Quality Diversity for Synthesizer Sound Matching

Abstract

It is difficult to adjust the parameters of a complex synthesizer to create the desired sound. As such, sound matching, the estimation of synthesis parameters that can replicate a certain sound, is a task that has often been researched, utilizing optimization methods such as genetic algorithm (GA). In this paper, we introduce a novelty-based objective for GA-based sound matching. Our contribution is two-fold. First, we show that the novelty objective is able to improve the quality of sound matching by maintaining phenotypic diversity in the population. Second, we introduce a quality diversity approach to the problem of sound matching, aiming to find a diverse set of matching sounds. We show that the novelty objective is effective in producing high-performing solutions that are diverse in terms of specified audio features. This approach allows for a new way of discovering sounds and exploring the capabilities of a synthesizer.