{"id":"W1643908042","doi":"","title":"A demonstration of a single channel blind noise reduction algorithm with live recordings","year":2014,"lang":"en","type":"article","venue":"Espace ÉTS (ETS)","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Value noise; Noise reduction; Noise (video); Noise measurement; Spectrogram; Noise floor; Algorithm; Computer science; Gradient noise; Frequency domain; Speech recognition; Reduction (mathematics); Signal-to-noise ratio (imaging); Mathematics; Artificial intelligence; Computer vision; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002720919,0.0001322353,0.0001635691,0.000137279,0.00009584188,0.0001345092,0.0002577019,0.00006549861,0.000005368791],"category_scores_gemma":[0.00004571821,0.0001155493,0.0000372869,0.0004619183,0.00006133693,0.000709823,0.00005349092,0.0001052464,0.00002597016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000359378,"about_ca_system_score_gemma":0.00006805839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003192254,"about_ca_topic_score_gemma":0.0000200052,"domain_scores_codex":[0.9989687,0.00004372263,0.0001625557,0.0003475109,0.0002515492,0.0002259895],"domain_scores_gemma":[0.9992049,0.00003407882,0.0002269857,0.0002793268,0.0001705592,0.00008419432],"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":[0.0001159404,0.000300882,0.0002671327,0.00006817342,0.00004032109,0.000009339852,0.007005293,0.0004783452,0.1649451,0.0005083308,0.0009794889,0.8252816],"study_design_scores_gemma":[0.001472649,0.001125911,0.0005573532,0.0003631334,0.00003038357,0.0001985898,0.0007533988,0.05836689,0.9337801,0.001709708,0.00116838,0.0004734426],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.188934,0.00006272272,0.8072571,0.001494849,0.0002585861,0.0001375778,7.477094e-7,0.0001145335,0.00173986],"genre_scores_gemma":[0.7885093,0.000006206342,0.2107403,0.00005947356,0.0001815062,0.000008940789,0.000001912015,0.00001115845,0.0004812452],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8248082,"threshold_uncertainty_score":0.4711963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01314881166501118,"score_gpt":0.2254209884009714,"score_spread":0.2122721767359602,"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."}}