{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:JNBN5XQ77S2LPIGXIJ6ZOQ2URR","short_pith_number":"pith:JNBN5XQ7","schema_version":"1.0","canonical_sha256":"4b42dede1ffcb4b7a0d7427d9743548c65585297c5472eba19e626cd139fbd99","source":{"kind":"arxiv","id":"1707.04877","version":1},"attestation_state":"computed","paper":{"title":"Optical Music Recognition with Convolutional Sequence-to-Sequence Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IR","cs.SD"],"primary_cat":"cs.CV","authors_text":"Eelco van der Wel, Karen Ullrich","submitted_at":"2017-07-16T13:11:22Z","abstract_excerpt":"Optical Music Recognition (OMR) is an important technology within Music Information Retrieval. Deep learning models show promising results on OMR tasks, but symbol-level annotated data sets of sufficient size to train such models are not available and difficult to develop. We present a deep learning architecture called a Convolutional Sequence-to-Sequence model to both move towards an end-to-end trainable OMR pipeline, and apply a learning process that trains on full sentences of sheet music instead of individually labeled symbols. The model is trained and evaluated on a human generated data s"},"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":"1707.04877","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2017-07-16T13:11:22Z","cross_cats_sorted":["cs.IR","cs.SD"],"title_canon_sha256":"d747ccd2f97e7cb0daded6aed2421a3d2509170f8270c458194ae19d9fb93c6f","abstract_canon_sha256":"552b24fbecde261466dc14f6cc0799d8e9cbb563ed07659c70db0101c5f36da1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:40:11.941271Z","signature_b64":"wZmOGn4HUi0We3oRW1Tn3yUKVvJGPE7raLAwJnC+HCViarC3Lb5Ne3bSgqbGHLDSGLe/Izg9y5I74A0hEXv3Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4b42dede1ffcb4b7a0d7427d9743548c65585297c5472eba19e626cd139fbd99","last_reissued_at":"2026-05-18T00:40:11.940613Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:40:11.940613Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optical Music Recognition with Convolutional Sequence-to-Sequence Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IR","cs.SD"],"primary_cat":"cs.CV","authors_text":"Eelco van der Wel, Karen Ullrich","submitted_at":"2017-07-16T13:11:22Z","abstract_excerpt":"Optical Music Recognition (OMR) is an important technology within Music Information Retrieval. Deep learning models show promising results on OMR tasks, but symbol-level annotated data sets of sufficient size to train such models are not available and difficult to develop. We present a deep learning architecture called a Convolutional Sequence-to-Sequence model to both move towards an end-to-end trainable OMR pipeline, and apply a learning process that trains on full sentences of sheet music instead of individually labeled symbols. The model is trained and evaluated on a human generated data s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.04877","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":""},"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":"1707.04877","created_at":"2026-05-18T00:40:11.940702+00:00"},{"alias_kind":"arxiv_version","alias_value":"1707.04877v1","created_at":"2026-05-18T00:40:11.940702+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.04877","created_at":"2026-05-18T00:40:11.940702+00:00"},{"alias_kind":"pith_short_12","alias_value":"JNBN5XQ77S2L","created_at":"2026-05-18T12:31:24.725408+00:00"},{"alias_kind":"pith_short_16","alias_value":"JNBN5XQ77S2LPIGX","created_at":"2026-05-18T12:31:24.725408+00:00"},{"alias_kind":"pith_short_8","alias_value":"JNBN5XQ7","created_at":"2026-05-18T12:31:24.725408+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.16446","citing_title":"A High-Accuracy Optical Music Recognition Method Based on Bottleneck Residual Convolutions","ref_index":26,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JNBN5XQ77S2LPIGXIJ6ZOQ2URR","json":"https://pith.science/pith/JNBN5XQ77S2LPIGXIJ6ZOQ2URR.json","graph_json":"https://pith.science/api/pith-number/JNBN5XQ77S2LPIGXIJ6ZOQ2URR/graph.json","events_json":"https://pith.science/api/pith-number/JNBN5XQ77S2LPIGXIJ6ZOQ2URR/events.json","paper":"https://pith.science/paper/JNBN5XQ7"},"agent_actions":{"view_html":"https://pith.science/pith/JNBN5XQ77S2LPIGXIJ6ZOQ2URR","download_json":"https://pith.science/pith/JNBN5XQ77S2LPIGXIJ6ZOQ2URR.json","view_paper":"https://pith.science/paper/JNBN5XQ7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1707.04877&json=true","fetch_graph":"https://pith.science/api/pith-number/JNBN5XQ77S2LPIGXIJ6ZOQ2URR/graph.json","fetch_events":"https://pith.science/api/pith-number/JNBN5XQ77S2LPIGXIJ6ZOQ2URR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JNBN5XQ77S2LPIGXIJ6ZOQ2URR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JNBN5XQ77S2LPIGXIJ6ZOQ2URR/action/storage_attestation","attest_author":"https://pith.science/pith/JNBN5XQ77S2LPIGXIJ6ZOQ2URR/action/author_attestation","sign_citation":"https://pith.science/pith/JNBN5XQ77S2LPIGXIJ6ZOQ2URR/action/citation_signature","submit_replication":"https://pith.science/pith/JNBN5XQ77S2LPIGXIJ6ZOQ2URR/action/replication_record"}},"created_at":"2026-05-18T00:40:11.940702+00:00","updated_at":"2026-05-18T00:40:11.940702+00:00"}