{"id":"W4389321189","doi":"10.1109/taslp.2023.3332544","title":"Time-Frequency Scattergrams for Biomedical Audio Signal Representation and Classification","year":2023,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Audio Speech and Language Processing","topic":"Phonocardiography and Auscultation Techniques","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Spectrogram; Speech recognition; Audio signal; Computer science; Representation (politics); SIGNAL (programming language); Natural sounds; Texture (cosmology); Bioacoustics; Artificial intelligence; Acoustics; Speech coding","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":[],"consensus_categories":[],"category_scores_codex":[0.0002597612,0.0001601892,0.000232555,0.0004393741,0.0002900502,0.00006842033,0.00006653952,0.0001342872,0.00004091262],"category_scores_gemma":[0.00005101813,0.0001435917,0.0001071006,0.0007513815,0.0001812459,0.0002025047,0.000002783887,0.0001810437,0.00001833326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002862308,"about_ca_system_score_gemma":0.00005357044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001546964,"about_ca_topic_score_gemma":0.000003810538,"domain_scores_codex":[0.9988554,0.00003381422,0.0002571013,0.0003912913,0.0002323504,0.0002300944],"domain_scores_gemma":[0.9993604,0.0001054949,0.00008744169,0.0002015198,0.00009592737,0.0001491641],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000106977,0.00008712176,0.0001626003,0.0002596108,0.00005983621,0.00002107378,0.00131658,0.000003632306,0.2805823,0.000003310659,0.0005372064,0.7168597],"study_design_scores_gemma":[0.00897222,0.002372175,0.02970432,0.002239406,0.001359709,0.001003883,0.01006094,0.02592226,0.9080873,0.00584098,0.002884587,0.001552264],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5363859,0.0009559362,0.4548834,0.004595984,0.0001500019,0.00124435,0.0001066708,0.00119521,0.0004825112],"genre_scores_gemma":[0.976261,0.0002808773,0.02197544,0.000287238,0.0001370722,0.0001944442,0.000157808,0.0000302542,0.000675819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7153075,"threshold_uncertainty_score":0.5855498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02717384920440576,"score_gpt":0.323343654410201,"score_spread":0.2961698052057952,"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."}}