{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:E7G43NJKIMJGSGRIUNI42CN2WC","short_pith_number":"pith:E7G43NJK","schema_version":"1.0","canonical_sha256":"27cdcdb52a4312691a28a351cd09bab0a0d9aae1f77435c298200952f46436f9","source":{"kind":"arxiv","id":"1710.06648","version":2},"attestation_state":"computed","paper":{"title":"Representation Learning of Music Using Artist Labels","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.AS"],"primary_cat":"cs.SD","authors_text":"Jangyeon Park, Jiyoung Park, Jongpil Lee, Juhan Nam, Jung-Woo Ha","submitted_at":"2017-10-18T09:42:16Z","abstract_excerpt":"In music domain, feature learning has been conducted mainly in two ways: unsupervised learning based on sparse representations or supervised learning by semantic labels such as music genre. However, finding discriminative features in an unsupervised way is challenging and supervised feature learning using semantic labels may involve noisy or expensive annotation. In this paper, we present a supervised feature learning approach using artist labels annotated in every single track as objective meta data. We propose two deep convolutional neural networks (DCNN) to learn the deep artist features. O"},"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":"1710.06648","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2017-10-18T09:42:16Z","cross_cats_sorted":["eess.AS"],"title_canon_sha256":"bea96b6483b2d7aa5816687b325bf8a952722b93997579e4e5912c57d771dfc2","abstract_canon_sha256":"33747e02465d20c81baace1f2ceddf28fa42c3ecedb03f124341586dc6afabe2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:12:58.663886Z","signature_b64":"vd5LNyPPJC3wG9tU+JJ085ZjD3BohKN872l0E+FlTmABafhjjxbJYXgKlqdTQFT7wDCsF1eHIuIli2p7rMU7DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"27cdcdb52a4312691a28a351cd09bab0a0d9aae1f77435c298200952f46436f9","last_reissued_at":"2026-05-18T00:12:58.663400Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:12:58.663400Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Representation Learning of Music Using Artist Labels","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.AS"],"primary_cat":"cs.SD","authors_text":"Jangyeon Park, Jiyoung Park, Jongpil Lee, Juhan Nam, Jung-Woo Ha","submitted_at":"2017-10-18T09:42:16Z","abstract_excerpt":"In music domain, feature learning has been conducted mainly in two ways: unsupervised learning based on sparse representations or supervised learning by semantic labels such as music genre. However, finding discriminative features in an unsupervised way is challenging and supervised feature learning using semantic labels may involve noisy or expensive annotation. In this paper, we present a supervised feature learning approach using artist labels annotated in every single track as objective meta data. We propose two deep convolutional neural networks (DCNN) to learn the deep artist features. O"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.06648","kind":"arxiv","version":2},"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":"1710.06648","created_at":"2026-05-18T00:12:58.663464+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.06648v2","created_at":"2026-05-18T00:12:58.663464+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.06648","created_at":"2026-05-18T00:12:58.663464+00:00"},{"alias_kind":"pith_short_12","alias_value":"E7G43NJKIMJG","created_at":"2026-05-18T12:31:12.930513+00:00"},{"alias_kind":"pith_short_16","alias_value":"E7G43NJKIMJGSGRI","created_at":"2026-05-18T12:31:12.930513+00:00"},{"alias_kind":"pith_short_8","alias_value":"E7G43NJK","created_at":"2026-05-18T12:31:12.930513+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/E7G43NJKIMJGSGRIUNI42CN2WC","json":"https://pith.science/pith/E7G43NJKIMJGSGRIUNI42CN2WC.json","graph_json":"https://pith.science/api/pith-number/E7G43NJKIMJGSGRIUNI42CN2WC/graph.json","events_json":"https://pith.science/api/pith-number/E7G43NJKIMJGSGRIUNI42CN2WC/events.json","paper":"https://pith.science/paper/E7G43NJK"},"agent_actions":{"view_html":"https://pith.science/pith/E7G43NJKIMJGSGRIUNI42CN2WC","download_json":"https://pith.science/pith/E7G43NJKIMJGSGRIUNI42CN2WC.json","view_paper":"https://pith.science/paper/E7G43NJK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.06648&json=true","fetch_graph":"https://pith.science/api/pith-number/E7G43NJKIMJGSGRIUNI42CN2WC/graph.json","fetch_events":"https://pith.science/api/pith-number/E7G43NJKIMJGSGRIUNI42CN2WC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E7G43NJKIMJGSGRIUNI42CN2WC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E7G43NJKIMJGSGRIUNI42CN2WC/action/storage_attestation","attest_author":"https://pith.science/pith/E7G43NJKIMJGSGRIUNI42CN2WC/action/author_attestation","sign_citation":"https://pith.science/pith/E7G43NJKIMJGSGRIUNI42CN2WC/action/citation_signature","submit_replication":"https://pith.science/pith/E7G43NJKIMJGSGRIUNI42CN2WC/action/replication_record"}},"created_at":"2026-05-18T00:12:58.663464+00:00","updated_at":"2026-05-18T00:12:58.663464+00:00"}