{"as_of":"2026-08-20T01:53:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6eb1dbe80cb81c05e78f982bf4eb2bf23233f6a0941fa3025082ca27f67a59e1","coverage":[{"denominator":37,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":37,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T10:22:33.353784Z","state":"measured"},{"denominator":38,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":38,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+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":"2026-08-14T10:22:33.229505Z","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":"2026-08-14T10:22:33.414746Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"cited_work":{"arxiv_id":"1908.11307","doi":null,"metadata_source":"pith","pith_arxiv_id":"1908.11307","snapshot_observed_at":"2026-08-14T10:22:33.414746Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","venue":"cs.SD","work_id":"9b22f500-3866-48fc-9719-49ff03f44118","year":2019},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.229505Z"},"links":{"cited_paper":"/paper/1908.11307","citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:deaf8cf0e401bd05bec3287d8b990775c1e6c170cd73ef051eaac607033668c5","observation_id":"27921f33-4000-4e64-a201-77c218087c65","resolution":{"observed_at":"2026-08-14T10:22:33.418476Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1908.11307/citation-record","integrity":"/paper/1908.11307/integrity","json":"/paper/1908.11307/citation-record.json","paper":"/paper/1908.11307"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"cited_work":{"arxiv_id":"1908.11307","doi":null,"metadata_source":"pith","pith_arxiv_id":"1908.11307","snapshot_observed_at":"2026-08-14T10:22:33.414746Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","venue":"cs.SD","work_id":"9b22f500-3866-48fc-9719-49ff03f44118","year":2019},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.229505Z"},"links":{"cited_paper":"/paper/1908.11307","citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:deaf8cf0e401bd05bec3287d8b990775c1e6c170cd73ef051eaac607033668c5","observation_id":"27921f33-4000-4e64-a201-77c218087c65","resolution":{"observed_at":"2026-08-14T10:22:33.418476Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.731010Z","title":null,"venue":null,"work_id":"95b39773-3997-492f-b4d8-edd741d9090a","year":null},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.234169Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:f38b893a709cf60f41443004ac50c4cd51723e00ac8c4766c3af5518a105cdbe","observation_id":"371e0131-1cc8-4d52-b907-069e01c1a220","resolution":{"observed_at":"2026-08-14T10:22:33.734098Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.721415Z","title":"This training is based on the LDA model [9, 24], which has TF masks and DoAs of sources as latent variables","venue":null,"work_id":"fc18192d-0578-4bd2-8060-cd0c3491158f","year":null},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.238382Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:df29d6f3f8e20c04068515f99c92a5e72dbc7f2fa7e94a888d64961c2a5bb25e","observation_id":"78508c90-deaa-4eb3-9b93-8014a1653cbc","resolution":{"observed_at":"2026-08-14T10:22:33.725249Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.712542Z","title":null,"venue":null,"work_id":"d9313750-780e-4e16-8112-b79aa9ce9885","year":null},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.242238Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:abd6e35d6e3dd154bcf51c9ba62c1412623319a561fcdf9383ee9b8bbbb5fb5a","observation_id":"4d0b03a9-1903-4a0b-bc61-6d56bd21c9f3","resolution":{"observed_at":"2026-08-14T10:22:33.715542Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.703316Z","title":null,"venue":null,"work_id":"661c82f2-6720-4688-8684-b5994254368a","year":null},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.246059Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:7082a7fa42f6de02b12caafe0c37e569d057b39f0e8f03f1f1329730cfff6d4c","observation_id":"a2e21c43-8a31-4929-9c3c-a2897b6749b2","resolution":{"observed_at":"2026-08-14T10:22:33.706540Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.693652Z","title":null,"venue":null,"work_id":"b1df067f-4a45-4a5e-8c50-6d0fe922200d","year":null},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.249659Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:2214a2c2a821be4cfda7c06cbc3630a12d57e6f176ed4ddfe01eec94dd1e37f3","observation_id":"2500b7c0-942f-42c1-8ef1-77a7a41a7426","resolution":{"observed_at":"2026-08-14T10:22:33.697339Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.684909Z","title":null,"venue":null,"work_id":"a0030aa7-d55e-47ad-b8e3-557f46cb58a7","year":null},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.253394Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:9c11a529a08e3908e4556c688d1b0c9a788727bcab380681eb56935297d50863","observation_id":"ce8c9fe1-eb01-4a5a-8fdc-c757a1c30818","resolution":{"observed_at":"2026-08-14T10:22:33.688053Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.675304Z","title":"The