{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002336878,0.000122263,0.0001345185,0.00008587145,0.00015699,0.0002583684,0.0005144459,0.00006512499,0.00002161993],"category_scores_gemma":[0.00006458732,0.0001057119,0.00005371696,0.0003567552,0.00002034317,0.0007360246,0.00007790622,0.0000673103,0.00004686563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000517826,"about_ca_system_score_gemma":0.00007350831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006502632,"about_ca_topic_score_gemma":0.000004729411,"domain_scores_codex":[0.9989352,0.0000121601,0.0001966819,0.0003654823,0.0001952484,0.0002952282],"domain_scores_gemma":[0.9993944,0.00005072528,0.00007523989,0.0002850525,0.0001119933,0.00008262297],"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":[0.00002138689,0.0002098107,0.000176704,0.0000511449,0.00001482348,0.000001684621,0.0003347041,0.03025665,0.007355445,0.002969628,0.008049225,0.9505588],"study_design_scores_gemma":[0.000470787,0.0000426928,0.0001844588,0.000006270236,0.00000487384,0.00001844573,0.00001833371,0.884819,0.1124715,0.0005645377,0.001246466,0.0001526574],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008324875,0.0000401472,0.9783745,0.00127608,0.00007645811,0.0003200775,9.660795e-7,0.0003672794,0.01121959],"genre_scores_gemma":[0.1585643,0.000001158074,0.8394594,0.0006068043,0.0001067571,0.00004420675,0.00001096657,0.000008276594,0.001198227],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9504061,"threshold_uncertainty_score":0.4310806,"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."}}