{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:TDHDGHN53HMD2NHQBEAT36AT2R","short_pith_number":"pith:TDHDGHN5","schema_version":"1.0","canonical_sha256":"98ce331dbdd9d83d34f009013df813d463e66b1eb316134f611798158bda0a94","source":{"kind":"arxiv","id":"2407.21658","version":1},"attestation_state":"computed","paper":{"title":"Beat this! Accurate beat tracking without DBN postprocessing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","eess.AS"],"primary_cat":"cs.SD","authors_text":"Francesco Foscarin, Gerhard Widmer, Jan Schl\\\"uter","submitted_at":"2024-07-31T14:59:17Z","abstract_excerpt":"We propose a system for tracking beats and downbeats with two objectives: generality across a diverse music range, and high accuracy. We achieve generality by training on multiple datasets -- including solo instrument recordings, pieces with time signature changes, and classical music with high tempo variations -- and by removing the commonly used Dynamic Bayesian Network (DBN) postprocessing, which introduces constraints on the meter and tempo. For high accuracy, among other improvements, we develop a loss function tolerant to small time shifts of annotations, and an architecture alternating "},"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":"2407.21658","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2024-07-31T14:59:17Z","cross_cats_sorted":["cs.LG","eess.AS"],"title_canon_sha256":"d11a92a1e7e5aca6c802c04266f41cb6f61ab3395e7b341f33bbcff8951578ae","abstract_canon_sha256":"4343cb379b2dab0f26ccc50fc499ec1b8864c8290593bd3262a641f81804aa8a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:50:42.045629Z","signature_b64":"rYRV643IiuwQ3gjqLU9yc6tMfSsxI8iUtzG3sc6krBV5MAl2/dfzo0HOwpoRQCn//S17CwfSm8KRxSqs0eM8AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"98ce331dbdd9d83d34f009013df813d463e66b1eb316134f611798158bda0a94","last_reissued_at":"2026-07-05T08:50:42.045216Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:50:42.045216Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Beat this! Accurate beat tracking without DBN postprocessing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","eess.AS"],"primary_cat":"cs.SD","authors_text":"Francesco Foscarin, Gerhard Widmer, Jan Schl\\\"uter","submitted_at":"2024-07-31T14:59:17Z","abstract_excerpt":"We propose a system for tracking beats and downbeats with two objectives: generality across a diverse music range, and high accuracy. We achieve generality by training on multiple datasets -- including solo instrument recordings, pieces with time signature changes, and classical music with high tempo variations -- and by removing the commonly used Dynamic Bayesian Network (DBN) postprocessing, which introduces constraints on the meter and tempo. For high accuracy, among other improvements, we develop a loss function tolerant to small time shifts of annotations, and an architecture alternating "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.21658","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/2407.21658/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":"2407.21658","created_at":"2026-07-05T08:50:42.045272+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.21658v1","created_at":"2026-07-05T08:50:42.045272+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.21658","created_at":"2026-07-05T08:50:42.045272+00:00"},{"alias_kind":"pith_short_12","alias_value":"TDHDGHN53HMD","created_at":"2026-07-05T08:50:42.045272+00:00"},{"alias_kind":"pith_short_16","alias_value":"TDHDGHN53HMD2NHQ","created_at":"2026-07-05T08:50:42.045272+00:00"},{"alias_kind":"pith_short_8","alias_value":"TDHDGHN5","created_at":"2026-07-05T08:50:42.045272+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.21661","citing_title":"UnityShots: Memory-Driven Multi-Shot Audio-Video Generation with Boundary-Aware Gating","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2606.31158","citing_title":"LLM-Powered Interactive Robotic Action Synthesis from Multimodal Speech, Gestures, and Music","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2605.24291","citing_title":"Rubato: Transcribing Piano Music with Timestamps","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12287","citing_title":"The SMC Blind Spot: A Failure Mode Analysis of State-of-the-Art Beat Tracking","ref_index":27,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TDHDGHN53HMD2NHQBEAT36AT2R","json":"https://pith.science/pith/TDHDGHN53HMD2NHQBEAT36AT2R.json","graph_json":"https://pith.science/api/pith-number/TDHDGHN53HMD2NHQBEAT36AT2R/graph.json","events_json":"https://pith.science/api/pith-number/TDHDGHN53HMD2NHQBEAT36AT2R/events.json","paper":"https://pith.science/paper/TDHDGHN5"},"agent_actions":{"view_html":"https://pith.science/pith/TDHDGHN53HMD2NHQBEAT36AT2R","download_json":"https://pith.science/pith/TDHDGHN53HMD2NHQBEAT36AT2R.json","view_paper":"https://pith.science/paper/TDHDGHN5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.21658&json=true","fetch_graph":"https://pith.science/api/pith-number/TDHDGHN53HMD2NHQBEAT36AT2R/graph.json","fetch_events":"https://pith.science/api/pith-number/TDHDGHN53HMD2NHQBEAT36AT2R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TDHDGHN53HMD2NHQBEAT36AT2R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TDHDGHN53HMD2NHQBEAT36AT2R/action/storage_attestation","attest_author":"https://pith.science/pith/TDHDGHN53HMD2NHQBEAT36AT2R/action/author_attestation","sign_citation":"https://pith.science/pith/TDHDGHN53HMD2NHQBEAT36AT2R/action/citation_signature","submit_replication":"https://pith.science/pith/TDHDGHN53HMD2NHQBEAT36AT2R/action/replication_record"}},"created_at":"2026-07-05T08:50:42.045272+00:00","updated_at":"2026-07-05T08:50:42.045272+00:00"}