Using Deep Learning and Low-Frequency Fourier Analysis to Predict Parameters of Coupled Non-Linear Oscillators for the Generation of Complex Rhythms

This paper describes a method for generating rhythmic structures and low frequency amplitude envelopes using non-linear coupled oscillators and a machine learning model. The method is based on the Kuramoto model, a mathematical model used to describe the collective behavior of a system of oscillators, and a multi-layer perceptron neural network. The goal of this approach is not to exactly reproduce input rhythms, but rather to develop a novel form of interaction with chaotic processes for experimental musical practice. The system consists of three components: the generative component producing rhythms, a method for the analysis of rhythmic structures, and a machine learning model that learns the relationships between the parameters of the rhythm generation and the analysis. The Kuramoto model was chosen due to its potential to mediate between periodicity and chaos, creating aesthetically rich and fruitful material for the author's musical practice. The other components serve to explore this model in new ways and to couple it to external rhythmic musical signals.