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Cited by 2 publications
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“…focus on the performers) [2,3] and violins (i.e. focus on the instrument) [36] used machine learning and statistical methods to distinguish good timbre from bad timbre, so we will do the same thing and rely on our "virtual ears" to provide judgements. Concretely, for each simulated note, we define the cost as the RMS of SVM judgements c [h] for all H frames.…”
Section: Validating the Bow Controllermentioning
confidence: 99%
See 1 more Smart Citation
“…focus on the performers) [2,3] and violins (i.e. focus on the instrument) [36] used machine learning and statistical methods to distinguish good timbre from bad timbre, so we will do the same thing and rely on our "virtual ears" to provide judgements. Concretely, for each simulated note, we define the cost as the RMS of SVM judgements c [h] for all H frames.…”
Section: Validating the Bow Controllermentioning
confidence: 99%
“…To address the problem of controlling the physical models, we turned to machine learning to train the system to perform virtual instruments. Previous uses of machine learning in performance focused on recognizing performer gestures [12], while machine learning has been used off-line to evaluate beginning violinists [2], recognize cellists from their timbre [3], rank violins (rather than violinists) [36], and estimate physical actions from violin audio [25]. Active learning [4] is a technique in machine learning in which the computer generates examples for the user to evaluate; it has been used to generate personalized audio equalizers [21].…”
Section: Related Workmentioning
confidence: 99%