{"id":"W4239925856","doi":"10.32920/ryerson.14645673","title":"Noise reduction in hearing aids using spectral subtraction techniques","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Intelligibility (philosophy); Subtraction; Hearing aid; Noise reduction; Speech recognition; Noise (video); Computer science; Algorithm; Mathematics; Acoustics; Artificial intelligence; Arithmetic; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0004136178,0.0006419062,0.0004423211,0.0006341043,0.0002228345,0.000797678,0.0005477173,0.0005145114,0.00282592],"category_scores_gemma":[0.0009151537,0.0001766435,0.0005193692,0.0004022932,0.0002442181,0.0005997465,0.0004532419,0.0003084565,0.001195918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000159483,"about_ca_system_score_gemma":0.0002301259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000324131,"about_ca_topic_score_gemma":0.000608522,"domain_scores_codex":[0.9996245,0.00006534947,0.00001834456,0.0000537984,0.0002172277,0.00002076878],"domain_scores_gemma":[0.9996668,0.0001504595,0.00003030794,0.00003332732,0.0001090483,0.00001012656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006366731,0.0001729194,0.0009855857,0.0004217461,0.00009504,0.0001308724,0.0001860049,0.01905505,0.3978141,0.002034084,0.0008026679,0.5776653],"study_design_scores_gemma":[0.00008228582,0.001537274,0.004747644,0.000109278,0.0002566087,0.001504249,0.0002119359,0.2825448,0.68599,0.002701295,0.02023997,0.00007463869],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1631228,0.00150328,0.8289157,0.0001059864,0.000113959,0.0000873038,0.00005294531,0.001184567,0.004913467],"genre_scores_gemma":[0.5055649,0.001888216,0.4834856,0.0001279265,0.00006509248,0.00008214756,0.0001994761,0.0002104793,0.008376249],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00282592,"threshold_uncertainty_score":0.009453654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04223485796805939,"score_gpt":0.3040969038750813,"score_spread":0.2618620459070219,"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."}}