Classical Music Composition Using State Space Models

TL;DR

This paper investigates the use of state space models, especially hidden Markov models, for algorithmic classical music composition, highlighting their strengths in harmony and limitations in melodic development.

Contribution

It demonstrates the application of HMMs to compose Romantic-era piano music and analyzes their effectiveness and shortcomings in mimicking human composition.

Findings

Models generate consonant harmonies effectively.

Limited melodic progression in generated pieces.

Models perform well with simple harmonic structures.

Abstract

Algorithmic composition of music has a long history and with the development of powerful deep learning methods, there has recently been increased interest in exploring algorithms and models to create art. We explore the utility of state space models, in particular hidden Markov models (HMMs) and variants, in composing classical piano pieces from the Romantic era and consider the models' ability to generate new pieces that sound like they were composed by a human. We find that the models we explored are fairly successful at generating new pieces that have largely consonant harmonies, especially when trained on original pieces with simple harmonic structure. However, we conclude that the major limitation in using these models to generate music that sounds like it was composed by a human is the lack of melodic progression in the composed pieces. We also examine the performance of the models in the context of music theory.