{"id":"W2929750792","doi":"10.1109/jstsp.2019.2909193","title":"Unsupervised Low Latency Speech Enhancement With RT-GCC-NMF","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; CHIST-ERA; Agence Nationale de la Recherche","keywords":"Non-negative matrix factorization; Computer science; Speech enhancement; Speech recognition; Intelligibility (philosophy); Spectrogram; Short-time Fourier transform; Source separation; PESQ; Artificial intelligence; Pattern recognition (psychology); Matrix decomposition; Fourier transform; Noise reduction; Mathematics","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":[0.000845643,0.001030906,0.0005281893,0.0004634614,0.0003109761,0.0004559,0.0009130473,0.0007988582,0.002532325],"category_scores_gemma":[0.002512713,0.0002778539,0.0005842797,0.0003079558,0.0004672481,0.0007970834,0.0009799962,0.0009300098,0.001817398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002958279,"about_ca_system_score_gemma":0.0006965857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001966731,"about_ca_topic_score_gemma":0.004135082,"domain_scores_codex":[0.9995577,0.00008782183,0.00002175925,0.00009899816,0.000186103,0.00004759515],"domain_scores_gemma":[0.9992306,0.0003057342,0.00005901118,0.0001531498,0.000215656,0.00003588732],"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":[0.000641463,0.0001898209,0.0008972865,0.0001878234,0.00009957715,0.0002258198,0.0001762128,0.1137713,0.1328663,0.003410075,0.005492304,0.742042],"study_design_scores_gemma":[0.00003850029,0.0001337304,0.0007026728,0.00002031204,0.00002046405,0.0002793137,0.00002886576,0.9316173,0.06064896,0.001572603,0.004911106,0.0000261922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01668784,0.0002972053,0.9782909,0.00008298252,0.00008424438,0.00005282865,0.00007529397,0.003034829,0.00139385],"genre_scores_gemma":[0.1878874,0.000165181,0.8060454,0.0002158953,0.0000625008,0.0001404214,0.0004675701,0.0004573888,0.004558458],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002532325,"threshold_uncertainty_score":0.008471489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009430378157480598,"score_gpt":0.2302141330364347,"score_spread":0.2207837548789541,"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."}}