{"id":"W2028507562","doi":"10.1121/1.2942659","title":"A framework for sound source separation using spectral clustering","year":2007,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Cluster analysis; Melody; Source separation; Artificial intelligence; Auditory scene analysis; Speech recognition; Pattern recognition (psychology); Similarity (geometry); Spectral clustering; Segmentation; Perception; Image (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":[0.001220606,0.001229242,0.00114189,0.002400196,0.001137749,0.002081946,0.002659159,0.001647922,0.004040518],"category_scores_gemma":[0.001916222,0.0006957667,0.001482573,0.002250645,0.001274269,0.001887861,0.002395084,0.001775404,0.003026146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008767837,"about_ca_system_score_gemma":0.001393727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003273009,"about_ca_topic_score_gemma":0.003527188,"domain_scores_codex":[0.9990707,0.0002162793,0.00005005231,0.0002228179,0.0003791447,0.00006114871],"domain_scores_gemma":[0.9995196,0.0001246002,0.00004632292,0.00009352902,0.0001760771,0.00003982454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000868349,0.00009147877,0.0003021552,0.0003358684,0.0001513049,0.0003041409,0.000368206,0.2126959,0.02900407,0.3859683,0.009048094,0.3616437],"study_design_scores_gemma":[0.00002330543,0.00006560832,0.0001964195,0.00004572226,0.00002944044,0.0002795541,0.00006616458,0.8236941,0.004057679,0.1445832,0.02689674,0.00006203171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001680265,0.00009144885,0.9990693,0.0000263485,0.00001441223,0.00001418531,0.00001494723,0.0001518606,0.0004494733],"genre_scores_gemma":[0.01804969,0.0003764728,0.9790876,0.00004974358,0.00009575523,0.0001399106,0.0001574264,0.0001458828,0.001897582],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004040518,"threshold_uncertainty_score":0.01351684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02759088404203538,"score_gpt":0.3228097289841277,"score_spread":0.2952188449420924,"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."}}