{"id":"W3142011739","doi":"10.1109/icassp.2002.1005726","title":"Improved spectral tracking using interpolated linear prediction parameters","year":2002,"lang":"en","type":"article","venue":"IEEE International Conference on Acoustics Speech and Signal Processing","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Tracking (education); Computer science; Interpolation (computer graphics); Linear prediction; Artificial intelligence; Computer vision; Algorithm","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":[],"consensus_categories":[],"category_scores_codex":[0.0001547076,0.0002419762,0.0001923619,0.0002527513,0.0002404855,0.0006944351,0.0007018108,0.0001132703,0.00006814954],"category_scores_gemma":[0.00005691917,0.0002296478,0.00004519284,0.0001913246,0.0001174976,0.001227295,0.0001301691,0.0004219202,0.000006093039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001070418,"about_ca_system_score_gemma":0.00005679334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001301115,"about_ca_topic_score_gemma":8.712501e-7,"domain_scores_codex":[0.998305,0.00003858592,0.0003914733,0.000539224,0.0004244427,0.0003012366],"domain_scores_gemma":[0.9990059,0.00006484139,0.0002394494,0.0002181414,0.0003396726,0.0001320411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004680465,0.0001401205,0.0000755303,0.00003020458,0.00002993276,0.00005413171,0.0003537897,0.001966145,0.6930744,0.001394055,0.0001234929,0.3027114],"study_design_scores_gemma":[0.0002607805,0.0001485851,0.00002678844,0.0002700179,0.000009574384,0.00008623922,0.00003711364,0.9367046,0.05840743,0.003787767,0.00004118745,0.0002198791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0507263,0.00004465328,0.9475067,0.0001900076,0.0003639823,0.0001446707,0.00002861332,0.0003310212,0.0006640721],"genre_scores_gemma":[0.7569307,0.00004465052,0.2426336,0.0001800747,0.0001179863,0.000005053258,0.000006603096,0.00001398824,0.00006732905],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9347385,"threshold_uncertainty_score":0.9364764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07952398660335253,"score_gpt":0.3189378977877926,"score_spread":0.2394139111844401,"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."}}