{"id":"W3193711300","doi":"10.1007/978-981-16-2674-6_21","title":"Acoustic Scene Classification Using Time–Frequency Energy Emphasis and Convolutional Recurrent Neural Networks","year":2021,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Music and Audio Processing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Spectrogram; Emphasis (telecommunications); Benchmark (surveying); Computer science; Convolutional neural network; Recurrent neural network; Artificial neural network; Energy (signal processing); Artificial intelligence; Speech recognition; Pattern recognition (psychology); Telecommunications; Mathematics; Statistics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002993435,0.0003654189,0.0005184864,0.0001752431,0.0002736866,0.0003195058,0.0002950346,0.0001993467,0.000008469518],"category_scores_gemma":[0.00001933072,0.0003684224,0.00006466502,0.0001211223,0.0001256113,0.0003786096,0.0003043955,0.0003112427,0.000001279367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001588222,"about_ca_system_score_gemma":0.00008514962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003173711,"about_ca_topic_score_gemma":0.000008895579,"domain_scores_codex":[0.9977334,0.00006725783,0.0007477077,0.0008192008,0.0002941143,0.000338356],"domain_scores_gemma":[0.9986826,0.0002099172,0.0005594495,0.0002796104,0.0001627607,0.000105694],"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":[0.000004597397,0.00002357864,0.0002139659,0.0003782751,0.00004082619,0.00004915563,0.0001778652,0.08414845,0.0000924225,0.177951,0.00003866913,0.7368812],"study_design_scores_gemma":[0.00009346119,0.00002908602,0.00002125032,0.002177186,0.0000198979,0.0001679918,0.00003289606,0.989953,0.000005855914,0.002003223,0.00511623,0.0003799267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002977641,0.1273059,0.8654819,0.00004220267,0.001508934,0.0001360061,0.000002925887,0.00006228579,0.00516215],"genre_scores_gemma":[0.9600759,0.01599934,0.01445428,0.0002970413,0.001629159,0.0000146911,0.00006844792,0.0000843259,0.007376865],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9597781,"threshold_uncertainty_score":0.9998768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03258779913234496,"score_gpt":0.2702312001364311,"score_spread":0.2376434010040862,"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."}}