  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.
