{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:PXSUWULOD6G66PUFDDZ2NOGHFY","short_pith_number":"pith:PXSUWULO","schema_version":"1.0","canonical_sha256":"7de54b516e1f8def3e8518f3a6b8c72e13888a2960fc0e0b74281c60435ac38f","source":{"kind":"arxiv","id":"1904.11148","version":1},"attestation_state":"computed","paper":{"title":"Divide and Conquer: A Deep CASA Approach to Talker-independent Monaural Speaker Separation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.AS"],"primary_cat":"cs.SD","authors_text":"Deliang Wang, Yuzhou Liu","submitted_at":"2019-04-25T03:57:11Z","abstract_excerpt":"We address talker-independent monaural speaker separation from the perspectives of deep learning and computational auditory scene analysis (CASA). Specifically, we decompose the multi-speaker separation task into the stages of simultaneous grouping and sequential grouping. Simultaneous grouping is first performed in each time frame by separating the spectra of different speakers with a permutation-invariantly trained neural network. In the second stage, the frame-level separated spectra are sequentially grouped to different speakers by a clustering network. The proposed deep CASA approach opti"},"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":"1904.11148","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2019-04-25T03:57:11Z","cross_cats_sorted":["cs.LG","eess.AS"],"title_canon_sha256":"4e61adbd31dc2a8574030c904c100aaae52389ff6b377d0dacf3d6e94e6a69da","abstract_canon_sha256":"60d73e1628895805ca11458ecbce4acfd26598d4aba8d25bae763fa97e57e699"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:47:45.655972Z","signature_b64":"98QSsBF/YgaTNvpBS41CsTC87J/WH2OYqgwLtw62+aawOK8LzD91fYVcuH+9o2wRk0G/SzkfFm+0HFTB0s0zBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7de54b516e1f8def3e8518f3a6b8c72e13888a2960fc0e0b74281c60435ac38f","last_reissued_at":"2026-05-17T23:47:45.655436Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:47:45.655436Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Divide and Conquer: A Deep CASA Approach to Talker-independent Monaural Speaker Separation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.AS"],"primary_cat":"cs.SD","authors_text":"Deliang Wang, Yuzhou Liu","submitted_at":"2019-04-25T03:57:11Z","abstract_excerpt":"We address talker-independent monaural speaker separation from the perspectives of deep learning and computational auditory scene analysis (CASA). Specifically, we decompose the multi-speaker separation task into the stages of simultaneous grouping and sequential grouping. Simultaneous grouping is first performed in each time frame by separating the spectra of different speakers with a permutation-invariantly trained neural network. In the second stage, the frame-level separated spectra are sequentially grouped to different speakers by a clustering network. The proposed deep CASA approach opti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.11148","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":"1904.11148","created_at":"2026-05-17T23:47:45.655527+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.11148v1","created_at":"2026-05-17T23:47:45.655527+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.11148","created_at":"2026-05-17T23:47:45.655527+00:00"},{"alias_kind":"pith_short_12","alias_value":"PXSUWULOD6G6","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_16","alias_value":"PXSUWULOD6G66PUF","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_8","alias_value":"PXSUWULO","created_at":"2026-05-18T12:33:24.271573+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/PXSUWULOD6G66PUFDDZ2NOGHFY","json":"https://pith.science/pith/PXSUWULOD6G66PUFDDZ2NOGHFY.json","graph_json":"https://pith.science/api/pith-number/PXSUWULOD6G66PUFDDZ2NOGHFY/graph.json","events_json":"https://pith.science/api/pith-number/PXSUWULOD6G66PUFDDZ2NOGHFY/events.json","paper":"https://pith.science/paper/PXSUWULO"},"agent_actions":{"view_html":"https://pith.science/pith/PXSUWULOD6G66PUFDDZ2NOGHFY","download_json":"https://pith.science/pith/PXSUWULOD6G66PUFDDZ2NOGHFY.json","view_paper":"https://pith.science/paper/PXSUWULO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.11148&json=true","fetch_graph":"https://pith.science/api/pith-number/PXSUWULOD6G66PUFDDZ2NOGHFY/graph.json","fetch_events":"https://pith.science/api/pith-number/PXSUWULOD6G66PUFDDZ2NOGHFY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PXSUWULOD6G66PUFDDZ2NOGHFY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PXSUWULOD6G66PUFDDZ2NOGHFY/action/storage_attestation","attest_author":"https://pith.science/pith/PXSUWULOD6G66PUFDDZ2NOGHFY/action/author_attestation","sign_citation":"https://pith.science/pith/PXSUWULOD6G66PUFDDZ2NOGHFY/action/citation_signature","submit_replication":"https://pith.science/pith/PXSUWULOD6G66PUFDDZ2NOGHFY/action/replication_record"}},"created_at":"2026-05-17T23:47:45.655527+00:00","updated_at":"2026-05-17T23:47:45.655527+00:00"}