{"id":"W4287021620","doi":"","title":"Unsupervised Blind Source Separation with Variational Auto-Encoders","year":2021,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Blind signal separation; Autoencoder; Computer science; Artificial intelligence; Source separation; Pattern recognition (psychology); Encoder; Algorithm; Artificial neural network; 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":[0.001169352,0.0009121912,0.0009584835,0.0005745433,0.0003750494,0.0009001691,0.001431826,0.001287965,0.001978846],"category_scores_gemma":[0.003760484,0.000981507,0.001322313,0.0006922062,0.0008572674,0.00127513,0.001837468,0.001645754,0.001131625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005095854,"about_ca_system_score_gemma":0.001441629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004332802,"about_ca_topic_score_gemma":0.006327393,"domain_scores_codex":[0.9992856,0.0002889811,0.00004398511,0.0001399516,0.0001756455,0.00006586757],"domain_scores_gemma":[0.9982631,0.00111231,0.00009019551,0.0002102463,0.0002603185,0.0000637933],"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":[0.0003720646,0.0001090916,0.0004524329,0.0002113737,0.0002246198,0.0000966002,0.0001205331,0.6361873,0.02280223,0.04493091,0.003383474,0.2911094],"study_design_scores_gemma":[0.00001078125,0.00001749507,0.00006426347,0.000005684983,0.000007801287,0.0000206857,0.000003795523,0.9903293,0.002435119,0.006469012,0.0006277134,0.000008405338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00358109,0.0001815242,0.9952939,0.00006509008,0.00003333298,0.00001208135,0.00004791858,0.000272969,0.0005120556],"genre_scores_gemma":[0.2690271,0.000584479,0.7197307,0.000150892,0.0001761148,0.000164541,0.0007345943,0.0003121999,0.009119495],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004332802,"threshold_uncertainty_score":0.008615196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01193594623501891,"score_gpt":0.2258128478040579,"score_spread":0.213876901569039,"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."}}