Mixture of Gaussian process experts for predicting sung melodic contour with expressive dynamic fluctuations
Yasunori Ohishi, Daichi Mochihashi, Hirokazu Kameoka, Kunio Kashino · 2014
We present a generative model for predicting the sung melodic contour, i.e., F0contour, with expressive dynamic fluctuations, such as vibrato and portamento, for a given musical score. Although several studies have attempted to characterize such fluctuations, no systematic method has been developed for generating the F0contour with them in connection with musical notes. In our model, the relationship between a musical note sequence and F0contour is directly learned by a mixture of Gaussian process experts. This approach allows us to automatically characterize the fluctuations by utilizing the kernel function for each Gaussian process expert and predict the F0contour for an arbitrary musical note sequence. Experimental results show that our model can better predict the F0contour than a baseline method can. Additionally, we discuss the effective musical contexts and the amount of training data for the prediction.