2020/10/13 by Rui Guo, Ivor Simpson, Guo, Rui +7 · 1 citation
Computer Science · Engineering · Neuroscience · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Music Technology and Sound Studies #Music and Audio Processing #Neuroscience and Music Perception #Sound (cs.SD) #Symbolic Computation (cs.SC) #cs.SC #cs.SD #eess.AS #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2010.06230
2020 Joint Conference on AI Music Creativity
openalex publication_date 2020/10/13 · arxiv created 2020/10/14 · arxiv updated 2020/10/15 · openalex created_date 2020/10/22 · openalex updated_date 2026/07/28
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.