What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Algorithm
His primary scientific interests are in Speech recognition, Algorithm, Signal processing, Adaptive filter and Blind signal separation.
The Speech recognition study combines topics in areas such as Acoustics, Reverberation, Robustness and Microphone.
Walter Kellermann works mostly in the field of Reverberation, limiting it down to concerns involving Speech enhancement and, occasionally, Benchmark and Background noise.
His Algorithm research is multidisciplinary, relying on both Normalization, Weighting, Coherence, Frequency domain and Noise.
His Adaptive filter research is multidisciplinary, incorporating perspectives in Filter and Nonlinear system.
The study incorporates disciplines such as Underdetermined system, Audio over Ethernet, Independent component analysis, Source separation and Generalization in addition to Blind signal separation.
His most cited work include:
- The reverb challenge: Acommon evaluation framework for dereverberation and recognition of reverberant speech (280 citations)
- A generalization of blind source separation algorithms for convolutive mixtures based on second-order statistics (237 citations)
- A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research (186 citations)
What are the main themes of his work throughout his whole career to date?
Walter Kellermann focuses on Speech recognition, Algorithm, Acoustics, Microphone and Adaptive filter.
His Speech recognition study combines topics from a wide range of disciplines, such as Robustness, Artificial intelligence, Reverberation and Signal processing.
His Algorithm study incorporates themes from Filter, Blind signal separation, Frequency domain, Noise and Nonlinear system.
His studies deal with areas such as Independent component analysis, Narrowband and Source separation as well as Blind signal separation.
The various areas that Walter Kellermann examines in his Noise study include Acoustic source localization and Interference.
Walter Kellermann works mostly in the field of Acoustics, limiting it down to topics relating to Microphone array and, in certain cases, Beamforming.
He most often published in these fields:
- Speech recognition (36.58%)
- Algorithm (31.12%)
- Acoustics (22.33%)
What were the highlights of his more recent work (between 2015-2021)?
- Algorithm (31.12%)
- Speech recognition (36.58%)
- Noise (9.98%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Algorithm, Speech recognition, Noise, Artificial intelligence and Microphone.
Walter Kellermann interconnects Filter, Blind signal separation, Signal processing, Particle filter and Robustness in the investigation of issues within Algorithm.
His Speech recognition study combines topics in areas such as Transfer function, Reduction, Loudspeaker, Humanoid robot and Speech enhancement.
His Artificial intelligence research includes themes of Computer vision and Pattern recognition.
His Microphone study integrates concerns from other disciplines, such as Wireless, Electronic engineering, Background noise and Reverberation.
Walter Kellermann combines subjects such as Adaptive filter and Linear filter with his study of Echo.
Between 2015 and 2021, his most popular works were:
- A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research (186 citations)
- The LOCATA Challenge Data Corpus for Acoustic Source Localization and Tracking (42 citations)
- The LOCATA Challenge: Acoustic Source Localization and Tracking (17 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Algorithm
His primary areas of investigation include Algorithm, Speech recognition, Reverberation, Noise and Acoustic source localization.
Walter Kellermann works in the field of Algorithm, namely Adaptive filter.
His Speech recognition research incorporates themes from Probability distribution, Reduction, Robustness, Artificial intelligence and Speech enhancement.
His research in Noise intersects with topics in Covariance matrix and Direction of arrival.
His Acoustic source localization research incorporates elements of Tracking, Real-time computing, Interference and Signal processing.
His research investigates the link between Signal processing and topics such as Speech processing that cross with problems in Array processing.
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