{"id":"W2152949589","doi":"10.1109/icassp.2006.1661203","title":"Speech Acquisition and Enhancement in a Reverberant, Cocktail-Party-Like Environment","year":2006,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Reverberation; Microphone; Computer science; Speech recognition; Speech enhancement; Impulse (physics); Impulse response; Microphone array; SIGNAL (programming language); Acoustics; Background noise; Telecommunications; Physics; Mathematics","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":[0.0001748585,0.00009554535,0.0001029096,0.00006440676,0.00005993579,0.0001049881,0.0001571867,0.0000318126,0.00005381917],"category_scores_gemma":[0.000001440184,0.00008581527,0.00001582945,0.0001223385,0.00002329145,0.0003368113,0.0001116256,0.00005420926,0.0001397822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005519113,"about_ca_system_score_gemma":0.00001525664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001046509,"about_ca_topic_score_gemma":0.00003185536,"domain_scores_codex":[0.9990857,0.00001972601,0.0001827931,0.0003074473,0.0001846358,0.0002197352],"domain_scores_gemma":[0.9997057,0.00001637147,0.00004314531,0.0001890607,0.000007531091,0.00003821372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003297177,0.0007806142,0.01897406,0.0001015388,0.00001738579,0.0002477573,0.0005975973,0.0002231822,0.4965766,0.005901032,0.01278532,0.463762],"study_design_scores_gemma":[0.001153574,0.0001164462,0.02766887,0.0000707353,0.000004438177,0.00004855676,0.00002950774,0.003523094,0.9437492,0.01168915,0.01156756,0.000378838],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4397036,0.000673473,0.5450321,0.001581242,0.000106181,0.0001966125,6.883971e-7,0.00007965075,0.01262643],"genre_scores_gemma":[0.8294906,0.00007921873,0.1663633,0.0009265066,0.00005227419,0.00001467435,0.000003344324,0.000005393862,0.003064715],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4633831,"threshold_uncertainty_score":0.3499445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005721869670230362,"score_gpt":0.2012126114285074,"score_spread":0.195490741758277,"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."}}