{"id":"W2147267580","doi":"10.1109/tmm.2010.2089786","title":"Training Surrogate Sensors in Musical Gesture Acquisition Systems","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Music and Audio Processing","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Gesture; Computer science; Microphone; SIGNAL (programming language); Speech recognition; Gesture recognition; Data acquisition; Artificial intelligence; Musical instrument; Human–computer interaction; Signal processing; Computer vision; Computer hardware; Digital signal processing; Acoustics","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":[0.001214867,0.0005622121,0.0007899021,0.0002681055,0.0002123224,0.0006144005,0.001015338,0.001278282,0.001938413],"category_scores_gemma":[0.004213281,0.0005231435,0.0003459612,0.0003140479,0.0005917876,0.001357066,0.001200781,0.001201119,0.0009312291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003237826,"about_ca_system_score_gemma":0.0003747747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004030525,"about_ca_topic_score_gemma":0.0005979015,"domain_scores_codex":[0.9992064,0.0002432687,0.00005904437,0.0001702744,0.0002711106,0.00004989838],"domain_scores_gemma":[0.9988111,0.0006044559,0.0001332084,0.0001658243,0.0002295514,0.00005584822],"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":[0.0008109749,0.0003910668,0.003131448,0.0004639728,0.00006143672,0.0002789278,0.0002982805,0.2902666,0.150977,0.006980627,0.002327283,0.5440124],"study_design_scores_gemma":[0.00002354983,0.0005112244,0.001232698,0.00002769101,0.00001052694,0.0001547771,0.00002953576,0.9605651,0.03253239,0.002487326,0.00240292,0.00002219125],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03719977,0.0004483891,0.9593807,0.0001899983,0.00007204345,0.00007861265,0.00006159976,0.001049518,0.001519387],"genre_scores_gemma":[0.6427583,0.0004498544,0.350675,0.0002060124,0.00005687411,0.0002806613,0.0003474515,0.00008716827,0.005138699],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001938413,"threshold_uncertainty_score":0.006484628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02475520679632822,"score_gpt":0.2567921720099744,"score_spread":0.2320369652136462,"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."}}