{"id":"W2128604088","doi":"10.1109/tsa.2003.818031","title":"Incorporating the human hearing properties in the signal subspace approach for speech enhancement","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Speech and Audio Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":171,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Speech recognition; Speech enhancement; Computer science; Signal subspace; Noise (video); Spectrogram; Noise reduction; Residual; Colors of noise; Subspace topology; Filter (signal processing); Artificial intelligence; Algorithm; Computer vision","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":[0.0003631875,0.0005648311,0.0003603335,0.0003027857,0.0001871996,0.0004266967,0.0002441204,0.0005556873,0.001995727],"category_scores_gemma":[0.0006798014,0.0002013846,0.0005405328,0.0002724043,0.0003328492,0.0008342257,0.0003276009,0.0004108854,0.001025173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008463814,"about_ca_system_score_gemma":0.0002652854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003972455,"about_ca_topic_score_gemma":0.0008388362,"domain_scores_codex":[0.9998577,0.00005346571,0.000008450925,0.00001854322,0.00005344889,0.000008488321],"domain_scores_gemma":[0.9998042,0.0001016985,0.00001157122,0.00003311524,0.00004353614,0.000005989078],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001994317,0.0000951621,0.0003680762,0.000258198,0.00006016653,0.000280279,0.0001586173,0.08580533,0.4519422,0.01788746,0.000867618,0.4420775],"study_design_scores_gemma":[0.00002614269,0.0006423569,0.001013364,0.00003118947,0.00006381241,0.001487559,0.00007736367,0.7922491,0.1775798,0.01217186,0.01458217,0.00007534271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005253729,0.0001625012,0.99368,0.00003809961,0.00001997902,0.00001430388,0.000009518229,0.0002532347,0.0005686007],"genre_scores_gemma":[0.1443982,0.001096294,0.8510427,0.000073806,0.00006021561,0.00005615828,0.00009345928,0.0000910809,0.003087987],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001995727,"threshold_uncertainty_score":0.006676316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04549423966659918,"score_gpt":0.264615521974624,"score_spread":0.2191212823080248,"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."}}