proposed method trains separation and localization networks by using a cost function based on a cGMM that has the TF masks and DoAs as latent variables","venue":null,"work_id":"b8b0947b-6469-4376-9570-331267601c9c","year":null},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.257181Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:8409c79678da10cd4b50d120e2e06baf3b3e8c0c0f0b1f407c78e5a48b575ef5","observation_id":"c33e67a0-b3c6-46a7-b9ec-8b9b4205beb5","resolution":{"observed_at":"2026-08-14T10:22:33.678838Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.665887Z","title":"Deep clustering: Discriminative embeddings for segmentation and separation,","venue":null,"work_id":"de091ade-11ca-4618-b940-2c96422b3a3f","year":2016},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.260531Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:3ee85227b52c18a57ad806c8d7513e1298b40cfbed6366c4248961210cb96c2f","observation_id":"1b91c240-a5dc-425c-8973-db229ff904a2","resolution":{"observed_at":"2026-08-14T10:22:33.669411Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.656597Z","title":"Multitalker speech separation with utterance-level permutation invariant training of deep recurrent neural networks,","venue":null,"work_id":"067ee7fd-694a-4e09-9cdc-8054655e40e3","year":1901},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.263745Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:94e0ed58b7e3a3f66f97c21610f7a2545a7998327184c1b54dd7d34c6b8aaeb8","observation_id":"2857491d-e8a2-4d4c-be52-13c626b254dc","resolution":{"observed_at":"2026-08-14T10:22:33.660094Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.647555Z","title":"Deep attractor networks for speaker re-identiﬁcation and blind source separation,","venue":null,"work_id":"5b7906ae-8e00-462b-95df-f9555df89539","year":2018},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.267109Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:5339dcb89d039769ae4fe1e5cc3b26df003c104a8ec0cd1fb578f8571a2a361d","observation_id":"c69a134d-0c67-4fa6-8ce0-39cfc7c8d6fa","resolution":{"observed_at":"2026-08-14T10:22:33.650933Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.637887Z","title":"Singing voice separation with deep U-Net convolutional networks,","venue":null,"work_id":"77cd48f3-6271-41cb-939a-3c3b96649c3b","year":2017},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.270248Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:0a4fd01e9039677fed4d1a2a0ef2330c57ccf56bffbac07f41a3762126755e84","observation_id":"564e3b35-c4b8-4401-a01b-e8688c330493","resolution":{"observed_at":"2026-08-14T10:22:33.641405Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.628008Z","title":"Deep clustering and conventional net- works for music separation: Stronger together,","venue":null,"work_id":"076120c6-c916-4a35-9452-3a9a726779ca","year":2017},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.273923Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:ba588709c6a4c683868bdaa6a8c9a6a1ba57b91054074843af9007154f598f8d","observation_id":"6c4f20e8-2ad7-4b90-bad2-12d861ad9680","resolution":{"observed_at":"2026-08-14T10:22:33.631437Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.618722Z","title":"Multichannel nonnegative matrix fac- torization in convolutive mixtures for audio source sepa- ration,","venue":null,"work_id":"e61147e7-9a44-40b1-a7a9-52453ae1c6eb","year":2010},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.277538Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:401197d978962a494a7b58dff57eb9f5758c99e1cb31777d33f2339ea5f4726b","observation_id":"4fd81b7e-7747-4c07-8fb4-8323491d5b0e","resolution":{"observed_at":"2026-08-14T10:22:33.622316Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.609482Z","title":"Real-time