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 ...
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.
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