{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:YRL4X7XS2TPE3QGBIQC5MVSJLK","short_pith_number":"pith:YRL4X7XS","schema_version":"1.0","canonical_sha256":"c457cbfef2d4de4dc0c14405d656495abf09a603b19ef333964d03eed5f60cd2","source":{"kind":"arxiv","id":"2306.16955","version":1},"attestation_state":"computed","paper":{"title":"Predicting Music Hierarchies with a Graph-Based Neural Decoder","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","eess.AS"],"primary_cat":"cs.SD","authors_text":"Daniel Harasim, Francesco Foscarin, Gerhard Widmer","submitted_at":"2023-06-29T13:59:18Z","abstract_excerpt":"This paper describes a data-driven framework to parse musical sequences into dependency trees, which are hierarchical structures used in music cognition research and music analysis. The parsing involves two steps. First, the input sequence is passed through a transformer encoder to enrich it with contextual information. Then, a classifier filters the graph of all possible dependency arcs to produce the dependency tree. One major benefit of this system is that it can be easily integrated into modern deep-learning pipelines. Moreover, since it does not rely on any particular symbolic grammar, it"},"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":"2306.16955","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2023-06-29T13:59:18Z","cross_cats_sorted":["cs.CL","eess.AS"],"title_canon_sha256":"8a239840b4be03c6510abf8b2b9cd817979526bf4e9b11f1c2db0ba60db52f35","abstract_canon_sha256":"bd9e18a9be281a20bf237999b07c4c69e6b5e2d97efbc8f63ffa6d05cd6653ba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:26:11.511117Z","signature_b64":"wsC6jNbWKO/p0iHR5Lz4bW8ODjs7xyqZIcPb7uQY0ge0+4Yjp3vcqeViqdvHmsvOZyNvbqVhUxT6B4ZCnonnDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c457cbfef2d4de4dc0c14405d656495abf09a603b19ef333964d03eed5f60cd2","last_reissued_at":"2026-07-05T06:26:11.510629Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:26:11.510629Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Predicting Music Hierarchies with a Graph-Based Neural Decoder","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","eess.AS"],"primary_cat":"cs.SD","authors_text":"Daniel Harasim, Francesco Foscarin, Gerhard Widmer","submitted_at":"2023-06-29T13:59:18Z","abstract_excerpt":"This paper describes a data-driven framework to parse musical sequences into dependency trees, which are hierarchical structures used in music cognition research and music analysis. The parsing involves two steps. First, the input sequence is passed through a transformer encoder to enrich it with contextual information. Then, a classifier filters the graph of all possible dependency arcs to produce the dependency tree. One major benefit of this system is that it can be easily integrated into modern deep-learning pipelines. Moreover, since it does not rely on any particular symbolic grammar, it"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.16955","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/2306.16955/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":"2306.16955","created_at":"2026-07-05T06:26:11.510688+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.16955v1","created_at":"2026-07-05T06:26:11.510688+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.16955","created_at":"2026-07-05T06:26:11.510688+00:00"},{"alias_kind":"pith_short_12","alias_value":"YRL4X7XS2TPE","created_at":"2026-07-05T06:26:11.510688+00:00"},{"alias_kind":"pith_short_16","alias_value":"YRL4X7XS2TPE3QGB","created_at":"2026-07-05T06:26:11.510688+00:00"},{"alias_kind":"pith_short_8","alias_value":"YRL4X7XS","created_at":"2026-07-05T06:26:11.510688+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/YRL4X7XS2TPE3QGBIQC5MVSJLK","json":"https://pith.science/pith/YRL4X7XS2TPE3QGBIQC5MVSJLK.json","graph_json":"https://pith.science/api/pith-number/YRL4X7XS2TPE3QGBIQC5MVSJLK/graph.json","events_json":"https://pith.science/api/pith-number/YRL4X7XS2TPE3QGBIQC5MVSJLK/events.json","paper":"https://pith.science/paper/YRL4X7XS"},"agent_actions":{"view_html":"https://pith.science/pith/YRL4X7XS2TPE3QGBIQC5MVSJLK","download_json":"https://pith.science/pith/YRL4X7XS2TPE3QGBIQC5MVSJLK.json","view_paper":"https://pith.science/paper/YRL4X7XS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.16955&json=true","fetch_graph":"https://pith.science/api/pith-number/YRL4X7XS2TPE3QGBIQC5MVSJLK/graph.json","fetch_events":"https://pith.science/api/pith-number/YRL4X7XS2TPE3QGBIQC5MVSJLK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YRL4X7XS2TPE3QGBIQC5MVSJLK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YRL4X7XS2TPE3QGBIQC5MVSJLK/action/storage_attestation","attest_author":"https://pith.science/pith/YRL4X7XS2TPE3QGBIQC5MVSJLK/action/author_attestation","sign_citation":"https://pith.science/pith/YRL4X7XS2TPE3QGBIQC5MVSJLK/action/citation_signature","submit_replication":"https://pith.science/pith/YRL4X7XS2TPE3QGBIQC5MVSJLK/action/replication_record"}},"created_at":"2026-07-05T06:26:11.510688+00:00","updated_at":"2026-07-05T06:26:11.510688+00:00"}