{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:3BX3DLFJBPNBMJW7PHBRVJDFZV","short_pith_number":"pith:3BX3DLFJ","canonical_record":{"source":{"id":"2002.00212","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2020-02-01T14:12:35Z","cross_cats_sorted":["cs.AI","eess.AS","stat.ML"],"title_canon_sha256":"352005fafb8a197651f5c1e4a4312709a24c04476d419b880b22b29a54260cc3","abstract_canon_sha256":"aa1d0adaa9893871cab9e8c332c149c3f6ef4950d3bc718b226085e5a665a2a8"},"schema_version":"1.0"},"canonical_sha256":"d86fb1aca90bda1626df79c31aa465cd679838278bab87d7c4478efa9d5d57d1","source":{"kind":"arxiv","id":"2002.00212","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.00212","created_at":"2026-07-05T01:25:52Z"},{"alias_kind":"arxiv_version","alias_value":"2002.00212v3","created_at":"2026-07-05T01:25:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.00212","created_at":"2026-07-05T01:25:52Z"},{"alias_kind":"pith_short_12","alias_value":"3BX3DLFJBPNB","created_at":"2026-07-05T01:25:52Z"},{"alias_kind":"pith_short_16","alias_value":"3BX3DLFJBPNBMJW7","created_at":"2026-07-05T01:25:52Z"},{"alias_kind":"pith_short_8","alias_value":"3BX3DLFJ","created_at":"2026-07-05T01:25:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:3BX3DLFJBPNBMJW7PHBRVJDFZV","target":"record","payload":{"canonical_record":{"source":{"id":"2002.00212","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2020-02-01T14:12:35Z","cross_cats_sorted":["cs.AI","eess.AS","stat.ML"],"title_canon_sha256":"352005fafb8a197651f5c1e4a4312709a24c04476d419b880b22b29a54260cc3","abstract_canon_sha256":"aa1d0adaa9893871cab9e8c332c149c3f6ef4950d3bc718b226085e5a665a2a8"},"schema_version":"1.0"},"canonical_sha256":"d86fb1aca90bda1626df79c31aa465cd679838278bab87d7c4478efa9d5d57d1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:25:52.597103Z","signature_b64":"vR40QI1whGlHQbO5EB0tx2/dXLBj+qRTyjYXLkF5sbJHp1lIMddkO1mwPgnRf0gj8dDxOAYLsCDS37wqsg8CAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d86fb1aca90bda1626df79c31aa465cd679838278bab87d7c4478efa9d5d57d1","last_reissued_at":"2026-07-05T01:25:52.596659Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:25:52.596659Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2002.00212","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:25:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zl8+MdzAflQn1aHdyr3/bAv+8Qmi3lZF63dJewyNcBg18z9xexNgUu/7sInGFPwmau71FG7KskaPZ68JrMEsCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T05:31:00.350713Z"},"content_sha256":"42e30f3f34c41ed3c80615b05e22f8ae26e482137b10dae41d02e598aeb9d14a","schema_version":"1.0","event_id":"sha256:42e30f3f34c41ed3c80615b05e22f8ae26e482137b10dae41d02e598aeb9d14a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:3BX3DLFJBPNBMJW7PHBRVJDFZV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","eess.AS","stat.ML"],"primary_cat":"cs.SD","authors_text":"Yi-Hsuan Yang, Yu-Siang Huang","submitted_at":"2020-02-01T14:12:35Z","abstract_excerpt":"A great number of deep learning based models have been recently proposed for automatic music composition. Among these models, the Transformer stands out as a prominent approach for generating expressive classical piano performance with a coherent structure of up to one minute. The model is powerful in that it learns abstractions of data on its own, without much human-imposed domain knowledge or constraints. In contrast with this general approach, this paper shows that Transformers can do even better for music modeling, when we improve the way a musical score is converted into the data fed to a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.00212","kind":"arxiv","version":3},"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/2002.00212/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:25:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RSOSlebzc6bWxSEKHHb7rx3YwfJhA6KYV2KOqT5lHSqT2Rh2p8beUIDyc8NJmieLCpg+pe53+bxJEDjdj8/QAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T05:31:00.351308Z"},"content_sha256":"57b567a936f6715be171b0f6e810176e910a763164a1516680e2ec19a24a3b80","schema_version":"1.0","event_id":"sha256:57b567a936f6715be171b0f6e810176e910a763164a1516680e2ec19a24a3b80"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3BX3DLFJBPNBMJW7PHBRVJDFZV/bundle.json","state_url":"https://pith.science/pith/3BX3DLFJBPNBMJW7PHBRVJDFZV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3BX3DLFJBPNBMJW7PHBRVJDFZV/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-09T05:31:00Z","links":{"resolver":"https://pith.science/pith/3BX3DLFJBPNBMJW7PHBRVJDFZV","bundle":"https://pith.science/pith/3BX3DLFJBPNBMJW7PHBRVJDFZV/bundle.json","state":"https://pith.science/pith/3BX3DLFJBPNBMJW7PHBRVJDFZV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3BX3DLFJBPNBMJW7PHBRVJDFZV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:3BX3DLFJBPNBMJW7PHBRVJDFZV","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"aa1d0adaa9893871cab9e8c332c149c3f6ef4950d3bc718b226085e5a665a2a8","cross_cats_sorted":["cs.AI","eess.AS","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2020-02-01T14:12:35Z","title_canon_sha256":"352005fafb8a197651f5c1e4a4312709a24c04476d419b880b22b29a54260cc3"},"schema_version":"1.0","source":{"id":"2002.00212","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.00212","created_at":"2026-07-05T01:25:52Z"},{"alias_kind":"arxiv_version","alias_value":"2002.00212v3","created_at":"2026-07-05T01:25:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.00212","created_at":"2026-07-05T01:25:52Z"},{"alias_kind":"pith_short_12","alias_value":"3BX3DLFJBPNB","created_at":"2026-07-05T01:25:52Z"},{"alias_kind":"pith_short_16","alias_value":"3BX3DLFJBPNBMJW7","created_at":"2026-07-05T01:25:52Z"},{"alias_kind":"pith_short_8","alias_value":"3BX3DLFJ","created_at":"2026-07-05T01:25:52Z"}],"graph_snapshots":[{"event_id":"sha256:57b567a936f6715be171b0f6e810176e910a763164a1516680e2ec19a24a3b80","target":"graph","created_at":"2026-07-05T01:25:52Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2002.00212/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"A great number of deep learning based models have been recently proposed for automatic music composition. Among these models, the Transformer stands out as a prominent approach for generating expressive classical piano performance with a coherent structure of up to one minute. The model is powerful in that it learns abstractions of data on its own, without much human-imposed domain knowledge or constraints. In contrast with this general approach, this paper shows that Transformers can do even better for music modeling, when we improve the way a musical score is converted into the data fed to a","authors_text":"Yi-Hsuan Yang, Yu-Siang Huang","cross_cats":["cs.AI","eess.AS","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2020-02-01T14:12:35Z","title":"Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.00212","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:42e30f3f34c41ed3c80615b05e22f8ae26e482137b10dae41d02e598aeb9d14a","target":"record","created_at":"2026-07-05T01:25:52Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"aa1d0adaa9893871cab9e8c332c149c3f6ef4950d3bc718b226085e5a665a2a8","cross_cats_sorted":["cs.AI","eess.AS","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2020-02-01T14:12:35Z","title_canon_sha256":"352005fafb8a197651f5c1e4a4312709a24c04476d419b880b22b29a54260cc3"},"schema_version":"1.0","source":{"id":"2002.00212","kind":"arxiv","version":3}},"canonical_sha256":"d86fb1aca90bda1626df79c31aa465cd679838278bab87d7c4478efa9d5d57d1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d86fb1aca90bda1626df79c31aa465cd679838278bab87d7c4478efa9d5d57d1","first_computed_at":"2026-07-05T01:25:52.596659Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:25:52.596659Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vR40QI1whGlHQbO5EB0tx2/dXLBj+qRTyjYXLkF5sbJHp1lIMddkO1mwPgnRf0gj8dDxOAYLsCDS37wqsg8CAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T01:25:52.597103Z","signed_message":"canonical_sha256_bytes"},"source_id":"2002.00212","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:42e30f3f34c41ed3c80615b05e22f8ae26e482137b10dae41d02e598aeb9d14a","sha256:57b567a936f6715be171b0f6e810176e910a763164a1516680e2ec19a24a3b80"],"state_sha256":"ee9f33be3825fa9dbc4836d6df750c4759687808df332771321c49e6b5894651"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OytIuWTxnehCaG3aCwkPChUZ+k8ZHpQrgXYbMl7qSq6xCcN9K2AtJu7PkoPkjocWJN8pjH5tTJhLx+maD/bNBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T05:31:00.355229Z","bundle_sha256":"cc98371d07a0e959967c8533cc1d2032046b4b2ef102dec819b52e41e88133dd"}}