{"id":"W2613787955","doi":"10.1109/iscslp.2016.7918416","title":"Interaural coherence induced ideal binary mask for binaural speech separation and dereverberation","year":2016,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Garron Family Cancer Centre","keywords":"Binaural recording; Computer science; Speech recognition; Coherence (philosophical gambling strategy); Speech enhancement; Interaural time difference; Noise reduction; Binary number; Noise (video); Reduction (mathematics); Source separation; Computational auditory scene analysis; Pattern recognition (psychology); Artificial intelligence; Mathematics; Image (mathematics)","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":[0.0004011257,0.0004512569,0.0003652564,0.000453013,0.0002400961,0.0004142618,0.0005546089,0.0003663591,0.001209092],"category_scores_gemma":[0.001004188,0.0001822168,0.000267095,0.0002781813,0.0002474239,0.0006106651,0.0004476985,0.0004033127,0.0004383214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003530848,"about_ca_system_score_gemma":0.0005915913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001706882,"about_ca_topic_score_gemma":0.004067041,"domain_scores_codex":[0.9997173,0.00004875132,0.00001815985,0.00007293977,0.0001097032,0.00003316805],"domain_scores_gemma":[0.9996641,0.00009340044,0.00004717477,0.00005651711,0.0001154152,0.00002345107],"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":[0.0005289411,0.0001203496,0.001941356,0.0001577324,0.0000548233,0.00005818295,0.0001072735,0.03606218,0.2535636,0.00302311,0.0008022408,0.7035803],"study_design_scores_gemma":[0.00002755467,0.0002427439,0.00627495,0.00001459795,0.00005937064,0.0002610229,0.00003729164,0.770816,0.2164199,0.001901581,0.003913509,0.00003144059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08213974,0.000421061,0.9143915,0.00007162875,0.00004153635,0.00006579716,0.00006572259,0.001093861,0.001709187],"genre_scores_gemma":[0.442625,0.0001671888,0.5542499,0.00008333821,0.00002355887,0.00006036226,0.0001627838,0.00008466773,0.002543224],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001706882,"threshold_uncertainty_score":0.004044771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02717652439149928,"score_gpt":0.2983006905461292,"score_spread":0.2711241661546299,"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."}}