{"id":"W2378962433","doi":"","title":"Implementation of a Improved G.726 Speech Compression Algorithm in Low Bit Rate","year":2009,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Speech coding; Bit rate; Encoding (memory); Algorithm; Bandwidth (computing); Speech recognition; Full Rate; Data compression; Process (computing); Codec2; Computer hardware; Linear predictive coding; Artificial intelligence; Telecommunications; Programming language","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000222727,0.0001905802,0.0002525109,0.0002991811,0.00009215985,0.00006991201,0.001218397,0.00007020632,0.00001198953],"category_scores_gemma":[5.849905e-7,0.0001896974,0.00006088709,0.000772283,0.00003284792,0.0005642494,0.0003733249,0.0001652265,0.00001520159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006787368,"about_ca_system_score_gemma":0.00005524634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006084782,"about_ca_topic_score_gemma":0.00001057549,"domain_scores_codex":[0.9983458,0.00007180178,0.0005952957,0.0005532783,0.000148552,0.0002852588],"domain_scores_gemma":[0.9986556,0.00006748686,0.000266473,0.0008100565,0.0001239888,0.00007639214],"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":[0.000002258203,0.0001319718,0.00003787094,0.00000908148,0.000002795328,0.000001315917,0.0001024126,0.00002748792,0.1506809,0.001931159,0.0004871755,0.8465856],"study_design_scores_gemma":[0.001268646,0.000142311,0.01038316,0.0001202118,0.000005870867,0.000020687,0.00003128776,0.07768593,0.8437822,0.02191372,0.04419263,0.0004533679],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00175779,0.00007284718,0.996195,0.0004364467,0.0000158971,0.001121249,0.00003462795,0.0002841561,0.0000820373],"genre_scores_gemma":[0.03369134,0.00003618307,0.9655564,0.0003727815,0.00004468392,0.0001958751,0.00007277352,0.00001033705,0.00001957644],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8461323,"threshold_uncertainty_score":0.7735634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006846922255266234,"score_gpt":0.2981943585585776,"score_spread":0.2913474363033114,"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."}}