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

A variational autoencoder for music generation controlled by tonal tension