{"id":"W1966053902","doi":"10.1109/mlsp.2009.5306201","title":"Gain estimation in model-based single channel speech separation","year":2009,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Carleton University; University of Toronto","funders":"","keywords":"Computer science; Channel (broadcasting); Source separation; Maximum likelihood; SIGNAL (programming language); Speech recognition; Energy (signal processing); Separation (statistics); Algorithm; Mathematics; Statistics; Machine learning; 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":[0.000806054,0.000784402,0.0008937313,0.0007149343,0.0002503103,0.0007095659,0.0005932742,0.0008242104,0.001174506],"category_scores_gemma":[0.00287282,0.0004702893,0.0006581528,0.0005385741,0.0004534664,0.001309924,0.0008254211,0.0008213929,0.001002465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000278607,"about_ca_system_score_gemma":0.0005099275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001365854,"about_ca_topic_score_gemma":0.001484514,"domain_scores_codex":[0.9994485,0.0001692306,0.00002300727,0.0001072696,0.0002071867,0.00004481445],"domain_scores_gemma":[0.9992442,0.0004739992,0.00006128199,0.0001013182,0.0001035841,0.00001562945],"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":[0.0006158733,0.0001047787,0.0009342824,0.0003278092,0.0001173862,0.0002465522,0.0002157091,0.2038796,0.1083714,0.005960994,0.001528685,0.6776969],"study_design_scores_gemma":[0.00002784872,0.0001000574,0.001002823,0.00001811116,0.00004502989,0.0003878653,0.0000361366,0.947804,0.04215023,0.006531,0.001863096,0.0000336976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01243835,0.0004086675,0.9857378,0.000053484,0.00002272819,0.00001549125,0.00002726449,0.0007697613,0.0005264733],"genre_scores_gemma":[0.4466935,0.001053582,0.5488101,0.00009541414,0.00007891311,0.0000791229,0.0002727651,0.0002660242,0.002650588],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001365854,"threshold_uncertainty_score":0.004262865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02479060202509689,"score_gpt":0.2863974212305902,"score_spread":0.2616068192054933,"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."}}