{"as_of":"2026-08-21T18:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ee355e589c7830fc3425516e5652f25a432c048c5686cf4a9a8f8ccf00a12d6c","coverage":[{"denominator":44,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":44,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T15:20:29.682290Z","state":"measured"},{"denominator":45,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":45,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+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":"2026-08-14T15:20:29.404068Z","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":"2026-08-14T15:20:29.807585Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"cited_work":{"arxiv_id":"1908.01768","doi":null,"metadata_source":"pith","pith_arxiv_id":"1908.01768","snapshot_observed_at":"2026-08-14T15:20:29.807585Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","venue":"eess.AS","work_id":"d4995f8f-543e-435f-9291-b0e7b66ffeeb","year":2019},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.404068Z"},"links":{"cited_paper":"/paper/1908.01768","citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:d85a3b7f4df57987d50d3451c04b8e573dcccf3db70f63f599feeaa68f97896d","observation_id":"d1c30595-5684-40ec-a014-01b2e4bf24a7","resolution":{"observed_at":"2026-08-14T15:20:29.813178Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1908.01768/citation-record","integrity":"/paper/1908.01768/integrity","json":"/paper/1908.01768/citation-record.json","paper":"/paper/1908.01768"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"cited_work":{"arxiv_id":"1908.01768","doi":null,"metadata_source":"pith","pith_arxiv_id":"1908.01768","snapshot_observed_at":"2026-08-14T15:20:29.807585Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","venue":"eess.AS","work_id":"d4995f8f-543e-435f-9291-b0e7b66ffeeb","year":2019},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.404068Z"},"links":{"cited_paper":"/paper/1908.01768","citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:d85a3b7f4df57987d50d3451c04b8e573dcccf3db70f63f599feeaa68f97896d","observation_id":"d1c30595-5684-40ec-a014-01b2e4bf24a7","resolution":{"observed_at":"2026-08-14T15:20:29.813178Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:30.570183Z","title":null,"venue":null,"work_id":"900c7318-fdb6-4327-8170-35dab1a1cd0d","year":null},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.411690Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:ed78be94d982d27b0733bcbdd44e5e962fd6e3e788ac82914f14ddb40113c395","observation_id":"bfc0692d-5982-40b1-8c5d-11d9219fde1f","resolution":{"observed_at":"2026-08-14T15:20:30.576370Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:30.553570Z","title":null,"venue":null,"work_id":"3ded9321-810d-4ced-a0c0-668c370930bd","year":null},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.419220Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:3029eff17cfded8859389b8e08a3824646d57868984b8568e27f3e7d26c5ccfc","observation_id":"c8a70b1e-a63b-46f0-b140-ff95de14e690","resolution":{"observed_at":"2026-08-14T15:20:30.559327Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:30.536038Z","title":"The GRID is a multi-speaker, sentence corpus [32], which has been used in monaural speech separation and recogni- tion challenge [33]","venue":null,"work_id":"28e02c4b-1f3e-49f2-a2f7-1d69735645aa","year":null},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.425569Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:e204d89ec73c9111950e0844c857cf53de74e8acb6bbae71449deed6a319bde4","observation_id":"baf4854f-e975-49bc-8cbc-02b03060db0b","resolution":{"observed_at":"2026-08-14T15:20:30.541884Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:30.519235Z","title":"A long-lasting problem in speech separation task is ﬁnding the correct label for each separated speech signal, which referred to as label permutation ambiguity","venue":null,"work_id":"48381b25-8a00-4e53-83fb-bc44b7e5df47","year":null},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.431108Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:72065c37c7c39288280d333a8c5eb0b99eb9330f65286e3a1506bb2189ef886d","observation_id":"7ad0ed54-4dd7-4b5c-9a80-672a9535e412","resolution":{"observed_at":"2026-08-14T15:20:30.525144Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:30.502407Z","title":"The