  Traditional voice conversion methods rely on parallel recordings of multiple
speakers pronouncing the same sentences. For real-world applications however,
parallel data is rarely available. We propose MelGAN-VC, a voice conversion
method that relies on non-parallel speech data and is able to convert audio
signals of arbitrary length from a source voice to a target voice. We firstly
compute spectrograms from waveform data and then perform a domain translation
using a Generative Adversarial Network (GAN) architecture. An additional
siamese network helps preserving speech information in the translation process,
without sacrificing the ability to flexibly model the style of the target
speaker. We test our framework with a dataset of clean speech recordings, as
well as with a collection of noisy real-world speech examples. Finally, we
apply the same method to perform music style transfer, translating arbitrarily
long music samples from one genre to another, and showing that our framework is
flexible and can be used for audio manipulation applications different from
voice conversion.
