{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002980619,0.0004869635,0.0002945901,0.0006088319,0.0002541341,0.0004790095,0.0006102354,0.0005571279,0.004905184],"category_scores_gemma":[0.0007040865,0.0001541924,0.0002024425,0.0004479867,0.0001901689,0.0004414799,0.0002173875,0.0005686856,0.002363371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002008053,"about_ca_system_score_gemma":0.0003697964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000815225,"about_ca_topic_score_gemma":0.0008658015,"domain_scores_codex":[0.9996806,0.00003371607,0.00002577755,0.00005018221,0.0001853675,0.0000243814],"domain_scores_gemma":[0.9995945,0.00005897252,0.0000342237,0.00007326647,0.0002216703,0.00001742471],"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":[0.000626699,0.00008865856,0.0007032849,0.0002350455,0.0000305143,0.0003557735,0.0001674626,0.004472014,0.4964524,0.004866823,0.00476201,0.4872392],"study_design_scores_gemma":[0.0001484547,0.0005920487,0.002150348,0.00003710901,0.00005870706,0.001993862,0.00004325082,0.1066131,0.8329281,0.0009117036,0.05445515,0.00006823557],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05441908,0.0005691249,0.9311946,0.0002966314,0.0004013994,0.0002197483,0.0003050935,0.00591827,0.006676134],"genre_scores_gemma":[0.2206161,0.0004023266,0.764207,0.0002077054,0.000113458,0.0001254193,0.0007917201,0.0003017457,0.01323444],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004905184,"threshold_uncertainty_score":0.01640952,"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."}}