7dd79d8dfc33fad6a9dc1f78a47fc51e659","observation_id":"8456a3a5-f0f0-4824-ac74-45a7c63b63d3","resolution":{"observed_at":"2026-07-08T14:25:02.629788Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.589378Z","title":"Chime-home: A dataset for sound source recognition in a do- mestic environment,","venue":null,"work_id":"f7fd0e76-7d35-4f1d-a4bf-1f9c570d8626","year":2015},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:a215cc865c3cf7c7a770827b6776650ae8602dc1cac3b9ef676acd7fb7327e09","observation_id":"7ee23827-6bcf-407b-b055-6bf24b741639","resolution":{"observed_at":"2026-07-08T14:25:02.590828Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.638214Z","title":"Few-shot open-set audio classification using attention information-fused prototypes,","venue":null,"work_id":"92936534-fb2a-4cc7-8f41-a459e37ebf10","year":1929},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:2bee4b7e3d7524f4aec17861793fad857e744421f11d2d2c2ddd3b179b9ae870","observation_id":"94f503e9-1f7c-43e1-93ee-716eb58af78d","resolution":{"observed_at":"2026-07-08T14:25:02.640595Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.625855Z","title":"Panns: Large-scale pretrained audio neural networks for audio pattern recognition","venue":null,"work_id":"49a781a3-ebe4-4f15-8ccd-bd48da39b80f","year":2020},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:a4f860e6fe5b1787a57f33e5a490a37184068f1f26218f31b616a2bdffc1b9bd","observation_id":"a427e31c-9e16-4f1a-8e9f-a087aecfeccc","resolution":{"observed_at":"2026-07-08T14:25:02.627226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.623953Z","title":"Beats: audio pre-training with acoustic tok- enizers,","venue":null,"work_id":"c010c100-fd85-4d4b-a702-7091cbf4323a","year":2023},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:d367d0c14f6fb0fee0745ee9051af03233f332d9e735931ccc5c90105f63bd96","observation_id":"7b47c1e8-2c22-42f5-b578-f37d37c19963","resolution":{"observed_at":"2026-07-08T14:25:02.625199Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.630529Z","title":"Atst: Audio representation learning with teacher-student transformer,","venue":null,"work_id":"98f24b53-0f42-46ea-998e-25f510baccae","year":2022},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:0846a6449612e3f1f10155cf7af95f44c2e5b86212c1debb0392d753db2dd6b9","observation_id":"dba62290-e85c-4a6a-a520-331581b021ea","resolution":{"observed_at":"2026-07-08T14:25:02.632524Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.636228Z","title":"Streaming audio transformers for online audio tagging,","venue":null,"work_id":"060593d5-1766-4b22-8ceb-58d9b6159da7","year":2024},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:e3f328cc4b6c58bd036747b6d9283f1d18ca108c58ad8b7682318b617e851959","observation_id":"fee00b69-a641-4693-b4ca-dd55386cf2c1","resolution":{"observed_at":"2026-07-08T14:25:02.637541Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.619798Z","title":"Sound event detection in domestic environments with weakly labeled data and soundscape synthesis,","venue":null,"work_id":"133b2811-d9cf-4db2-a6e3-6b5e9babe07e","year":2019},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:a7577b4aeef1d2f48b65819d756255834f2758b170cc7715d677e5fc4cc4d41f","observation_id":"a3262a87-e7a2-44d5-9ce1-73687bbba080","resolution":{"observed_at":"2026-07-08T14:25:02.621214Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.598071Z","title":"Domestic activities clustering from audio recordings using con- volutional capsule autoencoder network,","venue":null,"work_id":"ae62cb5d-d44a-4202-9a2e-4b9c7a101e5c","year":2021},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:320c4eedfeeafe9df3c4513823ff6bf50ea996b9f5b6448d013cb70dacf38f4d","observation_id":"d886c08f-3882-41ef-b51f-dff1e74ebcf2","resolution":{"observed_at":"2026-07-08T14:25:02.600298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.615178Z","title":"A Multi-Task Learning Frame- work for Sound Event Detection using High-level Acoustic Char- acteristics of Sounds,","venue":null,"work_id":"d46672fb-b116-40f9-a084-966927a1bbe5","year":2023},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:c198d974aa362cf42a43366631e1b48d4078066fd71a418d250d8a43687ba0c9","observation_id":"236bab84-eaa0-4478-aaf7-3d20dea7307d","resolution":{"observed_at":"2026-07-08T14:25:02.616958Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.617676Z","title":"Leverag- ing audio-tagging assisted sound event detection using weakified strong labels and frequency dynamic convolutions,","venue":null,"work_id":"ce268146-dd3f-4ef6-b141-e188e651ac63","year":2023},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:a94a75ba569e2cf86c98db201e03afd7357a1b8a3ddabf6c00d291a57f07f24b","observation_id":"5f81433b-52cf-4196-9722-7cb9dbdc910c","resolution":{"observed_at":"2026-07-08T14:25:02.619119Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.572812Z","title":"Htad: A home-tasks activities dataset with wrist-accelerometer and audio features,","venue":null,"work_id":"560fb666-732a-4ae9-a84b-b4c416b38d79","year":2021},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:3abcaf58ae70fb2efddcddad310a337695b747076fb5781c3e4ac3da470ca385","observation_id":"0d3ecf81-5a6d-4edc-b8ee-f487a7cc14cc","resolution":{"observed_at":"2026-07-08T14:25:02.574371Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.621916Z","title":"The cirdo corpus: comprehensive audio/video database of domestic falls of elderly