A CycleGAN for style transfer between drum and bass subgenres

In this work, we apply the CycleGAN image-to-image translation framework to Mel-scaled log-amplitude spectrograms, successfully realizing audio texture transfer between excerpts from two musically related genres. Such automatic musical transfer could provide music producers and DJs with new creative tools, e.g. to quickly prototype a remix of an existing song in another genre, or to use as an advanced effect during a live performance. We show that meaningful style transfer can be realized using only a limited amount of data and computational resources. A high-quality audio reconstruction is obtained from the generated amplitude spectrogram by simply using the phase of the original audio as an approximation for the phase of the generated spectrogram. This results in a significant quality improvement over traditional phase reconstruction methods.

Jaar van publicatie:2019

Toegankelijkheid:Open