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Effective 1 January 2021 Sharon Gannot, became an IEEE Fellow

Effective 1 January 2021 Sharon Gannot, became an IEEE Fellow, with the accompanying citation: “for contributions to acoustical modelling and statistical learning in speech enhancement”. Dr. Gannot’s research interests include statistical signal processing and machine learning. The methods he develops utilize either single- and multi-microphone (ad hoc) arrays, and are applied to speech enhancement, noise reduction and speaker separation and diarization, dereverberation, speaker localization and tracking.

He applies and develops tools in the following domains:

1. Data-driven methods, e.g. manifold learning and deep learning, variational auto-encoders;
2. Bayesian, e.g. variational-Bayes, Kalman and Wiener filtering, particle filtering, and non-Bayesian, e.g. recursive and distributed expectation-maximization;
3. Distributed algorithms for wireless ad hoc microphone networks;
4. Performance bounds.