{"id":"W4226208605","doi":"10.1109/taslp.2022.3169629","title":"End-to-End Brain-Driven Speech Enhancement in Multi-Talker Conditions","year":2022,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Audio Speech and Language Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Headphones; Speech recognition; Speech enhancement; Electroencephalography; Noise (video); Brain activity and meditation; Artificial neural network; Channel (broadcasting); Feature (linguistics); Artificial intelligence; Noise reduction; Acoustics; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003612715,0.0006239078,0.0004169778,0.0001803701,0.0001422748,0.0003492176,0.0004272987,0.0006537131,0.00154036],"category_scores_gemma":[0.000814466,0.0001859029,0.000353888,0.0001131959,0.0002486565,0.0004659318,0.0005982,0.0006677471,0.0006548657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001413233,"about_ca_system_score_gemma":0.000307178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007642413,"about_ca_topic_score_gemma":0.002573886,"domain_scores_codex":[0.9998534,0.00002781644,0.000008569845,0.00003768959,0.00004982029,0.0000226829],"domain_scores_gemma":[0.9998242,0.00008545442,0.00001224608,0.00001745039,0.00004778424,0.00001280429],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009789089,0.000251776,0.001068519,0.0002396651,0.0001486409,0.0005376397,0.000190637,0.0820144,0.3963512,0.001870658,0.002000683,0.5143473],"study_design_scores_gemma":[0.00003686514,0.0002965981,0.002475464,0.00002290111,0.00007026839,0.0006225209,0.00006024652,0.767511,0.223439,0.002377563,0.003058541,0.00002902627],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08705105,0.0005669424,0.9085096,0.0001404983,0.0001051067,0.00006185684,0.0001296189,0.001101811,0.002333499],"genre_scores_gemma":[0.6262019,0.0005838331,0.3627341,0.0002800444,0.00006371643,0.00009106516,0.0004344557,0.0001421837,0.009468674],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00154036,"threshold_uncertainty_score":0.005153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01906140709785276,"score_gpt":0.289076444176697,"score_spread":0.2700150370788442,"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."}}