people,","venue":null,"work_id":"8385b65a-c5cb-4cec-9ee0-2233230a30a0","year":2016},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:06d68775e2c96145783bef472a8f20f5d0d77832ebdf2d1c0c3dd40abaf30721","observation_id":"460eff82-7bff-49b0-9fe5-5484467d9eb2","resolution":{"observed_at":"2026-07-08T14:25:02.623343Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.601195Z","title":"Bi-modal multiperspective per- cussive (bimp) dataset for visual and audio human fall detection,","venue":null,"work_id":"09f2a958-f2a6-4767-9fc2-082b0ee1beb0","year":2025},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:863977643f10f06616db9c55aa50236cde508bf52bcfd8ecf5b4d5b63c09d544","observation_id":"8ddd7c4a-fbd4-4e53-ba27-80df4b192b72","resolution":{"observed_at":"2026-07-08T14:25:02.603131Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.606555Z","title":"Nonspeech7k dataset: Clas- sification and analysis of human non-speech sound,","venue":null,"work_id":"b1376428-841c-4d1d-b048-581d962aa58b","year":2023},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:10012589d3c5347390640dbfbc703977667c7a09459f62c643ff782193db3dbd","observation_id":"cf5fc140-34bf-4ee9-9de9-b93e523f416d","resolution":{"observed_at":"2026-07-08T14:25:02.608491Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.609563Z","title":"Emilia: An extensive, multilingual, and diverse speech dataset for large-scale speech generation,","venue":null,"work_id":"b17b5261-c69f-4a3f-9810-db4738281896","year":2024},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:52298e0ab6973bb87b2581738703875605762a76a5d432bbbc4c5aa6dc649a3a","observation_id":"46fd950a-5661-473a-bfc7-ae870cd6925e","resolution":{"observed_at":"2026-07-08T14:25:02.611144Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.04195","last_updated":"2025-08-06T08:25:26Z","snapshot_observed_at":"2026-08-06T17:34:54.346667Z","submitted_at":"2025-08-06T08:25:26Z","title":"NVSpeech: An Integrated and Scalable Pipeline for Human-Like Speech Modeling with Paralinguistic Vocalizations","version":1},"cited_work":{"arxiv_id":"2508.04195","doi":null,"metadata_source":"pith","pith_arxiv_id":"2508.04195","snapshot_observed_at":"2026-07-08T14:25:02.434510Z","title":"Nvspeech: An integrated and scalable pipeline for human-like speech modeling with paralinguistic vocalizations","venue":"cs.SD","work_id":"c4a043ef-1a77-44f9-acec-11c81b7809e4","year":2025},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"cited_paper":"/paper/2508.04195","citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:f9d2b78868c25d2a10a6cd130ba5083281aeefdb7c60e6820227f5be693b80f1","observation_id":"780fd3bc-2880-4093-a9f3-601039e19f43","resolution":{"observed_at":"2026-07-08T14:25:02.436023Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2508.05385","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.431484Z","title":"A scalable pipeline for enabling non-verbal speech generation and understanding","venue":null,"work_id":"e900e5c5-d317-479f-a7ed-77199ac289b8","year":2025},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:e5fc895252284abb51ce4c14ec47c406fbb1f2cdaa77bc2368b88e6bbc115a09","observation_id":"6bdaafff-d859-4285-b6f6-39844be3189c","resolution":{"observed_at":"2026-07-08T14:25:02.433251Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.05139","last_updated":"2025-02-07T18:15:57Z","snapshot_observed_at":"2026-07-06T20:33:01.960703Z","submitted_at":"2025-02-07T18:15:57Z","title":"Meta Audiobox Aesthetics: Unified Automatic Quality Assessment for Speech, Music, and Sound","version":1},"cited_work":{"arxiv_id":"2502.05139","doi":"10.48550/arxiv.2502.05139","metadata_source":"pith","pith_arxiv_id":"2502.05139","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Meta Audiobox Aesthetics: Unified Automatic Quality Assessment for Speech, Music, and Sound","venue":"cs.SD","work_id":"7cba92d8-f51e-4a64-8d1d-6d4cf1f56377","year":2025},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"cited_paper":"/paper/2502.05139","citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:4c6daf4a39788066b3dbf826ee857962f2a6ae43a55a55bb3e7e134fbd6a6f7b","observation_id":"b8bebbc3-730e-49bb-a052-e0b7737eba4d","resolution":{"observed_at":"2026-07-08T14:25:02.438599Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T14:25:02.612007Z","title":"Clap learning audio concepts from natural language supervision,","venue":null,"work_id":"bc0972ba-bebc-4280-8b5d-388df10ed100","year":2023},"citing_paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-08T14:20:24.828073Z"},"links":{"citing_paper":"/paper/2607.06179"},"observation_digest":"sha256:6f74374919a45467f0abf4eeff7eb9400b0e9741675d2f5e7eecb9bcc883ce63","observation_id":"d0f0ecfc-360c-481b-8a4d-a6f585af3a95","resolution":{"observed_at":"2026-07-08T14:25:02.614393Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2607.06179","last_updated":"2026-07-07T11:57:26Z","latest_version":1,"primary_category":"eess.AS","snapshot_observed_at":"2026-08-08T05:06:20.291056Z","submitted_at":"2026-07-07T11:57:26Z","title":"TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios"},"reference_resolution":{"displayed":34,"state_counts":{"malformed_identifier":1,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":3,"verified_exact":4,"verified_fuzzy":25},"total_outbound_references":34},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 1 inbound Pith citation observation for arXiv:2607.06179."}