{"id":"W3159394092","doi":"","title":"CLAR: Contrastive Learning of Auditory Representations","year":2021,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence and Statistics","topic":"Music and Audio Processing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Artificial intelligence; Supervised learning; Representation (politics); Machine learning; Speech recognition; Quality (philosophy); Raw data; Labeled data; Training set; Natural language processing; Artificial neural network","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":[0.001851677,0.001224972,0.0008240847,0.0007189726,0.0003036269,0.0008832323,0.002319245,0.001446684,0.002796844],"category_scores_gemma":[0.005263743,0.0004417769,0.0007510422,0.000473124,0.0008837045,0.001813502,0.001680667,0.002573234,0.001035883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005241156,"about_ca_system_score_gemma":0.0006479112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0019525,"about_ca_topic_score_gemma":0.003294135,"domain_scores_codex":[0.9992414,0.0002316579,0.00002579636,0.0002848439,0.0001426113,0.0000737728],"domain_scores_gemma":[0.9978513,0.001084998,0.000207907,0.0004216258,0.0003359622,0.00009833049],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004261073,0.0005078647,0.002300685,0.0002228392,0.0001783506,0.0001250733,0.0001518901,0.3935792,0.02385513,0.01062053,0.009058293,0.558974],"study_design_scores_gemma":[0.00001299879,0.00007089737,0.0001970831,0.000008324576,0.000008210535,0.00002340222,0.00000755313,0.9934055,0.002711916,0.003083734,0.0004636786,0.000006737424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03328876,0.0004233567,0.960657,0.000244164,0.00008651672,0.00008133387,0.0001748046,0.002989824,0.002054223],"genre_scores_gemma":[0.701417,0.0002592043,0.2889733,0.0005548405,0.0002021648,0.0002126077,0.001046074,0.0003841531,0.006950619],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002796844,"threshold_uncertainty_score":0.009792745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1028174873514848,"score_gpt":0.3563111225016691,"score_spread":0.2534936351501842,"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."}}