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The 4th Clarity Workshop on Machine Learning Challenges for Hearing Aids (Clarity-2023)
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Clarity-2023 will be a one-day workshop with a single track and a balance of oral and poster sessions.

There will be a session devoted to the outcomes of the 2nd Clarity Prediction Challenge.

Further details of keynote talkers and panel discussion sessions are to be announced.

Keynote talks

DeLiang Wang

DeLiang Wang
Ohio State University, US

Neural Spectrospatial Filtering

Neural Spectrospatial Filtering


As the most widely-used spatial filtering approach for multi-channel signal separation, beamforming extracts the target signal arriving from a specific direction. We present an emerging approach based on multi-channel complex spectral mapping, which trains a deep neural network (DNN) to directly estimate the real and imaginary spectrograms of the target signal from those of the multi-channel noisy mixture. In this all-neural approach, the trained DNN itself becomes a nonlinear, time-varying spectrospatial filter. How does this conceptually simple approach perform relative to commonly-used beamforming techniques on different array configurations and in different acoustic environments? We examine this issue systematically on speech dereverberation, speech enhancement, and speaker separation tasks. Comprehensive evaluations show that multi-channel complex spectral mapping achieves very competitive speech separation results compared to beamforming for different array geometries, and reduces to monaural complex spectral mapping in single-channel conditions, demonstrating the versatility of this new approach for multi-channel and single-channel speech separation. In addition, such an approach is computationally more efficient than popular mask-based beamforming. We conclude that this neural spectrospatial filter provides a broader approach than traditional and DNN-based beamforming.


DeLiang Wang received the B.S. degree and the M.S. degree from Peking (Beijing) University and the Ph.D. degree in 1991 from the University of Southern California all in computer science. Since 1991,he has been with the Department of Computer Science & Engineering and the Center for Cognitive and Brain Sciences at The Ohio State University, where he is a Professor and University Distinguished Scholar. He received the U.S. Office of Naval Research Young Investigator Award in 1996, the 2008 Helmholtz Award and 2020 Ada Lovelace Service Award from the International Neural Network Society (INNS), the 2007 Outstanding Paper Award of the IEEE ComputationalIntelligence Society and the 2019 Best Paper Award of the IEEE Signal Processing Society. He is an IEEE Fellow and ISCA Fellow. He currently serves as Co-Editor-in-Chief of Neural Networks, and a member of the INNS Board of Governors.