{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:7YCJCCNBCHE3BCOGZ22KDAMPZG","short_pith_number":"pith:7YCJCCNB","schema_version":"1.0","canonical_sha256":"fe049109a111c9b089c6ceb4a1818fc9aaddb6e9c320d5da9847f007c3e7468b","source":{"kind":"arxiv","id":"2309.02567","version":2},"attestation_state":"computed","paper":{"title":"Symbolic Music Representations for Classification Tasks: A Systematic Evaluation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MM","cs.SD"],"primary_cat":"eess.AS","authors_text":"Carlos Eduardo Cancino-Chac\\'on, Emmanouil Karystinaios, Gerhard Widmer, Huan Zhang, Simon Dixon","submitted_at":"2023-09-05T20:27:31Z","abstract_excerpt":"Music Information Retrieval (MIR) has seen a recent surge in deep learning-based approaches, which often involve encoding symbolic music (i.e., music represented in terms of discrete note events) in an image-like or language like fashion. However, symbolic music is neither an image nor a sentence, and research in the symbolic domain lacks a comprehensive overview of the different available representations. In this paper, we investigate matrix (piano roll), sequence, and graph representations and their corresponding neural architectures, in combination with symbolic scores and performances on t"},"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":"2309.02567","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2023-09-05T20:27:31Z","cross_cats_sorted":["cs.MM","cs.SD"],"title_canon_sha256":"4fbabde88f391155c1e26fb387adf8362e07cfa586f9be3a5f4837c032efea5d","abstract_canon_sha256":"931b18a21bf69c3f1464b3aa9f5f6f8a9604774c178b7615eb79f28e81c7cce1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:49:26.359962Z","signature_b64":"+BHNZm9lp9+fmFjq1RqEsTPCYHNQVBRVRqN84ucHxo3Q8kesB8a5KSvOm26D9jRBBPs8R9h0bpIl2kdBLXnRAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe049109a111c9b089c6ceb4a1818fc9aaddb6e9c320d5da9847f007c3e7468b","last_reissued_at":"2026-07-05T06:49:26.359541Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:49:26.359541Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Symbolic Music Representations for Classification Tasks: A Systematic Evaluation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MM","cs.SD"],"primary_cat":"eess.AS","authors_text":"Carlos Eduardo Cancino-Chac\\'on, Emmanouil Karystinaios, Gerhard Widmer, Huan Zhang, Simon Dixon","submitted_at":"2023-09-05T20:27:31Z","abstract_excerpt":"Music Information Retrieval (MIR) has seen a recent surge in deep learning-based approaches, which often involve encoding symbolic music (i.e., music represented in terms of discrete note events) in an image-like or language like fashion. However, symbolic music is neither an image nor a sentence, and research in the symbolic domain lacks a comprehensive overview of the different available representations. In this paper, we investigate matrix (piano roll), sequence, and graph representations and their corresponding neural architectures, in combination with symbolic scores and performances on t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.02567","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/2309.02567/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":"2309.02567","created_at":"2026-07-05T06:49:26.359603+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.02567v2","created_at":"2026-07-05T06:49:26.359603+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.02567","created_at":"2026-07-05T06:49:26.359603+00:00"},{"alias_kind":"pith_short_12","alias_value":"7YCJCCNBCHE3","created_at":"2026-07-05T06:49:26.359603+00:00"},{"alias_kind":"pith_short_16","alias_value":"7YCJCCNBCHE3BCOG","created_at":"2026-07-05T06:49:26.359603+00:00"},{"alias_kind":"pith_short_8","alias_value":"7YCJCCNB","created_at":"2026-07-05T06:49:26.359603+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2510.02171","citing_title":"Go witheFlow: Real-time Emotion Driven Audio Effects Modulation","ref_index":21,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7YCJCCNBCHE3BCOGZ22KDAMPZG","json":"https://pith.science/pith/7YCJCCNBCHE3BCOGZ22KDAMPZG.json","graph_json":"https://pith.science/api/pith-number/7YCJCCNBCHE3BCOGZ22KDAMPZG/graph.json","events_json":"https://pith.science/api/pith-number/7YCJCCNBCHE3BCOGZ22KDAMPZG/events.json","paper":"https://pith.science/paper/7YCJCCNB"},"agent_actions":{"view_html":"https://pith.science/pith/7YCJCCNBCHE3BCOGZ22KDAMPZG","download_json":"https://pith.science/pith/7YCJCCNBCHE3BCOGZ22KDAMPZG.json","view_paper":"https://pith.science/paper/7YCJCCNB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.02567&json=true","fetch_graph":"https://pith.science/api/pith-number/7YCJCCNBCHE3BCOGZ22KDAMPZG/graph.json","fetch_events":"https://pith.science/api/pith-number/7YCJCCNBCHE3BCOGZ22KDAMPZG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7YCJCCNBCHE3BCOGZ22KDAMPZG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7YCJCCNBCHE3BCOGZ22KDAMPZG/action/storage_attestation","attest_author":"https://pith.science/pith/7YCJCCNBCHE3BCOGZ22KDAMPZG/action/author_attestation","sign_citation":"https://pith.science/pith/7YCJCCNBCHE3BCOGZ22KDAMPZG/action/citation_signature","submit_replication":"https://pith.science/pith/7YCJCCNBCHE3BCOGZ22KDAMPZG/action/replication_record"}},"created_at":"2026-07-05T06:49:26.359603+00:00","updated_at":"2026-07-05T06:49:26.359603+00:00"}