Despite the innovations in deep learning and generative AI, creating long
term structure as well as the layers of repeated structure common in musical
works remains an open challenge in music generation. We propose an attention
layer that uses a novel approach applying user-supplied self-similarity
matrices to previous time steps, and demonstrate it in our Similarity
Incentivized Neural Generator (SING) system, a deep learning autonomous music
generation system with two layers. The first is a vanilla Long Short Term
Memory layer, and the second is the proposed attention layer. During
generation, this attention mechanism imposes a suggested structure from a
template piece on the generated music. We train SING on the MAESTRO dataset
using a novel variable batching method, and compare its performance to the same
model without the attention mechanism. The addition of our proposed attention
mechanism significantly improves the network's ability to replicate specific
structures, and it performs better on an unseen test set than a model without
the attention mechanism.

我们提出了一种注意力层的方法，使用用户提供的自相似矩阵来在生成音乐时添加模板结构，并通过与没有注意力机制的模型进行比较，证明了该方法显著提高了网络的特定结构复制能力，以及在未见测试集上的性能。