Quality-diversity for Synthesizer Sound Matching

Abstract

Synthesizers have become commonplace in music production due to their ability to create a diverse and unique timbre. However, many users find it difficult to control a synthesizer due to the complexity of the relationship between synthesis parameters and output sound. As such, the sound matching task focuses on the estimation of synthesis parameters that recreate the target sound as closely as possible within the capabilities of the synthesizer. In this paper, we introduce a quality-diversity approach to the problem of sound matching by introducing a novelty objective to genetic algorithm-based sound matching. We show that this QD method allows for the discovery of a collection of matches that are more diverse compared to matches found by standard genetic algorithms. These matches are spread across a space of interpretable spectral features, making it useful for the intuitive discovery of unique and useful matches.