During his lifetime, the Baroque composer J.S. Bach harmonized more than 300 chorale melodies, which collectively have become a pivotal body of music in Western music history and exemplify the composer's groundbreaking compositional techniques. Bach's harmonizations rest upon a series of compositional conventions that musicians today continue to emulate when learning to write in four-part counterpoint. By transforming the chorales into a statistical dataset, this thesis explores the ability of machine learning models to harmonically analyze chorale melodies and generate new harmonizations that exhibit the same compositional conventions Bach pioneered centuries ago.

• bach_code contains all code related to the thesis
• drafts contains the chapters and final thesis
• examples contains examples of a major and minor chorale harmonization, where the predicted harmonization (generated by a Random Forest model) is played side-by-side with Bach's harmonization

More examples will be available here soon.