{"id":"W2134356817","doi":"","title":"Multiband compression and contrast-enhancing frequency shaping in hearing aids","year":2004,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Formant; Contrast (vision); Acoustics; Hearing aid; Envelope (radar); Filter (signal processing); Amplitude; Computer science; Filter bank; Frequency domain; Speech recognition; Telecommunications; Physics; Artificial intelligence; Optics; Computer vision","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0002398137,0.0003315485,0.0001854619,0.0004156748,0.0001619249,0.0004441246,0.0004839683,0.0005606277,0.001141501],"category_scores_gemma":[0.0007641948,0.0002105596,0.000215635,0.0002570459,0.0002988053,0.0004774971,0.0003103775,0.0002721377,0.0004793233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000236522,"about_ca_system_score_gemma":0.0001938069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004817462,"about_ca_topic_score_gemma":0.0005695631,"domain_scores_codex":[0.9997979,0.00004286822,0.00001036919,0.00002528557,0.0001015109,0.00002202241],"domain_scores_gemma":[0.9996743,0.0001485464,0.00004039303,0.00003849679,0.00007979869,0.0000184422],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006489186,0.00006870218,0.0007481254,0.0001580544,0.00002798401,0.0003585017,0.0001325681,0.01737053,0.6406999,0.006681344,0.0005032569,0.3326021],"study_design_scores_gemma":[0.00006769817,0.000924982,0.002538191,0.00004144503,0.00005791967,0.002879225,0.00004649292,0.2265075,0.7514044,0.004337145,0.01115179,0.00004318377],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3089838,0.004186235,0.6781392,0.0001750032,0.0001401279,0.00008991545,0.00004426615,0.001204855,0.007036547],"genre_scores_gemma":[0.7788949,0.0009709263,0.2147641,0.00007589812,0.00007882393,0.00003515823,0.00003921918,0.00005521063,0.005085791],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001141501,"threshold_uncertainty_score":0.003818631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02088751651690359,"score_gpt":0.2385280048533684,"score_spread":0.2176404883364648,"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."}}