Towards Adaptive Music Generation by Reinforcement Learning of Musical Tension

Although music is often defined as the “language of emotion”, the exact nature of the relationship between musical parameters and the emotional response of the listener remains an open question. Whereas traditional psychological research usually focuses on an analytical approach, involving the rating of static sounds or preexisting musical pieces, we propose a synthetic approach based on a novel adaptive interactive music system controlled by an autonomous reinforcement learning agent. Preliminary results suggest an autonomous mapping from musical parameters (such as tempo, articulation and dynamics) to the perception of tension is possible. This paves the way for interesting applications in music therapy, interactive gaming, and physiologically-based musical instruments.