{"id":"W2162203224","doi":"10.1109/icassp.2006.1661250","title":"A Noise-Robust Fft-Based Spectrum for Audio Classification","year":2006,"lang":"en","type":"article","venue":"","topic":"Music and Audio Processing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Fast Fourier transform; Speech recognition; Robustness (evolution); Support vector machine; Classifier (UML); Noise (video); Pattern recognition (psychology); Artificial intelligence; Algorithm","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":[0.0004871241,0.0005104337,0.0004576755,0.0008000017,0.000224098,0.0003180502,0.000547646,0.0005523447,0.00215391],"category_scores_gemma":[0.001789342,0.0001496505,0.000470056,0.0005534872,0.0003007078,0.000872296,0.0003478518,0.0003508186,0.001043295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001849025,"about_ca_system_score_gemma":0.0002222444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005505193,"about_ca_topic_score_gemma":0.0007558744,"domain_scores_codex":[0.9996482,0.00007175058,0.00002395968,0.0000585676,0.0001820603,0.00001540722],"domain_scores_gemma":[0.9995778,0.0001483073,0.00002953044,0.00008777421,0.0001382934,0.00001823226],"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":[0.0005046733,0.0001610689,0.00103,0.000231631,0.00006852279,0.0002160161,0.00009203302,0.05436791,0.2654559,0.008473217,0.001758033,0.667641],"study_design_scores_gemma":[0.00003537546,0.000197557,0.002207483,0.00001869203,0.00003913172,0.0006471366,0.00002241104,0.9352007,0.05306161,0.004056745,0.004477303,0.00003594471],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02022305,0.0002045196,0.9779177,0.000044764,0.00004662374,0.00003524302,0.00004519247,0.000544087,0.0009388594],"genre_scores_gemma":[0.3474459,0.0004086928,0.6492817,0.00006799158,0.00009408437,0.0001076482,0.0003393334,0.0001262123,0.00212844],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00215391,"threshold_uncertainty_score":0.007205546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03290171046370842,"score_gpt":0.2383218928620199,"score_spread":0.2054201823983115,"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."}}