{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:WA7J2HDQDMIWOJHHXDLXRMJFGS","short_pith_number":"pith:WA7J2HDQ","schema_version":"1.0","canonical_sha256":"b03e9d1c701b116724e7b8d778b125348d55792412486d91076053baf30d26eb","source":{"kind":"arxiv","id":"2409.17898","version":1},"attestation_state":"computed","paper":{"title":"MC-SEMamba: A Simple Multi-channel Extension of SEMamba","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Fan-Gang Zeng, Hsin-Yi Lin, Rong Chao, Wen-Yuan Ting, Wenze Ren, Yu Tsao","submitted_at":"2024-09-26T14:48:21Z","abstract_excerpt":"Transformer-based models have become increasingly popular and have impacted speech-processing research owing to their exceptional performance in sequence modeling. Recently, a promising model architecture, Mamba, has emerged as a potential alternative to transformer-based models because of its efficient modeling of long sequences. In particular, models like SEMamba have demonstrated the effectiveness of the Mamba architecture in single-channel speech enhancement. This paper aims to adapt SEMamba for multi-channel applications with only a small increase in parameters. The resulting system, MC-S"},"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":"2409.17898","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2024-09-26T14:48:21Z","cross_cats_sorted":["cs.SD"],"title_canon_sha256":"21feebb59eba66697ed6211436f98f90f91577cbac190f9abb4506812c9cd208","abstract_canon_sha256":"76195e50ae4ecf856f6215e4df2827a6df8cf68df6470db54e6ff4705534829a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:12:17.256244Z","signature_b64":"d9jVwSVv7SUPX/GvMO90HD+ApY6Twx1EDOnkCnpzxQSGRzwNjtAS/FjAPLdY/rsqRqWeLW89xxb/bx6eJFnrCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b03e9d1c701b116724e7b8d778b125348d55792412486d91076053baf30d26eb","last_reissued_at":"2026-07-05T09:12:17.255743Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:12:17.255743Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MC-SEMamba: A Simple Multi-channel Extension of SEMamba","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Fan-Gang Zeng, Hsin-Yi Lin, Rong Chao, Wen-Yuan Ting, Wenze Ren, Yu Tsao","submitted_at":"2024-09-26T14:48:21Z","abstract_excerpt":"Transformer-based models have become increasingly popular and have impacted speech-processing research owing to their exceptional performance in sequence modeling. Recently, a promising model architecture, Mamba, has emerged as a potential alternative to transformer-based models because of its efficient modeling of long sequences. In particular, models like SEMamba have demonstrated the effectiveness of the Mamba architecture in single-channel speech enhancement. This paper aims to adapt SEMamba for multi-channel applications with only a small increase in parameters. The resulting system, MC-S"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.17898","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2409.17898/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2409.17898","created_at":"2026-07-05T09:12:17.255802+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.17898v1","created_at":"2026-07-05T09:12:17.255802+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.17898","created_at":"2026-07-05T09:12:17.255802+00:00"},{"alias_kind":"pith_short_12","alias_value":"WA7J2HDQDMIW","created_at":"2026-07-05T09:12:17.255802+00:00"},{"alias_kind":"pith_short_16","alias_value":"WA7J2HDQDMIWOJHH","created_at":"2026-07-05T09:12:17.255802+00:00"},{"alias_kind":"pith_short_8","alias_value":"WA7J2HDQ","created_at":"2026-07-05T09:12:17.255802+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/WA7J2HDQDMIWOJHHXDLXRMJFGS","json":"https://pith.science/pith/WA7J2HDQDMIWOJHHXDLXRMJFGS.json","graph_json":"https://pith.science/api/pith-number/WA7J2HDQDMIWOJHHXDLXRMJFGS/graph.json","events_json":"https://pith.science/api/pith-number/WA7J2HDQDMIWOJHHXDLXRMJFGS/events.json","paper":"https://pith.science/paper/WA7J2HDQ"},"agent_actions":{"view_html":"https://pith.science/pith/WA7J2HDQDMIWOJHHXDLXRMJFGS","download_json":"https://pith.science/pith/WA7J2HDQDMIWOJHHXDLXRMJFGS.json","view_paper":"https://pith.science/paper/WA7J2HDQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.17898&json=true","fetch_graph":"https://pith.science/api/pith-number/WA7J2HDQDMIWOJHHXDLXRMJFGS/graph.json","fetch_events":"https://pith.science/api/pith-number/WA7J2HDQDMIWOJHHXDLXRMJFGS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WA7J2HDQDMIWOJHHXDLXRMJFGS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WA7J2HDQDMIWOJHHXDLXRMJFGS/action/storage_attestation","attest_author":"https://pith.science/pith/WA7J2HDQDMIWOJHHXDLXRMJFGS/action/author_attestation","sign_citation":"https://pith.science/pith/WA7J2HDQDMIWOJHHXDLXRMJFGS/action/citation_signature","submit_replication":"https://pith.science/pith/WA7J2HDQDMIWOJHHXDLXRMJFGS/action/replication_record"}},"created_at":"2026-07-05T09:12:17.255802+00:00","updated_at":"2026-07-05T09:12:17.255802+00:00"}