  In recent years, neural network based methods have been proposed as a method
that cangenerate representations from music, but they are not human readable
and hardly analyzable oreditable by a human. To address this issue, we propose
a novel method to learn source-awarelatent representations of music through
Vector-Quantized Variational Auto-Encoder(VQ-VAE).We train our VQ-VAE to encode
an input mixture into a tensor of integers in a discrete latentspace, and
design them to have a decomposed structure which allows humans to manipulatethe
latent vector in a source-aware manner. This paper also shows that we can
generate basslines by estimating latent vectors in a discrete space.
