{"id":"W4393407675","doi":"10.1371/journal.pone.0299888","title":"Musical instrument classifier for early childhood percussion instruments","year":2024,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Music and Audio Processing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Holland Bloorview Kids Rehabilitation Hospital; Toronto Rehabilitation Institute; University of Toronto","funders":"University of Toronto; Bloorview Research Institute; Ontario Brain Institute","keywords":"Percussion; Musical instrument; Computer science; Classifier (UML); Artificial intelligence; Mel-frequency cepstrum; Feature vector; Speech recognition; Pattern recognition (psychology); Feature extraction; Acoustics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006475652,0.0008573813,0.0006819334,0.0009826008,0.0003440529,0.0005912537,0.001087419,0.0008367076,0.002407078],"category_scores_gemma":[0.001606897,0.0001200691,0.0006403762,0.0004676223,0.0001770958,0.0004329403,0.0006854881,0.0009644328,0.002356913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003691671,"about_ca_system_score_gemma":0.0005510083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003613229,"about_ca_topic_score_gemma":0.005527259,"domain_scores_codex":[0.999584,0.00003946858,0.00002663527,0.0001266546,0.0001305094,0.00009263473],"domain_scores_gemma":[0.9995809,0.00009791894,0.00002890704,0.00005403329,0.0001910097,0.0000471085],"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":[0.001127873,0.0007496377,0.05343691,0.0002779281,0.0002051887,0.0008002211,0.0002547633,0.03096409,0.07456312,0.000830596,0.02662079,0.8101689],"study_design_scores_gemma":[0.000183434,0.001002906,0.0893534,0.0001398532,0.0002079648,0.001189668,0.0006680034,0.8132805,0.06136075,0.001627505,0.03089063,0.00009541374],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6982719,0.002410913,0.2693895,0.0006428442,0.0008667269,0.0005855089,0.006606848,0.01085731,0.01036838],"genre_scores_gemma":[0.8240178,0.0006679481,0.1483698,0.0003431989,0.000156696,0.0005508517,0.01195878,0.0002179569,0.013717],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003613229,"threshold_uncertainty_score":0.008052468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05158355512396252,"score_gpt":0.2359382300677504,"score_spread":0.1843546749437878,"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."}}