{"id":"W2161161251","doi":"","title":"Efficient blind speech signal separation combining independent component analysis and beamforming","year":2007,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Blind signal separation; Beamforming; Independent component analysis; Computer science; Direction of arrival; Speech recognition; Source separation; Interference (communication); Independence (probability theory); Algorithm; Frequency domain; SIGNAL (programming language); Time domain; Computational complexity theory; Exploit; Pattern recognition (psychology); Artificial intelligence; Telecommunications; Mathematics; Computer vision; Statistics; Channel (broadcasting)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.001082102,0.0001487528,0.0001899294,0.001014765,0.0002521138,0.0002635544,0.0003333734,0.0001247224,0.0000152462],"category_scores_gemma":[0.00002934443,0.0001651697,0.00005541282,0.00100529,0.00004972656,0.0001140408,0.00008134961,0.000225519,0.00001051874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002860822,"about_ca_system_score_gemma":0.0002704096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005307209,"about_ca_topic_score_gemma":0.01565099,"domain_scores_codex":[0.9985471,0.00004003616,0.0003253344,0.0003272946,0.0003523963,0.0004078337],"domain_scores_gemma":[0.9988242,0.0001098537,0.0001159406,0.000319825,0.0001450557,0.0004851645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006493252,0.0003813433,0.01802137,0.00007839464,0.001014409,0.000893357,0.01963181,0.6274119,0.1009474,0.1188471,0.001988463,0.1107195],"study_design_scores_gemma":[0.000325007,0.00006414187,0.02911213,0.00001096895,0.0001092079,0.00002926378,0.0001955839,0.9655811,0.003369653,0.0004144284,0.0004805618,0.0003079427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3429894,0.00002278578,0.6556839,0.0001428393,0.0000695823,0.0001350343,0.000005186606,0.00007779048,0.0008735054],"genre_scores_gemma":[0.9213359,0.000003212164,0.07788593,0.0006462056,0.00003458244,0.00000301583,0.00001873312,0.000008696448,0.00006366471],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5783466,"threshold_uncertainty_score":0.873362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01447233015330201,"score_gpt":0.2704063059436457,"score_spread":0.2559339757903437,"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."}}