{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:H2B7426H7KINHX2CKON7CLXV2W","short_pith_number":"pith:H2B7426H","schema_version":"1.0","canonical_sha256":"3e83fe6bc7fa90d3df42539bf12ef5d596e2a6769ee89121022d5a47b1e360d2","source":{"kind":"arxiv","id":"2502.04711","version":1},"attestation_state":"computed","paper":{"title":"Dynamic Frequency-Adaptive Knowledge Distillation for Speech Enhancement","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["eess.AS"],"primary_cat":"cs.SD","authors_text":"Hanting Chen, Jian Li, Jie Hu, Lu Zhou, Siqi Liu, Xihao Yuan","submitted_at":"2025-02-07T07:25:59Z","abstract_excerpt":"Deep learning-based speech enhancement (SE) models have recently outperformed traditional techniques, yet their deployment on resource-constrained devices remains challenging due to high computational and memory demands. This paper introduces a novel dynamic frequency-adaptive knowledge distillation (DFKD) approach to effectively compress SE models. Our method dynamically assesses the model's output, distinguishing between high and low-frequency components, and adapts the learning objectives to meet the unique requirements of different frequency bands, capitalizing on the SE task's inherent ch"},"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":"2502.04711","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.SD","submitted_at":"2025-02-07T07:25:59Z","cross_cats_sorted":["eess.AS"],"title_canon_sha256":"7f1521ef4b8da7a471b3a3b340877fb23dc3649844001e2107f0616c513a885e","abstract_canon_sha256":"b6f95c9957a812db39ad93ce649bca5ebbafd04f1d74856611f0ba7519c61df6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:10:55.809876Z","signature_b64":"mNhn/gdP0dPkcp3ZFTqvRYHcjIro84CohQZbLbLrMqqpcf44jMreDkdocB/ggw0cA+vWQjzd7d1gSA4YTlM0BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3e83fe6bc7fa90d3df42539bf12ef5d596e2a6769ee89121022d5a47b1e360d2","last_reissued_at":"2026-07-05T10:10:55.809328Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:10:55.809328Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dynamic Frequency-Adaptive Knowledge Distillation for Speech Enhancement","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["eess.AS"],"primary_cat":"cs.SD","authors_text":"Hanting Chen, Jian Li, Jie Hu, Lu Zhou, Siqi Liu, Xihao Yuan","submitted_at":"2025-02-07T07:25:59Z","abstract_excerpt":"Deep learning-based speech enhancement (SE) models have recently outperformed traditional techniques, yet their deployment on resource-constrained devices remains challenging due to high computational and memory demands. This paper introduces a novel dynamic frequency-adaptive knowledge distillation (DFKD) approach to effectively compress SE models. Our method dynamically assesses the model's output, distinguishing between high and low-frequency components, and adapts the learning objectives to meet the unique requirements of different frequency bands, capitalizing on the SE task's inherent ch"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.04711","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/2502.04711/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":"2502.04711","created_at":"2026-07-05T10:10:55.809392+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.04711v1","created_at":"2026-07-05T10:10:55.809392+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.04711","created_at":"2026-07-05T10:10:55.809392+00:00"},{"alias_kind":"pith_short_12","alias_value":"H2B7426H7KIN","created_at":"2026-07-05T10:10:55.809392+00:00"},{"alias_kind":"pith_short_16","alias_value":"H2B7426H7KINHX2C","created_at":"2026-07-05T10:10:55.809392+00:00"},{"alias_kind":"pith_short_8","alias_value":"H2B7426H","created_at":"2026-07-05T10:10:55.809392+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.00405","citing_title":"SaD: A Scenario-Aware Discriminator for Speech Enhancement","ref_index":23,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/H2B7426H7KINHX2CKON7CLXV2W","json":"https://pith.science/pith/H2B7426H7KINHX2CKON7CLXV2W.json","graph_json":"https://pith.science/api/pith-number/H2B7426H7KINHX2CKON7CLXV2W/graph.json","events_json":"https://pith.science/api/pith-number/H2B7426H7KINHX2CKON7CLXV2W/events.json","paper":"https://pith.science/paper/H2B7426H"},"agent_actions":{"view_html":"https://pith.science/pith/H2B7426H7KINHX2CKON7CLXV2W","download_json":"https://pith.science/pith/H2B7426H7KINHX2CKON7CLXV2W.json","view_paper":"https://pith.science/paper/H2B7426H","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.04711&json=true","fetch_graph":"https://pith.science/api/pith-number/H2B7426H7KINHX2CKON7CLXV2W/graph.json","fetch_events":"https://pith.science/api/pith-number/H2B7426H7KINHX2CKON7CLXV2W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H2B7426H7KINHX2CKON7CLXV2W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H2B7426H7KINHX2CKON7CLXV2W/action/storage_attestation","attest_author":"https://pith.science/pith/H2B7426H7KINHX2CKON7CLXV2W/action/author_attestation","sign_citation":"https://pith.science/pith/H2B7426H7KINHX2CKON7CLXV2W/action/citation_signature","submit_replication":"https://pith.science/pith/H2B7426H7KINHX2CKON7CLXV2W/action/replication_record"}},"created_at":"2026-07-05T10:10:55.809392+00:00","updated_at":"2026-07-05T10:10:55.809392+00:00"}