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.