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个人简介
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.
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.
研究兴趣
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Yarden Menashri Sinai, Yaopeng X J Ma, Michal Abba Daleski,Sharon Gannot,Ronny P Bartsch,Ilanit Gordon
Frontiers in human neuroscience (2024): 1363891-1363891
CoRR (2024)
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IEEE/ACM International Conference on Human-Robot Interactionpp.865-869, (2024)
Authorea (Authorea) (2023)
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2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)pp.1-5, (2023)
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2023 31st European Signal Processing Conference (EUSIPCO)pp.416-420, (2023)
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ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)pp.1-5, (2023)
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