{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:56BLD2P3JJKEI5XRDMOVLBZB5O","short_pith_number":"pith:56BLD2P3","schema_version":"1.0","canonical_sha256":"ef82b1e9fb4a544476f11b1d558721ebad71327cb73ae3e5d819bd0ad1f57796","source":{"kind":"arxiv","id":"2607.26440","version":1},"attestation_state":"computed","paper":{"title":"Explicit Note-Event Tokenization and Pitch-Validity Constrained Decoding for MIDI-to-Tablature Transcription","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SD","authors_text":"Kai-Xi Hong, Ting-Kai Hsu, Wei-Chin Wang, Yu-Hua Chen","submitted_at":"2026-07-29T03:39:19Z","abstract_excerpt":"Guitar tablature transcription predicts the string and fret position for each note so that the resulting tablature reproduces the target musical part. Prior sequence-to-sequence approaches have shown promising results on large-scale datasets, but their generalization behavior across different dataset scales remains less explored. In this work, we propose a guitar tablature transcription framework with explicit note-event tokenization and regularized training. The proposed decoder token representation incorporates note-event tokens together with TAB tokens, allowing note boundaries, pitch-relat"},"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":"2607.26440","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2026-07-29T03:39:19Z","cross_cats_sorted":[],"title_canon_sha256":"85cd15586052835c0414766bf3d6f2ce1d0eb208d86862f0905192e23829c463","abstract_canon_sha256":"42b74cd2028cf0e623fb91baccabf42eaaeedc48006595e5b16baa4f5e830339"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ef82b1e9fb4a544476f11b1d558721ebad71327cb73ae3e5d819bd0ad1f57796","last_reissued_at":"2026-07-30T01:18:26.009490Z","signature_status":"unsigned_v0","first_computed_at":"2026-07-30T01:18:26.009490Z"},"graph_snapshot":{"paper":{"title":"Explicit Note-Event Tokenization and Pitch-Validity Constrained Decoding for MIDI-to-Tablature Transcription","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SD","authors_text":"Kai-Xi Hong, Ting-Kai Hsu, Wei-Chin Wang, Yu-Hua Chen","submitted_at":"2026-07-29T03:39:19Z","abstract_excerpt":"Guitar tablature transcription predicts the string and fret position for each note so that the resulting tablature reproduces the target musical part. Prior sequence-to-sequence approaches have shown promising results on large-scale datasets, but their generalization behavior across different dataset scales remains less explored. In this work, we propose a guitar tablature transcription framework with explicit note-event tokenization and regularized training. The proposed decoder token representation incorporates note-event tokens together with TAB tokens, allowing note boundaries, pitch-relat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.26440","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/2607.26440/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":"2607.26440","created_at":"2026-07-30T01:18:26.014656+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.26440v1","created_at":"2026-07-30T01:18:26.014656+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.26440","created_at":"2026-07-30T01:18:26.014656+00:00"},{"alias_kind":"pith_short_12","alias_value":"56BLD2P3JJKE","created_at":"2026-07-30T01:18:26.014656+00:00"},{"alias_kind":"pith_short_16","alias_value":"56BLD2P3JJKEI5XR","created_at":"2026-07-30T01:18:26.014656+00:00"},{"alias_kind":"pith_short_8","alias_value":"56BLD2P3","created_at":"2026-07-30T01:18:26.014656+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/56BLD2P3JJKEI5XRDMOVLBZB5O","json":"https://pith.science/pith/56BLD2P3JJKEI5XRDMOVLBZB5O.json","graph_json":"https://pith.science/api/pith-number/56BLD2P3JJKEI5XRDMOVLBZB5O/graph.json","events_json":"https://pith.science/api/pith-number/56BLD2P3JJKEI5XRDMOVLBZB5O/events.json","paper":"https://pith.science/paper/56BLD2P3"},"agent_actions":{"view_html":"https://pith.science/pith/56BLD2P3JJKEI5XRDMOVLBZB5O","download_json":"https://pith.science/pith/56BLD2P3JJKEI5XRDMOVLBZB5O.json","view_paper":"https://pith.science/paper/56BLD2P3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.26440&json=true","fetch_graph":"https://pith.science/api/pith-number/56BLD2P3JJKEI5XRDMOVLBZB5O/graph.json","fetch_events":"https://pith.science/api/pith-number/56BLD2P3JJKEI5XRDMOVLBZB5O/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/56BLD2P3JJKEI5XRDMOVLBZB5O/action/timestamp_anchor","attest_storage":"https://pith.science/pith/56BLD2P3JJKEI5XRDMOVLBZB5O/action/storage_attestation","attest_author":"https://pith.science/pith/56BLD2P3JJKEI5XRDMOVLBZB5O/action/author_attestation","sign_citation":"https://pith.science/pith/56BLD2P3JJKEI5XRDMOVLBZB5O/action/citation_signature","submit_replication":"https://pith.science/pith/56BLD2P3JJKEI5XRDMOVLBZB5O/action/replication_record"}},"created_at":"2026-07-30T01:18:26.014656+00:00","updated_at":"2026-07-30T01:18:26.014656+00:00"}