Symbolic Music Genre Transfer with CycleGAN

by   Gino Brunner, et al.
ETH Zurich

Deep generative models such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) have recently been applied to style and domain transfer for images, and in the case of VAEs, music. GAN-based models employing several generators and some form of cycle consistency loss have been among the most successful for image domain transfer. In this paper we apply such a model to symbolic music and show the feasibility of our approach for music genre transfer. Evaluations using separate genre classifiers show that the style transfer works well. In order to improve the fidelity of the transformed music, we add additional discriminators that cause the generators to keep the structure of the original music mostly intact, while still achieving strong genre transfer. Visual and audible results further show the potential of our approach. To the best of our knowledge, this paper represents the first application of GANs to symbolic music domain transfer.


MIDI-VAE: Modeling Dynamics and Instrumentation of Music with Applications to Style Transfer

We introduce MIDI-VAE, a neural network model based on Variational Autoe...

Music Sentiment Transfer

Music sentiment transfer is a completely novel task. Sentiment transfer ...

The Power of Reuse: A Multi-Scale Transformer Model for Structural Dynamic Segmentation in Symbolic Music Generation

Symbolic Music Generation relies on the contextual representation capabi...

CycleDRUMS: Automatic Drum Arrangement For Bass Lines Using CycleGAN

The two main research threads in computer-based music generation are: th...

Music Style Transfer: A Position Paper

Led by the success of neural style transfer on visual arts, there has be...

Music Style Transfer Issues: A Position Paper

Led by the success of neural style transfer on visual arts, there has be...

Supervised Symbolic Music Style Translation Using Synthetic Data

Research on style transfer and domain translation has clearly demonstrat...

Please sign up or login with your details

Forgot password? Click here to reset