Predicting pairwise pitch contour relations based on linguistic tone information in Beijing opera singing

Abstract

The similarity between linguistic tones and melodic pitch contours in Beijing Opera can be captured either by the contour shape of single syllable units, or by the pairwise pitch height relations in adjacent syllable units. In this paper, we investigate the latter problem with a novel machine learning approach, using techniques from time series data mining. Approximately 1300 pairwise contour segments are extracted from a selection of 20 arias. We then formulate the problem as a supervised machinelearning task of predicting types of pairwise melodic relations based on linguistic tone information. The results give a comparative view of fixed and mixed-effects models that achieved around 70% of maximum accuracy. We discuss the superiority of the current method to that of the unsupervised learning in single-syllable-unit contour analysis of similarity in Beijing Opera.