Ryan M. Corey is an Assistant Professor of Electrical and Computer Engineering at the University of Illinois Chicago and a Research Scientist at the Discovery Partners Institute, a Chicago-based innovation hub operated by the University of Illinois System and dedicated to equitable economic development. A hearing aid user for most of his life, Professor Corey studies audio and acoustic signal processing to improve the performance of assistive and augmentative hearing technology, especially in noisy environments. His research emphasizes spatial signal processing technologies, including microphone arrays, distributed sensor networks, and wireless audio systems, that can perform better than single-microphone devices in noisy environments. As the first dual-appointed faculty member at DPI, he aspires to build a research program that centers the voices of marginalized groups and engages closely with industry and with the local community, especially the disability community.

Ryan M. Corey and Andrew C. Singer, “Modeling the effects of dynamic range compression on signals in noise,” The Journal of the Acoustical Society of America, vol. 150, no. 1, pp. 159-170, 2021.

Ryan M. Corey, Uriah Jones, and Andrew C. Singer, “Acoustic effects of medical, cloth, and transparent face masks on speech signals,” The Journal of the Acoustical Society of America, vol. 148, no. 4, pp. 2371-5, 2020.

Ryan M. Corey, Evan M. Widloski, David Null, Brian Ricconi, Mark Johnson, Karen White, Jennifer R. Amos, Alex Pagano, Michael Oelze, Rachel Switzky, Matthew B. Wheeler, Eliot Bethke, Clifford Shipley, and Andrew C. Singer, “Low-complexity system and algorithm for an emergency ventilator sensor and alarm,” IEEE Transactions on Biomedical Circuits and Systems, vol. 14, no. 5, pp. 1088-1096, 2020.

Ryan M. Corey and Andrew C. Singer, “Adaptive binaural filtering for a multiple-talker listening system using remote and on-ear microphones,” IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2021.

Ryan M. Corey and Andrew C. Singer, “Motion-tolerant beamforming with deformable microphone arrays,” IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2019.

Ryan M. Corey, Matthew D. Skarha, and Andrew C. Singer, “Cooperative audio source separation and enhancement using distributed microphone arrays and wearable devices,” IEEE International Workshop on Computational Advances in Multisensor Adaptive Processing (CAMSAP), 2019.

Ryan M. Corey, Naoki Tsuda, and Andrew C. Singer, “Acoustic impulse responses for wearable audio devices,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019.

Ryan M. Corey and Andrew C. Singer, “Speech separation using partially asynchronous microphone arrays without resampling,” International Workshop on Acoustic Signal Enhancement (IWAENC), 2018.

• Google Scholar

Ph.D. in Electrical and Computer Engineering, University of Illinois Urbana-Champaign, 2019
M.S. in Electrical and Computer Engineering, University of Illinois Urbana-Champaign, 2014
B.S. in Electrical Engineering (Highest Honors), Princeton University, 2012

• IEEE Signal Processing Society
• Acoustical Society of America
• Audio Engineering Society

Professor Corey’s research is in the areas of audio and acoustic signal processing, sensor array and multichannel signal processing, adaptive signal processing, and machine learning for signal processing. His specific interests include:

• Augmented listening: Developing signal processing systems to alter and enhance how humans perceive the world, for example to immersively “re-mix” real-life sounds for augmented reality and hearing assistive systems
• Hearing assistive technology: Studying systems and algorithms for hearing devices, such as hearing aids and cochlear implants, and situational assistive devices, such as streaming systems and remote microphones
• Intelligent audio networks: Developing “cooperative” architectures to allow multiple sensing devices, such as wearable, installed, and mobile devices, to share data and improve performance at sensing and inference tasks
• Audio research tools: With collaborators from his DPI Science Team, developing an automated acoustic research system that can generate large, dynamic, and realistic data sets for signal processing and acoustics research