A quantitative comparison of glottal closure instant estimation algorithms on a large variety of singing sounds

Abstract

Glottal closure instant (GCI) estimation is a well-studied topic that plays a critical role in several speech processing applications. Many GCI estimation algorithms have been proposed in the literature and shown to provide excellent results on the speech signal. Nonetheless the efficiency of these algorithms for the analysis of the singing voice is still unknown. The goal of this paper is to assess the performance of existing GCI estimation methods on the singing voice with a quantitative comparison. A second goal is to provide a starting point for the adaptation of these algorithms to the singing voice by identifying weaknesses and strengths under different conditions. This study is carried out on a large database of singing sounds with synchronous electroglottography (EGG) recordings, containing a variety of singer categories and singing techniques. The evaluated algorithms are Dynamic Programming Phase Slope Algorithm (DYPSA), Hilbert Envelope-based detection (HE), Speech Event Detection using the Residual Excitation And a Mean-based Signal (SEDREAMS), Yet Another GCI Algorithm (YAGA) and Zero Frequency Resonator-based method (ZFR). The algorithms are evaluated in terms of both reliability and accuracy, over different singing categories, laryngeal mechanisms, and voice qualities. Additionally, the robustness of the algorithms to reverberation is analyzed.