{"id":"W2148415643","doi":"10.5281/zenodo.41292","title":"Pattern Extraction In Sparse Representations With Application To Audio Coding","year":2009,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Computer science; Codebook; Neural coding; Speech coding; Speech recognition; Coding (social sciences); Artificial intelligence; Matching pursuit; ENCODE; Pattern recognition (psychology); Lossless compression; Audio signal; Sparse approximation; Data compression; Compressed sensing; Mathematics","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":[0.0008567023,0.0007646843,0.001045699,0.001547652,0.0003712743,0.001247845,0.0008015002,0.0009721367,0.003502541],"category_scores_gemma":[0.004403406,0.0005005856,0.0007911213,0.002726971,0.0005207277,0.001156297,0.001051974,0.001328251,0.001328037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002153425,"about_ca_system_score_gemma":0.0005091742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002340417,"about_ca_topic_score_gemma":0.002667379,"domain_scores_codex":[0.9995773,0.0001149711,0.00003599216,0.0000650781,0.0001679449,0.00003875554],"domain_scores_gemma":[0.9983834,0.0009473602,0.00008730622,0.0001572104,0.0003820229,0.00004257116],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002158993,0.00008829052,0.0003835328,0.0002457054,0.00003989445,0.0001556029,0.0001124785,0.04727273,0.03867671,0.01221029,0.006045652,0.8945532],"study_design_scores_gemma":[0.00003703436,0.0001031317,0.0005294373,0.00003033156,0.00003494275,0.0002298432,0.00004463284,0.9649433,0.0151806,0.01353851,0.00530882,0.00001949682],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004243708,0.0004663019,0.9941067,0.0001359071,0.00006903846,0.00002420591,0.00007069622,0.000463725,0.0004196976],"genre_scores_gemma":[0.07462294,0.001251312,0.919355,0.00008879686,0.0002054523,0.0001037148,0.0006191302,0.0001680673,0.003585519],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003502541,"threshold_uncertainty_score":0.0117172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01787315784069582,"score_gpt":0.2959507290728859,"score_spread":0.2780775712321901,"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."}}