{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001102812,0.001059427,0.0007094104,0.001949985,0.0002864242,0.0008342627,0.0005134768,0.0009902776,0.002180568],"category_scores_gemma":[0.002691914,0.0001989658,0.001059896,0.002231257,0.0004206596,0.0008263438,0.0005686019,0.0009540516,0.001735829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004420406,"about_ca_system_score_gemma":0.0006574356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00400959,"about_ca_topic_score_gemma":0.002397273,"domain_scores_codex":[0.9994742,0.0001271117,0.00004621135,0.0001016871,0.0002080692,0.0000426878],"domain_scores_gemma":[0.9988588,0.0005274062,0.0001333146,0.0001205348,0.0003256621,0.00003418762],"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":[0.0003531537,0.000176031,0.004663948,0.0003009094,0.0001515693,0.0002552709,0.0002285215,0.1866738,0.05808667,0.009003183,0.005742507,0.7343645],"study_design_scores_gemma":[0.0000104554,0.0001148522,0.003638391,0.00004829287,0.00004032758,0.0001729315,0.00006874085,0.9772806,0.008441419,0.00533241,0.004822168,0.00002943757],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02220509,0.001322218,0.9735199,0.000238525,0.0001150391,0.00007638454,0.0003654748,0.001373228,0.0007840589],"genre_scores_gemma":[0.4309447,0.003551653,0.555841,0.0002323485,0.000274958,0.000421919,0.002734942,0.0002926658,0.005705955],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00400959,"threshold_uncertainty_score":0.007972538,"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."}}