{"id":"W2103734388","doi":"10.1109/icif.2003.177357","title":"Robust speech separation using two-stage independent component analysis","year":2003,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Independent component analysis; Kurtosis; Microphone; Computer science; Speech recognition; Noise (video); Blind signal separation; Set (abstract data type); Speech enhancement; Background noise; Source separation; SIGNAL (programming language); Pattern recognition (psychology); Artificial intelligence; Mathematics; Statistics; 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":[0.001027173,0.001658916,0.001148117,0.001242149,0.0004501182,0.001047109,0.00119953,0.001224269,0.003520956],"category_scores_gemma":[0.001766735,0.0006258985,0.001515226,0.001076343,0.000447761,0.001398211,0.001463529,0.001707084,0.003452439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000362439,"about_ca_system_score_gemma":0.0007758326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001105718,"about_ca_topic_score_gemma":0.001308371,"domain_scores_codex":[0.998965,0.0001992207,0.00006599506,0.0002726993,0.0004079037,0.0000892644],"domain_scores_gemma":[0.9994129,0.0001945987,0.00005018614,0.0001129888,0.0002037441,0.00002567368],"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":[0.000526613,0.0001892319,0.0004283867,0.0003545941,0.0003104985,0.0001784092,0.000128141,0.05099452,0.1406357,0.01280428,0.004972393,0.7884772],"study_design_scores_gemma":[0.00006075257,0.0002620635,0.00155556,0.00004168123,0.00009303857,0.000279155,0.00002705432,0.899701,0.07825334,0.005401738,0.01419823,0.0001263575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002007408,0.000309198,0.9956397,0.00005946902,0.00007035756,0.00005148053,0.00005778884,0.0009325375,0.0008720468],"genre_scores_gemma":[0.06197608,0.0004713232,0.9319533,0.0001255061,0.0001327327,0.0001703416,0.0004200642,0.000224421,0.004526291],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003520956,"threshold_uncertainty_score":0.01177883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06265238288013911,"score_gpt":0.3260595613328928,"score_spread":0.2634071784527536,"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."}}