Choir Transformer: Generating Polyphonic Music with Relative Attention on Transformer
This addresses the challenge of generating polyphonic music with correct melody and harmony for applications in music composition and style adaptation, representing an incremental advance over existing methods.
The paper tackles polyphonic music generation by proposing Choir Transformer, a neural network using relative positional attention and a new music representation, achieving a 4.06% improvement in accuracy over previous state-of-the-art methods and generating music with harmony metrics close to Bach's.
Polyphonic music generation is still a challenge direction due to its correct between generating melody and harmony. Most of the previous studies used RNN-based models. However, the RNN-based models are hard to establish the relationship between long-distance notes. In this paper, we propose a polyphonic music generation neural network named Choir Transformer[ https://github.com/Zjy0401/choir-transformer], with relative positional attention to better model the structure of music. We also proposed a music representation suitable for polyphonic music generation. The performance of Choir Transformer surpasses the previous state-of-the-art accuracy of 4.06%. We also measures the harmony metrics of polyphonic music. Experiments show that the harmony metrics are close to the music of Bach. In practical application, the generated melody and rhythm can be adjusted according to the specified input, with different styles of music like folk music or pop music and so on.