perception of speech under adverse conditions,","venue":null,"work_id":"86a0205f-621f-40b3-a650-7bb6ab8ed9bd","year":2004},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.436689Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:e8aac8f132ac948cf48bc06df133999b340ed03a7a49017c932f3702565f834f","observation_id":"8445e94c-3d4d-4a9f-9319-b7a35c1ce453","resolution":{"observed_at":"2026-08-14T15:20:30.507734Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:30.484596Z","title":null,"venue":null,"work_id":"f60c2160-252d-48ea-85c2-80ca9405bb3e","year":1994},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.443118Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:8ce81e9f5e45afe9905d33ad9729c5c3b2f8a06e8a8c6b5b88378e7acbb9040e","observation_id":"b8dbc153-ab9c-420f-a44d-d4695e866a58","resolution":{"observed_at":"2026-08-14T15:20:30.491135Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:30.467372Z","title":"Auditory grouping, i in hearing. hand- book of perception and cognition, bcj moore,","venue":null,"work_id":"15acc976-0e74-4b33-bcdb-313eb364d3ee","year":1995},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.448977Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:2932f62f8b6894e017772b6cbed16c68b8c0c877f4ff4179c24fe9a3f8857a52","observation_id":"a78bf3ce-abe4-46fb-ae59-e42e3c28b937","resolution":{"observed_at":"2026-08-14T15:20:30.472709Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:30.449777Z","title":"Divenyi, Speech separation by humans and machines","venue":null,"work_id":"5c409f18-05dc-4cc9-b5c1-d175cdc124be","year":2004},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.455920Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:bbedbe61e586f6d0c02f396a1abd35e7c73dadf7ebf711c8b3c7f376730f78c9","observation_id":"76745c39-867f-48d4-8424-b78a7472ae8f","resolution":{"observed_at":"2026-08-14T15:20:30.455517Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:30.430974Z","title":"An algorithm to increase intelligibility for hearing-impaired lis- teners in the presence of a competing talker,","venue":null,"work_id":"11cfc40e-079e-4469-a311-52d937970d43","year":2017},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.462747Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:2c6bcaaef20cdaa1f5d1a86ecf9e089bf71a35c98c97aefd259ed9004d2da31e","observation_id":"f7e83704-b4c1-40e7-820f-7067cdde5b2d","resolution":{"observed_at":"2026-08-14T15:20:30.437200Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:30.412272Z","title":"Eeg-informed attended speaker extraction from recorded speech mixtures with application in neuro-steered hearing prostheses,","venue":null,"work_id":"b99f4eb4-ab9c-4d28-843e-87a76455d655","year":2017},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.470796Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:54126f7fe133199d3a19872e62c0956d3a261bb9e30d5521d9d58df33621d09d","observation_id":"484a3842-426d-40f8-8d0f-2b19b935788d","resolution":{"observed_at":"2026-08-14T15:20:30.418056Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:30.388776Z","title":"Deep recurrent networks for separation and recognition of single- channel speech in nonstationary background audio,","venue":null,"work_id":"ae8f8a6b-fd36-4683-96a5-04e3a0537eba","year":2017},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.477340Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:8f668e532db80fb0956ecf25552046bfcbb18d80905382a9dc594e66d42da8f3","observation_id":"2d41bb52-2a1b-438f-85a1-a5377b5aa9d4","resolution":{"observed_at":"2026-08-14T15:20:30.395428Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:30.370111Z","title":"Single-channel mul- titalker speech recognition,","venue":null,"work_id":"07463dcd-7e18-4ec3-9b7d-ed6190db599a","year":2010},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.483440Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:c44d18b4e08ddc4c9595d5c62fbc4953c0d85973392e3f45f617aea51f08e708","observation_id":"73f9fe41-78af-4683-84a8-17b0f23a54a2","resolution":{"observed_at":"2026-08-14T15:20:30.376541Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:30.348480Z","title":"Deep neu- ral networks for single-channel multi-talker speech