{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:322RHONKP3ZOU2GSM5P743LRDT","short_pith_number":"pith:322RHONK","schema_version":"1.0","canonical_sha256":"deb513b9aa7ef2ea68d2675ffe6d711cccb79b21dd7c06127b52e0efc39b3388","source":{"kind":"arxiv","id":"2407.19823","version":1},"attestation_state":"computed","paper":{"title":"Analyzing and reducing the synthetic-to-real transfer gap in Music Information Retrieval: the task of automatic drum transcription","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IR","cs.LG","eess.AS"],"primary_cat":"cs.SD","authors_text":"Marco Alunno, Micka\\\"el Zehren, Paolo Bientinesi","submitted_at":"2024-07-29T09:17:16Z","abstract_excerpt":"Automatic drum transcription is a critical tool in Music Information Retrieval for extracting and analyzing the rhythm of a music track, but it is limited by the size of the datasets available for training. A popular method used to increase the amount of data is by generating them synthetically from music scores rendered with virtual instruments. This method can produce a virtually infinite quantity of tracks, but empirical evidence shows that models trained on previously created synthetic datasets do not transfer well to real tracks. In this work, besides increasing the amount of data, we ide"},"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":"2407.19823","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2024-07-29T09:17:16Z","cross_cats_sorted":["cs.IR","cs.LG","eess.AS"],"title_canon_sha256":"fb474d1ba9d11fd0d88c38dc86737d4ed38169e91b168609581abf549c9324b8","abstract_canon_sha256":"088492ef63aad5b7dccaf773928635aa0b3bbc82230714386f0dfb5350b1de7c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:49:44.837738Z","signature_b64":"z+jRp5Hq+eZ/jleSCU1bdKBXxal9OiUjfrCmFEUgNceQjUUpFonp7kSnA3CQpIOsQhFac08vGDI/ysyWx7qCBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"deb513b9aa7ef2ea68d2675ffe6d711cccb79b21dd7c06127b52e0efc39b3388","last_reissued_at":"2026-07-05T08:49:44.837302Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:49:44.837302Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Analyzing and reducing the synthetic-to-real transfer gap in Music Information Retrieval: the task of automatic drum transcription","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IR","cs.LG","eess.AS"],"primary_cat":"cs.SD","authors_text":"Marco Alunno, Micka\\\"el Zehren, Paolo Bientinesi","submitted_at":"2024-07-29T09:17:16Z","abstract_excerpt":"Automatic drum transcription is a critical tool in Music Information Retrieval for extracting and analyzing the rhythm of a music track, but it is limited by the size of the datasets available for training. A popular method used to increase the amount of data is by generating them synthetically from music scores rendered with virtual instruments. This method can produce a virtually infinite quantity of tracks, but empirical evidence shows that models trained on previously created synthetic datasets do not transfer well to real tracks. In this work, besides increasing the amount of data, we ide"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.19823","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/2407.19823/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":"2407.19823","created_at":"2026-07-05T08:49:44.837368+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.19823v1","created_at":"2026-07-05T08:49:44.837368+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.19823","created_at":"2026-07-05T08:49:44.837368+00:00"},{"alias_kind":"pith_short_12","alias_value":"322RHONKP3ZO","created_at":"2026-07-05T08:49:44.837368+00:00"},{"alias_kind":"pith_short_16","alias_value":"322RHONKP3ZOU2GS","created_at":"2026-07-05T08:49:44.837368+00:00"},{"alias_kind":"pith_short_8","alias_value":"322RHONK","created_at":"2026-07-05T08:49:44.837368+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.04742","citing_title":"Meta-learning-based percussion transcription and $t\\bar{a}la$ identification from low-resource audio","ref_index":19,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/322RHONKP3ZOU2GSM5P743LRDT","json":"https://pith.science/pith/322RHONKP3ZOU2GSM5P743LRDT.json","graph_json":"https://pith.science/api/pith-number/322RHONKP3ZOU2GSM5P743LRDT/graph.json","events_json":"https://pith.science/api/pith-number/322RHONKP3ZOU2GSM5P743LRDT/events.json","paper":"https://pith.science/paper/322RHONK"},"agent_actions":{"view_html":"https://pith.science/pith/322RHONKP3ZOU2GSM5P743LRDT","download_json":"https://pith.science/pith/322RHONKP3ZOU2GSM5P743LRDT.json","view_paper":"https://pith.science/paper/322RHONK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.19823&json=true","fetch_graph":"https://pith.science/api/pith-number/322RHONKP3ZOU2GSM5P743LRDT/graph.json","fetch_events":"https://pith.science/api/pith-number/322RHONKP3ZOU2GSM5P743LRDT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/322RHONKP3ZOU2GSM5P743LRDT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/322RHONKP3ZOU2GSM5P743LRDT/action/storage_attestation","attest_author":"https://pith.science/pith/322RHONKP3ZOU2GSM5P743LRDT/action/author_attestation","sign_citation":"https://pith.science/pith/322RHONKP3ZOU2GSM5P743LRDT/action/citation_signature","submit_replication":"https://pith.science/pith/322RHONKP3ZOU2GSM5P743LRDT/action/replication_record"}},"created_at":"2026-07-05T08:49:44.837368+00:00","updated_at":"2026-07-05T08:49:44.837368+00:00"}