Neural Style Transfer for Audio Spectograms

Understand

There has been fascinating work on creating artistic transformations of images by Gatys.

• This was revolutionary in how we can in some sense alter the 'style' of an image while generally preserving its 'content'.
• In our work, we present a method for creating new sounds using a similar approach, treating it as a style-transfer problem, starting from a random-noise input signal and iteratively using back-propagation to optimize the sound to conform to filter-outputs from a pre-trained neural architecture of interest.
• For demonstration, we investigate two different tasks, resulting in bandwidth expansion/compression, and timbral transfer from singing voice to musical instruments.