  Many of the music generation systems based on neural networks are fully
autonomous and do not offer control over the generation process. In this
research, we present a controllable music generation system in terms of tonal
tension. We incorporate two tonal tension measures based on the Spiral Array
Tension theory into a variational autoencoder model. This allows us to control
the direction of the tonal tension throughout the generated piece, as well as
the overall level of tonal tension. Given a seed musical fragment, stemming
from either the user input or from directly sampling from the latent space, the
model can generate variations of this original seed fragment with altered tonal
tension. This altered music still resembles the seed music rhythmically, but
the pitch of the notes are changed to match the desired tonal tension as
conditioned by the user.
