{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:OKFGU4AZKNAIHHSUX6TIGACXCV","short_pith_number":"pith:OKFGU4AZ","schema_version":"1.0","canonical_sha256":"728a6a70195340839e54bfa6830057155bc500516830dc2e29ccc0235d1ea29b","source":{"kind":"arxiv","id":"2206.03065","version":2},"attestation_state":"computed","paper":{"title":"Universal Speech Enhancement with Score-based Diffusion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.AS"],"primary_cat":"cs.SD","authors_text":"Davide Scaini, Joan Serr\\`a, Jordi Pons, R. Oguz Araz, Santiago Pascual","submitted_at":"2022-06-07T07:32:32Z","abstract_excerpt":"Removing background noise from speech audio has been the subject of considerable effort, especially in recent years due to the rise of virtual communication and amateur recordings. Yet background noise is not the only unpleasant disturbance that can prevent intelligibility: reverb, clipping, codec artifacts, problematic equalization, limited bandwidth, or inconsistent loudness are equally disturbing and ubiquitous. In this work, we propose to consider the task of speech enhancement as a holistic endeavor, and present a universal speech enhancement system that tackles 55 different distortions a"},"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":"2206.03065","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2022-06-07T07:32:32Z","cross_cats_sorted":["cs.LG","eess.AS"],"title_canon_sha256":"67767bfa70562335fb1bbc3116fe614e98414057c986038212222b30be347229","abstract_canon_sha256":"f704c1479cbc2a10177bc59198e69fa3473703b814dfd48ac02d7d39b1aa52f6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:58:04.504886Z","signature_b64":"Z8C0xINOpKRSeMgWSPih7PHVXR2kRXCs/USO83O+jDt45pBeXokikb2ciOv57OKOpnDGHdJRXJun27xZozIqDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"728a6a70195340839e54bfa6830057155bc500516830dc2e29ccc0235d1ea29b","last_reissued_at":"2026-07-05T04:58:04.504336Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:58:04.504336Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Universal Speech Enhancement with Score-based Diffusion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.AS"],"primary_cat":"cs.SD","authors_text":"Davide Scaini, Joan Serr\\`a, Jordi Pons, R. Oguz Araz, Santiago Pascual","submitted_at":"2022-06-07T07:32:32Z","abstract_excerpt":"Removing background noise from speech audio has been the subject of considerable effort, especially in recent years due to the rise of virtual communication and amateur recordings. Yet background noise is not the only unpleasant disturbance that can prevent intelligibility: reverb, clipping, codec artifacts, problematic equalization, limited bandwidth, or inconsistent loudness are equally disturbing and ubiquitous. In this work, we propose to consider the task of speech enhancement as a holistic endeavor, and present a universal speech enhancement system that tackles 55 different distortions a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.03065","kind":"arxiv","version":2},"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/2206.03065/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":"2206.03065","created_at":"2026-07-05T04:58:04.504398+00:00"},{"alias_kind":"arxiv_version","alias_value":"2206.03065v2","created_at":"2026-07-05T04:58:04.504398+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.03065","created_at":"2026-07-05T04:58:04.504398+00:00"},{"alias_kind":"pith_short_12","alias_value":"OKFGU4AZKNAI","created_at":"2026-07-05T04:58:04.504398+00:00"},{"alias_kind":"pith_short_16","alias_value":"OKFGU4AZKNAIHHSU","created_at":"2026-07-05T04:58:04.504398+00:00"},{"alias_kind":"pith_short_8","alias_value":"OKFGU4AZ","created_at":"2026-07-05T04:58:04.504398+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":8,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.25621","citing_title":"One Model, Many Latencies: Universal Speech Enhancement for Diverse Real-Time Applications","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2606.25621","citing_title":"One Model, Many Latencies: Universal Speech Enhancement for Diverse Real-Time Applications","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2606.22591","citing_title":"Bridging Self-Supervised Learning and Speech Enhancement: A Wav2Vec2-Conditioned Framework","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2605.16681","citing_title":"A Survey of Advancing Audio Super-Resolution and Bandwidth Extension from Discriminative to Generative Models","ref_index":50,"is_internal_anchor":false},{"citing_arxiv_id":"2605.16251","citing_title":"Real-time Speech Restoration using Data Prediction Mean Flows","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2603.02641","citing_title":"Rethinking Training Targets, Architectures and Data Quality for Universal Speech Enhancement","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2604.01120","citing_title":"Diff-VS: Efficient Audio-Aware Diffusion U-Net for Vocals Separation","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2604.14606","citing_title":"UniPASE: A Generative Model for Universal Speech Enhancement with High Fidelity and Low Hallucinations","ref_index":12,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OKFGU4AZKNAIHHSUX6TIGACXCV","json":"https://pith.science/pith/OKFGU4AZKNAIHHSUX6TIGACXCV.json","graph_json":"https://pith.science/api/pith-number/OKFGU4AZKNAIHHSUX6TIGACXCV/graph.json","events_json":"https://pith.science/api/pith-number/OKFGU4AZKNAIHHSUX6TIGACXCV/events.json","paper":"https://pith.science/paper/OKFGU4AZ"},"agent_actions":{"view_html":"https://pith.science/pith/OKFGU4AZKNAIHHSUX6TIGACXCV","download_json":"https://pith.science/pith/OKFGU4AZKNAIHHSUX6TIGACXCV.json","view_paper":"https://pith.science/paper/OKFGU4AZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2206.03065&json=true","fetch_graph":"https://pith.science/api/pith-number/OKFGU4AZKNAIHHSUX6TIGACXCV/graph.json","fetch_events":"https://pith.science/api/pith-number/OKFGU4AZKNAIHHSUX6TIGACXCV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OKFGU4AZKNAIHHSUX6TIGACXCV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OKFGU4AZKNAIHHSUX6TIGACXCV/action/storage_attestation","attest_author":"https://pith.science/pith/OKFGU4AZKNAIHHSUX6TIGACXCV/action/author_attestation","sign_citation":"https://pith.science/pith/OKFGU4AZKNAIHHSUX6TIGACXCV/action/citation_signature","submit_replication":"https://pith.science/pith/OKFGU4AZKNAIHHSUX6TIGACXCV/action/replication_record"}},"created_at":"2026-07-05T04:58:04.504398+00:00","updated_at":"2026-07-05T04:58:04.504398+00:00"}