{"id":"W101904975","doi":"10.1007/978-3-642-23250-3_4","title":"Multichannel Speech Enhancement with Gains","year":2011,"lang":"en","type":"book-chapter","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec; Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Speech enhancement; Microphone; Exploit; Microphone array; Speech recognition; Computer science; Noise (video); Focus (optics); Noise reduction; Acoustics; SIGNAL (programming language); Noise-canceling microphone; Artificial intelligence; Physics; Loudspeaker","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":[0.0001833967,0.0008796146,0.0003522549,0.0003828297,0.0001839204,0.0007094385,0.0005400965,0.0006146263,0.0211701],"category_scores_gemma":[0.0003060551,0.0002742117,0.0002787726,0.0003799905,0.0003793874,0.001142089,0.0006288883,0.0008607375,0.01288916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002514576,"about_ca_system_score_gemma":0.0001596863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002327787,"about_ca_topic_score_gemma":0.0006352279,"domain_scores_codex":[0.9998579,0.00001164107,0.000005279959,0.00003251422,0.00008247131,0.00001020975],"domain_scores_gemma":[0.9998707,0.0000526145,0.000007784663,0.00002535863,0.00003763209,0.000005889238],"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":[0.0001127139,0.00004268289,0.00007034608,0.0003978905,0.000031765,0.0001365634,0.00009166,0.002847123,0.1618679,0.02326748,0.01128271,0.7998511],"study_design_scores_gemma":[0.00003291514,0.0003012068,0.001072867,0.0002752756,0.0001354005,0.003270657,0.0001006271,0.03504819,0.3583341,0.02572707,0.5756407,0.00006095704],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01010982,0.01982277,0.7976503,0.0005633719,0.001173672,0.0001007527,0.0001879224,0.003335165,0.1670562],"genre_scores_gemma":[0.1038063,0.02426676,0.2909977,0.0006742398,0.001365055,0.0001096764,0.0004196612,0.0008101425,0.5775504],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0211701,"threshold_uncertainty_score":0.07082105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04089020264875105,"score_gpt":0.2390402451078015,"score_spread":0.1981500424590505,"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."}}