{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003662209,0.0006636999,0.0002921102,0.0002657206,0.000403762,0.0008128764,0.0006383948,0.0005281256,0.004021584],"category_scores_gemma":[0.001855245,0.0002229197,0.000238772,0.0001366083,0.0005318599,0.001021255,0.001559686,0.0003054485,0.0007516959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002116243,"about_ca_system_score_gemma":0.0001834538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004161738,"about_ca_topic_score_gemma":0.0007072523,"domain_scores_codex":[0.9995091,0.0001516654,0.00002088553,0.00008208479,0.0001919964,0.00004430132],"domain_scores_gemma":[0.9994354,0.0003439112,0.00004986842,0.00005399452,0.00009559505,0.00002121104],"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":[0.0006689524,0.0001993223,0.00117623,0.0006010844,0.00005682831,0.000623707,0.001104699,0.14755,0.5256448,0.02503176,0.0008415104,0.2965012],"study_design_scores_gemma":[0.000123162,0.001198115,0.003631433,0.00014624,0.0001172366,0.000886686,0.0004484223,0.7589858,0.1666909,0.03413529,0.03350941,0.0001272685],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0972674,0.0006719582,0.8880239,0.0001276792,0.00008751921,0.00006207664,0.00003794958,0.001693111,0.0120284],"genre_scores_gemma":[0.9108009,0.0003404935,0.08383711,0.00007814929,0.0000544508,0.000083479,0.00003596467,0.0001272387,0.004642276],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004021584,"threshold_uncertainty_score":0.01345354,"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."}}