{"id":"W3169359258","doi":"","title":"Study of the Non-negative Matrix Factorization behavior to estimate the urban traffic sound levels","year":2019,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Non-negative matrix factorization; Computer science; Noise (video); Matrix decomposition; Sound (geography); Traffic noise; Mixing (physics); Matrix (chemical analysis); Source separation; Artificial intelligence; Speech recognition; Acoustics; Image (mathematics); Noise reduction","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001248331,0.0005506418,0.0003187559,0.0004353311,0.0002525231,0.0004408103,0.0003140401,0.0005836402,0.001016798],"category_scores_gemma":[0.008543902,0.0001981687,0.0003393324,0.0002962665,0.0003034602,0.0006164111,0.0002494562,0.0006400208,0.0002127881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002146741,"about_ca_system_score_gemma":0.0003870785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005388361,"about_ca_topic_score_gemma":0.004612886,"domain_scores_codex":[0.9997352,0.000114222,0.00001001384,0.00005738729,0.00005552161,0.00002766216],"domain_scores_gemma":[0.9921098,0.006825631,0.000236409,0.0001920923,0.0005529413,0.00008314152],"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":[0.0006338273,0.0002620368,0.01229839,0.0003857418,0.0001963053,0.0004331816,0.0005844472,0.7527375,0.05529938,0.01524368,0.003070946,0.1588546],"study_design_scores_gemma":[0.000003103219,0.00001969209,0.00101855,0.000003410124,0.000003902269,0.0000275793,0.0000154135,0.9974213,0.0009357331,0.0004650371,0.00008298029,0.00000323449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2907473,0.0007240228,0.7062841,0.0004083937,0.00006315241,0.00004059496,0.0001387746,0.0003137763,0.001279884],"genre_scores_gemma":[0.9031661,0.0002143179,0.09425657,0.00007655601,0.00005008098,0.00003573247,0.0002997621,0.00006005902,0.001840982],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005388361,"threshold_uncertainty_score":0.01071399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02437047376377993,"score_gpt":0.294798704552846,"score_spread":0.270428230789066,"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."}}