{"id":"W2131754512","doi":"10.1109/icassp.2002.5743773","title":"Signal subspace speech enhancement with perceptual post-filtering","year":2002,"lang":"en","type":"article","venue":"IEEE International Conference on Acoustics Speech and Signal Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Speech recognition; Speech enhancement; Signal subspace; Subspace topology; Computer science; Filter (signal processing); Distortion (music); SIGNAL (programming language); Noise (video); Perception; Speech processing; Computational auditory scene analysis; Acoustics; Artificial intelligence; Computer vision; Telecommunications; Psychology; Physics; Amplifier","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":[0.0003643276,0.0006800758,0.0004232944,0.0004028559,0.0002926755,0.0004777478,0.0005668079,0.0005147539,0.002890703],"category_scores_gemma":[0.0007190529,0.0002302778,0.0004730041,0.0003205595,0.0003841482,0.0007106873,0.0006221264,0.0005746487,0.001289081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001182029,"about_ca_system_score_gemma":0.0003039467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002337609,"about_ca_topic_score_gemma":0.0005490358,"domain_scores_codex":[0.9997018,0.00004831909,0.00001711038,0.00005143109,0.0001567046,0.0000246015],"domain_scores_gemma":[0.9996459,0.00007972698,0.0000372481,0.00006930335,0.0001498004,0.00001790383],"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":[0.0002658394,0.00009380424,0.0001909394,0.0002124295,0.00002961073,0.0001294791,0.00009728866,0.005605331,0.6791015,0.006322543,0.0006057367,0.3073453],"study_design_scores_gemma":[0.00003981705,0.001130102,0.001192168,0.00002684318,0.00005995207,0.0009995581,0.00005376863,0.1546468,0.8141478,0.004447827,0.02320388,0.00005144003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008758347,0.000206517,0.9893338,0.00002845745,0.00004166213,0.00005035276,0.00001321722,0.0005486839,0.001018852],"genre_scores_gemma":[0.1258544,0.0004579966,0.8683735,0.00007785117,0.00007636592,0.0001125412,0.00009153045,0.0001078813,0.004847963],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002890703,"threshold_uncertainty_score":0.009670377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04445270449140182,"score_gpt":0.2737615278008637,"score_spread":0.2293088233094619,"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."}}