Identifying Critical Decision Points in Musical Compositions using Machine Learning

In musical compositions, identifying critical points that reveal atypical and unexpected decisions is valuable from a compositional perspective as these points arguably contribute to the enjoyment of listening to music and are useful for applications such as automatic music generation and music understanding. In this study, we suggest a machine learning-based approach for identifying critical decision points, where we utilise two long short-term memory (LSTM) models that originally function as generative networks and are repurposed in our case to identify critical decision points. These models are trained on musical corpora from the classical period and the 20th century providing different angles to the analysis. We demonstrate this approach using two short musical examples and an excerpt from Chopin's Nocturne in E flat major (Op. 9 No. 2). We compare our suggested machine-learning-based approach to two time series analysis methods as the baselines, evaluate the results, and suggest some future directions for this approach.