recognition,","venue":null,"work_id":"6cbc87c0-cb4e-42d4-b259-ad4c5168da01","year":2015},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.488886Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:a1aad41f613c21ec11c424e15e3f42c0729221f20e60e1d367e5225958ec0b02","observation_id":"dbb523bd-e7da-466b-a6e5-c076b0c8ab74","resolution":{"observed_at":"2026-08-14T15:20:30.354799Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:30.317472Z","title":"Multi-speaker conversations, cross-talk, and diarization for speaker recognition,","venue":null,"work_id":"151079dc-1bae-4798-9b17-c3d797d23377","year":2017},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.495505Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:6b41de398791a84b1136834a4b66ef0ca7383c6cab02404f852bb2c775fe6a19","observation_id":"9916df4f-2de8-463e-9b42-f037bd7cdc62","resolution":{"observed_at":"2026-08-14T15:20:30.325342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.07881","last_updated":"2019-02-21T06:32:21Z","snapshot_observed_at":"2026-08-14T17:13:26.602627Z","submitted_at":"2019-02-21T06:32:21Z","title":"All-neural online source separation, counting, and diarization for meeting analysis","version":1},"cited_work":{"arxiv_id":"1902.07881","doi":null,"metadata_source":"pith","pith_arxiv_id":"1902.07881","snapshot_observed_at":"2026-08-14T15:20:29.781171Z","title":"All-neural online source separation, counting, and diarization for meeting analysis","venue":"eess.AS","work_id":"c8295061-1164-46af-9c8c-3b9b1a6dfc93","year":2019},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.501091Z"},"links":{"cited_paper":"/paper/1902.07881","citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:ffe8d9d6e8a7af22f1ae779a514d466ac1044c1d21a23a2064c1c28fa5cc06dc","observation_id":"4c3e0e54-347c-41dd-861b-933720ac4a4b","resolution":{"observed_at":"2026-08-14T15:20:29.787083Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:29.506911Z","title":"Jointly aligning and predicting continuous emotion annotations,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.506911Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:b30f5f6c4646e29410da88493f5c3d50b36a24ca05ab8ecaf60d2ec91bc44675","observation_id":"6e7a7b2c-301e-406e-9e2a-af0ab2340228","resolution":{"observed_at":"2026-08-14T15:20:29.506911Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.03256","last_updated":"2017-06-10T17:26:20Z","snapshot_observed_at":"2026-08-14T20:58:14.483044Z","submitted_at":"2017-06-10T17:26:20Z","title":"Progressive Neural Networks for Transfer Learning in Emotion Recognition","version":1},"cited_work":{"arxiv_id":"1706.03256","doi":null,"metadata_source":"pith","pith_arxiv_id":"1706.03256","snapshot_observed_at":"2026-08-14T15:20:29.748366Z","title":"Progressive Neural Networks for Transfer Learning in Emotion Recognition","venue":"cs.LG","work_id":"5eec2650-ee06-4e87-9600-bdacee541ee7","year":2017},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.513891Z"},"links":{"cited_paper":"/paper/1706.03256","citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:cb5bf47f5916971e0772389f175980bf85bfcdc9975dbc9bf5d0aadc434527bd","observation_id":"19021b35-a0de-40b8-b5d7-bc27d66a6860","resolution":{"observed_at":"2026-08-14T15:20:29.756396Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:30.276207Z","title":"A robust text depen- dent speaker identiﬁcation using neural responses from the model of the auditory system,","venue":null,"work_id":"9f3bd37f-9595-4346-8146-fb2b31880b61","year":2019},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.520220Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:b1478dbc0dba31ac8cc4cc14ddeb280a37d349d0d826c099c93313c5fdffe967","observation_id":"f2877708-3683-4590-8046-8800b9a86458","resolution":{"observed_at":"2026-08-14T15:20:30.281874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:30.256255Z","title":"Speaker recognition by machines and humans: A tutorial review,","venue":null,"work_id":"9bcce2f2-57a2-4569-8869-06913d1f6bd9","year":2015},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.533652Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:16cfdba9ab4c2682b243dce0120b6fcd17825092390a2ef932cb1071d097ec72","observation_id":"74ee84ae-9ed6-4953-81bb-3e9151f7a30e","resolution":{"observed_at":"2026-08-14T15:20:30.263194Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:30.238174Z","title":"Computational auditory scene analysis: