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Robert Rehr
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Books and Theses
- 2018
- [b1]Robert Rehr:
Robust Speech Enhancement Using Statistical Signal Processing and Machine-Learning (Robuste Sprachverbesserung mittels statistischer Signalverarbeitung und maschinellem Lernen). University of Hamburg, Germany, 2018
Journal Articles
- 2021
- [j6]Robert Rehr
, Timo Gerkmann
SNR-Based Features and Diverse Training Data for Robust DNN-Based Speech Enhancement. IEEE ACM Trans. Audio Speech Lang. Process. 29: 1937-1949 (2021) - 2020
- [j5]Lux Li, Robert Rehr, Patrick Bruns
A Survey on Probabilistic Models in Human Perception and Machines. Frontiers Robotics AI 7: 85 (2020) - 2018
- [j4]Robert Rehr, Timo Gerkmann
On the Importance of Super-Gaussian Speech Priors for Machine-Learning Based Speech Enhancement. IEEE ACM Trans. Audio Speech Lang. Process. 26(2): 357-366 (2018) - 2017
- [j3]Robert Rehr, Timo Gerkmann
An Analysis of Adaptive Recursive Smoothing with Applications to Noise PSD Estimation. IEEE ACM Trans. Audio Speech Lang. Process. 25(2): 397-408 (2017) - 2015
- [j2]Benjamin Cauchi, Ina Kodrasi
Combination of MVDR beamforming and single-channel spectral processing for enhancing noisy and reverberant speech. EURASIP J. Adv. Signal Process. 2015: 61 (2015) - [j1]Feifei Xiong, Bernd T. Meyer, Niko Moritz, Robert Rehr, Jörn Anemüller, Timo Gerkmann
Front-end technologies for robust ASR in reverberant environments - spectral enhancement-based dereverberation and auditory modulation filterbank features. EURASIP J. Adv. Signal Process. 2015: 70 (2015)
Conference and Workshop Papers
- 2019
- [c10]Robert Rehr, Timo Gerkmann
An Analysis of Noise-aware Features in Combination with the Size and Diversity of Training Data for DNN-based Speech Enhancement. ICASSP 2019: 601-605 - [c9]Kristina Tesch
On Nonlinear Spatial Filtering in Multichannel Speech Enhancement. INTERSPEECH 2019: 91-95 - 2018
- [c8]Robert Rehr, Timo Gerkmann:
Robust DNN-Based Speech Enhancement with Limited Training Data. ITG Symposium on Speech Communication 2018: 1-5 - 2017
- [c7]Robert Rehr, Timo Gerkmann
MixMax Approximation as a Super-Gaussian Log-Spectral Amplitude Estimator for Speech Enhancement. INTERSPEECH 2017: 1983-1987 - 2016
- [c6]Robert Rehr, Timo Gerkmann:
A Combination of Pre-Trained Approaches and Generic Methods for an Improved Speech Enhancement. ITG Symposium on Speech Communication 2016: 1-5 - [c5]Robert Rehr, Timo Gerkmann
BIAS correction methods for adaptive recursive smoothing with applications in noise PSD estimation. ICASSP 2016: 206-210 - 2015
- [c4]Robert Rehr, Timo Gerkmann
Cepstral noise subtraction for robust automatic speech recognition. ICASSP 2015: 375-378 - [c3]Robert Rehr, Timo Gerkmann
On the bias of adaptive first-order recursive smoothing. WASPAA 2015: 1-5 - 2014
- [c2]Robert Rehr, Martin Krawczyk, Timo Gerkmann
A posteriori voiced/unvoiced probability estimation based on a sinusoidal model. ICASSP 2014: 6944-6948 - 2013
- [c1]Martin Krawczyk, Robert Rehr, Timo Gerkmann:
Phase-sensitive real-time capable speech enhancement under voiced-unvoiced uncertainty. EUSIPCO 2013: 1-5
Informal and Other Publications
- 2020
- [i3]Robert Rehr, Timo Gerkmann:
SNR-Based Features and Diverse Training Data for Robust DNN-Based Speech Enhancement. CoRR abs/2004.03512 (2020) - 2017
- [i2]Robert Rehr, Timo Gerkmann:
On the Importance of Super-Gaussian Speech Priors for Pre-Trained Speech Enhancement. CoRR abs/1703.05003 (2017) - [i1]Robert Rehr, Timo Gerkmann:
Improving the Generalizability of Deep Neural Network Based Speech Enhancement. CoRR abs/1709.02175 (2017)
Coauthor Index
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