independent vector analysis for con- volutive blind source separation,","venue":null,"work_id":"5d480237-ee1a-4c45-beee-fd8a717e2cc5","year":2010},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.280804Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:693f680ed8a04939db50984ea02fb7f19dc1bdeda74afc0f5ae2669b368f605d","observation_id":"0d5c6f2a-2fbd-4121-a520-c62bc8477693","resolution":{"observed_at":"2026-08-14T10:22:33.612875Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.600285Z","title":"Stable and fast update rules for independent vector analysis based on auxiliary function technique,","venue":null,"work_id":"3a29dc37-36a4-4474-b7dd-185a4a868edb","year":2011},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.284038Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:8981d0f3f2235b158c2db6a24ddf1c952483a3d7ea15834468c29d431249d8d7","observation_id":"e8e0f050-e57e-4763-9505-bc434ab06e04","resolution":{"observed_at":"2026-08-14T10:22:33.603563Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.590759Z","title":"Bayesian nonparametrics for micro- phone array processing,","venue":null,"work_id":"703a815a-c65c-4a3a-980d-c35438057d4b","year":2014},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.287191Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:0c5f01feb41c6c98080f657a9eaa4061a1aa09e489b044be34acb5ed7cd36c2b","observation_id":"7ea946ae-ebe5-444c-b3f1-220ce80f3a64","resolution":{"observed_at":"2026-08-14T10:22:33.594094Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.581671Z","title":"Robust MVDR beamforming using time-frequency masks for online/ofﬂine ASR in noise,","venue":null,"work_id":"1a5a97c4-6160-48ca-964a-ca552b168fd5","year":2016},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.290362Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:1b0a5aa5c5f40d53298e0617a755cc22d38db448dfbef0aa0cdbc4f8817305fc","observation_id":"336c4904-8ed4-4690-b290-c77b3cb295ce","resolution":{"observed_at":"2026-08-14T10:22:33.584967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.572354Z","title":"Permutation-free CGMM: Complex Gaussian mixture model with inverse Wishart mixture model based spatial prior for permutation-free source separation and source counting,","venue":null,"work_id":"ab0833f6-ab25-4318-b307-d90bac3b1e03","year":2018},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.293638Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:858db1ac6e5dcdeb17ddb3d860dd601733d8428a784d164e86a0e044bc84ce5d","observation_id":"f01b1e20-1990-4b11-ac34-a6ede07401e5","resolution":{"observed_at":"2026-08-14T10:22:33.575850Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.562900Z","title":"Unsupervised training of a deep clus- tering model for multichannel blind source separation,","venue":null,"work_id":"55f45023-5a9c-4b65-8a2a-c26b9c3d8baf","year":2019},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.297142Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:d9d57a5fb13dd32e5f54b74ce164b223ebfaa4a88c65f1d50bbae74c1a9732fa","observation_id":"e7152035-1091-4387-822e-216a42527753","resolution":{"observed_at":"2026-08-14T10:22:33.566402Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.552929Z","title":"Bootstrapping single-channel source separation via unsupervised spatial clustering on stereo mixtures,","venue":null,"work_id":"0dfd7a92-6770-4a90-b13a-c4da18126675","year":2019},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.300861Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:9aee73b1e171a33c6239a166138d209f323ea74143023e00206d054efee071f9","observation_id":"18b69a59-6ecd-4959-9014-5bf3c3284efe","resolution":{"observed_at":"2026-08-14T10:22:33.557254Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.543305Z","title":"Unsupervised deep clustering for source separation: Direct learning from mixtures using spatial information,","venue":null,"work_id":"af25592a-82aa-49bb-94fe-d8bc12741e1a","year":2019},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.304358Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:c8e6579e7e0ee0ac200ce942ca78edbc0295bda7dd31a0c3e58aa4d1ce3f5f97","observation_id":"b9ccc221-045b-4647-9fe1-60abeac1791a","resolution":{"observed_at":"2026-08-14T10:22:33.546979Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.01578","last_updated":"2019-04-08T12:00:46Z","snapshot_observed_at":"2026-08-14T16:53:03.751685Z","submitted_at":"2019-04-02T12:10:23Z","title":"Unsupervised