Classification of phonation types in singing voice using wavelet scattering network-based features.

Abstract

The automatic classification of phonation types in singing voice is essential for tasks such as identification of singing style. In this study, it is proposed to use wavelet scattering network (WSN)-based features for classification of phonation types in singing voice. WSN, which has a close similarity with auditory physiological models, generates acoustic features that greatly characterize the information related to pitch, formants, and timbre. Hence, the WSN-based features can effectively capture the discriminative information across phonation types in singing voice. The experimental results show that the proposed WSN-based features improved phonation classification accuracy by at least 9% compared to state-of-the-art features.