{"id":"W2182981075","doi":"10.5281/zenodo.1415705","title":"Evaluating The Genre Classification Performance Of Lyrical Features Relative To Audio, Symbolic And Cultural Features.","year":2010,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Music and Audio Processing","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Lyrics; Computer science; Music information retrieval; Musical; Feature (linguistics); Feature extraction; Artificial intelligence; Natural language processing; Speech recognition; Linguistics; Art","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003449533,0.001325749,0.001111291,0.003189797,0.0004786038,0.002075177,0.000903081,0.001633074,0.003665613],"category_scores_gemma":[0.008913957,0.0001856126,0.0006536383,0.001633642,0.0002823615,0.001042531,0.0008602332,0.0008469336,0.003733031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003197424,"about_ca_system_score_gemma":0.0004026362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004145452,"about_ca_topic_score_gemma":0.004615284,"domain_scores_codex":[0.9983689,0.0005706565,0.0001660401,0.0003311831,0.0003724299,0.0001908065],"domain_scores_gemma":[0.9934179,0.003895287,0.0002758128,0.0005148596,0.001260165,0.0006360163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.01345147,0.001987423,0.0670143,0.001094195,0.001213638,0.0003734506,0.000343053,0.01601373,0.07005211,0.0006146986,0.04616646,0.7816755],"study_design_scores_gemma":[0.001099888,0.003758747,0.1631407,0.0002069328,0.001301914,0.0007370375,0.001698845,0.7264421,0.08856042,0.001655358,0.01119951,0.0001986098],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.937193,0.004991145,0.03208253,0.0007252944,0.001218327,0.0003186586,0.006001273,0.004033354,0.01343634],"genre_scores_gemma":[0.9376838,0.0009382843,0.03645567,0.0002234023,0.0004317229,0.0001301634,0.01464707,0.0003547509,0.009135135],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004145452,"threshold_uncertainty_score":0.01824307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06282934531584099,"score_gpt":0.3124865194336757,"score_spread":0.2496571741178347,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}