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。

合唱变换器：使用变换器上的相对注意力生成多音乐

Choir Transformer: Generating Polyphonic Music with Relative Attention  on Transformer

In this paper, we propose a generic technique to model temporal dependencies
and sequences using a combination of a recurrent neural network and a Deep
Belief Network. Our technique, RNN-DBN, is an amalgamation of the memory state
of the RNN that allows it to provide temporal information and a multi-layer DBN
that helps in high level representation of the data. This makes RNN-DBNs ideal
for sequence generation. Further, the use of a DBN in conjunction with the RNN
makes this model capable of significantly more complex data representation than
an RBM. We apply this technique to the task of polyphonic music generation.

该论文提出了一种泛用的技术，将递归神经网络和深度置信网络相结合，用于建模时间依赖性和序列生成，特别适用于多声部音乐生成任务。