Michael is a Research Assistant in the Sound and Music Computing Lab, holding a M.Sc in Psychology with specialization in computational analysis of music preference. Their primary research interests include applying machine learning, motion-capture and signal processing for music cognition research. Michael is also interested in creating open-access databases which facilitate new research and discovery for global digital music behaviours. Their current research initiatives in the Sound and Music Computing Lab include music-based therapies for people with Parkinson’s disease, and developing vocal features extraction algorithms for language-learning and music preference founded on cognitive research.
- Barone, M.D., Bansal, J., & Woolhouse, M.H. (2017). Acoustic Features Influence Musical Choices Across Multiple Genres. Frontiers in Psychology, 8, 931. http://doi.org/10.3389/fpsyg.2017.00931
- Barone, M.D., Dacosta, K., Vigliensoni, G., & Woolhouse, M.H. GRAIL: Database Linking Music Metadata Across Artist, Release, and Track. Proceedings of the 4th International Workshop on Digital Libraries for Musicology, Suzhou, October 2017.