In this paper, we propose SinTra, an auto-regressive sequential generative
model that can learn from a single multi-track music segment, to generate
coherent, aesthetic, and variable polyphonic music of multi-instruments with an
arbitrary length of bar. For this task, to ensure the relevance of generated
samples and training music, we present a novel pitch-group representation.
SinTra, consisting of a pyramid of Transformer-XL with a multi-scale training
strategy, can learn both the musical structure and the relative positional
relationship between notes of the single training music segment. Additionally,
for maintaining the inter-track correlation, we use the convolution operation
to process multi-track music, and when decoding, the tracks are independent to
each other to prevent interference. We evaluate SinTra with both subjective
study and objective metrics. The comparison results show that our framework can
learn information from a single music segment more sufficiently than Music
Transformer. Also the comparison between SinTra and its variant, i.e., the
single-stage SinTra with the first stage only, shows that the pyramid structure
can effectively suppress overly-fragmented notes.

该论文提出了一种基于 SinTra 的自回归序列生成模型，可以从单个多轨音乐片段中学习，生成多个乐器的一致、美学、可变的多声部音乐，采用新颖的音高组表示法来确保生成样本的相关性和训练音乐，通过金字塔形的 Transformer-XL 与多尺度训练策略相结合的自回归序列生成模型可学习从单个训练音乐片段中的音乐结构和相对位置关系。