{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000694452,0.001241642,0.001353956,0.001315749,0.0004310528,0.001036164,0.0006695278,0.001215348,0.002440822],"category_scores_gemma":[0.001617431,0.0004956059,0.0008878536,0.001448197,0.0004939535,0.001965443,0.001284053,0.0008052364,0.003360602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002624714,"about_ca_system_score_gemma":0.000979675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001089848,"about_ca_topic_score_gemma":0.001228578,"domain_scores_codex":[0.9991639,0.0001726383,0.00005744739,0.0001472231,0.0003875571,0.00007126999],"domain_scores_gemma":[0.9994579,0.0002120979,0.00003350984,0.00006281848,0.000213607,0.00002009891],"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":[0.0002950567,0.00007807583,0.0002789695,0.000285041,0.00009430596,0.0001196949,0.0000805017,0.04869309,0.1225601,0.01108088,0.003599148,0.812835],"study_design_scores_gemma":[0.00008352005,0.0001629555,0.0007267349,0.00005141013,0.00009546998,0.0005009275,0.00004550422,0.8741941,0.08961833,0.01785524,0.01658295,0.00008277791],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002388115,0.0003588849,0.9957511,0.00005421337,0.00004276701,0.00001986681,0.00002855158,0.0004738664,0.0008824095],"genre_scores_gemma":[0.09312116,0.001403598,0.8994319,0.0001249905,0.0002231303,0.0001581656,0.0004860884,0.0001486643,0.004902227],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002440822,"threshold_uncertainty_score":0.0081653,"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."}}