{"id":"W4401325927","doi":"10.1109/lsp.2024.3438089","title":"Cross-Terms and Spectral Leakage Minimization in Time-Frequency Distribution","year":2024,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Leakage (economics); Minification; Computer science; Time–frequency analysis; Spectral leakage; Algorithm; Mathematical optimization; Mathematics; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0002666447,0.0001748255,0.0001410869,0.0001472415,0.0001625781,0.001876141,0.0003630181,0.00006777282,0.00001425584],"category_scores_gemma":[0.00001394985,0.0001652914,0.00003647934,0.0006419134,0.0001185486,0.001774144,0.00004503355,0.0002263668,0.00003423812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001049635,"about_ca_system_score_gemma":0.00008059236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000774513,"about_ca_topic_score_gemma":0.000001393355,"domain_scores_codex":[0.9985311,0.00003376277,0.0002608669,0.0005202221,0.0002899219,0.0003641474],"domain_scores_gemma":[0.9996743,0.00004207934,0.00006050747,0.0001229797,0.00002688999,0.00007326157],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001563131,0.00005543693,0.01056685,0.0005080704,0.00001372756,0.0004975454,0.00126392,0.001048324,0.6872721,0.0001535226,0.00123871,0.2973661],"study_design_scores_gemma":[0.001214041,0.0001593795,0.02610467,0.00230646,0.00003251891,0.0004227817,0.00001963712,0.3967368,0.5614716,0.009246837,0.0008229281,0.001462309],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4867649,0.0005801648,0.5100728,0.00193441,0.0001571586,0.00007682876,0.000003993936,0.0002553521,0.0001543928],"genre_scores_gemma":[0.9897841,0.000006185785,0.009187602,0.0006981463,0.0001965273,0.000008274319,0.00001536739,0.00001595832,0.00008788278],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5030192,"threshold_uncertainty_score":0.99916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00886271731325808,"score_gpt":0.2457083375972374,"score_spread":0.2368456202839793,"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."}}