{"id":"W2898963093","doi":"10.1109/icassp.2019.8683800","title":"End-to-end Sound Source Separation Conditioned on Instrument Labels","year":2019,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Monaural; Source separation; Separation (statistics); Bottleneck; Computer science; End-to-end principle; Artificial intelligence; Speech recognition; Acoustics; Machine learning; Physics","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":[0.0008397475,0.001659746,0.0009867775,0.0002286019,0.0004659136,0.001140518,0.001937747,0.0018373,0.01194227],"category_scores_gemma":[0.002928398,0.0005278329,0.0006171668,0.0002824627,0.0007743952,0.003037435,0.002829938,0.003741506,0.004884075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005102842,"about_ca_system_score_gemma":0.00123568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001568847,"about_ca_topic_score_gemma":0.003760505,"domain_scores_codex":[0.9996209,0.00006003782,0.00001872916,0.0001097184,0.0001059079,0.00008471204],"domain_scores_gemma":[0.999175,0.0003034706,0.00005115769,0.0002077433,0.0001729778,0.00008973319],"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":[0.002515613,0.0006600675,0.001603429,0.0003055795,0.000202223,0.0002784685,0.0001500208,0.2167321,0.1383218,0.02029624,0.008647257,0.6102872],"study_design_scores_gemma":[0.00005552258,0.0001362645,0.0003689982,0.00001646383,0.0000260138,0.0000904281,0.00002609575,0.9039811,0.07761806,0.0156921,0.001965353,0.00002356073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0133771,0.00007510126,0.9823726,0.0001575721,0.00007813877,0.00003504194,0.0001236229,0.001978477,0.001802248],"genre_scores_gemma":[0.4178995,0.0001754784,0.5634222,0.0005130171,0.0001052466,0.0001270258,0.001011971,0.0006851669,0.01606043],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01194227,"threshold_uncertainty_score":0.03995085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01678665798965863,"score_gpt":0.2705405133659615,"score_spread":0.2537538553763029,"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."}}