{"id":"W2883127466","doi":"10.1109/icassp.2018.8461788","title":"Multi-Scenario Deep Learning for Multi-Speaker Source Separation","year":2018,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministère de la Santé et des Services sociaux","keywords":"Computer science; Source separation; Matching (statistics); Separation (statistics); Artificial intelligence; Deep learning; Data modeling; Machine learning; Database","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":[0.001294124,0.001061089,0.0005550179,0.0005977323,0.0002994581,0.000634669,0.001159219,0.00126982,0.002797311],"category_scores_gemma":[0.002376095,0.0005562153,0.0007099149,0.0006119466,0.0004522079,0.002096942,0.00209804,0.00253259,0.001086647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006195867,"about_ca_system_score_gemma":0.0006595618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001982603,"about_ca_topic_score_gemma":0.003115521,"domain_scores_codex":[0.9994066,0.0002128382,0.00003040112,0.0001389845,0.0001206351,0.00009052241],"domain_scores_gemma":[0.9993563,0.0003277163,0.00004374207,0.00009320408,0.0001299742,0.00004900851],"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":[0.0004036156,0.0002233092,0.001303332,0.0002207856,0.000253967,0.0002570479,0.0001483816,0.6259634,0.02524236,0.01369492,0.00462768,0.3276612],"study_design_scores_gemma":[0.000003935616,0.00002213186,0.0001424129,0.000006340919,0.000008759777,0.00003464873,0.00001100297,0.9895757,0.00285801,0.006703336,0.0006266797,0.000006969838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01487804,0.0008221862,0.9816441,0.0002230667,0.00005902872,0.00002601727,0.0001267012,0.0009043853,0.001316473],"genre_scores_gemma":[0.755901,0.0009704488,0.2366287,0.0002767023,0.0001094145,0.00009941286,0.001264074,0.0001695294,0.004580698],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002797311,"threshold_uncertainty_score":0.009357929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04451585460580375,"score_gpt":0.3239678072055122,"score_spread":0.2794519525997085,"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."}}