Principles, algorithms, application","venue":null,"work_id":"991e3cd3-d707-4466-87c6-0fea373033b7","year":2006},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.541101Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:a2e80b5a4072506c5dcbbbc6f7f580e51c7366f796711967c6b03a356eac16b3","observation_id":"0d0bdba4-0089-4c4b-b0c9-c04072e2d893","resolution":{"observed_at":"2026-08-14T15:20:30.244611Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:29.546769Z","title":"Independent component analysis, a new concept?","venue":null,"work_id":null,"year":1994},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.546769Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:7dcffe8731c2e8a546331d52764495458b6b8ecb1ea0353451100b84a3b7a6d7","observation_id":"af1cfc9e-d248-4d63-b29b-5716bfad3978","resolution":{"observed_at":"2026-08-14T15:20:29.546769Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:20:30.208215Z","title":"One microphone source separation,","venue":null,"work_id":"dddfc67d-ea06-4380-aeff-f23a66b31ae8","year":2001},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.552499Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:8fd037f750e47dabbdaf009a021446b929cb1cd403a84c90e030e9922e63ce03","observation_id":"eebc0cae-dd79-4fee-b598-95c5791cfa32","resolution":{"observed_at":"2026-08-14T15:20:30.213430Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:30.182552Z","title":"Convolutive speech bases and their applica- tion to supervised speech separation,","venue":null,"work_id":"b588f078-f84f-444f-afb8-5c120f1cb06f","year":2007},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.558503Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:8619d8a47466b13720df400d7f31b5215fdb085fabcb7e4090a17428d9de232c","observation_id":"933baa65-489e-4326-b000-fdfc3b4a38fa","resolution":{"observed_at":"2026-08-14T15:20:30.189169Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:30.158673Z","title":"Supervised speech enhancement using online group-sparse convolutive nmf,","venue":null,"work_id":"ca1690a4-4b73-4d12-b1b0-08ee82b32ae8","year":2016},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.565685Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:e9c8b0ddecac582d085df9b2e402049a9e3cb6561ee5f7f1ea1fe4853c48506b","observation_id":"441c7873-8c9b-4c35-9611-91deb6fdc8e0","resolution":{"observed_at":"2026-08-14T15:20:30.165708Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:30.141109Z","title":"On training targets for supervised speech separation,","venue":null,"work_id":"7aec08eb-9051-4c1c-9083-7119ab64c85f","year":2014},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.571112Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:3f845d4f66f10e42180918f34439e551886bb9258479ec221ff246c0f0145a57","observation_id":"1aa477a2-daa1-4f07-b5e2-79ce8bf474fb","resolution":{"observed_at":"2026-08-14T15:20:30.146640Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:30.118481Z","title":"Joint optimization of masks and deep recurrent neural networks for monaural source separation,","venue":null,"work_id":"59b41c74-c152-4907-8671-3c71a7eeabcc","year":2015},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.578671Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:e69f4a351df09a29a61ffdf72841c97a5553e2690099e6df636d9c3e72cbc39a","observation_id":"bf028d8e-6626-45da-9d47-ae159921016e","resolution":{"observed_at":"2026-08-14T15:20:30.123839Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:30.100529Z","title":"A deep ensemble learning method for monaural speech separation,","venue":null,"work_id":"cf08cf18-425e-4e50-b0d8-4c12dde7fe9c","year":2016},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.587397Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:6ecc2a1697ad6fed851d268d9242b5882e91ed630ec76bfb478ede3eab42be63","observation_id":"d2cf1475-dbaa-4ae4-a208-7c3d0c87cc12","resolution":{"observed_at":"2026-08-14T15:20:30.106595Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:30.075096Z","title":"Convolutional neu- ral network-based speech enhancement for cochlear implant re- cipients,","venue":null,"work_id":"2d25c9ef-8c3f-4f93-ac0d-fcfef4ce7c9e","year":2019},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.595388Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:1b70a52ccd3415c259f591d5d18b60f1b32bcba6dab03df94e30c859a746ffa5","observation_id":"9541543a-73e5-423f-ac2a-fbb8b2d1f478","resolution":{"observed_at":"2026-08-14T15:20:30.084573Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:29.602002Z","title":"Deep