{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007837105,0.0005468743,0.0004482408,0.0008553854,0.0002327667,0.0004531514,0.0005716562,0.0006600822,0.001279728],"category_scores_gemma":[0.002871617,0.0001574742,0.0005310969,0.0006475843,0.0003369398,0.001110865,0.0003647258,0.000444363,0.0005643677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002272801,"about_ca_system_score_gemma":0.0002929421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001340849,"about_ca_topic_score_gemma":0.001058004,"domain_scores_codex":[0.9995496,0.00008316037,0.00003036818,0.00007979613,0.0002349944,0.00002212075],"domain_scores_gemma":[0.9993184,0.0003339072,0.00005046258,0.00008071182,0.0001956585,0.00002087796],"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":[0.0003982823,0.000147617,0.001725448,0.0002582334,0.00008319395,0.0004266632,0.0001487915,0.223068,0.1305624,0.01579918,0.001260459,0.6261218],"study_design_scores_gemma":[0.000008206818,0.00006838823,0.0008804355,0.0000107468,0.00001591254,0.0002112862,0.00001355731,0.983248,0.01236875,0.001645495,0.001514058,0.00001518185],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01898236,0.0004128435,0.979115,0.00006027315,0.00004929763,0.00003068655,0.00003182281,0.0004004794,0.0009172569],"genre_scores_gemma":[0.4167438,0.0006628778,0.5802635,0.00007433951,0.0001208006,0.00007417118,0.000215387,0.0001228685,0.001722291],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001340849,"threshold_uncertainty_score":0.004281163,"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."}}