{"id":"W2097999418","doi":"10.1109/tasl.2007.907569","title":"A Noise-Robust FFT-Based Auditory Spectrum With Application in Audio Classification","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Audio Speech and Language Processing","topic":"Music and Audio Processing","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Mel-frequency cepstrum; Fast Fourier transform; Speech recognition; Computer science; Noise (video); Robustness (evolution); Bandwidth (computing); Pattern recognition (psychology); Artificial intelligence; Feature extraction; Algorithm; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005471646,0.0002593013,0.000228304,0.0004320469,0.0003567674,0.0002308524,0.0003133003,0.0001307863,0.00001399037],"category_scores_gemma":[0.000006819391,0.0002310679,0.00004451852,0.001028575,0.0001120779,0.0006227723,0.000003017975,0.0004070467,0.0000163465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001536295,"about_ca_system_score_gemma":0.0002145666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007205902,"about_ca_topic_score_gemma":0.0006152451,"domain_scores_codex":[0.9981236,0.00004301462,0.0003410207,0.0006549045,0.0003782498,0.00045919],"domain_scores_gemma":[0.9991137,0.00008782541,0.0002055442,0.0003762201,0.0000672387,0.0001494468],"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":[0.0001189622,0.0002810052,0.0003260404,0.0002084953,0.00001077095,0.0000859714,0.001973811,0.002084876,0.03193561,0.0000582129,0.00004009987,0.9628761],"study_design_scores_gemma":[0.00423945,0.0004546534,0.01622802,0.001217762,0.00009182858,0.000330686,0.002224567,0.6462054,0.3258203,0.0003029538,0.001127102,0.001757247],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04773935,0.0003079167,0.9492238,0.001007159,0.0001283303,0.0002678007,0.000002184922,0.0002750084,0.001048484],"genre_scores_gemma":[0.9324693,0.00001067759,0.06654237,0.0006171175,0.0001255699,0.00004031702,0.00000298717,0.00002575212,0.000165871],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9611189,"threshold_uncertainty_score":0.9422674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01178304780719417,"score_gpt":0.2402740827436147,"score_spread":0.2284910349364206,"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."}}