{"id":"W2617002574","doi":"10.1109/taslp.2017.2690558","title":"Combining Temporal Features by Local Binary Pattern for Acoustic Scene Classification","year":2017,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Audio Speech and Language Processing","topic":"Music and Audio Processing","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mel-frequency cepstrum; Local binary patterns; Computer science; Pattern recognition (psychology); Artificial intelligence; Classifier (UML); Centroid; Support vector machine; Binary number; Feature extraction; Speech recognition; Frequency domain; Feature (linguistics); Computer vision; Histogram; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0002754343,0.0002660248,0.000260137,0.0001281296,0.001885861,0.001070414,0.000843751,0.0001481795,0.00001201209],"category_scores_gemma":[0.0000418766,0.0002439978,0.00007812679,0.000131051,0.0001881507,0.0009844452,0.00002199833,0.0003238998,0.000005923379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000532993,"about_ca_system_score_gemma":0.0001158851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000827145,"about_ca_topic_score_gemma":0.00005946116,"domain_scores_codex":[0.9984039,0.00003162475,0.0002636681,0.0006165511,0.0002742645,0.0004099631],"domain_scores_gemma":[0.9986657,0.00007901406,0.0002892558,0.0007120515,0.0000985412,0.0001554362],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002545267,0.000100168,0.00009715519,0.0002031221,0.0000149678,0.00001783887,0.00115633,0.00007815346,0.02270458,0.000004072148,0.0006093502,0.9749888],"study_design_scores_gemma":[0.00644088,0.0009423773,0.006468088,0.002420718,0.0002772418,0.0004236248,0.004416375,0.6707128,0.3011428,0.001479036,0.002711453,0.002564574],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02454647,0.001119625,0.9709875,0.00232043,0.0003323261,0.0002410225,0.00002344921,0.0002167873,0.0002123982],"genre_scores_gemma":[0.9656686,0.00003237964,0.03262091,0.0008112231,0.0001131263,0.00005014358,0.00001288079,0.00002993639,0.0006607823],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9724242,"threshold_uncertainty_score":0.9999666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0232413096621984,"score_gpt":0.2901684639987911,"score_spread":0.2669271543365928,"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."}}