Title: Learning a Latent Space of Multitrack Measures,
Abstract: Discovering and exploring the underlying structure of multi-instrumental
music using learning-based approaches remains an open problem. We extend the
recent MusicVAE model to represent multitrack polyphonic measures as vectors in
a latent space. Our approach enables several useful operations such as
generating plausible measures from scratch, interpolating between measures in a
musically meaningful way, and manipulating specific musical attributes. We also
introduce chord conditioning, which allows all of these operations to be
performed while keeping harmony fixed, and allows chords to be changed while
maintaining musical "style". By generating a sequence of measures over a
predefined chord progression, our model can produce music with convincing
long-term structure. We demonstrate that our latent space model makes it
possible to intuitively control and generate musical sequences with rich
instrumentation (see this https URL for generated audio).