  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.