training of neural mask-based beamforming","version":2},"cited_work":{"arxiv_id":"1904.01578","doi":null,"metadata_source":"pith","pith_arxiv_id":"1904.01578","snapshot_observed_at":"2026-08-14T10:22:33.399156Z","title":"Unsupervised training of neural mask-based beamforming","venue":"cs.SD","work_id":"0322525f-87f4-438d-b1fe-03cd6fa595a8","year":2019},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.307731Z"},"links":{"cited_paper":"/paper/1904.01578","citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:88bb7796eb5c4644c5a1eae70fdc3fe30f5f7de70ac0ba16a357e1dff78bca70","observation_id":"16c45cc3-1ba8-4567-b9b8-b0957df53522","resolution":{"observed_at":"2026-08-14T10:22:33.404698Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.534063Z","title":"Measuring dependence of bin- wise separated signals for permutation alignment in frequency-domain BSS,","venue":null,"work_id":"d7a149cb-83cf-43e7-8244-77cf1cc950b3","year":2007},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.311619Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:ba6f156965d900606503f43181cceb551beefbd99987095f3fb4be77ba875e28","observation_id":"438af49d-8372-4595-85a8-a8e18e9f7830","resolution":{"observed_at":"2026-08-14T10:22:33.537573Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-08-14T23:50:45.029465Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-14T10:22:33.314871Z","title":"Auto-encoding variational Bayes,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.314871Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:4099357ab43dd0c314ead93d738df3c883b9d2b2cb966e16b3d199c3637a7d8a","observation_id":"1eaab787-0fa1-4562-8b56-23a7ed0b5dbc","resolution":{"observed_at":"2026-08-14T10:22:33.314871Z","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":"2026-08-14T10:22:33.524943Z","title":"Collapsed variational Dirichlet pro- cess mixture models","venue":null,"work_id":"1a071aff-f4f5-4e04-b760-b87e69e77b56","year":2007},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.318146Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:51bd063aaeaa62c46c1d25ae392e1388c11faa3f469f53995afe721e00173512","observation_id":"82ce9441-5efa-45ed-9e15-142db3dfac2e","resolution":{"observed_at":"2026-08-14T10:22:33.528258Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.515102Z","title":"Blind sparse source separation for unknown number of sources using Gaussian mixture model ﬁtting with Dirichlet prior,","venue":null,"work_id":"b87abc26-3f57-4035-b76d-bd1fabaaaf48","year":2009},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.321624Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:a77c331f1d3fcefdb27dc509c88c6611e4a935f4a41893f9810f420aa83738a2","observation_id":"899098dc-84f6-47c2-a962-635730716882","resolution":{"observed_at":"2026-08-14T10:22:33.518543Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.505769Z","title":"An em algorithm for localizing multiple sound sources in reverberant environments,","venue":null,"work_id":"913816e4-2dab-4963-bcdf-b47a3844ea52","year":2007},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.324813Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:59d4e350085a93f4263c7afcf32862bcaf32f3593fa5bb161f95b8146610fec6","observation_id":"a3030959-2afb-4698-bf8b-3040bf2576c3","resolution":{"observed_at":"2026-08-14T10:22:33.509185Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.496006Z","title":"Complex angular central gaussian mix- ture model for directional statistics in mask-based mi- crophone array signal processing,","venue":null,"work_id":"d6cda81f-971b-4eee-a942-87c9f5ad364b","year":2016},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.328161Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:8b3ce20954b19c30aedc513e778be24f13a00db4e048d7a491e58a04f284c07d","observation_id":"f7ed04f2-da13-454f-9c3e-9200f9355564","resolution":{"observed_at":"2026-08-14T10:22:33.499695Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.485737Z","title":"Audio-visual scene analysis with self- supervised multisensory