Symbolic Guitar Music Style Transfer with Playable Guitar Tablatures

Abstract

In the task of music style transfer, the symbolic music representation based on Musical Instrument Digital Interface (MIDI) files has always been a popular research medium. By using such representation, some mature models for image style transfer can also be applied to this scenario, such as Cycle-consistent Generative Adversarial Networks (CycleGAN). However, this MIDI-based data representation is not suitable for guitar music because it does not support unique expressive information of guitar playing, such as bending, sliding, or other playing techniques. DadaGP, a dataset made up of guitar-specific format files (tablatures) and their rendered text-like tokens, enables us to perform symbolic guitar music style transfer leveraging expressive guitar playing information, and to produce playable guitar tablatures. We first adopt K-hot encoding to transform the task from sequence generation to binary classification of multiple variables, and use top-$k$ sampling to reproduce sequences from output K-hot vectors. We then propose a novel model we call CycleGMT, a CycleGAN-based model for symbolic guitar music style transfer. Finally, to mitigate the severe sparsity in the data and its resulting content loss, we adopt a skip connection between the input and output of the model, successfully achieving style-transferred music whose quality being competitive with human-composed remixes, while the musical complexity of the style-transferred music can be controlled by adjusting the value of $k$ in top-$k$ sampling.