{"id":"W3045302321","doi":"10.48550/arxiv.2007.13703","title":"From Sound Representation to Model Robustness","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Robustness (evolution); Computer science; Spectrogram; Pattern recognition (psychology); Artificial intelligence; Mel-frequency cepstrum; Speech recognition; Convolutional neural network; Residual; Short-time Fourier transform; Discrete wavelet transform; Feature extraction; Wavelet; Fourier transform; Wavelet transform; Mathematics; Algorithm","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":[],"consensus_categories":[],"category_scores_codex":[0.00005356222,0.0001880555,0.0002003958,0.0001294421,0.0001358012,0.0001457239,0.001470731,0.0001777659,0.00001650637],"category_scores_gemma":[0.00001404407,0.0002392296,0.0001372047,0.0006289817,0.00003040346,0.0002452138,0.001841215,0.0003168846,0.00009474497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001269078,"about_ca_system_score_gemma":0.00008856905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004256179,"about_ca_topic_score_gemma":0.00002730291,"domain_scores_codex":[0.9984303,0.00004186085,0.0001549997,0.001131583,0.00007495489,0.0001663376],"domain_scores_gemma":[0.9983545,0.00003648355,0.0001444399,0.001163302,0.0001135571,0.0001877354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007645148,0.0000240315,0.00006435106,0.000006047354,0.00002005881,0.00001690968,0.0001837689,0.9082599,0.0001072309,0.08938701,0.001334974,0.0005880409],"study_design_scores_gemma":[0.00006798098,0.00001204552,0.0001212026,0.00001026065,0.00001881606,4.181546e-7,0.00003154697,0.7702314,0.0004703191,0.2286622,0.0001721584,0.0002016979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0905173,0.000004764862,0.9063842,0.0007691821,0.0001168642,0.0003191093,0.00002895476,0.0006056373,0.001254032],"genre_scores_gemma":[0.9425544,0.00001762393,0.05617443,0.0002378778,0.00009430791,0.000006910279,0.00002401717,0.00001336247,0.0008770853],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8520371,"threshold_uncertainty_score":0.9755502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1415479592791728,"score_gpt":0.228850258799886,"score_spread":0.0873022995207132,"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."}}