Extracting Patterns in Music for Composition via Markov Chains

The goal of this paper is to describe a new approach to algorithmic music composition that uses pattern extraction techniques to find patterns in a set of existing musical sequences, and then to use these patterns to compose music via a Markov chain. The transition probabilities of the Markov chain are learned from the musical sequences from which the patterns were extracted. These transitions determine which of the extracted patterns can follow other patterns. Our pattern matching phase considers three dimensions: time, pitch, and duration. Patterns of notes are considered to be equivalent under shifts in time, the baseline note of the pattern, and multiplicative changes of duration across all notes in the pattern. We give experimental results using classical music as training sequences to show the viability of our method in composing novel musical sequences. Keywords:Intelligent Systems; Data Mining; Machine Learning