{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:T5FLWXUSBHLFFKJXOL7UCYHTFF","short_pith_number":"pith:T5FLWXUS","schema_version":"1.0","canonical_sha256":"9f4abb5e9209d652a93772ff4160f32964346e34c802af8db438de513741178d","source":{"kind":"arxiv","id":"1806.08086","version":1},"attestation_state":"computed","paper":{"title":"Towards Automated Single Channel Source Separation using Neural Networks","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Akshay Soni, Arpita Gang, Pravesh Biyani","submitted_at":"2018-06-21T07:03:51Z","abstract_excerpt":"Many applications of single channel source separation (SCSS) including automatic speech recognition (ASR), hearing aids etc. require an estimation of only one source from a mixture of many sources. Treating this special case as a regular SCSS problem where in all constituent sources are given equal priority in terms of reconstruction may result in a suboptimal separation performance. In this paper, we tackle the one source separation problem by suitably modifying the orthodox SCSS framework and focus only on one source at a time. The proposed approach is a generic framework that can be applied"},"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":"1806.08086","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"eess.AS","submitted_at":"2018-06-21T07:03:51Z","cross_cats_sorted":["cs.SD"],"title_canon_sha256":"1ad640dddf9de38602d3df58c7fd990af434c058af046e570f388c5fab034c0b","abstract_canon_sha256":"73c746e9e7f6a0a47a56305333b44baeb089f0c5cdd621db0a9a5addcf5d95fa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:12:42.092238Z","signature_b64":"I2meRCrl+Ll3BREGw5nwvr21m9gnQfSWX1ux4fo2iPoVLKtdEjAWGOv/v+xjZErWXuK0whG1OFlpZKk3ahSEBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9f4abb5e9209d652a93772ff4160f32964346e34c802af8db438de513741178d","last_reissued_at":"2026-05-18T00:12:42.091733Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:12:42.091733Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Automated Single Channel Source Separation using Neural Networks","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Akshay Soni, Arpita Gang, Pravesh Biyani","submitted_at":"2018-06-21T07:03:51Z","abstract_excerpt":"Many applications of single channel source separation (SCSS) including automatic speech recognition (ASR), hearing aids etc. require an estimation of only one source from a mixture of many sources. Treating this special case as a regular SCSS problem where in all constituent sources are given equal priority in terms of reconstruction may result in a suboptimal separation performance. In this paper, we tackle the one source separation problem by suitably modifying the orthodox SCSS framework and focus only on one source at a time. The proposed approach is a generic framework that can be applied"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.08086","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":"1806.08086","created_at":"2026-05-18T00:12:42.091806+00:00"},{"alias_kind":"arxiv_version","alias_value":"1806.08086v1","created_at":"2026-05-18T00:12:42.091806+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.08086","created_at":"2026-05-18T00:12:42.091806+00:00"},{"alias_kind":"pith_short_12","alias_value":"T5FLWXUSBHLF","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_16","alias_value":"T5FLWXUSBHLFFKJX","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_8","alias_value":"T5FLWXUS","created_at":"2026-05-18T12:32:53.628368+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/T5FLWXUSBHLFFKJXOL7UCYHTFF","json":"https://pith.science/pith/T5FLWXUSBHLFFKJXOL7UCYHTFF.json","graph_json":"https://pith.science/api/pith-number/T5FLWXUSBHLFFKJXOL7UCYHTFF/graph.json","events_json":"https://pith.science/api/pith-number/T5FLWXUSBHLFFKJXOL7UCYHTFF/events.json","paper":"https://pith.science/paper/T5FLWXUS"},"agent_actions":{"view_html":"https://pith.science/pith/T5FLWXUSBHLFFKJXOL7UCYHTFF","download_json":"https://pith.science/pith/T5FLWXUSBHLFFKJXOL7UCYHTFF.json","view_paper":"https://pith.science/paper/T5FLWXUS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1806.08086&json=true","fetch_graph":"https://pith.science/api/pith-number/T5FLWXUSBHLFFKJXOL7UCYHTFF/graph.json","fetch_events":"https://pith.science/api/pith-number/T5FLWXUSBHLFFKJXOL7UCYHTFF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T5FLWXUSBHLFFKJXOL7UCYHTFF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T5FLWXUSBHLFFKJXOL7UCYHTFF/action/storage_attestation","attest_author":"https://pith.science/pith/T5FLWXUSBHLFFKJXOL7UCYHTFF/action/author_attestation","sign_citation":"https://pith.science/pith/T5FLWXUSBHLFFKJXOL7UCYHTFF/action/citation_signature","submit_replication":"https://pith.science/pith/T5FLWXUSBHLFFKJXOL7UCYHTFF/action/replication_record"}},"created_at":"2026-05-18T00:12:42.091806+00:00","updated_at":"2026-05-18T00:12:42.091806+00:00"}