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[
this https URL], 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.

提出了一种适用于多声部音乐生成的神经网络模型 Choir Transformer，并引入了相对位置注意力机制以更好地建模音乐的结构，提出了适用于多声部音乐生成的音乐表示方法。Choir Transformer 的性能超过了之前最先进的准确率达到 4.06%。实验证明其和巴赫音乐的和声度接近。在实际应用中，生成的旋律和节奏可以根据特定输入进行调整，从而产生不同风格的音乐，如民谣音乐或流行音乐等。