{"id":"W4392902959","doi":"10.1109/icassp48485.2024.10447655","title":"ASPED: An Audio Dataset for Detecting Pedestrians","year":2024,"lang":"en","type":"article","venue":"","topic":"Music and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Task (project management); Pedestrian; Pedestrian detection; Scale (ratio); Artificial intelligence; Speech recognition; Engineering; Geography; Cartography; Transport engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006395826,0.003400815,0.001505042,0.003933486,0.001044234,0.001180906,0.002057365,0.002289278,0.01122877],"category_scores_gemma":[0.002375383,0.0004463753,0.001227747,0.00202798,0.0003496452,0.0009902413,0.001789812,0.001334587,0.01708839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006250288,"about_ca_system_score_gemma":0.001170811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01081474,"about_ca_topic_score_gemma":0.03016736,"domain_scores_codex":[0.9987963,0.0001573783,0.0001275629,0.0003430647,0.0004157396,0.0001598991],"domain_scores_gemma":[0.9987319,0.0002077835,0.0001105104,0.000270351,0.0004577935,0.000221651],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001899809,0.0008682473,0.01258215,0.00246645,0.0003870819,0.001017269,0.0002240164,0.002750931,0.02601135,0.001241657,0.7875522,0.1629988],"study_design_scores_gemma":[0.0009870274,0.001121534,0.1259001,0.0005931476,0.0005271616,0.005866112,0.0009050504,0.04145196,0.0370494,0.00472988,0.7804975,0.00037108],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.05006413,0.002517814,0.03056588,0.0004868053,0.001146799,0.0009861856,0.8787096,0.02097308,0.01454978],"genre_scores_gemma":[0.03176718,0.0004884888,0.02513302,0.0002737462,0.0001935106,0.0007025864,0.9363524,0.000384086,0.004704989],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01122877,"threshold_uncertainty_score":0.03756398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05140978155896479,"score_gpt":0.3252451145803849,"score_spread":0.27383533302142,"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."}}