{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:Z6RAKFA3Q3SRDWS3VRS7WPWGUZ","short_pith_number":"pith:Z6RAKFA3","schema_version":"1.0","canonical_sha256":"cfa205141b86e511da5bac65fb3ec6a67944c14fa416cfec88b53409eacd039d","source":{"kind":"arxiv","id":"2406.09819","version":1},"attestation_state":"computed","paper":{"title":"Enhanced Deep Speech Separation in Clustered Ad Hoc Distributed Microphone Environments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.AS","authors_text":"Hong-Goo Kang, JiHyun Kim, Nilesh Madhu, Stijn Kindt","submitted_at":"2024-06-14T08:22:18Z","abstract_excerpt":"Ad-hoc distributed microphone environments, where microphone locations and numbers are unpredictable, present a challenge to traditional deep learning models, which typically require fixed architectures. To tailor deep learning models to accommodate arbitrary array configurations, the Transform-Average-Concatenate (TAC) layer was previously introduced. In this work, we integrate TAC layers with dual-path transformers for speech separation from two simultaneous talkers in realistic settings. However, the distributed nature makes it hard to fuse information across microphones efficiently. Theref"},"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":"2406.09819","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2024-06-14T08:22:18Z","cross_cats_sorted":[],"title_canon_sha256":"6609eeac867ee12f94c806e2e92e57c51164a501f593b38eaa0d9d68bfd5298f","abstract_canon_sha256":"3622cc058f7d066f6cc9ec0d587cb3498dca87076dac56d028caa83bea9f210e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:32:00.633650Z","signature_b64":"mtWfd9i7ac9gD7cjvmD7YGTMOT9/vsK7Fhmsko7aqeIQlZND7oEkYb5BiQ6IKPk7vkrui4rFH3dhe3ViedHmBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cfa205141b86e511da5bac65fb3ec6a67944c14fa416cfec88b53409eacd039d","last_reissued_at":"2026-07-05T08:32:00.633185Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:32:00.633185Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhanced Deep Speech Separation in Clustered Ad Hoc Distributed Microphone Environments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.AS","authors_text":"Hong-Goo Kang, JiHyun Kim, Nilesh Madhu, Stijn Kindt","submitted_at":"2024-06-14T08:22:18Z","abstract_excerpt":"Ad-hoc distributed microphone environments, where microphone locations and numbers are unpredictable, present a challenge to traditional deep learning models, which typically require fixed architectures. To tailor deep learning models to accommodate arbitrary array configurations, the Transform-Average-Concatenate (TAC) layer was previously introduced. In this work, we integrate TAC layers with dual-path transformers for speech separation from two simultaneous talkers in realistic settings. However, the distributed nature makes it hard to fuse information across microphones efficiently. Theref"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.09819","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/2406.09819/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":"2406.09819","created_at":"2026-07-05T08:32:00.633237+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.09819v1","created_at":"2026-07-05T08:32:00.633237+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.09819","created_at":"2026-07-05T08:32:00.633237+00:00"},{"alias_kind":"pith_short_12","alias_value":"Z6RAKFA3Q3SR","created_at":"2026-07-05T08:32:00.633237+00:00"},{"alias_kind":"pith_short_16","alias_value":"Z6RAKFA3Q3SRDWS3","created_at":"2026-07-05T08:32:00.633237+00:00"},{"alias_kind":"pith_short_8","alias_value":"Z6RAKFA3","created_at":"2026-07-05T08:32:00.633237+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/Z6RAKFA3Q3SRDWS3VRS7WPWGUZ","json":"https://pith.science/pith/Z6RAKFA3Q3SRDWS3VRS7WPWGUZ.json","graph_json":"https://pith.science/api/pith-number/Z6RAKFA3Q3SRDWS3VRS7WPWGUZ/graph.json","events_json":"https://pith.science/api/pith-number/Z6RAKFA3Q3SRDWS3VRS7WPWGUZ/events.json","paper":"https://pith.science/paper/Z6RAKFA3"},"agent_actions":{"view_html":"https://pith.science/pith/Z6RAKFA3Q3SRDWS3VRS7WPWGUZ","download_json":"https://pith.science/pith/Z6RAKFA3Q3SRDWS3VRS7WPWGUZ.json","view_paper":"https://pith.science/paper/Z6RAKFA3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.09819&json=true","fetch_graph":"https://pith.science/api/pith-number/Z6RAKFA3Q3SRDWS3VRS7WPWGUZ/graph.json","fetch_events":"https://pith.science/api/pith-number/Z6RAKFA3Q3SRDWS3VRS7WPWGUZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Z6RAKFA3Q3SRDWS3VRS7WPWGUZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Z6RAKFA3Q3SRDWS3VRS7WPWGUZ/action/storage_attestation","attest_author":"https://pith.science/pith/Z6RAKFA3Q3SRDWS3VRS7WPWGUZ/action/author_attestation","sign_citation":"https://pith.science/pith/Z6RAKFA3Q3SRDWS3VRS7WPWGUZ/action/citation_signature","submit_replication":"https://pith.science/pith/Z6RAKFA3Q3SRDWS3VRS7WPWGUZ/action/replication_record"}},"created_at":"2026-07-05T08:32:00.633237+00:00","updated_at":"2026-07-05T08:32:00.633237+00:00"}