{"id":"W4213366994","doi":"10.1109/lsp.2022.3150258","title":"CNN-RNN and Data Augmentation Using Deep Convolutional Generative Adversarial Network for Environmental Sound Classification","year":2022,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Music and Audio Processing","field":"Computer Science","cited_by":118,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Kids Brain Health Network","keywords":"Computer science; Artificial intelligence; Deep learning; Convolutional neural network; Recurrent neural network; Normalization (sociology); Transfer of learning; Feature extraction; Pattern recognition (psychology); Generative adversarial network; Feature (linguistics); Feature learning; Machine learning; Spectrogram; Generative grammar; Artificial neural network","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"],"consensus_categories":[],"category_scores_codex":[0.000492412,0.0001627205,0.0001398406,0.00006240523,0.00162839,0.0003180867,0.0005701435,0.00003353046,0.00002333344],"category_scores_gemma":[0.00000567551,0.0001820519,0.00002987746,0.0001797277,0.0001403621,0.001200603,0.0002717354,0.0001318653,0.000001296712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002133641,"about_ca_system_score_gemma":0.0001290599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005295794,"about_ca_topic_score_gemma":0.000001232818,"domain_scores_codex":[0.9982762,0.0001038821,0.0002654631,0.0006699229,0.0003784431,0.0003060705],"domain_scores_gemma":[0.9993305,0.00007865141,0.0002807637,0.0002286026,0.00001934093,0.00006219777],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001791481,0.0001423604,0.001343477,0.0001128913,0.00009693112,0.00001231265,0.002930125,0.3739235,0.4460857,0.000813145,0.009769082,0.1645914],"study_design_scores_gemma":[0.0006585901,0.00003820235,0.000322695,0.00001423605,0.00003354566,0.00002541715,0.0001703009,0.9958134,0.0004506659,0.001237974,0.0009981397,0.0002367942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06995725,0.0003278333,0.9275495,0.001378899,0.0004416506,0.0002419965,0.00003754456,0.00004877119,0.00001649804],"genre_scores_gemma":[0.8919743,0.000001698028,0.1025597,0.004400355,0.0007718157,0.00004536475,0.0002123086,0.00001638541,0.00001804458],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8249898,"threshold_uncertainty_score":0.9996713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07475645323796545,"score_gpt":0.2863002993979042,"score_spread":0.2115438461599388,"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."}}