{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:REGGSXHW7ZVSXGGU464WY4FIQP","short_pith_number":"pith:REGGSXHW","schema_version":"1.0","canonical_sha256":"890c695cf6fe6b2b98d4e7b96c70a883dfecba48dafd95b8da4613d870f1b8b4","source":{"kind":"arxiv","id":"2204.04646","version":1},"attestation_state":"computed","paper":{"title":"Deep Embeddings for Robust User-Based Amateur Vocal Percussion Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IR","eess.AS"],"primary_cat":"cs.SD","authors_text":"Alejandro Delgado, Charalampos Saitis, Emir Demirel, Mark Sandler, Vinod Subramanian","submitted_at":"2022-04-10T10:26:11Z","abstract_excerpt":"Vocal Percussion Transcription (VPT) is concerned with the automatic detection and classification of vocal percussion sound events, allowing music creators and producers to sketch drum lines on the fly. Classifier algorithms in VPT systems learn best from small user-specific datasets, which usually restrict modelling to small input feature sets to avoid data overfitting. This study explores several deep supervised learning strategies to obtain informative feature sets for amateur vocal percussion classification. We evaluated the performance of these sets on regular vocal percussion classificat"},"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":"2204.04646","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2022-04-10T10:26:11Z","cross_cats_sorted":["cs.IR","eess.AS"],"title_canon_sha256":"26d383afbc3893726fee98a4ac86fa315bbb7ebc4aab888c565b78e33b523f5f","abstract_canon_sha256":"d91cf717d58214f8f686eaf88fb975e53e0f160031d123942c96dcaf4d698861"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:13:00.040874Z","signature_b64":"YxRqAuipJjeED7X6kp3ulaZOlV2ix/YDZbcl6a4/OEtSeL5MtE4doJi0lV6fB4HRx8C4dGNjTW6MUeTZrTWbBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"890c695cf6fe6b2b98d4e7b96c70a883dfecba48dafd95b8da4613d870f1b8b4","last_reissued_at":"2026-07-05T04:13:00.040495Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:13:00.040495Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Embeddings for Robust User-Based Amateur Vocal Percussion Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IR","eess.AS"],"primary_cat":"cs.SD","authors_text":"Alejandro Delgado, Charalampos Saitis, Emir Demirel, Mark Sandler, Vinod Subramanian","submitted_at":"2022-04-10T10:26:11Z","abstract_excerpt":"Vocal Percussion Transcription (VPT) is concerned with the automatic detection and classification of vocal percussion sound events, allowing music creators and producers to sketch drum lines on the fly. Classifier algorithms in VPT systems learn best from small user-specific datasets, which usually restrict modelling to small input feature sets to avoid data overfitting. This study explores several deep supervised learning strategies to obtain informative feature sets for amateur vocal percussion classification. We evaluated the performance of these sets on regular vocal percussion classificat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.04646","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/2204.04646/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":"2204.04646","created_at":"2026-07-05T04:13:00.040560+00:00"},{"alias_kind":"arxiv_version","alias_value":"2204.04646v1","created_at":"2026-07-05T04:13:00.040560+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.04646","created_at":"2026-07-05T04:13:00.040560+00:00"},{"alias_kind":"pith_short_12","alias_value":"REGGSXHW7ZVS","created_at":"2026-07-05T04:13:00.040560+00:00"},{"alias_kind":"pith_short_16","alias_value":"REGGSXHW7ZVSXGGU","created_at":"2026-07-05T04:13:00.040560+00:00"},{"alias_kind":"pith_short_8","alias_value":"REGGSXHW","created_at":"2026-07-05T04:13:00.040560+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/REGGSXHW7ZVSXGGU464WY4FIQP","json":"https://pith.science/pith/REGGSXHW7ZVSXGGU464WY4FIQP.json","graph_json":"https://pith.science/api/pith-number/REGGSXHW7ZVSXGGU464WY4FIQP/graph.json","events_json":"https://pith.science/api/pith-number/REGGSXHW7ZVSXGGU464WY4FIQP/events.json","paper":"https://pith.science/paper/REGGSXHW"},"agent_actions":{"view_html":"https://pith.science/pith/REGGSXHW7ZVSXGGU464WY4FIQP","download_json":"https://pith.science/pith/REGGSXHW7ZVSXGGU464WY4FIQP.json","view_paper":"https://pith.science/paper/REGGSXHW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2204.04646&json=true","fetch_graph":"https://pith.science/api/pith-number/REGGSXHW7ZVSXGGU464WY4FIQP/graph.json","fetch_events":"https://pith.science/api/pith-number/REGGSXHW7ZVSXGGU464WY4FIQP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/REGGSXHW7ZVSXGGU464WY4FIQP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/REGGSXHW7ZVSXGGU464WY4FIQP/action/storage_attestation","attest_author":"https://pith.science/pith/REGGSXHW7ZVSXGGU464WY4FIQP/action/author_attestation","sign_citation":"https://pith.science/pith/REGGSXHW7ZVSXGGU464WY4FIQP/action/citation_signature","submit_replication":"https://pith.science/pith/REGGSXHW7ZVSXGGU464WY4FIQP/action/replication_record"}},"created_at":"2026-07-05T04:13:00.040560+00:00","updated_at":"2026-07-05T04:13:00.040560+00:00"}