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This paper introduces a new score-informed method for the segmentation ofjingju a cappella singing phrase into syllables. The proposed method estimatesthe most likely sequence of syllable boundaries given the estimated syllableonset detection function (ODF) and its score. Throughout the paper, we firstexamine the jingju syllables structure and propose a definition of the term"syllable onset". Then, we identify which are the challenges that jingju acappella singing poses. Further, we investigate how to improve the syllable ODFestimation with convolutional neural networks (CNNs). We propose a novel CNNarchitecture that allows to efficiently capture different time-frequency scalesfor estimating syllable onsets. In addition, we propose using a score-informedViterbi algorithm -instead of thresholding the onset function-, because theavailable musical knowledge we have (the score) can be used to inform theViterbi algorithm in order to overcome the identified challenges. The proposedmethod outperforms the state-of-the-art in syllable segmentation for jingju acappella singing. We further provide an analysis of the segmentation errorswhich points possible research directions.

Score-informed syllable segmentation for a cappella singing voice with convolutional neural networks