{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:KXOZWITFEVMAHIFCTXBVITANTC","short_pith_number":"pith:KXOZWITF","schema_version":"1.0","canonical_sha256":"55dd9b2265255803a0a29dc3544c0d989cb8e10b28fa99e494eba23f1172632b","source":{"kind":"arxiv","id":"1901.02053","version":1},"attestation_state":"computed","paper":{"title":"Detecting the Trend in Musical Taste over the Decade -- A Novel Feature Extraction Algorithm to Classify Musical Content with Simple Features","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SD","eess.AS"],"primary_cat":"cs.IR","authors_text":"Anish Acharya","submitted_at":"2018-12-19T03:53:55Z","abstract_excerpt":"This work proposes a novel feature selection algorithm to classify Songs into different groups. Classification of musical content is often a non-trivial job and still relatively less explored area. The main idea conveyed in this article is to come up with a new feature selection scheme that does the classification job elegantly and with high accuracy but with simpler but wisely chosen small number of features thus being less prone to over-fitting. This uses a very basic general idea about the structure of the audio signal which is generally in the shape of a trapezium. So, using this general i"},"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":"1901.02053","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2018-12-19T03:53:55Z","cross_cats_sorted":["cs.LG","cs.SD","eess.AS"],"title_canon_sha256":"3d2398db99a612005bb4ef48a54468d6a106fdc7ece0577dc3cd1cfc24522e8a","abstract_canon_sha256":"8e778a965f754ad69c9b3c14ff6e67721c8641478a31cd66818eabfc87c0384d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:56:44.104138Z","signature_b64":"GfGUlxxG9Z6rx0QYTMAjwrD85aJsZyn6mEeK5q1mP13gqBuBSmVRNst1TaGYdh3FLQz3CAqC7wJ3JiCwfIYXBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"55dd9b2265255803a0a29dc3544c0d989cb8e10b28fa99e494eba23f1172632b","last_reissued_at":"2026-05-17T23:56:44.103640Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:56:44.103640Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Detecting the Trend in Musical Taste over the Decade -- A Novel Feature Extraction Algorithm to Classify Musical Content with Simple Features","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SD","eess.AS"],"primary_cat":"cs.IR","authors_text":"Anish Acharya","submitted_at":"2018-12-19T03:53:55Z","abstract_excerpt":"This work proposes a novel feature selection algorithm to classify Songs into different groups. Classification of musical content is often a non-trivial job and still relatively less explored area. The main idea conveyed in this article is to come up with a new feature selection scheme that does the classification job elegantly and with high accuracy but with simpler but wisely chosen small number of features thus being less prone to over-fitting. This uses a very basic general idea about the structure of the audio signal which is generally in the shape of a trapezium. So, using this general i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.02053","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":"1901.02053","created_at":"2026-05-17T23:56:44.103718+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.02053v1","created_at":"2026-05-17T23:56:44.103718+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.02053","created_at":"2026-05-17T23:56:44.103718+00:00"},{"alias_kind":"pith_short_12","alias_value":"KXOZWITFEVMA","created_at":"2026-05-18T12:32:33.847187+00:00"},{"alias_kind":"pith_short_16","alias_value":"KXOZWITFEVMAHIFC","created_at":"2026-05-18T12:32:33.847187+00:00"},{"alias_kind":"pith_short_8","alias_value":"KXOZWITF","created_at":"2026-05-18T12:32:33.847187+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/KXOZWITFEVMAHIFCTXBVITANTC","json":"https://pith.science/pith/KXOZWITFEVMAHIFCTXBVITANTC.json","graph_json":"https://pith.science/api/pith-number/KXOZWITFEVMAHIFCTXBVITANTC/graph.json","events_json":"https://pith.science/api/pith-number/KXOZWITFEVMAHIFCTXBVITANTC/events.json","paper":"https://pith.science/paper/KXOZWITF"},"agent_actions":{"view_html":"https://pith.science/pith/KXOZWITFEVMAHIFCTXBVITANTC","download_json":"https://pith.science/pith/KXOZWITFEVMAHIFCTXBVITANTC.json","view_paper":"https://pith.science/paper/KXOZWITF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.02053&json=true","fetch_graph":"https://pith.science/api/pith-number/KXOZWITFEVMAHIFCTXBVITANTC/graph.json","fetch_events":"https://pith.science/api/pith-number/KXOZWITFEVMAHIFCTXBVITANTC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KXOZWITFEVMAHIFCTXBVITANTC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KXOZWITFEVMAHIFCTXBVITANTC/action/storage_attestation","attest_author":"https://pith.science/pith/KXOZWITFEVMAHIFCTXBVITANTC/action/author_attestation","sign_citation":"https://pith.science/pith/KXOZWITFEVMAHIFCTXBVITANTC/action/citation_signature","submit_replication":"https://pith.science/pith/KXOZWITFEVMAHIFCTXBVITANTC/action/replication_record"}},"created_at":"2026-05-17T23:56:44.103718+00:00","updated_at":"2026-05-17T23:56:44.103718+00:00"}