Computational analysis and modeling of expressive timing in Chopin's Mazurkas
Zhengshan Shi · Zenodo (CERN European Organization for Nuclear Research) · 2021
Performers' distortion of notated rhythms in a musical score is a significant factor in the production of convincingly expressive music interpretations. Sometimes exaggerated, and sometimes subtle, these distortions are driven by a variety of factors, including schematic features (both structural such as phrase boundaries and surface events such as recurrent rhythmic patterns), as well as relatively rare veridical events that characterize the individuality and uniqueness of a particular piece. Performers tend to adopt similar pervasive approaches to interpreting schemas, resulting in common performance practices, while often formulating less common approaches to the interpretation of veridical events. Furthermore, some performers choose anomalous interpretations of schemas. We present a machine learning model of expressive performance of Chopin Mazurkas and a critical analysis of the output based upon statistical analyses of the musical scores and of recorded performances. We compare the timings of recorded human performances of selected Mazurkas by Frédéric Chopin with performances of the same works generated by a neural network trained with recorded human performances of the entire corpus. This paper demonstrates that while machine learning succeeds, to some degree, in expressive interpretation of schemata, convincingly capturing performance characteristics remains very much a work in progress.