Music Style Analysis among Haydn, Mozart and Beethoven: an Unsupervised Machine Learning Approach

Different musicians have quite different styles, which has influenced by their different historical backgrounds, personalities, and experiences. In this paper, we propose an approach to extract melody based features from sheet music, as well as an unsupervised clustering method for discovering music styles. Since that existing corpus is not sufficient for this research in terms of completeness or data format, a new corpus of Haydn, Mozart and Beethoven in MusicXML format is created for research. By applying this approach, similar and different styles are discovered. The analysis results conform to the Implication-Realization model, one of the most significant modern theories of melodic expectation, which confirms the validity of our approach.