{"id":"W4399783724","doi":"10.54097/ohpdubg1","title":"Research on the Recognition and Application of Montreal Forced Aligner for Singing Audio","year":2024,"lang":"en","type":"article","venue":"Journal of Computing and Electronic Information Management","topic":"Music and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Singing; Speech recognition; Computer science; Acoustics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002954181,0.001017179,0.000757446,0.001639597,0.000626541,0.001519558,0.001738206,0.001263954,0.006277926],"category_scores_gemma":[0.007569106,0.0005274591,0.0007407191,0.001720704,0.0009062109,0.002320868,0.0005223695,0.000966263,0.002820368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001048236,"about_ca_system_score_gemma":0.001854385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02905756,"about_ca_topic_score_gemma":0.02481797,"domain_scores_codex":[0.9982493,0.0003170545,0.00008559617,0.0006590684,0.0005624648,0.0001264912],"domain_scores_gemma":[0.9949579,0.001895377,0.0002250351,0.00076173,0.002006894,0.0001531623],"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":[0.0002680845,0.0001145352,0.003699084,0.0004796656,0.0001306014,0.0002101616,0.0005189185,0.008896329,0.2092606,0.004142015,0.002726769,0.7695531],"study_design_scores_gemma":[0.00005624658,0.00133637,0.03211382,0.0002456262,0.0003444985,0.002480626,0.0009015594,0.2898549,0.5681325,0.004384835,0.09986827,0.00028075],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1142119,0.01333244,0.8519635,0.0007277468,0.0004532364,0.0003383946,0.000699687,0.005286738,0.01298642],"genre_scores_gemma":[0.3728741,0.00640957,0.599018,0.000365381,0.0003257354,0.0001763053,0.002055671,0.0005382384,0.01823701],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02905756,"threshold_uncertainty_score":0.05777687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02254529312320128,"score_gpt":0.2998860715592169,"score_spread":0.2773407784360156,"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."}}