{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:3O4IO6GINX27ION7NLZN3HILCN","short_pith_number":"pith:3O4IO6GI","canonical_record":{"source":{"id":"2107.10388","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2021-07-21T23:07:22Z","cross_cats_sorted":["eess.AS"],"title_canon_sha256":"5fbf0e98f12f7f758ac2dd2112a814f8a3dcf6cca4bb4f6ec96db676d4ad1135","abstract_canon_sha256":"537e4231ad0cf1664bab78d64c8a006c8a70f50b51b76a8502b3a2cca1e6e219"},"schema_version":"1.0"},"canonical_sha256":"dbb88778c86df5f439bf6af2dd9d0b1346d4e5007b5bc697c01e9409d64e19ae","source":{"kind":"arxiv","id":"2107.10388","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.10388","created_at":"2026-07-05T04:10:36Z"},{"alias_kind":"arxiv_version","alias_value":"2107.10388v4","created_at":"2026-07-05T04:10:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.10388","created_at":"2026-07-05T04:10:36Z"},{"alias_kind":"pith_short_12","alias_value":"3O4IO6GINX27","created_at":"2026-07-05T04:10:36Z"},{"alias_kind":"pith_short_16","alias_value":"3O4IO6GINX27ION7","created_at":"2026-07-05T04:10:36Z"},{"alias_kind":"pith_short_8","alias_value":"3O4IO6GI","created_at":"2026-07-05T04:10:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:3O4IO6GINX27ION7NLZN3HILCN","target":"record","payload":{"canonical_record":{"source":{"id":"2107.10388","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2021-07-21T23:07:22Z","cross_cats_sorted":["eess.AS"],"title_canon_sha256":"5fbf0e98f12f7f758ac2dd2112a814f8a3dcf6cca4bb4f6ec96db676d4ad1135","abstract_canon_sha256":"537e4231ad0cf1664bab78d64c8a006c8a70f50b51b76a8502b3a2cca1e6e219"},"schema_version":"1.0"},"canonical_sha256":"dbb88778c86df5f439bf6af2dd9d0b1346d4e5007b5bc697c01e9409d64e19ae","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:10:36.248147Z","signature_b64":"HwcO5tL+FvT0yIdCXmaGVy7onHgwFCTbGMzDi+1gXADHYjUJZZxSrXaW9o3ixZkhmpuLGK7huBJz9AYOuHQaDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dbb88778c86df5f439bf6af2dd9d0b1346d4e5007b5bc697c01e9409d64e19ae","last_reissued_at":"2026-07-05T04:10:36.247701Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:10:36.247701Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2107.10388","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:10:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hxfahlDYsr4gwrgQSC8rEEvE4Gvg51bP2KTwwEhYXs/yNFlL/6N9Ov04uiGOq4P75T8bHFEmyNIn7PvggmCOAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T23:05:07.567074Z"},"content_sha256":"8659c6b3e60d9ee665746c34a9f64682ebfc9382d90d6d552677f58702265af9","schema_version":"1.0","event_id":"sha256:8659c6b3e60d9ee665746c34a9f64682ebfc9382d90d6d552677f58702265af9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:3O4IO6GINX27ION7NLZN3HILCN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"JS Fake Chorales: a Synthetic Dataset of Polyphonic Music with Human Annotation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.AS"],"primary_cat":"cs.SD","authors_text":"Omar Peracha","submitted_at":"2021-07-21T23:07:22Z","abstract_excerpt":"High-quality datasets for learning-based modelling of polyphonic symbolic music remain less readily-accessible at scale than in other domains, such as language modelling or image classification. Deep learning algorithms show great potential for enabling the widespread use of interactive music generation technology in consumer applications, but the lack of large-scale datasets remains a bottleneck for the development of algorithms that can consistently generate high-quality outputs. We propose that models with narrow expertise can serve as a source of high-quality scalable synthetic data, and o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.10388","kind":"arxiv","version":4},"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/2107.10388/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":"2026-07-05T04:10:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"upg9zplW9mG1P6d39FbYeHx/r9gQJTNUXLdJMO+FiYlzS5f2FbFyeXoCaPIArzPcnb1k4Pqm8tQ3ljkvgQ7aCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T23:05:07.567581Z"},"content_sha256":"a87155115b330654371de5bbcf3a14cd12e2f2d69cc48bdbe7f6c1f6f271122c","schema_version":"1.0","event_id":"sha256:a87155115b330654371de5bbcf3a14cd12e2f2d69cc48bdbe7f6c1f6f271122c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3O4IO6GINX27ION7NLZN3HILCN/bundle.json","state_url":"https://pith.science/pith/3O4IO6GINX27