{"id":"W2166428112","doi":"10.5281/zenodo.1176986","title":"Grassp: Gesturally-Realized Audio, Speech And Song Performance","year":2006,"lang":"en","type":"article","venue":"","topic":"Music and Audio Processing","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Interface (matter); Gesture; Speech recognition; Jitter; Musical expression; Audio signal processing; Sound recording and reproduction; Sound (geography); Speech synthesis; User interface; Speech processing; Human–computer interaction; Multimedia; Audio signal; Musical; Speech coding; Artificial intelligence; Acoustics","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":[0.0007533069,0.001121162,0.0004997091,0.0002583094,0.0004073816,0.0009451678,0.002097363,0.000704216,0.03084927],"category_scores_gemma":[0.001219582,0.0004802723,0.000450269,0.0001689947,0.0007735098,0.001341116,0.0028452,0.001137057,0.01184945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001991818,"about_ca_system_score_gemma":0.0004481872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006083812,"about_ca_topic_score_gemma":0.001180641,"domain_scores_codex":[0.999415,0.00009358025,0.00002404057,0.0001076221,0.0002632889,0.00009643257],"domain_scores_gemma":[0.9995556,0.0001254737,0.0000259744,0.0001084442,0.00005076261,0.0001336816],"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":[0.0027671,0.0005097124,0.002327403,0.0009620416,0.00007419431,0.002246031,0.00263499,0.007444777,0.3919302,0.0228654,0.04901326,0.5172248],"study_design_scores_gemma":[0.0007479643,0.002832508,0.01169126,0.0001864136,0.0001107052,0.005487062,0.0007248427,0.1311245,0.30972,0.01840455,0.5185993,0.0003709386],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05204497,0.0001815393,0.8192955,0.0001500998,0.0001105364,0.0006335509,0.0009622431,0.09970406,0.0269175],"genre_scores_gemma":[0.2583724,0.0003288775,0.6798543,0.000319554,0.00008538463,0.00138447,0.003081514,0.008040294,0.04853338],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03084927,"threshold_uncertainty_score":0.1032011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007491374669156958,"score_gpt":0.2025796765667273,"score_spread":0.1950883018975703,"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."}}