{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:HVSAMUH6SHRD2DVRLWOHDSEYDC","short_pith_number":"pith:HVSAMUH6","schema_version":"1.0","canonical_sha256":"3d640650fe91e23d0eb15d9c71c898188ea9078018e64f677677997830a6dcf9","source":{"kind":"arxiv","id":"1511.05520","version":1},"attestation_state":"computed","paper":{"title":"Automatic Instrument Recognition in Polyphonic Music Using Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR","cs.LG","cs.NE"],"primary_cat":"cs.SD","authors_text":"Jiyuan Qian, Peter Li, Tian Wang","submitted_at":"2015-11-17T19:43:53Z","abstract_excerpt":"Traditional methods to tackle many music information retrieval tasks typically follow a two-step architecture: feature engineering followed by a simple learning algorithm. In these \"shallow\" architectures, feature engineering and learning are typically disjoint and unrelated. Additionally, feature engineering is difficult, and typically depends on extensive domain expertise.\n  In this paper, we present an application of convolutional neural networks for the task of automatic musical instrument identification. In this model, feature extraction and learning algorithms are trained together in an "},"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":"1511.05520","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2015-11-17T19:43:53Z","cross_cats_sorted":["cs.IR","cs.LG","cs.NE"],"title_canon_sha256":"e21ed38f6ff969e547ac0ee0bd133ed5986254180bcc8b7e740ef24c818a33b1","abstract_canon_sha256":"d168ef33e4045e3403b39ac9f43e5b94ee864c559ff92044cc6465e474b41a86"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:26:40.039104Z","signature_b64":"LnEhZOX4Dw7UzuKKsDrApnx6Ilku6WBMZoj28A7qx8d1VPGXablLjj4fx7uPhQLGAxRcDSFKtxwFk43fQxFSDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3d640650fe91e23d0eb15d9c71c898188ea9078018e64f677677997830a6dcf9","last_reissued_at":"2026-05-18T01:26:40.038438Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:26:40.038438Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automatic Instrument Recognition in Polyphonic Music Using Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR","cs.LG","cs.NE"],"primary_cat":"cs.SD","authors_text":"Jiyuan Qian, Peter Li, Tian Wang","submitted_at":"2015-11-17T19:43:53Z","abstract_excerpt":"Traditional methods to tackle many music information retrieval tasks typically follow a two-step architecture: feature engineering followed by a simple learning algorithm. In these \"shallow\" architectures, feature engineering and learning are typically disjoint and unrelated. Additionally, feature engineering is difficult, and typically depends on extensive domain expertise.\n  In this paper, we present an application of convolutional neural networks for the task of automatic musical instrument identification. In this model, feature extraction and learning algorithms are trained together in an "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.05520","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":"1511.05520","created_at":"2026-05-18T01:26:40.038538+00:00"},{"alias_kind":"arxiv_version","alias_value":"1511.05520v1","created_at":"2026-05-18T01:26:40.038538+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.05520","created_at":"2026-05-18T01:26:40.038538+00:00"},{"alias_kind":"pith_short_12","alias_value":"HVSAMUH6SHRD","created_at":"2026-05-18T12:29:25.134429+00:00"},{"alias_kind":"pith_short_16","alias_value":"HVSAMUH6SHRD2DVR","created_at":"2026-05-18T12:29:25.134429+00:00"},{"alias_kind":"pith_short_8","alias_value":"HVSAMUH6","created_at":"2026-05-18T12:29:25.134429+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"1907.04294","citing_title":"An Attention Mechanism for Musical Instrument Recognition","ref_index":37,"is_internal_anchor":true},{"citing_arxiv_id":"1907.08520","citing_title":"Data Augmentation for Instrument Classification Robust to Audio Effects","ref_index":17,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HVSAMUH6SHRD2DVRLWOHDSEYDC","json":"https://pith.science/pith/HVSAMUH6SHRD2DVRLWOHDSEYDC.json","graph_json":"https://pith.science/api/pith-number/HVSAMUH6SHRD2DVRLWOHDSEYDC/graph.json","events_json":"https://pith.science/api/pith-number/HVSAMUH6SHRD2DVRLWOHDSEYDC/events.json","paper":"https://pith.science/paper/HVSAMUH6"},"agent_actions":{"view_html":"https://pith.science/pith/HVSAMUH6SHRD2DVRLWOHDSEYDC","download_json":"https://pith.science/pith/HVSAMUH6SHRD2DVRLWOHDSEYDC.json","view_paper":"https://pith.science/paper/HVSAMUH6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1511.05520&json=true","fetch_graph":"https://pith.science/api/pith-number/HVSAMUH6SHRD2DVRLWOHDSEYDC/graph.json","fetch_events":"https://pith.science/api/pith-number/HVSAMUH6SHRD2DVRLWOHDSEYDC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HVSAMUH6SHRD2DVRLWOHDSEYDC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HVSAMUH6SHRD2DVRLWOHDSEYDC/action/storage_attestation","attest_author":"https://pith.science/pith/HVSAMUH6SHRD2DVRLWOHDSEYDC/action/author_attestation","sign_citation":"https://pith.science/pith/HVSAMUH6SHRD2DVRLWOHDSEYDC/action/citation_signature","submit_replication":"https://pith.science/pith/HVSAMUH6SHRD2DVRLWOHDSEYDC/action/replication_record"}},"created_at":"2026-05-18T01:26:40.038538+00:00","updated_at":"2026-05-18T01:26:40.038538+00:00"}