A machine-learning approach to autonomous music composition

Abstract

Several music composition systems exist that generate blues chord progressions, jazz improvisation, or classical pieces. Such systems often work by applying a set of rules explicitly provided to the system to determine what sequence of output values is appropriate. Others use pattern recognition and generation techniques such as Markov models. These systems often suffer from mediocre performance and limited generality. We propose a system that goes from raw musical data to feature vector representation to classification models. We employ sliding window sequential machine learning techniques to generate classifiers that correspond to a training set of musical data. Our approach has the advantage of greater generality than explicitly specified musical grammar rules and the potential to apply a wide variety of powerful existing non-sequential learning algorithms. We present the design and implementation of the composition system. We demonstrate the efficacy of the method, show and analyze successful samples of its output, and discuss ways in which it might be improved.