{"id":"W2950129007","doi":"10.82308/31575","title":"Auditory-based noise-robust audio classification algorithms","year":2008,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Music and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Noise (video); Speech recognition; Frequency domain; Audio analyzer; Computational complexity theory; Gaussian noise; Fast Fourier transform; Algorithm; Pattern recognition (psychology); Artificial intelligence; Speech coding; Audio signal processing; Audio signal","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00142323,0.001360298,0.001370472,0.001723514,0.0006645461,0.00146462,0.002454612,0.001328694,0.004876991],"category_scores_gemma":[0.004602443,0.0003489337,0.001296977,0.001185771,0.0005665451,0.001504542,0.001337316,0.001270766,0.003431973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007517006,"about_ca_system_score_gemma":0.00115021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002443226,"about_ca_topic_score_gemma":0.002259134,"domain_scores_codex":[0.9988523,0.0001169285,0.00009448954,0.0003234263,0.000496852,0.0001159844],"domain_scores_gemma":[0.9988747,0.0003415839,0.0001166713,0.0001076178,0.0005184819,0.00004087809],"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":[0.0002904814,0.0001661547,0.0006297388,0.0001380761,0.00006143158,0.00009436363,0.00006146089,0.1192818,0.01844129,0.006656835,0.003186333,0.850992],"study_design_scores_gemma":[0.00002542738,0.00006233985,0.0004585781,0.00001864253,0.00002507192,0.0001085764,0.0000273564,0.985268,0.007368391,0.004401953,0.002219114,0.00001645461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007942859,0.0005243607,0.9877204,0.00009701386,0.00009167716,0.00009697121,0.00007266037,0.001502746,0.001951193],"genre_scores_gemma":[0.2099004,0.0008545113,0.7785637,0.0003710685,0.0002824262,0.0003307617,0.0008737267,0.0002652596,0.00855799],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004876991,"threshold_uncertainty_score":0.01631522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04787507847716791,"score_gpt":0.2347461009938439,"score_spread":0.1868710225166759,"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."}}