{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005628188,0.0009184574,0.0009664237,0.003789794,0.0003485172,0.0006930762,0.00064617,0.0007465499,0.002005546],"category_scores_gemma":[0.001532932,0.0002164495,0.0007433128,0.002940772,0.0002635907,0.001343122,0.0008865948,0.0007140236,0.001629649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002958144,"about_ca_system_score_gemma":0.0004987761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002775287,"about_ca_topic_score_gemma":0.003666719,"domain_scores_codex":[0.999361,0.00006415469,0.00004424122,0.0001526744,0.0002833146,0.00009474534],"domain_scores_gemma":[0.9993383,0.0001269945,0.00005585048,0.00009559005,0.0003280449,0.00005512441],"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":[0.000240271,0.0001582959,0.002879675,0.0001115852,0.0000634235,0.0001118739,0.00003982359,0.009888219,0.06885943,0.0007119827,0.003190068,0.9137455],"study_design_scores_gemma":[0.00002890443,0.0003244664,0.01245851,0.00003391636,0.0002054356,0.0005246873,0.0001475957,0.9138052,0.060355,0.003255961,0.008783231,0.00007715408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1090702,0.001392547,0.8791868,0.0002454343,0.0003607712,0.0002121694,0.0007631039,0.004130574,0.004638366],"genre_scores_gemma":[0.6195664,0.0007603265,0.3728471,0.0001751768,0.0002349205,0.0001941195,0.001757756,0.0001975218,0.004266628],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003789794,"threshold_uncertainty_score":0.006709218,"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."}}