Gaussian Process Synthesis of Artificial Sounds

Abstract

In this paper, we propose Gaussian Process (GP) sound synthesis. A GP is used to sample random continuous functions, which are then used for wavetable or waveshaping synthesis. The shape of the sampled functions is controlled with the kernel function of the GP. Sampling multiple times from the same GP generates perceptually similar but non-identical sounds. Since there are many ways to choose the kernel function and its parameters, an interface aids the user in sound selection. The interface is based on a two-dimensional visualization of the sounds grouped by their similarity as judged by a t-SNE analysis of their Mel Frequency Cepstral Coefficient (MFCC) representations.