{"id":"W2057220308","doi":"10.1007/s11265-008-0274-7","title":"Monaural Speech Separation Based on Gain Adapted Minimum Mean Square Error Estimation","year":2008,"lang":"en","type":"article","venue":"Journal of Signal Processing Systems","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University; Queen's University","funders":"","keywords":"Monaural; Estimator; Mean squared error; Computer science; Speech recognition; SIGNAL (programming language); Minimum mean square error; Source separation; Separation (statistics); Mathematics; 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":[0.0007292145,0.0007636292,0.001068353,0.000525411,0.0003970677,0.0008303619,0.000588279,0.001104259,0.001748032],"category_scores_gemma":[0.002057859,0.0003894689,0.0005705357,0.0004572645,0.0002302155,0.0009854509,0.0009432269,0.0009221811,0.001392559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002029258,"about_ca_system_score_gemma":0.0006820705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009286476,"about_ca_topic_score_gemma":0.002810558,"domain_scores_codex":[0.9994856,0.000137148,0.00003631507,0.00009862371,0.0001909749,0.00005125741],"domain_scores_gemma":[0.9991972,0.0003599271,0.00004564643,0.00009845684,0.0002609435,0.00003784322],"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":[0.001511496,0.0001890718,0.0009349377,0.0001787726,0.0001588861,0.0001515476,0.0001026464,0.03279208,0.2839356,0.00394647,0.001658054,0.6744404],"study_design_scores_gemma":[0.00008428917,0.0001902965,0.002775672,0.00002113916,0.000107301,0.0004422274,0.00003132049,0.8845453,0.1063172,0.002354181,0.003072489,0.00005860229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01728657,0.0003697022,0.9801009,0.00009313647,0.0001495482,0.00002559248,0.00003778787,0.0008334868,0.001103363],"genre_scores_gemma":[0.2786294,0.0003853293,0.7150351,0.0001865525,0.0001578816,0.00008219922,0.0002391681,0.0001788847,0.005105523],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001748032,"threshold_uncertainty_score":0.005847752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03629040671234737,"score_gpt":0.2861034167571161,"score_spread":0.2498130100447688,"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."}}