What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Speech recognition
- Artificial neural network
DeLiang Wang spends much of his time researching Speech recognition, Artificial intelligence, Pattern recognition, Artificial neural network and Speech processing.
His Speech recognition research incorporates elements of Speech enhancement and Noise.
His Artificial intelligence research focuses on Computer vision and how it relates to Theory of computation, Information processing and Computation.
His work deals with themes such as Feature and Robustness, which intersect with Pattern recognition.
His study in the field of Hebbian theory also crosses realms of Concatenation.
His Speech processing research is multidisciplinary, relying on both Reverberation, Spectrogram, Speech coding, Binary classification and Supervised learning.
His most cited work include:
- Computational Auditory Scene Analysis: Principles, Algorithms, and Applications (766 citations)
- On training targets for supervised speech separation (613 citations)
- Supervised Speech Separation Based on Deep Learning: An Overview (493 citations)
What are the main themes of his work throughout his whole career to date?
DeLiang Wang mainly focuses on Speech recognition, Artificial intelligence, Pattern recognition, Speech processing and Artificial neural network.
His Speech recognition research is multidisciplinary, incorporating perspectives in Speech enhancement and Reverberation.
The concepts of his Artificial intelligence study are interwoven with issues in Noise and Computer vision.
DeLiang Wang combines subjects such as Supervised learning, Feature and Robustness with his study of Pattern recognition.
His studies examine the connections between Speech processing and genetics, as well as such issues in Hidden Markov model, with regards to Pitch detection algorithm.
His Artificial neural network research includes elements of Synchronization and Topology.
He most often published in these fields:
- Speech recognition (65.90%)
- Artificial intelligence (46.54%)
- Pattern recognition (26.73%)
What were the highlights of his more recent work (between 2017-2021)?
- Speech recognition (65.90%)
- Artificial intelligence (46.54%)
- Deep learning (9.68%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Speech recognition, Artificial intelligence, Deep learning, Speech enhancement and Intelligibility.
Specifically, his work in Speech recognition is concerned with the study of Monaural.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Acoustics, Masking and Pattern recognition.
DeLiang Wang has included themes like Cluster analysis, Noise reduction, Computational auditory scene analysis and Background noise in his Deep learning study.
His Speech enhancement research incorporates themes from Noise measurement, Spectrogram and Feature extraction.
His research investigates the link between Intelligibility and topics such as Algorithm that cross with problems in Hearing impaired.
Between 2017 and 2021, his most popular works were:
- Supervised Speech Separation Based on Deep Learning: An Overview (493 citations)
- A Convolutional Recurrent Neural Network for Real-Time Speech Enhancement. (117 citations)
- A New Framework for CNN-Based Speech Enhancement in the Time Domain (71 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Speech recognition
Speech recognition, Speech enhancement, Artificial intelligence, Deep learning and Convolutional neural network are his primary areas of study.
His Speech recognition study is mostly concerned with Monaural, Intelligibility and Speech processing.
His Speech enhancement study incorporates themes from Time domain, Supervised learning, Noise measurement and Recurrent neural network.
DeLiang Wang has researched Artificial intelligence in several fields, including Reverberation and Computer vision.
His research in Deep learning intersects with topics in Background noise, Echo, Microphone, Artificial neural network and Cluster analysis.
His studies in Microphone integrate themes in fields like Masking and Pattern recognition.
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