{"id":"W2107886012","doi":"10.1109/mmsp.2006.285299","title":"Adaptive Feature Selection for Speech / Music Classification","year":2006,"lang":"en","type":"article","venue":"","topic":"Music and Audio Processing","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Feature (linguistics); Pattern recognition (psychology); Speech recognition; Signal-to-noise ratio (imaging); Artificial intelligence; Feature selection; Energy (signal processing); Noise (video); Feature extraction; Selection (genetic algorithm); Mathematics; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000889744,0.00006213458,0.00005560217,0.0000429412,0.000135403,0.0001083577,0.0001614561,0.0000487209,0.00001177158],"category_scores_gemma":[0.00000610791,0.00005222971,0.00002952564,0.0002603251,0.00001070045,0.0003302357,0.00001898579,0.00005001524,0.00001407948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003212086,"about_ca_system_score_gemma":0.00004053609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002698427,"about_ca_topic_score_gemma":0.00005778846,"domain_scores_codex":[0.9994695,0.00000913556,0.00007313192,0.0002222046,0.00009626702,0.0001297332],"domain_scores_gemma":[0.9997017,0.00002395858,0.00005292522,0.0001062991,0.00009754164,0.00001757816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008962625,0.00006171117,0.0003504689,0.00001736727,0.000006176969,5.140052e-7,0.000116681,0.00009054074,0.01966546,0.3457142,0.2711822,0.3627857],"study_design_scores_gemma":[0.0004627437,0.0001062668,0.010507,0.00001972136,0.000009349052,0.00002300623,0.00005052874,0.8198557,0.04870296,0.04744813,0.07251125,0.0003033703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002417231,0.00002665074,0.9741402,0.002038483,0.0001369682,0.0001266374,3.957798e-7,0.0001683938,0.02094505],"genre_scores_gemma":[0.6273094,4.467055e-7,0.3633897,0.0006342191,0.0002713864,0.00001882702,0.000004043678,0.000004602614,0.008367372],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8197652,"threshold_uncertainty_score":0.2129866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0335770781608573,"score_gpt":0.2463010420801415,"score_spread":0.2127239639192842,"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."}}