What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Algorithm
His primary areas of investigation include Speech enhancement, Speech recognition, Algorithm, Noise and Speech processing.
His research in Speech enhancement tackles topics such as Estimator which are related to areas like Signal-to-noise ratio and Estimation theory.
The various areas that Israel Cohen examines in his Speech recognition study include Reverberation, Microphone, Noise, Adaptive filter and Noise reduction.
His Algorithm research includes elements of Transfer function, Lateral resolution, Statistics, Mathematical optimization and System identification.
Israel Cohen focuses mostly in the field of Noise, narrowing it down to matters related to Gaussian noise and, in some cases, Noise measurement.
His work carried out in the field of Speech processing brings together such families of science as Smoothing, Detection theory, Background noise and White noise.
His most cited work include:
- Noise spectrum estimation in adverse environments: improved minima controlled recursive averaging (833 citations)
- Pearson Correlation Coefficient (663 citations)
- Noise estimation by minima controlled recursive averaging for robust speech enhancement (548 citations)
What are the main themes of his work throughout his whole career to date?
Speech recognition, Algorithm, Speech enhancement, Beamforming and Microphone are his primary areas of study.
His research in Speech recognition focuses on subjects like Noise measurement, which are connected to Gaussian noise.
His Algorithm research incorporates elements of Fourier transform, Mathematical optimization, Robustness and Signal processing.
His study looks at the intersection of Speech enhancement and topics like Statistical model with Pattern recognition.
His Beamforming study incorporates themes from Microphone array, Narrowband and White noise.
His research investigates the connection between Microphone and topics such as Acoustics that intersect with problems in Adaptive beamformer.
He most often published in these fields:
- Speech recognition (34.95%)
- Algorithm (33.33%)
- Speech enhancement (23.62%)
What were the highlights of his more recent work (between 2018-2021)?
- Beamforming (19.42%)
- Algorithm (33.33%)
- Microphone (19.09%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Beamforming, Algorithm, Microphone, Kronecker product and White noise.
His studies in Algorithm integrate themes in fields like Filter, Intelligibility, Signal, Noise reduction and Robustness.
His Noise reduction research is multidisciplinary, incorporating elements of Speech enhancement, Signal enhancement and Linear filter.
He has researched Microphone in several fields, including Acoustics, Noise, Differential, Structure and Flexibility.
His Noise study combines topics from a wide range of disciplines, such as Reverberation and Joint.
His studies deal with areas such as Parameter identification problem and Gaussian noise as well as Signal-to-noise ratio.
Between 2018 and 2021, his most popular works were:
- An End-to-End Multimodal Voice Activity Detection Using WaveNet Encoder and Residual Networks (16 citations)
- A Simple Theory and New Method of Differential Beamforming With Uniform Linear Microphone Arrays (13 citations)
- Differential Kronecker Product Beamforming (10 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Algorithm
His primary scientific interests are in Algorithm, Beamforming, Microphone, Kronecker product and White noise.
His Algorithm research includes elements of Underwater acoustics and Frequency domain.
His biological study deals with issues like Microphone array, which deal with fields such as Speech processing, Sound pressure and Array aperture.
His study in Speech processing is interdisciplinary in nature, drawing from both Signal, Complex conjugate, Noise reduction and Linear filter.
The concepts of his Microphone study are interwoven with issues in Differential, Noise and Flexibility.
His White noise research is multidisciplinary, incorporating perspectives in Acoustics, Broadband, Differential and Joint.
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