{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002573085,0.0005226795,0.000472414,0.000278852,0.0001373084,0.0004460391,0.0006099031,0.0007324175,0.003570769],"category_scores_gemma":[0.0006669384,0.0002410325,0.0003565526,0.0002382714,0.0002570915,0.0006018095,0.0005251367,0.0003793825,0.001438566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001183398,"about_ca_system_score_gemma":0.0002270949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003753983,"about_ca_topic_score_gemma":0.0006322158,"domain_scores_codex":[0.9996833,0.00005751822,0.00001200244,0.00006011541,0.0001572244,0.00002987682],"domain_scores_gemma":[0.9996688,0.000115879,0.0000284736,0.00005117409,0.0001202468,0.00001535148],"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":[0.0006521328,0.00009170268,0.000653458,0.0002302582,0.00005212714,0.0002111705,0.0000828468,0.01120252,0.6627132,0.00121151,0.0008498901,0.3220492],"study_design_scores_gemma":[0.0001586062,0.00128688,0.007504685,0.00003965778,0.0001584513,0.002454253,0.00005704917,0.3945022,0.5731443,0.00146986,0.01912036,0.0001037237],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06342715,0.0004691021,0.9312459,0.00009416758,0.000135934,0.00006771398,0.00006320322,0.002031489,0.002465392],"genre_scores_gemma":[0.3428119,0.0004150901,0.6500581,0.0001020115,0.0001132,0.00008559033,0.0001635327,0.0001121071,0.006138476],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003570769,"threshold_uncertainty_score":0.01194543,"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."}}