{"id":"W2100820878","doi":"10.1109/iros.2004.1389723","title":"Enhanced robot audition based on microphone array source separation with post-filter","year":2005,"lang":"en","type":"preprint","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":136,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Microphone array; Computer science; Filter (signal processing); Microphone; Robot; Noise-canceling microphone; Source separation; Acoustics; Separation (statistics); Speech recognition; Artificial intelligence; Computer vision; Physics; Sound pressure; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000166261,0.0003675868,0.0002858786,0.0002117878,0.0001594907,0.0005440122,0.0007170189,0.000214006,0.000145144],"category_scores_gemma":[0.0000173411,0.0002935203,0.00009763652,0.0002300245,0.00003939382,0.0003298216,0.0001604326,0.0004663204,0.0002696559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001429729,"about_ca_system_score_gemma":0.0003125604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002136072,"about_ca_topic_score_gemma":0.00003659313,"domain_scores_codex":[0.9979287,0.00006107742,0.000282805,0.000920018,0.0004597454,0.000347595],"domain_scores_gemma":[0.9984339,0.00004798761,0.0003035385,0.0008639904,0.0002419677,0.000108558],"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":[0.00008815702,0.0001810418,0.00001284003,0.00008026657,0.00002392654,0.000007071004,0.0003841709,0.1402516,0.8053933,0.00001354454,0.001197522,0.05236657],"study_design_scores_gemma":[0.0004185111,0.0001956243,0.0002868955,0.0003013536,0.00001074201,0.000006709869,0.000009817009,0.01424449,0.9833131,0.0001433679,0.0006166242,0.0004527866],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01320672,0.00003205311,0.9742355,0.004129692,0.0003155,0.000302093,0.000004186391,0.0004051095,0.007369155],"genre_scores_gemma":[0.5161629,0.000004127397,0.4778503,0.003618947,0.0003129224,0.00004479275,0.00007877757,0.00002277542,0.001904472],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5029562,"threshold_uncertainty_score":0.9999517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01035412407496789,"score_gpt":0.2456421257564639,"score_spread":0.235288001681496,"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."}}