{"id":"W2103741299","doi":"10.1109/hscma.2008.4538687","title":"Voice Separation Using Ratchet FAP Algorithm","year":2008,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Ratchet; Computer science; Separation (statistics); Scheme (mathematics); Noise (video); Adaptation (eye); Algorithm; Source separation; Adaptive filter; Speech recognition; Artificial intelligence; Mathematics; Machine learning","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":[0.000511693,0.0005939365,0.0004640562,0.0005222415,0.0003582488,0.0005165915,0.0005370995,0.0006726656,0.002739382],"category_scores_gemma":[0.001620134,0.000193791,0.0004173812,0.0004588552,0.0004162484,0.0007825904,0.0007335323,0.0008762088,0.001726083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001818974,"about_ca_system_score_gemma":0.0004521713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001066621,"about_ca_topic_score_gemma":0.001102551,"domain_scores_codex":[0.9996257,0.0001096845,0.00001979218,0.00007905562,0.0001344857,0.0000311965],"domain_scores_gemma":[0.9996179,0.0001295324,0.00003502622,0.0000682216,0.0001309894,0.00001830252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002720954,0.00004659675,0.0004408605,0.00009456548,0.00006447822,0.0001624912,0.0001322558,0.07597373,0.0721641,0.01919564,0.002207169,0.829246],"study_design_scores_gemma":[0.00003005465,0.0001426677,0.0005562406,0.00001485336,0.00002496939,0.0005035466,0.00002371197,0.9363851,0.04513729,0.008542864,0.008587213,0.00005148897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003711209,0.0001031423,0.9949227,0.00003266146,0.00003302815,0.00001930048,0.000008568375,0.0004123011,0.0007571317],"genre_scores_gemma":[0.1752295,0.0002787764,0.8188598,0.0001031147,0.00009850955,0.00009626245,0.00009245462,0.0001385956,0.005102926],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002739382,"threshold_uncertainty_score":0.009164095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04651311106219896,"score_gpt":0.3172213803726227,"score_spread":0.2707082693104237,"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."}}