| 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 | 56 | 2020 |
| 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 | 33 | 2019 |
| 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 | 32 | 2020 |
| 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 | 27 | 2018 |
| 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 | 27 | 2016 |
| 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 | 22 | 2020 |
| Y-Net FCRN for Acoustic Echo and Noise Suppression E Seidel, J Franzen, M Strake, T Fingscheidt arXiv preprint arXiv:2103.17189, 2021 | 18 | 2021 |
| 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 | 14 | 2022 |
| 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 | 13 | 2021 |
| 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 | 8 | 2019 |
| 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 | 4 | 2019 |
| 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 | 4 | 2018 |
| 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 | 3 | 2022 |
| 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, ... 11th SESAR Innovation Days, 1-8, 2021 | 3 | 2021 |
| 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 | 2 | 2022 |
| 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 | 1 | 2023 |
Tim FingscheidtITG Fellow, Professor, Technische Universität Braunschweig, GermanyVerified email at tu-bs.de
