{"id":"W2124020622","doi":"10.1109/ccece.2004.1349639","title":"Respiratory sounds classification using Gaussian mixture models","year":2004,"lang":"en","type":"article","venue":"","topic":"Phonocardiography and Auscultation Techniques","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Pattern recognition (psychology); Vector quantization; Mixture model; Artificial intelligence; Speech recognition; Multilayer perceptron; Feature vector; Computer science; Mel-frequency cepstrum; Feature extraction; Perceptron; Artificial neural network; Learning vector quantization; Wavelet transform; Hidden Markov model; Gaussian; Dimension (graph theory); Cepstrum; Wavelet; Mathematics","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":[0.00145587,0.0005554381,0.0009276267,0.001457191,0.0002153078,0.0008622598,0.0005356278,0.0008092144,0.0008418109],"category_scores_gemma":[0.003323282,0.0002702989,0.0008748044,0.0008092387,0.0002741252,0.0007111626,0.0004424736,0.000620839,0.0009189598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003747987,"about_ca_system_score_gemma":0.0003540125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004376656,"about_ca_topic_score_gemma":0.002167141,"domain_scores_codex":[0.9991591,0.0002931324,0.00004516749,0.0001557842,0.0002634327,0.00008340566],"domain_scores_gemma":[0.9993062,0.0003742649,0.00004397871,0.00005877811,0.0001943035,0.00002253792],"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":[0.0006672183,0.0001651514,0.009205887,0.000177724,0.0002086674,0.0001539259,0.0001694997,0.2396187,0.03425949,0.005378949,0.004095526,0.7058992],"study_design_scores_gemma":[0.00001275704,0.00006335618,0.003930599,0.00001354102,0.00002992525,0.00006534113,0.00001936334,0.9893308,0.003417268,0.001897017,0.001193733,0.00002630493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04407819,0.001137539,0.951822,0.0001217148,0.00009731102,0.000055489,0.0001424852,0.001582944,0.0009622934],"genre_scores_gemma":[0.7697397,0.001172479,0.2242779,0.00009390194,0.0001303256,0.0001156082,0.0007269138,0.0001292962,0.003613859],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004376656,"threshold_uncertainty_score":0.008702338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07813691648283683,"score_gpt":0.3263275344647923,"score_spread":0.2481906179819555,"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."}}