{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:E2KSIQIZLV5ZDNJZPOZ75NKARE","short_pith_number":"pith:E2KSIQIZ","canonical_record":{"source":{"id":"1611.00379","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2016-11-01T20:35:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"78e9791be721704d76ad1d1740453f118d5d806ded35b2a682debe6ba0fcd223","abstract_canon_sha256":"31b4695e0150ec4267bed5223ff5d0f9b6e8d125e130345794f2022a4bb2b2ff"},"schema_version":"1.0"},"canonical_sha256":"26952441195d7b91b5397bb3feb540891d75a1a98f5ab4d4b681ba6fda3c44b3","source":{"kind":"arxiv","id":"1611.00379","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.00379","created_at":"2026-05-18T01:00:32Z"},{"alias_kind":"arxiv_version","alias_value":"1611.00379v1","created_at":"2026-05-18T01:00:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.00379","created_at":"2026-05-18T01:00:32Z"},{"alias_kind":"pith_short_12","alias_value":"E2KSIQIZLV5Z","created_at":"2026-05-18T12:30:12Z"},{"alias_kind":"pith_short_16","alias_value":"E2KSIQIZLV5ZDNJZ","created_at":"2026-05-18T12:30:12Z"},{"alias_kind":"pith_short_8","alias_value":"E2KSIQIZ","created_at":"2026-05-18T12:30:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:E2KSIQIZLV5ZDNJZPOZ75NKARE","target":"record","payload":{"canonical_record":{"source":{"id":"1611.00379","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2016-11-01T20:35:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"78e9791be721704d76ad1d1740453f118d5d806ded35b2a682debe6ba0fcd223","abstract_canon_sha256":"31b4695e0150ec4267bed5223ff5d0f9b6e8d125e130345794f2022a4bb2b2ff"},"schema_version":"1.0"},"canonical_sha256":"26952441195d7b91b5397bb3feb540891d75a1a98f5ab4d4b681ba6fda3c44b3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:00:32.993146Z","signature_b64":"z9s1rBr2Oldtm12O/MVN2Th1rnHOhKwukoGWad2SiZpTcfBq0q8W9b8o91k9IH5h4slRLUHJasMT3Fqpw3OVBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"26952441195d7b91b5397bb3feb540891d75a1a98f5ab4d4b681ba6fda3c44b3","last_reissued_at":"2026-05-18T01:00:32.992517Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:00:32.992517Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1611.00379","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:00:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rMz4iYca9vkcbGb+Zb6REpkjKluKHZxlVYNBigc8AvE+Fz3x2gkyyl961QsxS8jEF2nx6BcQPf/idMQ+N0KKAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T14:59:51.605355Z"},"content_sha256":"8e3f72115b70beeff171f91def2337a4b71a55b85b8361796479eeba9c97de9d","schema_version":"1.0","event_id":"sha256:8e3f72115b70beeff171f91def2337a4b71a55b85b8361796479eeba9c97de9d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:E2KSIQIZLV5ZDNJZPOZ75NKARE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"The Machine Learning Algorithm as Creative Musical Tool","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.HC","authors_text":"Baptiste Caramiaux, Rebecca Fiebrink","submitted_at":"2016-11-01T20:35:46Z","abstract_excerpt":"Machine learning is the capacity of a computational system to learn structures from datasets in order to make predictions on newly seen data. Such an approach offers a significant advantage in music scenarios in which musicians can teach the system to learn an idiosyncratic style, or can break the rules to explore the system's capacity in unexpected ways. In this chapter we draw on music, machine learning, and human-computer interaction to elucidate an understanding of machine learning algorithms as creative tools for music and the sonic arts. We motivate a new understanding of learning algori"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.00379","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:00:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xNupWVQ7Z6h8ddGPotmNsmFR6ixFEiGNHmTJuD7rgouHVNjm44vNqq49BVwPnoi7usdnlh21UGTQcBU2bp7qDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T14:59:51.605712Z"},"content_sha256":"c3083b2c9bb37704ca8f5b2f7f1087373d228f27a6a2998b320a5e4598151f0d","schema_version":"1.0","event_id":"sha256:c3083b2c9bb37704ca8f5b2f7f1087373d228f27a6a2998b320a5e4598151f0d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/E2KSIQIZLV5ZDNJZPOZ75NKARE/bundle.json","state_url":"https://pith.science/pith/E2KSIQIZLV5ZDNJZPOZ75NKARE