An LSTM-Based Dynamic Chord Progression Generation System for Interactive Music Performance

In this paper, we describe an interactive generative music system, designed to handle polyphonic guitar music. We formulate the problem of chord progression generation as a prediction problem. Thus, we propose utilization of an LSTM-based network architecture incorporating neural attention that is able to learn a mapping between symbolic representations of polyphonic chord progressions and future chord candidates. Furthermore, we have developed a virtual air-guitar controller, utilizing a Kinect device, that uses the above architecture in order to change in real time the guitar chord mapping, depending on the performer's previous performance. The whole system was evaluated both objectively and subjectively. The goal of the objective evaluation was to measure the ability of the system to correctly generate chord candidates for existing chord progressions, as well as identify the type of errors. The subjective evaluation mainly focused on the longer-term behavior of the system, regarding the musical coherence and the variety of the generated progressions. The results were encouraging regarding the ability of our system to generate sound chord progressions, while highlighting a number of issues that require to be resolved.