  Neural style transfer, allowing to apply the artistic style of one image to
another, has become one of the most widely showcased computer vision
applications shortly after its introduction. In contrast, related tasks in the
music audio domain remained, until recently, largely untackled. While several
style conversion methods tailored to musical signals have been proposed, most
lack the 'one-shot' capability of classical image style transfer algorithms. On
the other hand, the results of existing one-shot audio style transfer methods
on musical inputs are not as compelling. In this work, we are specifically
interested in the problem of one-shot timbre transfer. We present a novel
method for this task, based on an extension of the vector-quantized variational
autoencoder (VQ-VAE), along with a simple self-supervised learning strategy
designed to obtain disentangled representations of timbre and pitch. We
evaluate the method using a set of objective metrics and show that it is able
to outperform selected baselines.
