A singing voice synthesis system based on sinusoidal modeling

Although sinusoidal models have been demonstrated to be capable of high-quality musical instrument synthesis [1], speech modification [2], and speech synthesis [3], little exploration of the application of these models to the synthesis of singing voice has been undertaken. In this paper, we propose a system framework similar to that employed in concatenation-based text-to-speech synthesizers, and describe its extension to the synthesis of singing voice. The power and flexibility of the sinusoidal model used in the waveform synthesis portion of the system [1] enables high-quality, computationally-efficient synthesis and the incorporation of musical qualities such as vibrate and spectral tilt variation. Modeling of segmental phonetic characteristics is achieved by employing a ''unit selection'' procedure that selects sinusoidally-modeled segments from an inventory of singing voice data collected from a human vocalist. The system, called LYRICOS, is capable of synthesizing very natural-sounding singing that maintains the characteristics and perceived identity of the analyzed vocalist.