{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:BUS4CMQFMIEHGVEBQIOG3I2CFA","short_pith_number":"pith:BUS4CMQF","schema_version":"1.0","canonical_sha256":"0d25c132056208735481821c6da342282ca3d0d6470833f78cc09b088e69cd8b","source":{"kind":"arxiv","id":"2501.10525","version":2},"attestation_state":"computed","paper":{"title":"DFingerNet: Noise-Adaptive Speech Enhancement for Hearing Aids","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.AS","eess.SP"],"primary_cat":"cs.SD","authors_text":"Andreas Triantafyllopoulos, Bj\\\"orn W. Schuller, Hendrik Schr\\\"oter, Iosif Tsangko, Michael M\\\"uller","submitted_at":"2025-01-17T19:56:22Z","abstract_excerpt":"The DeepFilterNet (DFN) architecture was recently proposed as a deep learning model suited for hearing aid devices. Despite its competitive performance on numerous benchmarks, it still follows a `one-size-fits-all' approach, which aims to train a single, monolithic architecture that generalises across different noises and environments. However, its limited size and computation budget can hamper its generalisability. Recent work has shown that in-context adaptation can improve performance by conditioning the denoising process on additional information extracted from background recordings to mit"},"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":"2501.10525","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2025-01-17T19:56:22Z","cross_cats_sorted":["cs.LG","eess.AS","eess.SP"],"title_canon_sha256":"cfc026e4b0876b84593c19ba084dbaefa08f12c0f8287ee849f9c0fe58e7e02d","abstract_canon_sha256":"d4b406aba3a97b9d26942a27c1c49bfe676bfb92e79bc29afbfb762593687ae5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:04:22.573417Z","signature_b64":"2P7I0L5RXkM4PavUVURuhzB+P+XpYh7+IWfHKuYwDvQdV5XhjnsFAxbEaWvtP1J6U+1h3OB3u0PU8kNLDrivAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0d25c132056208735481821c6da342282ca3d0d6470833f78cc09b088e69cd8b","last_reissued_at":"2026-07-05T10:04:22.572909Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:04:22.572909Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DFingerNet: Noise-Adaptive Speech Enhancement for Hearing Aids","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.AS","eess.SP"],"primary_cat":"cs.SD","authors_text":"Andreas Triantafyllopoulos, Bj\\\"orn W. Schuller, Hendrik Schr\\\"oter, Iosif Tsangko, Michael M\\\"uller","submitted_at":"2025-01-17T19:56:22Z","abstract_excerpt":"The DeepFilterNet (DFN) architecture was recently proposed as a deep learning model suited for hearing aid devices. Despite its competitive performance on numerous benchmarks, it still follows a `one-size-fits-all' approach, which aims to train a single, monolithic architecture that generalises across different noises and environments. However, its limited size and computation budget can hamper its generalisability. Recent work has shown that in-context adaptation can improve performance by conditioning the denoising process on additional information extracted from background recordings to mit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.10525","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/2501.10525/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":"2501.10525","created_at":"2026-07-05T10:04:22.572971+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.10525v2","created_at":"2026-07-05T10:04:22.572971+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.10525","created_at":"2026-07-05T10:04:22.572971+00:00"},{"alias_kind":"pith_short_12","alias_value":"BUS4CMQFMIEH","created_at":"2026-07-05T10:04:22.572971+00:00"},{"alias_kind":"pith_short_16","alias_value":"BUS4CMQFMIEHGVEB","created_at":"2026-07-05T10:04:22.572971+00:00"},{"alias_kind":"pith_short_8","alias_value":"BUS4CMQF","created_at":"2026-07-05T10:04:22.572971+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/BUS4CMQFMIEHGVEBQIOG3I2CFA","json":"https://pith.science/pith/BUS4CMQFMIEHGVEBQIOG3I2CFA.json","graph_json":"https://pith.science/api/pith-number/BUS4CMQFMIEHGVEBQIOG3I2CFA/graph.json","events_json":"https://pith.science/api/pith-number/BUS4CMQFMIEHGVEBQIOG3I2CFA/events.json","paper":"https://pith.science/paper/BUS4CMQF"},"agent_actions":{"view_html":"https://pith.science/pith/BUS4CMQFMIEHGVEBQIOG3I2CFA","download_json":"https://pith.science/pith/BUS4CMQFMIEHGVEBQIOG3I2CFA.json","view_paper":"https://pith.science/paper/BUS4CMQF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.10525&json=true","fetch_graph":"https://pith.science/api/pith-number/BUS4CMQFMIEHGVEBQIOG3I2CFA/graph.json","fetch_events":"https://pith.science/api/pith-number/BUS4CMQFMIEHGVEBQIOG3I2CFA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BUS4CMQFMIEHGVEBQIOG3I2CFA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BUS4CMQFMIEHGVEBQIOG3I2CFA/action/storage_attestation","attest_author":"https://pith.science/pith/BUS4CMQFMIEHGVEBQIOG3I2CFA/action/author_attestation","sign_citation":"https://pith.science/pith/BUS4CMQFMIEHGVEBQIOG3I2CFA/action/citation_signature","submit_replication":"https://pith.science/pith/BUS4CMQFMIEHGVEBQIOG3I2CFA/action/replication_record"}},"created_at":"2026-07-05T10:04:22.572971+00:00","updated_at":"2026-07-05T10:04:22.572971+00:00"}