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Maximilian Strake
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Fully convolutional recurrent networks for speech enhancement
M Strake, B Defraene, K Fluyt, W Tirry, T Fingscheidt
ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020
542020
Separated noise suppression and speech restoration: LSTM-based speech enhancement in two stages
M Strake, B Defraene, K Fluyt, W Tirry, T Fingscheidt
2019 IEEE Workshop on Applications of Signal Processing to Audio and …, 2019
322019
Speech enhancement by LSTM-based noise suppression followed by CNN-based speech restoration
M Strake, B Defraene, K Fluyt, W Tirry, T Fingscheidt
EURASIP Journal on Advances in Signal Processing 2020, 1-26, 2020
302020
A simple cepstral domain DNN approach to artificial speech bandwidth extension
J Abel, M Strake, T Fingscheidt
2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018
272018
Artificial bandwidth extension using deep neural networks for spectral envelope estimation
J Abel, M Strake, T Fingscheidt
2016 IEEE International Workshop on Acoustic Signal Enhancement (IWAENC), 1-5, 2016
272016
INTERSPEECH 2020 Deep Noise Suppression Challenge: A Fully Convolutional Recurrent Network (FCRN) for Joint Dereverberation and Denoising.
M Strake, B Defraene, K Fluyt, W Tirry, T Fingscheidt
INTERSPEECH, 2467-2471, 2020
222020
Y-Net FCRN for Acoustic Echo and Noise Suppression
E Seidel, J Franzen, M Strake, T Fingscheidt
arXiv preprint arXiv:2103.17189, 2021
172021
Deep noise suppression with non-intrusive pesqnet supervision enabling the use of real training data
Z Xu, M Strake, T Fingscheidt
arXiv preprint arXiv:2103.17088, 2021
132021
Deep noise suppression maximizing non-differentiable PESQ mediated by a non-intrusive PESQNet
Z Xu, M Strake, T Fingscheidt
IEEE/ACM Transactions on Audio, Speech, and Language Processing 30, 1572-1585, 2022
122022
Concatenated identical DNN (CI-DNN) to reduce noise-type dependence in DNN-based speech enhancement
Z Xu, M Strake, T Fingscheidt
2019 27th European Signal Processing Conference (EUSIPCO), 1-5, 2019
72019
On temporal context information for hybrid BLSTM-based phoneme recognition
T Lohrenz, M Strake, T Fingscheidt
2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU …, 2019
42019
DenseNet BLSTM for acoustic modeling in robust asr
M Strake, P Behr, T Lohrenz, T Fingscheidt
2018 IEEE Spoken Language Technology Workshop (SLT), 6-12, 2018
42018
Self-Attention With Restricted Time Context And Resolution In Dnn Speech Enhancement
M Strake, A Behlke, T Fingscheidt
2022 International Workshop on Acoustic Signal Enhancement (IWAENC), 1-5, 2022
32022
Easy Adaptation of Speech Recognition to Different Air Traffic Control Environments using the DeepSpeech Engine
M Kleinert, N Venkatarathinam, H Helmke, O Ohneiser, M Strake, ...
32021
Does a PESQNet (Loss) Require a Clean Reference Input? The Original PESQ Does, But ACR Listening Tests Don’t
Z Xu, M Strake, T Fingscheidt
2022 International Workshop on Acoustic Signal Enhancement (IWAENC), 1-5, 2022
22022
EffCRN: An Efficient Convolutional Recurrent Network for High-Performance Speech Enhancement
M Sach, J Franzen, B Defraene, K Fluyt, M Strake, W Tirry, T Fingscheidt
arXiv preprint arXiv:2306.02778, 2023
12023
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