{"id":"W1566614593","doi":"10.1007/978-3-642-15615-1_17","title":"A New Microphone Array Speech Enhancement Method Based on AR Model","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Grace (Canada)","funders":"","keywords":"Computer science; Speech enhancement; Microphone; Autoregressive model; Speech recognition; Microphone array; Speech processing; Noise (video); Linear prediction; Channel (broadcasting); Artificial intelligence; Noise reduction; Mathematics; Telecommunications","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":[0.0003536295,0.0009780471,0.0007641973,0.0004529016,0.0002053573,0.0004908726,0.0006688365,0.0008276457,0.004659259],"category_scores_gemma":[0.0004599366,0.0004530987,0.0009474791,0.0004414662,0.0001914512,0.001053704,0.0004788232,0.0008952002,0.004013014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001104012,"about_ca_system_score_gemma":0.0002279474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004402104,"about_ca_topic_score_gemma":0.000828306,"domain_scores_codex":[0.9995924,0.00005904809,0.00001976841,0.0001007282,0.0002047372,0.00002339834],"domain_scores_gemma":[0.9997396,0.00006929801,0.00001442771,0.00003709779,0.0001239424,0.00001568517],"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":[0.0002841172,0.00007144367,0.0002211193,0.0002267971,0.00007493904,0.0001566506,0.00006114083,0.005110976,0.468412,0.002071754,0.0034595,0.5198496],"study_design_scores_gemma":[0.0001046116,0.0006699315,0.002491334,0.00004965937,0.0003696553,0.0032742,0.00006253829,0.5022334,0.4227886,0.001593752,0.06620815,0.0001541143],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003063683,0.000622332,0.9932806,0.00004402437,0.0002310625,0.00002778535,0.00004692027,0.001144756,0.001538965],"genre_scores_gemma":[0.05314402,0.001396305,0.9299524,0.0001919732,0.0002807474,0.00009228605,0.0003583537,0.0002562401,0.01432757],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004659259,"threshold_uncertainty_score":0.01558679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0160747252750402,"score_gpt":0.2657513431590596,"score_spread":0.2496766178840194,"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."}}