Existing approaches for generating multitrack music with transformer models
have been limited in terms of the number of instruments, the length of the
music segments and slow inference. This is partly due to the memory
requirements of the lengthy input sequences necessitated by existing
representations. In this work, we propose a new multitrack music representation
that allows a diverse set of instruments while keeping a short sequence length.
Our proposed Multitrack Music Transformer (MMT) achieves comparable performance
with state-of-the-art systems, landing in between two recently proposed models
in a subjective listening test, while achieving substantial speedups and memory
reductions over both, making the method attractive for real time improvisation
or near real time creative applications. Further, we propose a new measure for
analyzing musical self-attention and show that the trained model attends more
to notes that form a consonant interval with the current note and to notes that
are 4N beats away from the current step.

提出了一种新型的多轨音乐表示方式，可以同时包含多种不同类型的乐器，并实现了基于音乐自注意力的实时即兴演奏，该方法在速度和内存占用方面都具有优势。