{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:UVWZV44J3ORLC3VJM7XOQYWROE","short_pith_number":"pith:UVWZV44J","schema_version":"1.0","canonical_sha256":"a56d9af389dba2b16ea967eee862d17126fbbd64b4d694fd0b3913a0b1eaf769","source":{"kind":"arxiv","id":"1807.06391","version":1},"attestation_state":"computed","paper":{"title":"Learning to Listen, Read, and Follow: Score Following as a Reinforcement Learning Game","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SD","eess.AS"],"primary_cat":"cs.AI","authors_text":"Florian Henkel, Gerhard Widmer, Matthias Dorfer","submitted_at":"2018-07-17T12:49:18Z","abstract_excerpt":"Score following is the process of tracking a musical performance (audio) with respect to a known symbolic representation (a score). We start this paper by formulating score following as a multimodal Markov Decision Process, the mathematical foundation for sequential decision making. Given this formal definition, we address the score following task with state-of-the-art deep reinforcement learning (RL) algorithms such as synchronous advantage actor critic (A2C). In particular, we design multimodal RL agents that simultaneously learn to listen to music, read the scores from images of sheet music"},"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":"1807.06391","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-07-17T12:49:18Z","cross_cats_sorted":["cs.LG","cs.SD","eess.AS"],"title_canon_sha256":"d07338050a0e25666b1f9af71d4b36c74a2b01800928868711dd08772ea57e61","abstract_canon_sha256":"916f0da1c41740921178764b79b69ae157332204403b5e768745b523e9ee6149"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:10:33.182956Z","signature_b64":"Gk7s3qSLplE4WFFIRb4eVfhX/Ln7/0JnUBZwaOymN5oifzBhBcXTKLyQrBSbjsMY6C7Ojy3Ut6M0xivQfqdPBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a56d9af389dba2b16ea967eee862d17126fbbd64b4d694fd0b3913a0b1eaf769","last_reissued_at":"2026-05-18T00:10:33.182157Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:10:33.182157Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning to Listen, Read, and Follow: Score Following as a Reinforcement Learning Game","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SD","eess.AS"],"primary_cat":"cs.AI","authors_text":"Florian Henkel, Gerhard Widmer, Matthias Dorfer","submitted_at":"2018-07-17T12:49:18Z","abstract_excerpt":"Score following is the process of tracking a musical performance (audio) with respect to a known symbolic representation (a score). We start this paper by formulating score following as a multimodal Markov Decision Process, the mathematical foundation for sequential decision making. Given this formal definition, we address the score following task with state-of-the-art deep reinforcement learning (RL) algorithms such as synchronous advantage actor critic (A2C). In particular, we design multimodal RL agents that simultaneously learn to listen to music, read the scores from images of sheet music"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.06391","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":"1807.06391","created_at":"2026-05-18T00:10:33.182272+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.06391v1","created_at":"2026-05-18T00:10:33.182272+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.06391","created_at":"2026-05-18T00:10:33.182272+00:00"},{"alias_kind":"pith_short_12","alias_value":"UVWZV44J3ORL","created_at":"2026-05-18T12:32:56.356000+00:00"},{"alias_kind":"pith_short_16","alias_value":"UVWZV44J3ORLC3VJ","created_at":"2026-05-18T12:32:56.356000+00:00"},{"alias_kind":"pith_short_8","alias_value":"UVWZV44J","created_at":"2026-05-18T12:32:56.356000+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/UVWZV44J3ORLC3VJM7XOQYWROE","json":"https://pith.science/pith/UVWZV44J3ORLC3VJM7XOQYWROE.json","graph_json":"https://pith.science/api/pith-number/UVWZV44J3ORLC3VJM7XOQYWROE/graph.json","events_json":"https://pith.science/api/pith-number/UVWZV44J3ORLC3VJM7XOQYWROE/events.json","paper":"https://pith.science/paper/UVWZV44J"},"agent_actions":{"view_html":"https://pith.science/pith/UVWZV44J3ORLC3VJM7XOQYWROE","download_json":"https://pith.science/pith/UVWZV44J3ORLC3VJM7XOQYWROE.json","view_paper":"https://pith.science/paper/UVWZV44J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.06391&json=true","fetch_graph":"https://pith.science/api/pith-number/UVWZV44J3ORLC3VJM7XOQYWROE/graph.json","fetch_events":"https://pith.science/api/pith-number/UVWZV44J3ORLC3VJM7XOQYWROE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UVWZV44J3ORLC3VJM7XOQYWROE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UVWZV44J3ORLC3VJM7XOQYWROE/action/storage_attestation","attest_author":"https://pith.science/pith/UVWZV44J3ORLC3VJM7XOQYWROE/action/author_attestation","sign_citation":"https://pith.science/pith/UVWZV44J3ORLC3VJM7XOQYWROE/action/citation_signature","submit_replication":"https://pith.science/pith/UVWZV44J3ORLC3VJM7XOQYWROE/action/replication_record"}},"created_at":"2026-05-18T00:10:33.182272+00:00","updated_at":"2026-05-18T00:10:33.182272+00:00"}