What is she best known for?
The fields of study she is best known for:
- Statistics
- Artificial intelligence
- Algorithm
Sharon Gannot mostly deals with Speech recognition, Speech enhancement, Noise, Speech processing and Algorithm.
The concepts of her Speech recognition study are interwoven with issues in Transfer function, Noise reduction, Interference and Reverberation.
Her Speech enhancement research is multidisciplinary, incorporating elements of Beamforming, Adaptive beamformer and Microphone array, Microphone.
Sharon Gannot has researched Noise in several fields, including Estimation theory and Signal.
Her research investigates the connection between Speech processing and topics such as Background noise that intersect with problems in Linear predictive coding, Speech coding and Voice activity detection.
Her Algorithm study incorporates themes from Colors of noise, Extended Kalman filter, Artificial intelligence and Pattern recognition.
Her most cited work include:
- Signal enhancement using beamforming and nonstationarity with applications to speech (560 citations)
- Iterative and sequential Kalman filter-based speech enhancement algorithms (224 citations)
- A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation (221 citations)
What are the main themes of her work throughout her whole career to date?
Her primary areas of study are Speech recognition, Algorithm, Speech enhancement, Microphone and Noise.
Her Speech recognition study combines topics from a wide range of disciplines, such as Transfer function, Noise reduction, Reverberation and Beamforming.
Sharon Gannot has included themes like Direction of arrival and Expectation–maximization algorithm in her Algorithm study.
The study incorporates disciplines such as Adaptive beamformer, Noise power, Spectral density and Minimum-variance unbiased estimator in addition to Speech enhancement.
Her research integrates issues of Intelligibility, Signal-to-noise ratio, Pattern recognition and Wireless in her study of Microphone.
Her Speech processing research incorporates elements of Background noise and Signal processing.
She most often published in these fields:
- Speech recognition (44.70%)
- Algorithm (35.76%)
- Speech enhancement (31.46%)
What were the highlights of her more recent work (between 2019-2021)?
- Algorithm (35.76%)
- Microphone (28.15%)
- Noise (25.83%)
In recent papers she was focusing on the following fields of study:
The scientist’s investigation covers issues in Algorithm, Microphone, Noise, Direction of arrival and Pattern recognition.
Her Algorithm study integrates concerns from other disciplines, such as Probabilistic logic, Bayesian hierarchical modeling, Minimum-variance unbiased estimator and Expectation–maximization algorithm.
Her Microphone research includes elements of Speech recognition, Joint, Cluster analysis and Signal processing.
Speech recognition is frequently linked to Speech enhancement in her study.
Her work on Noise measurement as part of general Noise research is often related to Acoustic source localization, thus linking different fields of science.
Her research in Pattern recognition intersects with topics in Supervised learning, Feature, Artificial intelligence and Transfer function.
Between 2019 and 2021, her most popular works were:
- Unsupervised Multiple Source Localization Using Relative Harmonic Coefficients (6 citations)
- Global and Local Simplex Representations for Multichannel Source Separation (5 citations)
- Semi-Supervised Source Localization with Deep Generative Modeling (3 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Algorithm
Her primary areas of investigation include Algorithm, Pattern recognition, Artificial intelligence, Reverberation and Direction of arrival.
The various areas that Sharon Gannot examines in her Algorithm study include Calibration, Microphone and Expectation–maximization algorithm.
Her study in Pattern recognition is interdisciplinary in nature, drawing from both Supervised learning, Source localization and Transfer function.
In the subject of general Artificial intelligence, her work in Autoencoder, Cluster analysis and Image is often linked to Network architecture and Set, thereby combining diverse domains of study.
Many of her research projects under Reverberation are closely connected to Impulse with Impulse, tying the diverse disciplines of science together.
Her work deals with themes such as Signal, Noise, Noise power, Rank and Noise reduction, which intersect with Estimator.
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