features,","venue":null,"work_id":"bf7ee1fb-6998-4849-ae06-85c5df62d32f","year":2018},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.331327Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:77df176573bf0c4869bace21a7591337ad49996306acf2f7d5f1e01ff39811c4","observation_id":"15d04bdc-3f04-430a-9adf-9bda4b2b4bcb","resolution":{"observed_at":"2026-08-14T10:22:33.489091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.476485Z","title":"Self-supervised audio-visual co-segmentation,","venue":null,"work_id":"2550e94a-e94d-4480-bc5a-7529fd55a54d","year":2019},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.334520Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:bf866a789ce4b85f44e247fc09979f873c60d46af6a92db08d68f3541193f174","observation_id":"f962a638-2c48-4e5e-bda0-5f48c4d1999d","resolution":{"observed_at":"2026-08-14T10:22:33.479805Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.466741Z","title":"Bayesian uniﬁcation of sound source localization and separation with permutation resolu- tion,","venue":null,"work_id":"0dbaf800-cb98-4b6c-aa10-ef23b2c6d2ff","year":2012},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.337619Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:bc545fd5a2a97f07069a28c3dae8a9ad1ea786fde32dd47294a3b167ecb20b8b","observation_id":"230a1f9c-54a5-4ddd-8b32-fcf3a5b3f9ea","resolution":{"observed_at":"2026-08-14T10:22:33.470484Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.456275Z","title":"Black box variational inference,","venue":null,"work_id":"b9b840a8-63ae-4e1a-9d6f-8960a7b3e40a","year":2014},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.341135Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:18d31d3417e717cf51c9ebf60deb8e737a07b006a7cd27e769c3661afaba6add","observation_id":"edcfc137-950a-4aa7-8c4d-a7836937baaf","resolution":{"observed_at":"2026-08-14T10:22:33.460067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.445565Z","title":"Image method for efﬁciently simu- lating small-room acoustics,","venue":null,"work_id":"8864f197-c5cf-455d-9236-3401afb373ce","year":1979},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.344213Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:cda1434d9bca6ebd0daae404643d623d1ae844d2580bcf33d7fa792bf06e1763","observation_id":"29deaf25-7aac-4d3d-a5ec-4c154f39c495","resolution":{"observed_at":"2026-08-14T10:22:33.449664Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-17T19:26:44.032537Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-14T10:22:33.347370Z","title":"Adam: A method for stochastic optimization,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.347370Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:1068fecc4e527443ea16c89dcefc6eb8b94d340b11267417b6f7b304c5c03c1f","observation_id":"805cd9de-a85f-4e56-b323-c387ab6166fb","resolution":{"observed_at":"2026-08-14T10:22:33.347370Z","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":"2026-08-14T10:22:33.435429Z","title":"Relaxation of rank-1 spatial con- straint in overdetermined blind source separation,","venue":null,"work_id":"9645f78f-dc6c-4176-a0e7-e504b87e2ae0","year":2015},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.350508Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:ba33f4efd00baacad184583fccf24aab0d5ed549752ea4155524992ae68da717","observation_id":"1e314a6c-48cb-482a-beb2-6a251658a39d","resolution":{"observed_at":"2026-08-14T10:22:33.438900Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-14T10:22:33.425264Z","title":"Performance measurement in blind audio source separation,","venue":null,"work_id":"2ed33d31-2f45-45a1-948a-0b6349778895","year":2006},"citing_paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T10:22:33.353784Z"},"links":{"citing_paper":"/paper/1908.11307"},"observation_digest":"sha256:ce34bc2572c7568a90b89e2cbbb71d204cbde0dece67fe009a704639919a3d26","observation_id":"a4ba8b7a-8215-4fb8-8910-c5250439424b","resolution":{"observed_at":"2026-08-14T10:22:33.428932Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.11307","last_updated":"2019-08-29T15:45:20Z","latest_version":1,"primary_category":"cs.SD","snapshot_observed_at":"2026-08-19T08:54:17.377951Z","submitted_at":"2019-08-29T15:45:20Z","title":"Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model"},"reference_resolution":{"displayed":37,"state_counts":{"malformed_identifier":1,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":6,"verified_exact":1,"verified_fuzzy":28},"total_outbound_references":37},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 1 inbound Pith citation observation for arXiv:1908.11307."}