The Intelligent Signal Processing Laboratory (ISPLab) pursues original, innovative, and frontier research in the theory, design, and implementation of digital filters, fuzzy neural networks, deep neural network and learning, discrete Gabor transforms, and design optimization algorithms to build intelligent signal processing systems for real-time analysis and processing of signals and data.
The ISPLab accepts Ph.D. and M.A.Sc. students under the supervision of Dr. H. K. Kwan. We offer financial support to highly qualified and motivated graduate applicants with a suitable academic background and a strong interest in successfully completing a Ph.D. (or M.A.Sc.) research in our area of specialization. Our Ph.D. and M.A.Sc. students are regular full-time students in the Department of Electrical and Computer Engineering of the Faculty of Engineering and are admitted under the normal University of Windsor's graduate admission procedures.
We train and support (with funding) Postdoctoral Fellows and welcome international and national collaborations with Visiting Scholars of common research interests and prior successful journal publication experience in our area of specialization.
Our research is supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC).
Demo 1: Qing Pan, Teng Gao, Jian Zhou, Huabin Wang, Liang Tao, Hon Keung Kwan, "CycleGAN with dual adversarial loss for bone-conducted speech enhancement," arXiv:2111.01430v1, November, 2021. https://arxiv.org/pdf/2111.01430.pdf
Demo 2: Teng Gao, Qing Pan, Jian Zhou, Huabin Wang, Liang Tao, Hon Keung Kwan, "A novel attention-guided generative adversarial network for whisper-to-normal speech conversion," Regular paper, Cognitive Computation, 16 January 2023. https://doi.org/10.1007/s12559-023-10108-9
Demo 3: Jian Zhou, Yuting Hu, Hailun Lian, Huabin Wang, Liang Tao, Hon Keung Kwan, "Multimodal voice conversion under adverse environment using a deep convolutional neural network," IEEE Access, volume 7, pages 170878 - 170887, 26 November 2019. https://doi.org/10.1109/ACCESS.2019.2955982
Demo 4: Huabin Wang, Rui Cheng, Jian Zhou, Liang Tao, Hon Keung Kwan, "Multistage model for robust face alignment using deep neural networks," Cognitive Computation, Regular paper, volume 13, number 2, pages 1-17, 7 March 2022. https://doi.org/10.1007/s12559-021-09846-5