What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Algorithm
His main research concerns Speech recognition, Speech enhancement, Speech processing, Intelligibility and Algorithm. His Speech recognition study incorporates themes from Time–frequency analysis, Noise reduction, Artificial intelligence and Noise measurement, Noise. His Noise reduction research includes elements of Signal and Microphone.
As a part of the same scientific study, he usually deals with the Speech enhancement, concentrating on Minimum mean square error and frequently concerns with Simple function, White noise and Discrete Fourier transform. His Intelligibility research is multidisciplinary, incorporating perspectives in Weighting and Monaural. As part of one scientific family, Jesper Jensen deals mainly with the area of Algorithm, narrowing it down to issues related to the Estimator, and often Reverberation.
His most cited work include:
- An Algorithm for Intelligibility Prediction of Time–Frequency Weighted Noisy Speech (1017 citations)
- Permutation invariant training of deep models for speaker-independent multi-talker speech separation (336 citations)
- Multitalker Speech Separation With Utterance-Level Permutation Invariant Training of Deep Recurrent Neural Networks (334 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Speech recognition, Algorithm, Speech enhancement, Intelligibility and Estimator. His work deals with themes such as Noise measurement, Noise and Noise reduction, Artificial intelligence, which intersect with Speech recognition. In Algorithm, Jesper Jensen works on issues like Fundamental frequency, which are connected to Additive white Gaussian noise.
His Speech enhancement research is multidisciplinary, incorporating elements of Filter, Linear filter, Signal-to-noise ratio, Minimum mean square error and Distortion. His Intelligibility research is multidisciplinary, relying on both Weighting, Time–frequency analysis, Reverberation and Monaural. The Estimator study combines topics in areas such as Mean squared error, Wiener filter and Direction of arrival.
He most often published in these fields:
- Speech recognition (52.33%)
- Algorithm (29.86%)
- Speech enhancement (23.84%)
What were the highlights of his more recent work (between 2018-2021)?
- Speech recognition (52.33%)
- Algorithm (29.86%)
- Artificial intelligence (13.70%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Speech recognition, Algorithm, Artificial intelligence, Intelligibility and Speech enhancement. His work on Keyword spotting and Speech processing as part of general Speech recognition research is frequently linked to Context, bridging the gap between disciplines. His research in Algorithm intersects with topics in Reverberation, Bayesian inference, Noise, Noise reduction and Robustness.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Computer vision and Pattern recognition. His Intelligibility research includes elements of Mutual information, Microphone and Monaural. He interconnects Lombard effect, Noise measurement, Noise and Signal processing in the investigation of issues within Speech enhancement.
Between 2018 and 2021, his most popular works were:
- On Loss Functions for Supervised Monaural Time-Domain Speech Enhancement (35 citations)
- On Training Targets and Objective Functions for Deep-learning-based Audio-visual Speech Enhancement (13 citations)
- An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation (10 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Algorithm
His scientific interests lie mostly in Speech recognition, Artificial intelligence, Speech enhancement, Intelligibility and Deep learning. His Speech recognition study combines topics in areas such as Microphone array and Hearing aid. His Artificial intelligence study incorporates themes from Ranging and Pattern recognition.
His Speech enhancement research is multidisciplinary, incorporating elements of Lombard effect, Noise measurement and Reverberation. The study incorporates disciplines such as Transfer function, Time–frequency analysis, Noise and Algorithm, Source separation in addition to Noise measurement. His research in Intelligibility intersects with topics in Estimator, Speech quality and Spectral amplitude.
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