{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:QP42AOGG5E5ZBK7RYAOPAQ46N5","short_pith_number":"pith:QP42AOGG","schema_version":"1.0","canonical_sha256":"83f9a038c6e93b90abf1c01cf0439e6f7d8acc8133964e85c5a41bfaeb8a2496","source":{"kind":"arxiv","id":"2406.13471","version":1},"attestation_state":"computed","paper":{"title":"Diffusion-based Generative Modeling with Discriminative Guidance for Streamable Speech Enhancement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Chenda Li, Samuele Cornell, Shinji Watanabe, Yanmin Qian","submitted_at":"2024-06-19T11:51:37Z","abstract_excerpt":"Diffusion-based generative models (DGMs) have recently attracted attention in speech enhancement research (SE) as previous works showed a remarkable generalization capability. However, DGMs are also computationally intensive, as they usually require many iterations in the reverse diffusion process (RDP), making them impractical for streaming SE systems. In this paper, we propose to use discriminative scores from discriminative models in the first steps of the RDP. These discriminative scores require only one forward pass with the discriminative model for multiple RDP steps, thus greatly reduci"},"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":"2406.13471","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2024-06-19T11:51:37Z","cross_cats_sorted":["cs.SD"],"title_canon_sha256":"3614f91b99e9de13ed35b0afe28906215107b54c9df7ac81a796d61f0860d61c","abstract_canon_sha256":"8a44a1d7cd956960c76c5e48c8579a24900acc6c29f718aeb153b214cd75f915"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:34:30.494008Z","signature_b64":"Lpf28J08G3OpkhKjncml5j5p4rpS2wh+UMn4Ap4M8i7mGximeoiJ1SHzDXRiUUrqJdwAr/ljxzCTnt5qdG/xCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"83f9a038c6e93b90abf1c01cf0439e6f7d8acc8133964e85c5a41bfaeb8a2496","last_reissued_at":"2026-07-05T08:34:30.493589Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:34:30.493589Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Diffusion-based Generative Modeling with Discriminative Guidance for Streamable Speech Enhancement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Chenda Li, Samuele Cornell, Shinji Watanabe, Yanmin Qian","submitted_at":"2024-06-19T11:51:37Z","abstract_excerpt":"Diffusion-based generative models (DGMs) have recently attracted attention in speech enhancement research (SE) as previous works showed a remarkable generalization capability. However, DGMs are also computationally intensive, as they usually require many iterations in the reverse diffusion process (RDP), making them impractical for streaming SE systems. In this paper, we propose to use discriminative scores from discriminative models in the first steps of the RDP. These discriminative scores require only one forward pass with the discriminative model for multiple RDP steps, thus greatly reduci"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.13471","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/2406.13471/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":"2406.13471","created_at":"2026-07-05T08:34:30.493654+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.13471v1","created_at":"2026-07-05T08:34:30.493654+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.13471","created_at":"2026-07-05T08:34:30.493654+00:00"},{"alias_kind":"pith_short_12","alias_value":"QP42AOGG5E5Z","created_at":"2026-07-05T08:34:30.493654+00:00"},{"alias_kind":"pith_short_16","alias_value":"QP42AOGG5E5ZBK7R","created_at":"2026-07-05T08:34:30.493654+00:00"},{"alias_kind":"pith_short_8","alias_value":"QP42AOGG","created_at":"2026-07-05T08:34:30.493654+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.14890","citing_title":"Scale This, Not That: Investigating Key Dataset Attributes for Efficient Speech Enhancement Scaling","ref_index":23,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QP42AOGG5E5ZBK7RYAOPAQ46N5","json":"https://pith.science/pith/QP42AOGG5E5ZBK7RYAOPAQ46N5.json","graph_json":"https://pith.science/api/pith-number/QP42AOGG5E5ZBK7RYAOPAQ46N5/graph.json","events_json":"https://pith.science/api/pith-number/QP42AOGG5E5ZBK7RYAOPAQ46N5/events.json","paper":"https://pith.science/paper/QP42AOGG"},"agent_actions":{"view_html":"https://pith.science/pith/QP42AOGG5E5ZBK7RYAOPAQ46N5","download_json":"https://pith.science/pith/QP42AOGG5E5ZBK7RYAOPAQ46N5.json","view_paper":"https://pith.science/paper/QP42AOGG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.13471&json=true","fetch_graph":"https://pith.science/api/pith-number/QP42AOGG5E5ZBK7RYAOPAQ46N5/graph.json","fetch_events":"https://pith.science/api/pith-number/QP42AOGG5E5ZBK7RYAOPAQ46N5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QP42AOGG5E5ZBK7RYAOPAQ46N5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QP42AOGG5E5ZBK7RYAOPAQ46N5/action/storage_attestation","attest_author":"https://pith.science/pith/QP42AOGG5E5ZBK7RYAOPAQ46N5/action/author_attestation","sign_citation":"https://pith.science/pith/QP42AOGG5E5ZBK7RYAOPAQ46N5/action/citation_signature","submit_replication":"https://pith.science/pith/QP42AOGG5E5ZBK7RYAOPAQ46N5/action/replication_record"}},"created_at":"2026-07-05T08:34:30.493654+00:00","updated_at":"2026-07-05T08:34:30.493654+00:00"}