clus- tering: Discriminative embeddings for segmentation and separa- tion,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.602002Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:480125e74b2902080bc31049f53bc544fff17d5875569e053b996f809f13408d","observation_id":"8b171cdd-f62c-4b64-8d1b-ac566114e68c","resolution":{"observed_at":"2026-08-14T15:20:29.602002Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1607.02173","last_updated":"2016-07-07T21:06:48Z","snapshot_observed_at":"2026-08-14T21:49:19.478287Z","submitted_at":"2016-07-07T21:06:48Z","title":"Single-Channel Multi-Speaker Separation using Deep Clustering","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1607.02173","snapshot_observed_at":"2026-08-14T15:20:29.608107Z","title":"Single-channel multi-speaker separation using deep clustering,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.608107Z"},"links":{"cited_paper":"/paper/1607.02173","citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:a542c708d1ab93714b6d29b795d1b9e65ac8c2bd1bef663008fd43ae33ecf7ff","observation_id":"808c30d0-e9ca-410b-ae9f-cd43e66686ee","resolution":{"observed_at":"2026-08-14T15:20:29.608107Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:20:30.039220Z","title":"Deep attractor network for single-microphone speaker separation,","venue":null,"work_id":"198c9687-928b-4649-8637-282ddb9e11f7","year":2017},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.613778Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:4a7d15d41acb309c4464070c93aaa2f15a1ead2e740651a351088d5b19a599b0","observation_id":"bca57cc0-3633-4c1f-94e8-5aaf44cd867d","resolution":{"observed_at":"2026-08-14T15:20:30.045507Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:30.014327Z","title":"Permutation invari- ant training of deep models for speaker-independent multi-talker speech separation,","venue":null,"work_id":"8bd1935a-b24a-4e6d-aed8-4159410fc398","year":2017},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.619674Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:29ca176f393312c341155bd2ff3f17f8cf1d124231e92975a0f6436514e62890","observation_id":"6ce08327-26fb-4c59-8a71-9bbf13f66393","resolution":{"observed_at":"2026-08-14T15:20:30.024618Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:29.990998Z","title":"Multitalker speech separation with utterance- level permutation invariant training of deep recurrent neural net- works,","venue":null,"work_id":"75a921b8-b329-41bd-b8c6-29791855b591","year":1901},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.624728Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:a045ee32b4ad270530301ef62342164d2c91d9f5f01307e06aea3dd0d697fb58","observation_id":"1175545c-4583-406a-858e-e6dd5238818f","resolution":{"observed_at":"2026-08-14T15:20:29.997508Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:29.973045Z","title":"Complex ratio mask- ing for monaural speech separation,","venue":null,"work_id":"2fcbc6b2-9f79-4b9a-8f14-5b50dfc99112","year":2016},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.631319Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:c9a595ab9e2a8d33a63c319a3110818144f6aafc0a6c35e61580ff9c2b7aa20e","observation_id":"5fb62346-2a55-4dfd-9e58-febb1b5aabab","resolution":{"observed_at":"2026-08-14T15:20:29.978763Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:29.955515Z","title":"Soft-dtw: a differentiable loss func- tion for time-series,","venue":null,"work_id":"90fcced5-8765-40a7-b549-6c85fb76e45f","year":2017},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.636644Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:452e568d8dd23ce65e4b72385fdbbf99c2944cc485acc017efa76a2e425410b7","observation_id":"3ed0ef82-5a03-4873-a8e2-4211cbdaccab","resolution":{"observed_at":"2026-08-14T15:20:29.961058Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:29.642658Z","title":"An audio- visual corpus for speech perception and automatic speech recog- nition,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.642658Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:539375f18bed5a0053894c87ba1d49102d313ebffd3da0c60e7d20d1aa90a2a6","observation_id":"ad1a4f0c-90fa-418e-91c7-bf97376914a9","resolution":{"observed_at":"2026-08-14T15:20:29.642658Z","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":"2026-08-14T15:20:29.648458Z","title":"Monaural