My current research interests include audio representation learning, open-set machine listening, and leveraging language models for audio understanding. My prior research background is in the areas of spatial audio reproduction and networked music performance.
Research: Selected publications
Sridhar, S., and Cartwright, M. (2024). Compositional Audio Representation Learning. Under review.
May, L., Ohshiro, K., Dang, K., Sridhar, S., Pai, J., Fuentes, M., Lee, S., and Cartwright, M. . Unspoken Sound: Identifying Trends in Non-Speech Audio Captioning on YouTube. In Proceedings of the CHI Conference on Human Factors in Computing Systems, 2024.
Sridhar, S., and Cartwright, M. (2023). Multi-label open-set audio classification. Workshop on the Detection and Classification of Acoustic Scenes and Events, 2023 (DCASE 2023). 21-22 September 2023, Tampere, Finland.
Lostanlen, V., Sridhar, S., McFee, B., Farnsworth, A., & Bello, J. P. (2020). Learning the Helix Topology of Musical Pitch [ISSN: 2379-190X]. ICASSP 2020 - 2020 IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP), 11–15.
Hupke, R., Peissig, J., Genovese, A., Sridhar, S., & Roginska, A. (2020). Impact of Source Panning on a Global Metronome in Rhythmic Networked Music Performance [ISSN: 2305-7254]. 2020 27th Conference of Open Innovations Association (FRUCT), 73–83.
Hupke, R., Sridhar, S., Genovese, A., Nophut, M., Preihs, S., Beyer, T., Roginska, A., & Peissig, J. (2019). A Latency Measurement Method for Networked Music Performances. Audio Engineering Society, 2019.
Kim, S., & Sridhar, S. (2019). Multichannel Audio Implementation for Virtual Reality. Audio Engineering Society, 2019.
