{"id":"W2121166798","doi":"10.1109/mue.2007.147","title":"Maximum Likelihood Study for Sound Pattern Separation and Recognition","year":2007,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"McGill University; National Science Foundation","keywords":"Separation (statistics); Sound (geography); Computer science; Maximum likelihood; Source separation; Speech recognition; Acoustics; Pattern recognition (psychology); Artificial intelligence; Mathematics; Statistics; Physics; Machine learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005009036,0.0009289445,0.001314316,0.001612891,0.0006499506,0.001720523,0.001259488,0.001307185,0.003578898],"category_scores_gemma":[0.02759141,0.0008589395,0.001098857,0.001531713,0.001580345,0.003280118,0.001256266,0.001818423,0.00140095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001219397,"about_ca_system_score_gemma":0.001147115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002540463,"about_ca_topic_score_gemma":0.001083328,"domain_scores_codex":[0.9972575,0.001295666,0.000161019,0.0003862936,0.000758741,0.0001408221],"domain_scores_gemma":[0.9866173,0.01156209,0.0004285747,0.0003571824,0.0009214846,0.0001133479],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004363808,0.0001469355,0.002999331,0.0006067225,0.0002119025,0.0005263457,0.0005337717,0.3737252,0.01280414,0.3211527,0.004250738,0.2826059],"study_design_scores_gemma":[0.00001637398,0.00003192242,0.0003142417,0.00001215148,0.00001163739,0.0001101855,0.00001223192,0.9682064,0.0017379,0.02790869,0.00162171,0.00001659337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002521944,0.0006517897,0.9958413,0.0001786807,0.00001441313,0.00001421873,0.00001479534,0.0000853792,0.0006773062],"genre_scores_gemma":[0.3283958,0.003175918,0.6537122,0.0002826565,0.0007144682,0.0004400464,0.0004784446,0.0003279924,0.01247248],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005009036,"threshold_uncertainty_score":0.02649063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03509969255903399,"score_gpt":0.3275787450879732,"score_spread":0.2924790525289392,"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."}}