Download Universal Audio Synthesizer Control with Normalizing Flows
The ubiquity of sound synthesizers have reshaped music production and even entirely define new music genres. However, the increasing complexity and number of parameters in modern synthesizers make them harder to master. Hence, the development of methods allowing to easily create and explore with synthesizers is a crucial need. Here, we introduce a radically novel formulation of audio synthesizer control by formalizing it as finding an organized continuous latent space of audio that represents the capabilities of a synthesizer and map this space to the space of synthesis parameter. By using this formulation, we show that we can address simultaneously automatic parameter inference, macro-control learning and audio-based preset exploration within a single model. To solve this new formulation, we rely on Variational Auto-Encoders (VAE) and Normalizing Flows (NF) to organize and map the respective auditory and parameter spaces. We introduce a new type of NF named regression flows that allow to perform an invertible mapping between separate latent spaces, while steering the organization of some of the latent dimensions. We evaluate our proposal against a large set of baseline models and show its superiority in both parameter inference and audio reconstruction. We also show that the model disentangles the major factors of audio variations as latent dimensions, that can be directly used as macro-parameters. Finally, we discuss the use of our model in several creative applications and introduce real-time implementations in Ableton Live
Download Quality Diversity for Synthesizer Sound Matching
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.