Semantic Control Over Neurally Synthesized Audio via Latent Disentanglement

Advances in deep generative models have made it possible to synthesize high-fidelity audio, yet giving users precise, continuous control over the semantic qualities of the
generated sound remains challenging. This thesis tackles the problem by combining variational auto-encoders (VAEs) with a latent disentanglement strategy inspired by
Fader Networks [1][2]. In this model the encoder learns a compressed latent space invariant to desired control attributes, allowing for precise control over said attributes
before the latent representation is passed to the decoder via a "fader" like mechanism. Attributes are computed via a learned linear regression coefficient trained in
a supervised manner on continuous attribute labels derived from a synthetic footstep sound effects dataset. During training, adversarial and reconstruction losses  encourage orthogonality between the latent codes and control attributes, ensuring that adjusting one attribute adjusts only the desired content while leaving the rest  unchanged. This thesis details the state of the art and core concepts behind the methods used before diving into the details of the implementation. Finally, the results will be presented and discussed.