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/E2KSIQIZLV5ZDNJZPOZ75NKARE/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":"2026-06-01T14:59:51Z","links":{"resolver":"https://pith.science/pith/E2KSIQIZLV5ZDNJZPOZ75NKARE","bundle":"https://pith.science/pith/E2KSIQIZLV5ZDNJZPOZ75NKARE/bundle.json","state":"https://pith.science/pith/E2KSIQIZLV5ZDNJZPOZ75NKARE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/E2KSIQIZLV5ZDNJZPOZ75NKARE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:E2KSIQIZLV5ZDNJZPOZ75NKARE","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":"31b4695e0150ec4267bed5223ff5d0f9b6e8d125e130345794f2022a4bb2b2ff","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2016-11-01T20:35:46Z","title_canon_sha256":"78e9791be721704d76ad1d1740453f118d5d806ded35b2a682debe6ba0fcd223"},"schema_version":"1.0","source":{"id":"1611.00379","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.00379","created_at":"2026-05-18T01:00:32Z"},{"alias_kind":"arxiv_version","alias_value":"1611.00379v1","created_at":"2026-05-18T01:00:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.00379","created_at":"2026-05-18T01:00:32Z"},{"alias_kind":"pith_short_12","alias_value":"E2KSIQIZLV5Z","created_at":"2026-05-18T12:30:12Z"},{"alias_kind":"pith_short_16","alias_value":"E2KSIQIZLV5ZDNJZ","created_at":"2026-05-18T12:30:12Z"},{"alias_kind":"pith_short_8","alias_value":"E2KSIQIZ","created_at":"2026-05-18T12:30:12Z"}],"graph_snapshots":[{"event_id":"sha256:c3083b2c9bb37704ca8f5b2f7f1087373d228f27a6a2998b320a5e4598151f0d","target":"graph","created_at":"2026-05-18T01:00:32Z","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"},"paper":{"abstract_excerpt":"Machine learning is the capacity of a computational system to learn structures from datasets in order to make predictions on newly seen data. Such an approach offers a significant advantage in music scenarios in which musicians can teach the system to learn an idiosyncratic style, or can break the rules to explore the system's capacity in unexpected ways. In this chapter we draw on music, machine learning, and human-computer interaction to elucidate an understanding of machine learning algorithms as creative tools for music and the sonic arts. We motivate a new understanding of learning algori","authors_text":"Baptiste Caramiaux, Rebecca Fiebrink","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2016-11-01T20:35:46Z","title":"The Machine Learning Algorithm as Creative Musical Tool"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.00379","kind":"arxiv","version":1},"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":"sha256:8e3f72115b70beeff171f91def2337a4b71a55b85b8361796479eeba9c97de9d","target":"record","created_at":"2026-05-18T01:00:32Z","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":"31b4695e0150ec4267bed5223ff5d0f9b6e8d125e130345794f2022a4bb2b2ff","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2016-11-01T20:35:46Z","title_canon_sha256":"78e9791be721704d76ad1d1740453f118d5d806ded35b2a682debe6ba0fcd223"},"schema_version":"1.0","source":{"id":"1611.00379","kind":"arxiv","version":1}},"canonical_sha256":"26952441195d7b91b5397bb3feb540891d75a1a98f5ab4d4b681ba6fda3c44b3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"26952441195d7b91b5397bb3feb540891d75a1a98f5ab4d4b681ba6fda3c44b3","first_computed_at":"2026-05-18T01:00:32.992517Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:00:32.992517Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"z9s1rBr2Oldtm12O/MVN2Th1rnHOhKwukoGWad2SiZpTcfBq0q8W9b8o91k9IH5h4slRLUHJasMT3Fqpw3OVBA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:00:32.993146Z","signed_message":"canonical_sha256_bytes"},"source_id":"1611.00379","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8e3f72115b70beeff171f91def2337a4b71a55b85b8361796479eeba9c97de9d","sha256:c3083b2c9bb37704ca8f5b2f7f1087373d228f27a6a2998b320a5e4598151f0d"],"state_sha256":"3ad23b1c374a25bc7bc93d45b3488eb338a9c3d8deecb8e5582bc53ea9b7bf51"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"x5VKhPLRWd8K3XePbA0JoPlQ1wpIy41RGbIMLaxl6c9I2RV1Bgq+Voypcrm0sJgU8BXszRNsA3Z4AsiDoMlqCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T14:59:51.607623Z","bundle_sha256":"c598c4774e2bf68f55a8922f5447ec27d3c6e121faf1b9cb7174de08c90fe3c0"}}