{"id":"W2107629484","doi":"10.1109/iscas.2005.1465294","title":"A Robust Pitch Estimation Approach for Colored Noise-Corrupted Speech","year":2005,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Colors of noise; Autocorrelation; Noise (video); Pitch detection algorithm; Speech recognition; Colored; Computer science; Speech enhancement; Noise measurement; SIGNAL (programming language); Acoustics; Speech processing; Mathematics; Noise reduction; Artificial intelligence; Physics; Statistics","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":[0.0006886501,0.0008715126,0.0008932967,0.0007708748,0.0002874937,0.0009095832,0.0008926186,0.000745652,0.001705629],"category_scores_gemma":[0.002160609,0.0004725282,0.0007209792,0.0004249484,0.0003242018,0.0008520287,0.0007644085,0.0006710002,0.001080894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002943526,"about_ca_system_score_gemma":0.000573042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001276465,"about_ca_topic_score_gemma":0.00182054,"domain_scores_codex":[0.999313,0.0001020699,0.00005088273,0.0001885676,0.0002990567,0.00004644391],"domain_scores_gemma":[0.9994154,0.0001861531,0.00007542787,0.0001035382,0.0001916691,0.00002781869],"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":[0.0005365583,0.00007167262,0.0009355716,0.0002434099,0.0002090521,0.0003207782,0.0001656366,0.06458043,0.2479834,0.004706129,0.001345967,0.6789014],"study_design_scores_gemma":[0.00003824174,0.0002511459,0.002356132,0.00002962226,0.0001371367,0.0006173511,0.00004936475,0.8473402,0.140979,0.0020411,0.00607485,0.00008582035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01081909,0.0003030729,0.9875871,0.00002786013,0.00004725226,0.00001981252,0.000038907,0.0006754546,0.0004814659],"genre_scores_gemma":[0.1567488,0.0003995163,0.8397391,0.00005175672,0.00008026328,0.00005352702,0.0002405095,0.0001290804,0.002557414],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001705629,"threshold_uncertainty_score":0.005705833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03409288089425652,"score_gpt":0.2523032239041736,"score_spread":0.2182103430099171,"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."}}