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

Learning source-aware representations of music in a discrete latent space