{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:PDRDEPYRMFFGXZ7PQB24ICNJ6J","short_pith_number":"pith:PDRDEPYR","canonical_record":{"source":{"id":"1606.02096","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2016-06-07T11:07:56Z","cross_cats_sorted":["cs.MM","cs.SD"],"title_canon_sha256":"c83af9bc8353d95a91e664033c8cfc8822062dc8483f531c81acfbf6c37db98a","abstract_canon_sha256":"1cbeed744319764b28f960bf78d870e0b3ba772e72b201614182de11fd13e7a1"},"schema_version":"1.0"},"canonical_sha256":"78e2323f11614a6be7ef8075c409a9f24eefdbb29524c69bcc371036bfaadcb0","source":{"kind":"arxiv","id":"1606.02096","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1606.02096","created_at":"2026-05-18T01:12:44Z"},{"alias_kind":"arxiv_version","alias_value":"1606.02096v1","created_at":"2026-05-18T01:12:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.02096","created_at":"2026-05-18T01:12:44Z"},{"alias_kind":"pith_short_12","alias_value":"PDRDEPYRMFFG","created_at":"2026-05-18T12:30:39Z"},{"alias_kind":"pith_short_16","alias_value":"PDRDEPYRMFFGXZ7P","created_at":"2026-05-18T12:30:39Z"},{"alias_kind":"pith_short_8","alias_value":"PDRDEPYR","created_at":"2026-05-18T12:30:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:PDRDEPYRMFFGXZ7PQB24ICNJ6J","target":"record","payload":{"canonical_record":{"source":{"id":"1606.02096","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2016-06-07T11:07:56Z","cross_cats_sorted":["cs.MM","cs.SD"],"title_canon_sha256":"c83af9bc8353d95a91e664033c8cfc8822062dc8483f531c81acfbf6c37db98a","abstract_canon_sha256":"1cbeed744319764b28f960bf78d870e0b3ba772e72b201614182de11fd13e7a1"},"schema_version":"1.0"},"canonical_sha256":"78e2323f11614a6be7ef8075c409a9f24eefdbb29524c69bcc371036bfaadcb0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:12:44.629297Z","signature_b64":"BH9oi0nRHSXXRIDKqpG9hAR8Ms7KM3ynFtrcHy4lLxC+22QoFCEfYsTjCLPEKaiLwZfhlWUUr9ed5uY0YljWCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"78e2323f11614a6be7ef8075c409a9f24eefdbb29524c69bcc371036bfaadcb0","last_reissued_at":"2026-05-18T01:12:44.628966Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:12:44.628966Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1606.02096","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":"2026-05-18T01:12:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9dtzjUjQf0RzoGF7onXX04SvqfyAHLTP7QFn2dL0t55Rtym6im2WunCXDc26fT6OdwrBJa/jiRwPLkIhtRtCCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T02:37:17.400322Z"},"content_sha256":"727c064ef654904c7906cebc0923615910aa529981cd01b5742726ee7142fd2f","schema_version":"1.0","event_id":"sha256:727c064ef654904c7906cebc0923615910aa529981cd01b5742726ee7142fd2f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:PDRDEPYRMFFGXZ7PQB24ICNJ6J","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Towards Playlist Generation Algorithms Using RNNs Trained on Within-Track Transitions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.MM","cs.SD"],"primary_cat":"cs.AI","authors_text":"George Fazekas, Keunwoo Choi, Mark Sandler","submitted_at":"2016-06-07T11:07:56Z","abstract_excerpt":"We introduce a novel playlist generation algorithm that focuses on the quality of transitions using a recurrent neural network (RNN). The proposed model assumes that optimal transitions between tracks can be modelled and predicted by internal transitions within music tracks. We introduce modelling sequences of high-level music descriptors using RNNs and discuss an experiment involving different similarity functions, where the sequences are provided by a musical structural analysis algorithm. Qualitative observations show that the proposed approach can effectively model transitions of music tra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.02096","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":"2026-05-18T01:12:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"98wOT2XIzxnQigvBVk/AfR13VPUSidz4/JzLGCPxRg+4tidFJDWSKVrgxfx13N4O4J+r4Z1Hl5aqzeEXvfgFDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T02:37:17.400676Z"},"content_sha256":"58ec5e032ba1f68471e199cd333001e57f0d875b00e08100263391dc3933923b","schema_version":"1.0","event_id":"sha256:58ec5e032ba1f68471e199cd333001e57f0d875b00e08100263391dc3933923b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PDRDEPYRMFFGXZ7PQB24ICNJ6J/bundle.json","state_url":"https://pith.science/pith/PDRDEPYRMFFGXZ7PQB24ICNJ6J/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PDRDEPYRMFFGXZ7PQB24ICNJ6J/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":"2026-05-30T02:37:17Z","links":{"resolver":"https://pith.science/pith/PDRDEPYRMFFGXZ7PQB24ICNJ6J","bundle":"https://pith.science/pith/PDRDEPYRMFFGXZ7PQB24ICNJ6J/bundle.json","state":"https://pith.science/pith/PDRDEPYRMFFGXZ7PQB24ICNJ6J/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PDRDEPYRMFFGXZ7PQB24ICNJ6J/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:PDRDEPYRMFFGXZ7PQB24ICNJ6J","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":"1cbeed744319764b28f960bf78d870e0b3ba772e72b201614182de11fd13e7a1","cross_cats_sorted":["cs.MM","cs.SD"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2016-06-07T11:07:56Z","title_canon_sha256":"c83af9bc8353d95a91e664033c8cfc8822062dc8483f531c81acfbf6c37db98a"},"schema_version":"1.0","source":{"id":"1606.02096","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1606.02096","created_at":"2026-05-18T01:12:44Z"},{"alias_kind":"arxiv_version","alias_value":"1606.02096v1","created_at":"2026-05-18T01:12:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.02096","created_at":"2026-05-18T01:12:44Z"},{"alias_kind":"pith_short_12","alias_value":"PDRDEPYRMFFG","created_at":"2026-05-18T12:30:39Z"},{"alias_kind":"pith_short_16","alias_value":"PDRDEPYRMFFGXZ7P","created_at":"2026-05-18T12:30:39Z"},{"alias_kind":"pith_short_8","alias_value":"PDRDEPYR","created_at":"2026-05-18T12:30:39Z"}],"graph_snapshots":[{"event_id":"sha256:58ec5e032ba1f68471e199cd333001e57f0d875b00e08100263391dc3933923b","target":"graph","created_at":"2026-05-18T01:12:44Z","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":"We introduce a novel playlist generation algorithm that focuses on the quality of transitions using a recurrent neural network (RNN). The proposed model assumes that optimal transitions between tracks can be modelled and predicted by internal transitions within music tracks. We introduce modelling sequences of high-level music descriptors using RNNs and discuss an experiment involving different similarity functions, where the sequences are provided by a musical structural analysis algorithm. Qualitative observations show that the proposed approach can effectively model transitions of music tra","authors_text":"George Fazekas, Keunwoo Choi, Mark Sandler","cross_cats":["cs.MM","cs.SD"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2016-06-07T11:07:56Z","title":"Towards Playlist Generation Algorithms Using RNNs Trained on Within-Track Transitions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.02096","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":"sha256:727c064ef654904c7906cebc0923615910aa529981cd01b5742726ee7142fd2f","target":"record","created_at":"2026-05-18T01:12:44Z","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":"1cbeed744319764b28f960bf78d870e0b3ba772e72b201614182de11fd13e7a1","cross_cats_sorted":["cs.MM","cs.SD"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2016-06-07T11:07:56Z","title_canon_sha256":"c83af9bc8353d95a91e664033c8cfc8822062dc8483f531c81acfbf6c37db98a"},"schema_version":"1.0","source":{"id":"1606.02096","kind":"arxiv","version":1}},"canonical_sha256":"78e2323f11614a6be7ef8075c409a9f24eefdbb29524c69bcc371036bfaadcb0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"78e2323f11614a6be7ef8075c409a9f24eefdbb29524c69bcc371036bfaadcb0","first_computed_at":"2026-05-18T01:12:44.628966Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:12:44.628966Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BH9oi0nRHSXXRIDKqpG9hAR8Ms7KM3ynFtrcHy4lLxC+22QoFCEfYsTjCLPEKaiLwZfhlWUUr9ed5uY0YljWCg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:12:44.629297Z","signed_message":"canonical_sha256_bytes"},"source_id":"1606.02096","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:727c064ef654904c7906cebc0923615910aa529981cd01b5742726ee7142fd2f","sha256:58ec5e032ba1f68471e199cd333001e57f0d875b00e08100263391dc3933923b"],"state_sha256":"acfe67dfebb69081210e796d8d07b79faaf7daf630e710e4927411bdcb816dd1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oXy1q3JaeciwH0oybGPeyBq0UNXKlWmisT7S7/MTPIUEUaQUYok88e0WcxYNP61ssYNmzwD4512Bsv5FT5QRBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T02:37:17.402966Z","bundle_sha256":"fe7cd12d1a326b17545df55f0435dbf9fcc94027b27eb7faf7ea13d35a893fd6"}}