{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:KK5MDKCPLJUVDJ3O3KW2LFLNWH","short_pith_number":"pith:KK5MDKCP","schema_version":"1.0","canonical_sha256":"52bac1a84f5a6951a76edaada5956db1da7f9b95d066810e36164a01c0009849","source":{"kind":"arxiv","id":"1705.04662","version":1},"attestation_state":"computed","paper":{"title":"Monaural Audio Speaker Separation with Source Contrastive Estimation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","stat.ML"],"primary_cat":"cs.SD","authors_text":"Abhinav Ganesh, Cory Stephenson, Karl Ni, Patrick Callier","submitted_at":"2017-05-12T17:23:02Z","abstract_excerpt":"We propose an algorithm to separate simultaneously speaking persons from each other, the \"cocktail party problem\", using a single microphone. Our approach involves a deep recurrent neural networks regression to a vector space that is descriptive of independent speakers. Such a vector space can embed empirically determined speaker characteristics and is optimized by distinguishing between speaker masks. We call this technique source-contrastive estimation. The methodology is inspired by negative sampling, which has seen success in natural language processing, where an embedding is learned by co"},"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":"1705.04662","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2017-05-12T17:23:02Z","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"title_canon_sha256":"ec136f3737639caea49f21cff6e365f5dc9805f3a6b2662fc02d6c44336d3158","abstract_canon_sha256":"e1ae55d275e3af62a54529304c60bde661f41b9e81c7f350309d032752ec59c0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:44:13.170063Z","signature_b64":"GoXr3RUyOj5BRh/Or6qKoZkAi2HWzBFLo0gIneaH4VRvpmtqP1DSoTSy0v31varYpbq9EbNT+OjGzOafkUiCCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"52bac1a84f5a6951a76edaada5956db1da7f9b95d066810e36164a01c0009849","last_reissued_at":"2026-05-18T00:44:13.169552Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:44:13.169552Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Monaural Audio Speaker Separation with Source Contrastive Estimation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","stat.ML"],"primary_cat":"cs.SD","authors_text":"Abhinav Ganesh, Cory Stephenson, Karl Ni, Patrick Callier","submitted_at":"2017-05-12T17:23:02Z","abstract_excerpt":"We propose an algorithm to separate simultaneously speaking persons from each other, the \"cocktail party problem\", using a single microphone. Our approach involves a deep recurrent neural networks regression to a vector space that is descriptive of independent speakers. Such a vector space can embed empirically determined speaker characteristics and is optimized by distinguishing between speaker masks. We call this technique source-contrastive estimation. The methodology is inspired by negative sampling, which has seen success in natural language processing, where an embedding is learned by co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.04662","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":"1705.04662","created_at":"2026-05-18T00:44:13.169641+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.04662v1","created_at":"2026-05-18T00:44:13.169641+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.04662","created_at":"2026-05-18T00:44:13.169641+00:00"},{"alias_kind":"pith_short_12","alias_value":"KK5MDKCPLJUV","created_at":"2026-05-18T12:31:24.725408+00:00"},{"alias_kind":"pith_short_16","alias_value":"KK5MDKCPLJUVDJ3O","created_at":"2026-05-18T12:31:24.725408+00:00"},{"alias_kind":"pith_short_8","alias_value":"KK5MDKCP","created_at":"2026-05-18T12:31:24.725408+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/KK5MDKCPLJUVDJ3O3KW2LFLNWH","json":"https://pith.science/pith/KK5MDKCPLJUVDJ3O3KW2LFLNWH.json","graph_json":"https://pith.science/api/pith-number/KK5MDKCPLJUVDJ3O3KW2LFLNWH/graph.json","events_json":"https://pith.science/api/pith-number/KK5MDKCPLJUVDJ3O3KW2LFLNWH/events.json","paper":"https://pith.science/paper/KK5MDKCP"},"agent_actions":{"view_html":"https://pith.science/pith/KK5MDKCPLJUVDJ3O3KW2LFLNWH","download_json":"https://pith.science/pith/KK5MDKCPLJUVDJ3O3KW2LFLNWH.json","view_paper":"https://pith.science/paper/KK5MDKCP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.04662&json=true","fetch_graph":"https://pith.science/api/pith-number/KK5MDKCPLJUVDJ3O3KW2LFLNWH/graph.json","fetch_events":"https://pith.science/api/pith-number/KK5MDKCPLJUVDJ3O3KW2LFLNWH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KK5MDKCPLJUVDJ3O3KW2LFLNWH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KK5MDKCPLJUVDJ3O3KW2LFLNWH/action/storage_attestation","attest_author":"https://pith.science/pith/KK5MDKCPLJUVDJ3O3KW2LFLNWH/action/author_attestation","sign_citation":"https://pith.science/pith/KK5MDKCPLJUVDJ3O3KW2LFLNWH/action/citation_signature","submit_replication":"https://pith.science/pith/KK5MDKCPLJUVDJ3O3KW2LFLNWH/action/replication_record"}},"created_at":"2026-05-18T00:44:13.169641+00:00","updated_at":"2026-05-18T00:44:13.169641+00:00"}