{"id":"W2063111556","doi":"10.1121/1.4787140","title":"Automatic detection of head voice in sung musical signals via machine learning classification of time-varying partial intensities","year":2006,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Music and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Mel-frequency cepstrum; Speech recognition; SIGNAL (programming language); Support vector machine; Classifier (UML); Artificial intelligence; Fourier transform; Signal processing; Acoustics; Pattern recognition (psychology); Feature extraction; Mathematics; Telecommunications; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000993441,0.00009773762,0.0003445173,0.00003638359,0.0001160799,0.00001658765,0.0005057086,0.00005140353,0.00001089739],"category_scores_gemma":[0.0002111954,0.00005834864,0.0002129123,0.0004332551,0.0004130294,0.0001833639,0.0001459286,0.0003935191,9.92899e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004923099,"about_ca_system_score_gemma":0.00007057402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001838554,"about_ca_topic_score_gemma":0.000001516182,"domain_scores_codex":[0.9983345,0.0002790104,0.0006824563,0.0000878547,0.0004524839,0.0001637437],"domain_scores_gemma":[0.9978807,0.0006455551,0.00105101,0.0001785071,0.000215526,0.00002872798],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004271951,0.000128806,0.0002590614,0.00009348128,0.00003872813,4.781469e-7,0.001545009,0.04284687,0.8993004,0.000007558718,0.0001716791,0.05556522],"study_design_scores_gemma":[0.0002043033,0.0001497044,0.004507395,0.0001839924,0.00005812205,0.00002695145,0.0002333504,0.9649465,0.02902107,0.0005810806,0.00002969528,0.00005783206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2948519,0.0001229649,0.7036343,0.001251522,0.00005613635,0.00004696612,4.494476e-7,0.00000845586,0.00002724456],"genre_scores_gemma":[0.9775811,0.00002022826,0.02208715,0.0002119948,0.00007989853,5.007661e-7,1.735274e-7,0.000006110655,0.00001281403],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9220996,"threshold_uncertainty_score":0.2379388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01505170702891597,"score_gpt":0.2469508848562025,"score_spread":0.2318991778272865,"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."}}