{"id":"W1505714377","doi":"","title":"Backward Linear Prediction for Lossless Coding of Stereo Audio","year":2004,"lang":"en","type":"article","venue":"Journal of the Audio Engineering Society","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Linear prediction; Lossless compression; Computer science; Speech coding; Entropy encoding; Audio signal; Quantization (signal processing); Vector quantization; Speech recognition; Algorithm; Data compression; Entropy (arrow of time); Residual; Decoding methods; Redundancy (engineering)","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":[0.0004032493,0.0003332556,0.0002350636,0.0002237549,0.000203623,0.0003444349,0.0004330681,0.0004346251,0.00191484],"category_scores_gemma":[0.001272236,0.0001586454,0.0001482784,0.0002865023,0.0003545793,0.000477134,0.0005930348,0.0007884826,0.0007393545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004193754,"about_ca_system_score_gemma":0.0004786295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002062752,"about_ca_topic_score_gemma":0.002882313,"domain_scores_codex":[0.9997148,0.0000600843,0.00001125437,0.00002338965,0.0001668275,0.00002372109],"domain_scores_gemma":[0.9996701,0.0001662007,0.00003530474,0.00004880079,0.00006919409,0.00001034525],"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":[0.0003849946,0.0001026689,0.0007765632,0.0002167719,0.00001866634,0.0004945127,0.0002058471,0.2794612,0.1584286,0.113119,0.006613926,0.4401772],"study_design_scores_gemma":[0.000007828887,0.00002373222,0.0001107648,0.00001266261,0.000002625602,0.00008313283,0.000005695826,0.9750389,0.01615328,0.006878024,0.001676576,0.000006780198],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01705515,0.0005637311,0.9788897,0.0001701419,0.00005857012,0.00002378919,0.00005523657,0.0004712015,0.002712528],"genre_scores_gemma":[0.577849,0.001353481,0.4101753,0.0002448642,0.0001038741,0.0001263466,0.00029208,0.0001328546,0.00972218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002062752,"threshold_uncertainty_score":0.006405771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01228170684807887,"score_gpt":0.2421416069253494,"score_spread":0.2298599000772706,"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."}}