{"id":"W2143382543","doi":"10.1109/icassp.1997.596191","title":"Blind separation and restoration of signals mixed in convolutive environment","year":2002,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Entropy (arrow of time); Speech recognition; Artificial neural network; Blind signal separation; Set (abstract data type); Scheme (mathematics); Artificial intelligence; Pattern recognition (psychology); Mathematics; Telecommunications; Channel (broadcasting)","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":[0.0004281893,0.000476191,0.0004457189,0.0004386814,0.0002481698,0.0003564544,0.0003859985,0.0005687753,0.0007073457],"category_scores_gemma":[0.001144617,0.0001951221,0.0003178031,0.0002949397,0.000673231,0.0007521739,0.0004833047,0.0004518562,0.0002105255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001996754,"about_ca_system_score_gemma":0.000260791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003949004,"about_ca_topic_score_gemma":0.0007956291,"domain_scores_codex":[0.9997367,0.00007272912,0.00001689989,0.0000539536,0.00008733964,0.00003233022],"domain_scores_gemma":[0.999713,0.0001439446,0.00004284075,0.00003697368,0.00004891733,0.00001442153],"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":[0.0007746132,0.0001075902,0.000926826,0.0003431288,0.0001081788,0.0003153559,0.0002380447,0.1574045,0.2331679,0.02935903,0.000734404,0.5765204],"study_design_scores_gemma":[0.00002357392,0.0002100565,0.001072958,0.00002177635,0.0000367472,0.000494385,0.00003566249,0.8719329,0.108489,0.01387088,0.003770106,0.00004192669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03751158,0.0005287792,0.9605234,0.00004944868,0.00004739668,0.00001543208,0.00001301481,0.0002670312,0.001043865],"genre_scores_gemma":[0.4679485,0.0006472236,0.5279522,0.00008324884,0.00007969506,0.0000330761,0.00005075644,0.00005237761,0.003152979],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0007073457,"threshold_uncertainty_score":0.002366364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03544803569215658,"score_gpt":0.267702736774622,"score_spread":0.2322547010824654,"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."}}