Predictive Controlled Music

TL;DR

This paper introduces predictive controlled music (PCM), a novel algorithmic composition method combining model predictive control with neural networks to generate music through optimization and feedback mechanisms.

Contribution

It presents a new approach integrating MPC with neural network-based evaluation and constraints for music generation, advancing algorithmic composition techniques.

Findings

PCM effectively predicts and optimizes musical notes.

Neural networks improve evaluation and constraint modeling.

Numerical examples demonstrate the method's viability.

Abstract

This paper presents a new approach to algorithmic composition, called predictive controlled music (PCM), which combines model predictive control (MPC) with music generation. PCM uses dynamic models to predict and optimize the music generation process, where musical notes are computed in a manner similar to an MPC problem by optimizing a performance measure. A feedforward neural network-based assessment function is used to evaluate the generated musical score, which serves as the objective function of the PCM optimization problem. Furthermore, a recurrent neural network model is employed to capture the relationships among the variables in the musical notes, and this model is then used to define the constraints in the PCM. Similar to MPC, the proposed PCM computes musical notes in a receding-horizon manner, leading to feedback controlled prediction. Numerical examples are presented to illustrate the PCM generation method.