In this project, I extended the automatic accompaniment system proposed in [2] by applying Decision Tree to chord tone determination and by conducting two experiments for verifying the effectiveness of the system. The system aims to assist music lovers in writing a complete song with sophisticated chord progressions. The system takes melody as input, and harmonizes it with style-specific accompaniments. The system is hybrid: statistical learning such as Support Vector Machine (SVM) maintains the style by learning from only a few pieces from users' favoriate bands/song writers, while music knowledge such as Neo-Riemannian Transform ensures the generated chords are resolved musically correctly.

In [2], the learned model by SVM is difficult for human to analyze. In this project I used Decision Tree such that the resulting model is easier to understand and is musically meaningful for analysis. In addition, two experiments are conducted for this project. In the first experiment, the system harmonizes the melody of the song "across the universe" from Beatles using three different rock bands' styles: Radiohead, U2, and Green Day. The second experiment is a turing test: the system generates a new accompaniment for a melody of Radiohead's song based on the other songs of the band.