speech separation and recognition challenge,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.648458Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:9a780d4a572aa767581b7c48d3ffee037e535cbd51063bf69bb02dcff4c03735","observation_id":"58c49a48-a3da-47fa-b4b6-b6d54174932c","resolution":{"observed_at":"2026-08-14T15:20:29.648458Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:20:29.915124Z","title":"Teager–kaiser energy operators for overlapped speech detection,","venue":null,"work_id":"5a7ad069-7260-4e22-bd89-72df455b4a84","year":2017},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.653179Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:f6db80fdbd4382a4a18a2d7a2a9813e76fbc93946ae16b7d30315e85f6df4a73","observation_id":"452b56ea-a1e8-4b3e-9a6e-f4d53c0944f0","resolution":{"observed_at":"2026-08-14T15:20:29.921007Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:29.898783Z","title":"Assessing speaker en- gagement in 2-person debates: Overlap detection in united states presidential debates,","venue":null,"work_id":"8a440a72-7a08-4eee-a3aa-22b526145d99","year":2018},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.658513Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:815fb883de158146015f183f184e773db32ae637b5c42d27d6abe12ef829c759","observation_id":"6cde6463-3bc1-4cf2-8fca-4d16d62806ac","resolution":{"observed_at":"2026-08-14T15:20:29.903884Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:29.882514Z","title":"Speech separation based on signal-noise-dependent deep neural networks for robust speech recognition,","venue":null,"work_id":"e5e2ceaf-b64f-4490-b87f-558b8823c2db","year":2015},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.663835Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:8c9a4ebedc21068fce4ee1ff2868e76588dc0f7b5b32bc35c69fa81bc0ce083e","observation_id":"f1c8d2f2-abac-478e-8816-706a33d1df6f","resolution":{"observed_at":"2026-08-14T15:20:29.887618Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:29.862168Z","title":"Performance measure- ment in blind audio source separation,","venue":null,"work_id":"fe477fed-9905-46d4-a2f0-c87586f1f41a","year":2006},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.668737Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:6f2b002fc0c0c3a619b1fc0284db6bd65b2967319053c3f5d71d2663a580f1db","observation_id":"c14124d4-5c99-4867-b5e9-f43bd587bf3e","resolution":{"observed_at":"2026-08-14T15:20:29.868783Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:29.843575Z","title":"Modeling perceptual similarity of audio signals for blind source separation evaluation,","venue":null,"work_id":"7eb23de8-bd99-437d-95e3-78ece50ecf16","year":2007},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.675130Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:af62ed70696d5582cce5d2922a07e8e2cd8d9940f3279ce1d2a901da4b16f306","observation_id":"cfd946b8-0cce-43ed-be5c-df5d43944aab","resolution":{"observed_at":"2026-08-14T15:20:29.849740Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-14T15:20:29.826040Z","title":"Long short-term memory for speaker gen- eralization in supervised speech separation,","venue":null,"work_id":"58b722d7-0e3a-4105-9c72-e63ee0f17280","year":2017},"citing_paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-14T15:20:29.682290Z"},"links":{"citing_paper":"/paper/1908.01768"},"observation_digest":"sha256:5e9aa24357f9d6fb03445d808a530b0de1ad53d0b316523d4f1ffdbf71d8edf2","observation_id":"2c6a43c9-c5b8-4d7f-8911-128b35ff9090","resolution":{"observed_at":"2026-08-14T15:20:29.831508Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.01768","last_updated":"2019-08-04T17:42:31Z","latest_version":1,"primary_category":"eess.AS","snapshot_observed_at":"2026-08-19T05:16:27.315057Z","submitted_at":"2019-08-04T17:42:31Z","title":"Probabilistic Permutation Invariant Training for Speech Separation"},"reference_resolution":{"displayed":44,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":9,"verified_exact":2,"verified_fuzzy":32},"total_outbound_references":44},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 1 inbound Pith citation observation for arXiv:1908.01768."}