{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:GJKMJGTOEQKP5SHTH76AEY64GS","short_pith_number":"pith:GJKMJGTO","schema_version":"1.0","canonical_sha256":"3254c49a6e2414fec8f33ffc0263dc34b2c4a3eff832618db642e403d500fa5b","source":{"kind":"arxiv","id":"1806.10307","version":1},"attestation_state":"computed","paper":{"title":"Independent Deeply Learned Matrix Analysis for Multichannel Audio Source Separation","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Daichi Kitamura, Hayato Sumino, Hiroshi Saruwatari, Nobutaka Ono, Norihiro Takamune, Shinichi Mogami, Shinnosuke Takamichi","submitted_at":"2018-06-27T05:52:40Z","abstract_excerpt":"In this paper, we address a multichannel audio source separation task and propose a new efficient method called independent deeply learned matrix analysis (IDLMA). IDLMA estimates the demixing matrix in a blind manner and updates the time-frequency structures of each source using a pretrained deep neural network (DNN). Also, we introduce a complex Student's t-distribution as a generalized source generative model including both complex Gaussian and Cauchy distributions. Experiments are conducted using music signals with a training dataset, and the results show the validity of the proposed metho"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1806.10307","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"eess.AS","submitted_at":"2018-06-27T05:52:40Z","cross_cats_sorted":["cs.SD"],"title_canon_sha256":"83f6ef85295d0c10603b9f72372468c4d1904a1c552060ac9f64d5fc3c62b56b","abstract_canon_sha256":"31167ceb4725606809458d56249fbc53f2733f93abca196bba74398bfc679a74"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:12:12.872974Z","signature_b64":"nc7D1ibn3pg0Y9itfXiqpB4EApAnaO65hfr72GnSDZGRkqrDBBtP+UGpm9zDsUqgWcBGgiOx+LZFyctX1aVGBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3254c49a6e2414fec8f33ffc0263dc34b2c4a3eff832618db642e403d500fa5b","last_reissued_at":"2026-05-18T00:12:12.872336Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:12:12.872336Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Independent Deeply Learned Matrix Analysis for Multichannel Audio Source Separation","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Daichi Kitamura, Hayato Sumino, Hiroshi Saruwatari, Nobutaka Ono, Norihiro Takamune, Shinichi Mogami, Shinnosuke Takamichi","submitted_at":"2018-06-27T05:52:40Z","abstract_excerpt":"In this paper, we address a multichannel audio source separation task and propose a new efficient method called independent deeply learned matrix analysis (IDLMA). IDLMA estimates the demixing matrix in a blind manner and updates the time-frequency structures of each source using a pretrained deep neural network (DNN). Also, we introduce a complex Student's t-distribution as a generalized source generative model including both complex Gaussian and Cauchy distributions. Experiments are conducted using music signals with a training dataset, and the results show the validity of the proposed metho"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.10307","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1806.10307","created_at":"2026-05-18T00:12:12.872447+00:00"},{"alias_kind":"arxiv_version","alias_value":"1806.10307v1","created_at":"2026-05-18T00:12:12.872447+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.10307","created_at":"2026-05-18T00:12:12.872447+00:00"},{"alias_kind":"pith_short_12","alias_value":"GJKMJGTOEQKP","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_16","alias_value":"GJKMJGTOEQKP5SHT","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_8","alias_value":"GJKMJGTO","created_at":"2026-05-18T12:32:25.280505+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GJKMJGTOEQKP5SHTH76AEY64GS","json":"https://pith.science/pith/GJKMJGTOEQKP5SHTH76AEY64GS.json","graph_json":"https://pith.science/api/pith-number/GJKMJGTOEQKP5SHTH76AEY64GS/graph.json","events_json":"https://pith.science/api/pith-number/GJKMJGTOEQKP5SHTH76AEY64GS/events.json","paper":"https://pith.science/paper/GJKMJGTO"},"agent_actions":{"view_html":"https://pith.science/pith/GJKMJGTOEQKP5SHTH76AEY64GS","download_json":"https://pith.science/pith/GJKMJGTOEQKP5SHTH76AEY64GS.json","view_paper":"https://pith.science/paper/GJKMJGTO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1806.10307&json=true","fetch_graph":"https://pith.science/api/pith-number/GJKMJGTOEQKP5SHTH76AEY64GS/graph.json","fetch_events":"https://pith.science/api/pith-number/GJKMJGTOEQKP5SHTH76AEY64GS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GJKMJGTOEQKP5SHTH76AEY64GS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GJKMJGTOEQKP5SHTH76AEY64GS/action/storage_attestation","attest_author":"https://pith.science/pith/GJKMJGTOEQKP5SHTH76AEY64GS/action/author_attestation","sign_citation":"https://pith.science/pith/GJKMJGTOEQKP5SHTH76AEY64GS/action/citation_signature","submit_replication":"https://pith.science/pith/GJKMJGTOEQKP5SHTH76AEY64GS/action/replication_record"}},"created_at":"2026-05-18T00:12:12.872447+00:00","updated_at":"2026-05-18T00:12:12.872447+00:00"}