{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:MKYCT5JLIWIKZSUEWJVV6QXVGZ","short_pith_number":"pith:MKYCT5JL","schema_version":"1.0","canonical_sha256":"62b029f52b4590acca84b26b5f42f53668b569e14d5c558313a1bb875a541a61","source":{"kind":"arxiv","id":"2505.23784","version":1},"attestation_state":"computed","paper":{"title":"Learning Normal Patterns in Musical Loops","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR","cs.LG","cs.MM","eess.AS"],"primary_cat":"cs.SD","authors_text":"Bernt Arild Bremdal, B{\\o}rre Bang, Rune Dalmo, Shayan Dadman","submitted_at":"2025-05-22T19:52:00Z","abstract_excerpt":"This paper introduces an unsupervised framework for detecting audio patterns in musical samples (loops) through anomaly detection techniques, addressing challenges in music information retrieval (MIR). Existing methods are often constrained by reliance on handcrafted features, domain-specific limitations, or dependence on iterative user interaction. We address these limitations through an architecture combining deep feature extraction with unsupervised anomaly detection. Our approach leverages a pre-trained Hierarchical Token-semantic Audio Transformer (HTS-AT), paired with a Feature Fusion Me"},"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":"2505.23784","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2025-05-22T19:52:00Z","cross_cats_sorted":["cs.IR","cs.LG","cs.MM","eess.AS"],"title_canon_sha256":"e1545255e68a43c2201a5ed1b7eb98cbe2f55667f053a08332cbe9031415edfc","abstract_canon_sha256":"5ef15a042bc456779edbed419f32ae307ce264410e02788e97eed1b23870f61c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:12:35.278971Z","signature_b64":"qKhpl6I8cWgrTD2Q2NphIo4ucfsVfy5HqufLuHyV1zuwRymhZh9nOsu0N2uClR58ZHjtnmMIq0yFTBeBapY6Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"62b029f52b4590acca84b26b5f42f53668b569e14d5c558313a1bb875a541a61","last_reissued_at":"2026-07-05T11:12:35.278481Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:12:35.278481Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Normal Patterns in Musical Loops","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR","cs.LG","cs.MM","eess.AS"],"primary_cat":"cs.SD","authors_text":"Bernt Arild Bremdal, B{\\o}rre Bang, Rune Dalmo, Shayan Dadman","submitted_at":"2025-05-22T19:52:00Z","abstract_excerpt":"This paper introduces an unsupervised framework for detecting audio patterns in musical samples (loops) through anomaly detection techniques, addressing challenges in music information retrieval (MIR). Existing methods are often constrained by reliance on handcrafted features, domain-specific limitations, or dependence on iterative user interaction. We address these limitations through an architecture combining deep feature extraction with unsupervised anomaly detection. Our approach leverages a pre-trained Hierarchical Token-semantic Audio Transformer (HTS-AT), paired with a Feature Fusion Me"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.23784","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/2505.23784/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":"2505.23784","created_at":"2026-07-05T11:12:35.278551+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.23784v1","created_at":"2026-07-05T11:12:35.278551+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.23784","created_at":"2026-07-05T11:12:35.278551+00:00"},{"alias_kind":"pith_short_12","alias_value":"MKYCT5JLIWIK","created_at":"2026-07-05T11:12:35.278551+00:00"},{"alias_kind":"pith_short_16","alias_value":"MKYCT5JLIWIKZSUE","created_at":"2026-07-05T11:12:35.278551+00:00"},{"alias_kind":"pith_short_8","alias_value":"MKYCT5JL","created_at":"2026-07-05T11:12:35.278551+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/MKYCT5JLIWIKZSUEWJVV6QXVGZ","json":"https://pith.science/pith/MKYCT5JLIWIKZSUEWJVV6QXVGZ.json","graph_json":"https://pith.science/api/pith-number/MKYCT5JLIWIKZSUEWJVV6QXVGZ/graph.json","events_json":"https://pith.science/api/pith-number/MKYCT5JLIWIKZSUEWJVV6QXVGZ/events.json","paper":"https://pith.science/paper/MKYCT5JL"},"agent_actions":{"view_html":"https://pith.science/pith/MKYCT5JLIWIKZSUEWJVV6QXVGZ","download_json":"https://pith.science/pith/MKYCT5JLIWIKZSUEWJVV6QXVGZ.json","view_paper":"https://pith.science/paper/MKYCT5JL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.23784&json=true","fetch_graph":"https://pith.science/api/pith-number/MKYCT5JLIWIKZSUEWJVV6QXVGZ/graph.json","fetch_events":"https://pith.science/api/pith-number/MKYCT5JLIWIKZSUEWJVV6QXVGZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MKYCT5JLIWIKZSUEWJVV6QXVGZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MKYCT5JLIWIKZSUEWJVV6QXVGZ/action/storage_attestation","attest_author":"https://pith.science/pith/MKYCT5JLIWIKZSUEWJVV6QXVGZ/action/author_attestation","sign_citation":"https://pith.science/pith/MKYCT5JLIWIKZSUEWJVV6QXVGZ/action/citation_signature","submit_replication":"https://pith.science/pith/MKYCT5JLIWIKZSUEWJVV6QXVGZ/action/replication_record"}},"created_at":"2026-07-05T11:12:35.278551+00:00","updated_at":"2026-07-05T11:12:35.278551+00:00"}