Ni-Hahn S., Xu W., Yin J., Zhu R., Mak S., Jiang Y., Rudin C. (2024). A New Dataset, Notation Software, and Representation for Computational Schenkerian Analysis. Conference of the International Society for Music Information Retrieval (ISMIR). [paper | software | public data]
Hahn, S., Yin, J., Zhu, R., Xu, W., Jiang, Y., Mak, S., and Rudin, C. (2024). SentHYMNent: An Interpretable and Sentiment-Driven Model for Algorithmic Melody Harmonization. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). [paper]
Hahn S., Zhu R., Mak S., Rudin C., and Jiang Y. (2023). An Interpretable, Flexible, and Interactive Probabilistic Framework for Melody Generation. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). [paper]
Hahn S., Zhu R.&, Yin J.&, Jiang Y., Mak S., Rudin C. (2023). New Orleans: An Adventure In Music. Conference on Neural Information Processing Systems (NeurIPS) Creative Track Video Presentation. https://neurips.cc/virtual/2023/event/81844.
Jin, L., Liu, B., Zhao, F., Hahn, S., Dong, B., Song, R., Elston, T.C., Xu, Y. & Hahn, K. M. (2020). Deep learning enables structured illumination microscopy with low light levels and enhanced speed. Nature Communications, 11(1), 1-7. [paper]
Hahn, S. E. (2019). Continuous Harmonic Structure in JS Bach’s Triple Fugues in “The Well-Tempered Clavier” and “Art of Fugue” (Master thesis, University of North Texas). (https://digital.library.unt.edu/ark:/67531/metadc1538652)