{"id":"W168718579","doi":"10.1007/3-540-27489-8_7","title":"Signal Subspace Techniques for Speech Enhancement","year":2005,"lang":"en","type":"book-chapter","venue":"Signals and communication technology","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Speech enhancement; Speech recognition; Subspace topology; SIGNAL (programming language); Residual; Frequency domain; Distortion (music); Signal subspace; Noise (video); Perspective (graphical); Domain (mathematical analysis); Artificial intelligence; Algorithm; Noise reduction; Telecommunications; Mathematics; 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":[0.0001725062,0.001091346,0.0006048085,0.0006849784,0.0002485283,0.0007092789,0.0006031147,0.000666332,0.01779679],"category_scores_gemma":[0.0003796597,0.0003233067,0.0003680673,0.001055328,0.0003519947,0.001187433,0.0005331056,0.00109401,0.01482735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001624693,"about_ca_system_score_gemma":0.0001975923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002773516,"about_ca_topic_score_gemma":0.0005954839,"domain_scores_codex":[0.9998283,0.00002033247,0.000007809243,0.00002550842,0.0001083034,0.000009524004],"domain_scores_gemma":[0.9998218,0.00005866196,0.00000873078,0.00003255854,0.00007139491,0.000006844828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004737773,0.00003870654,0.00003849638,0.0002539545,0.00002229232,0.00006409216,0.00006357028,0.004089661,0.0712386,0.01915822,0.01820809,0.8867769],"study_design_scores_gemma":[0.00003847691,0.0003293031,0.0009167272,0.0002204244,0.00009182229,0.002159333,0.0001375687,0.136553,0.1838592,0.05667964,0.6189164,0.00009807823],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002394493,0.01230918,0.9516637,0.0002004575,0.0004708724,0.00004741595,0.0001097267,0.002082241,0.03072186],"genre_scores_gemma":[0.03585772,0.02716808,0.6994397,0.0003495172,0.0008670283,0.0001482853,0.0008342051,0.0007532578,0.2345822],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01779679,"threshold_uncertainty_score":0.05953616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01862485102146598,"score_gpt":0.2668659399757683,"score_spread":0.2482410889543023,"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."}}