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Intelligent Signal Processing Laboratory

About ISPLab

The Intelligent Signal Processing Laboratory (ISPLab) pursues original, innovative, and frontier research in the design/theory, realization, and applications of digital filters, deep and fuzzy neural networks, discrete Gabor transform, and optimization algorithms for the analysis, and modeling and prediction of signals (and smart data) for building real-time intelligent signal processing systems.

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 to successfully complete 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 welcome international and national collaborations with other researchers (including visiting scholars and postdoctoral fellows) of common research interests.

Our research is supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC). 

Demo 1: Teng Gao, Qing Pan, Jian Zhou, Huabin Wang, Liang Tao, and Hon Keung Kwan, "Attention-Guided Generative Adversarial Network for Whisper to Normal Speech Conversion," 2021.


Demo 2: Qing Pan, Teng Gao, Jian Zhou, Huabin Wang, Liang Tao, and Hon Keung Kwan, "Parallel CycleGAN with Dual Adversarial Loss for Bone-Conducted Speech Enhancement," 2021.


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, DOI: 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, March 2021. Preprint arXiv:2002.01075, 2020.