Timbre Transfer with Variational Auto Encoding and Cycle-Consistent Adversarial Networks

TL;DR

This paper explores deep learning methods combining Variational Autoencoders and GANs for timbre transfer across speakers and instruments, demonstrating improved reconstruction and transfer quality on multiple datasets.

Contribution

It introduces a novel approach integrating VAEs and GANs for timbre transfer, comparing different architectures and loss functions for enhanced performance.

Findings

Many-to-many transfer outperforms one-to-one in reconstruction.

Basic residual blocks are more effective than bottleneck designs.

Cyclic loss choice has minimal impact on transfer quality.

Abstract

This research project investigates the application of deep learning to timbre transfer, where the timbre of a source audio can be converted to the timbre of a target audio with minimal loss in quality. The adopted approach combines Variational Autoencoders with Generative Adversarial Networks to construct meaningful representations of the source audio and produce realistic generations of the target audio and is applied to the Flickr 8k Audio dataset for transferring the vocal timbre between speakers and the URMP dataset for transferring the musical timbre between instruments. Furthermore, variations of the adopted approach are trained, and generalised performance is compared using the metrics SSIM (Structural Similarity Index) and FAD (Frech\'et Audio Distance). It was found that a many-to-many approach supersedes a one-to-one approach in terms of reconstructive capabilities, and that the adoption of a basic over a bottleneck residual block design is more suitable for enriching content information about a latent space. It was also found that the decision on whether cyclic loss takes on a variational autoencoder or vanilla autoencoder approach does not have a significant impact on reconstructive and adversarial translation aspects of the model.