{"id":"W2342593223","doi":"10.3390/app6050125","title":"Augmenting Environmental Interaction in Audio Feedback Systems","year":2016,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Music Technology and Sound Studies","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Timbre; Audio feedback; Computer science; Audio signal; Reverberation; Feedback loop; SIGNAL (programming language); Speech recognition; Acoustics; Noise (video); Ambient noise level; Environmental noise; Audio signal processing; Artificial intelligence; Sound (geography); Speech coding; Physics","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":[0.0004416996,0.00007600028,0.00009730553,0.0001122613,0.0002355575,0.00007013479,0.0005599686,0.00004024146,0.00001184007],"category_scores_gemma":[0.00001093333,0.00004802247,0.00001584608,0.0002361644,0.0002885831,0.0003268643,0.0002408347,0.00005760219,0.0001270298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005717049,"about_ca_system_score_gemma":0.00001300192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001210837,"about_ca_topic_score_gemma":0.0000139761,"domain_scores_codex":[0.9990785,0.0000185898,0.0001523346,0.0003348721,0.0001809444,0.0002347733],"domain_scores_gemma":[0.9996394,0.0001002834,0.00006671095,0.0001712447,0.000002480557,0.00001986856],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.000007594304,0.0001135165,0.04082792,0.00001078293,0.0000208717,0.00001095827,0.003183994,0.00009834128,0.06330045,0.7309012,0.001423391,0.160101],"study_design_scores_gemma":[0.008734715,0.00115675,0.440306,0.001008364,0.00004260911,0.0003107573,0.03728052,0.03709926,0.08122811,0.2008401,0.1869676,0.005025194],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9289842,0.0001495451,0.04492114,0.001089928,0.0007129983,0.0001539613,6.796709e-7,0.0001462193,0.0238413],"genre_scores_gemma":[0.9984106,0.00001885705,0.001205924,0.00006575638,0.00002497855,0.00002974811,7.343683e-8,0.000001496359,0.0002425292],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5300611,"threshold_uncertainty_score":0.1958299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01753199371350622,"score_gpt":0.2259644579445225,"score_spread":0.2084324642310163,"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."}}