Sharon Gannot received the BSc degree (summa cum laude) from the Technion-Israel Institute of Technology, Haifa, Israel, in 1986, and the MSc (cum laude) and PhD degrees from Tel-Aviv University, Tel Aviv, Israel, in 1995 and 2000, respectively, all in electrical engineering. In 2001, he held a postdoctoral position with the Department of Electrical Engineering, KU Leuven, Leuven, Belgium. From 2002 to 2003, he held a Research and Teaching position with the Faculty of Electrical Engineering, Technion–Israel Institute of Technology.
He is currently a Full Professor with the Faculty of Engineering, Bar-Ilan University, Ramat-Gan, Israel, where he is heading Data Science Program. He also serves as the Faculty Vice Dean.
Dr. Gannot's reserach lab, the Acoustic Signal Processing Laboratory, inaugurated in 2009, is characterized by a controllable reverberation time and equipped with state-of-the-art multichannel transmission, acquisition and measurement capabilities. A database of impulse responses recorded in the lab is available.
In 2018-2019, Dr. Gannot was a part-time Professor at the Technical Faculty of IT and Design, Aalborg University, Denmark.
Dr. Gannot was an Associate Editor for the EURASIP Journal of Advances in Signal Processing during 2003–2012, and an Editor for several special issues on multi-microphone speech processing of the same journal. He was a Guest Editor for several special issues for Elsevier Speech Communication and Signal Processing journals. He was also the Lead Guest Editor of a special issue for the IEEE Journal of Selected Topics in Signal processing, 2019, Special Issue on Acoustic Source Localization and Tracking in Dynamic Real-life Scenes. Dr. Gannot was an Associate Editor for the IEEE Transactions on Audio, Speech, and Language Processing in 2009–2013, and the Area Chair for the same journal in 2013–2017. He is currently a Moderator for arXiv in the field of audio and speech processing. He is also a reviewer for many IEEE journals and conferences.
Since January 2010, Dr. Gannot has been a Member of the Audio and Acoustic Signal Processing (AASP) technical committee of the IEEE Signal Processing Society (SPS) and served as the Committee Chair, 2017-2018. Currently, he is a member of the Data Science Initiative of IEEE SPS. Dr. Gannot is also a member of the IEEE SPS, Conferences Board since 2019 and a member of the Executive Subcommittee of this board since 2020. From 2020 he is also a member of the IEEE SPS Education Board. He is a member of the Special Area Team on Acoustic, Speech and Music Signal Processing of the European Association on Signal Processing (EURASIP), 2016-2021 and a member of the European Acoustics Association (EAA), Audio Signal Processing Technical Committee, since 2018. From 2005, Dr. Gannot has been a member of the technical and steering committee of the International Workshop on Acoustic Signal Enhancement (IWAENC) and a member of the Steering Committee, the Latent Variable Analysis and Signal Separation (LVA/ICA), since Oct. 2019.
Dr. Gannot was the General Co-Chair of IWAENC 2010 held in Tel-Aviv, Israel in August 2010. He was the General Co-Chair of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) in October 2013. He also served as a Technical Co-Chair, the international conference on Latent Variable Analysis and Signal Separation, LVA/ICA, University of Surrey, UK, July 2018.
Dr. Gannot was selected (with colleagues) to present tutorial sessions at IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2012, EUSIPCO 2012, ICASSP 2013, European Signal Processing Conference (EUSIPCO) 2013 and EUSIPCO 2019, and was a keynote speaker for IWAENC, Aachen, Germany, 2012; International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA) Grenoble, France, 2017, Informationstechnische Gesellschaft im VDE (ITG) Conference on Speech Communication, Oldenburg, Germany 2018 and Audio Analysis Workshop, Aalborg, Denmark 2018. He is the recipient of Bar-Ilan University Outstanding Lecturer Award in 2010 and 2014 and the Rector Innovation in Research Award in 2018. He is also a co-recipient of eleven best paper awards.
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:
- Data-driven methods, e.g. manifold learning and deep learning, variational auto-encoders;
- Bayesian, e.g. variational-Bayes, Kalman and Wiener filtering, particle filtering, and non-Bayesian, e.g. recursive and distributed expectation-maximization;
- Distributed algorithms for wireless ad hoc microphone networks;
- Performance bounds.