Feature Extraction and Machine Learning on Symbolic Music using the music21 Toolkit

Machine learning and artificial intelligence have great potential to help researchers understand and classify musical scores and other symbolic musical data, but the difficulty of preparing and extracting characteristics (features) from symbolic scores has hindered musicologists (and others who examine scores closely) from using these techniques. This paper describes the “feature” capabilities of music21, a general-purpose, open source toolkit for analyzing, searching, and transforming symbolic music data. The features module of music21 integrates standard featureextraction tools provided by other toolkits, includes new tools, and also allows researchers to write new and powerful extraction methods quickly. These developments take advantage of the system’s built-in capacities to parse diverse data formats and to manipulate complex scores (e.g., by reducing them to a series of chords, determining key or metrical strength automatically, or integrating audio data). This paper’s demonstrations combine music21 with the data mining toolkits Orange and Weka to distinguish works by Monteverdi from works by Bach and German folk music from Chinese folk music.