ION7NLZN3HILCN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3O4IO6GINX27ION7NLZN3HILCN/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":"2026-08-21T23:05:07Z","links":{"resolver":"https://pith.science/pith/3O4IO6GINX27ION7NLZN3HILCN","bundle":"https://pith.science/pith/3O4IO6GINX27ION7NLZN3HILCN/bundle.json","state":"https://pith.science/pith/3O4IO6GINX27ION7NLZN3HILCN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3O4IO6GINX27ION7NLZN3HILCN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:3O4IO6GINX27ION7NLZN3HILCN","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":"537e4231ad0cf1664bab78d64c8a006c8a70f50b51b76a8502b3a2cca1e6e219","cross_cats_sorted":["eess.AS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2021-07-21T23:07:22Z","title_canon_sha256":"5fbf0e98f12f7f758ac2dd2112a814f8a3dcf6cca4bb4f6ec96db676d4ad1135"},"schema_version":"1.0","source":{"id":"2107.10388","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.10388","created_at":"2026-07-05T04:10:36Z"},{"alias_kind":"arxiv_version","alias_value":"2107.10388v4","created_at":"2026-07-05T04:10:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.10388","created_at":"2026-07-05T04:10:36Z"},{"alias_kind":"pith_short_12","alias_value":"3O4IO6GINX27","created_at":"2026-07-05T04:10:36Z"},{"alias_kind":"pith_short_16","alias_value":"3O4IO6GINX27ION7","created_at":"2026-07-05T04:10:36Z"},{"alias_kind":"pith_short_8","alias_value":"3O4IO6GI","created_at":"2026-07-05T04:10:36Z"}],"graph_snapshots":[{"event_id":"sha256:a87155115b330654371de5bbcf3a14cd12e2f2d69cc48bdbe7f6c1f6f271122c","target":"graph","created_at":"2026-07-05T04:10:36Z","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/2107.10388/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"High-quality datasets for learning-based modelling of polyphonic symbolic music remain less readily-accessible at scale than in other domains, such as language modelling or image classification. Deep learning algorithms show great potential for enabling the widespread use of interactive music generation technology in consumer applications, but the lack of large-scale datasets remains a bottleneck for the development of algorithms that can consistently generate high-quality outputs. We propose that models with narrow expertise can serve as a source of high-quality scalable synthetic data, and o","authors_text":"Omar Peracha","cross_cats":["eess.AS"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2021-07-21T23:07:22Z","title":"JS Fake Chorales: a Synthetic Dataset of Polyphonic Music with Human Annotation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.10388","kind":"arxiv","version":4},"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":"sha256:8659c6b3e60d9ee665746c34a9f64682ebfc9382d90d6d552677f58702265af9","target":"record","created_at":"2026-07-05T04:10:36Z","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":"537e4231ad0cf1664bab78d64c8a006c8a70f50b51b76a8502b3a2cca1e6e219","cross_cats_sorted":["eess.AS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2021-07-21T23:07:22Z","title_canon_sha256":"5fbf0e98f12f7f758ac2dd2112a814f8a3dcf6cca4bb4f6ec96db676d4ad1135"},"schema_version":"1.0","source":{"id":"2107.10388","kind":"arxiv","version":4}},"canonical_sha256":"dbb88778c86df5f439bf6af2dd9d0b1346d4e5007b5bc697c01e9409d64e19ae","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dbb88778c86df5f439bf6af2dd9d0b1346d4e5007b5bc697c01e9409d64e19ae","first_computed_at":"2026-07-05T04:10:36.247701Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:10:36.247701Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HwcO5tL+FvT0yIdCXmaGVy7onHgwFCTbGMzDi+1gXADHYjUJZZxSrXaW9o3ixZkhmpuLGK7huBJz9AYOuHQaDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:10:36.248147Z","signed_message":"canonical_sha256_bytes"},"source_id":"2107.10388","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8659c6b3e60d9ee665746c34a9f64682ebfc9382d90d6d552677f58702265af9","sha256:a87155115b330654371de5bbcf3a14cd12e2f2d69cc48bdbe7f6c1f6f271122c"],"state_sha256":"1729abb3b636a2796fdeca9364e30599be8c874ff5febfd2e2feb837c084cc89"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ze55D03ca2DMQ6aTfkkaVYZpex/Ff8MB52oYc8WwdZLJgr1cZQ1Smc4aDFo7Dr6lunGzwz/yRD/oLYeoxsHEDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T23:05:07.572493Z","bundle_sha256":"4b8414ebca41d50339011d90f5ef0008ec48d2919df16fbad7cf0929ee98e60d"}}