{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:KPAPOF6IWWGRC5Q3NO7G4MKSUV","short_pith_number":"pith:KPAPOF6I","schema_version":"1.0","canonical_sha256":"53c0f717c8b58d11761b6bbe6e3152a56bcd2a92bdba64455da60da4cf4f879c","source":{"kind":"arxiv","id":"1712.08370","version":1},"attestation_state":"computed","paper":{"title":"Music Genre Classification with Paralleling Recurrent Convolutional Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR","eess.AS"],"primary_cat":"cs.SD","authors_text":"Jianing Yao, Lin Feng, Shenlan Liu","submitted_at":"2017-12-22T09:49:26Z","abstract_excerpt":"Deep learning has been demonstrated its effectiveness and efficiency in music genre classification. However, the existing achievements still have several shortcomings which impair the performance of this classification task. In this paper, we propose a hybrid architecture which consists of the paralleling CNN and Bi-RNN blocks. They focus on spatial features and temporal frame orders extraction respectively. Then the two outputs are fused into one powerful representation of musical signals and fed into softmax function for classification. The paralleling network guarantees the extracting featu"},"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":"1712.08370","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2017-12-22T09:49:26Z","cross_cats_sorted":["cs.IR","eess.AS"],"title_canon_sha256":"e090756433b17b75343698d23f02bff2ac5f9c9b12c186a8f92c5c3cf74e3bef","abstract_canon_sha256":"af6fca8d23c4e69a3f70215f2e4f1522dbe1e08d924dc426f09bb6334881ecb7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:27:23.640079Z","signature_b64":"diF9sK9igxCtfH1CqOL/MQO0+YiRr36SuPs3UYAnIg5wGyJ6IffspijZGTPF3gh9LgpK8bhoSD1TzTfX2y4JDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"53c0f717c8b58d11761b6bbe6e3152a56bcd2a92bdba64455da60da4cf4f879c","last_reissued_at":"2026-05-18T00:27:23.639486Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:27:23.639486Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Music Genre Classification with Paralleling Recurrent Convolutional Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR","eess.AS"],"primary_cat":"cs.SD","authors_text":"Jianing Yao, Lin Feng, Shenlan Liu","submitted_at":"2017-12-22T09:49:26Z","abstract_excerpt":"Deep learning has been demonstrated its effectiveness and efficiency in music genre classification. However, the existing achievements still have several shortcomings which impair the performance of this classification task. In this paper, we propose a hybrid architecture which consists of the paralleling CNN and Bi-RNN blocks. They focus on spatial features and temporal frame orders extraction respectively. Then the two outputs are fused into one powerful representation of musical signals and fed into softmax function for classification. The paralleling network guarantees the extracting featu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.08370","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1712.08370","created_at":"2026-05-18T00:27:23.639568+00:00"},{"alias_kind":"arxiv_version","alias_value":"1712.08370v1","created_at":"2026-05-18T00:27:23.639568+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.08370","created_at":"2026-05-18T00:27:23.639568+00:00"},{"alias_kind":"pith_short_12","alias_value":"KPAPOF6IWWGR","created_at":"2026-05-18T12:31:24.725408+00:00"},{"alias_kind":"pith_short_16","alias_value":"KPAPOF6IWWGRC5Q3","created_at":"2026-05-18T12:31:24.725408+00:00"},{"alias_kind":"pith_short_8","alias_value":"KPAPOF6I","created_at":"2026-05-18T12:31:24.725408+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/KPAPOF6IWWGRC5Q3NO7G4MKSUV","json":"https://pith.science/pith/KPAPOF6IWWGRC5Q3NO7G4MKSUV.json","graph_json":"https://pith.science/api/pith-number/KPAPOF6IWWGRC5Q3NO7G4MKSUV/graph.json","events_json":"https://pith.science/api/pith-number/KPAPOF6IWWGRC5Q3NO7G4MKSUV/events.json","paper":"https://pith.science/paper/KPAPOF6I"},"agent_actions":{"view_html":"https://pith.science/pith/KPAPOF6IWWGRC5Q3NO7G4MKSUV","download_json":"https://pith.science/pith/KPAPOF6IWWGRC5Q3NO7G4MKSUV.json","view_paper":"https://pith.science/paper/KPAPOF6I","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1712.08370&json=true","fetch_graph":"https://pith.science/api/pith-number/KPAPOF6IWWGRC5Q3NO7G4MKSUV/graph.json","fetch_events":"https://pith.science/api/pith-number/KPAPOF6IWWGRC5Q3NO7G4MKSUV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KPAPOF6IWWGRC5Q3NO7G4MKSUV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KPAPOF6IWWGRC5Q3NO7G4MKSUV/action/storage_attestation","attest_author":"https://pith.science/pith/KPAPOF6IWWGRC5Q3NO7G4MKSUV/action/author_attestation","sign_citation":"https://pith.science/pith/KPAPOF6IWWGRC5Q3NO7G4MKSUV/action/citation_signature","submit_replication":"https://pith.science/pith/KPAPOF6IWWGRC5Q3NO7G4MKSUV/action/replication_record"}},"created_at":"2026-05-18T00:27:23.639568+00:00","updated_at":"2026-05-18T00:27:23.639568+00:00"}