Download Interpretable timbre synthesis using variational autoencoders regularized on timbre descriptors Controllable timbre synthesis has been a subject of research for several decades, and deep neural networks have been the most successful in this area. Deep generative models such as Variational Autoencoders (VAEs) have the ability to generate a high-level representation of audio while providing a structured latent space. Despite their advantages, the interpretability of these latent spaces in terms of human perception is often limited. To address this limitation and enhance the control over timbre generation, we propose a regularized VAE-based latent space that incorporates timbre descriptors. Moreover, we suggest a more concise representation of sound by utilizing its harmonic content, in order to minimize the dimensionality of the latent space.
Download Generative Latent Spaces for Neural Synthesis of Audio Textures This paper investigates the synthesis of audio textures and the
structure of generative latent spaces using Variational Autoencoders (VAEs) within two paradigms of neural audio synthesis:
DSP-inspired and data-driven approaches. For each paradigm, we
propose VAE-based frameworks that allow fine-grained temporal
control. We introduce datasets across three categories of environmental sounds to support our investigations. We evaluate and compare the models’ reconstruction performance using objective metrics, and investigate their generative capabilities and latent space
structure through latent space interpolations.