| 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 | 24 | 2020 |
| 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 | 24 | 2016 |
| 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 | 21 | 2018 |
| 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 | 16 | 2019 |
| 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 | 9 | 2020 |
| 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 | 8 | 2021 |
| 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), 1-26, 2020 | 6 | 2020 |
| Y-Net FCRN for Acoustic Echo and Noise Suppression E Seidel, J Franzen, M Strake, T Fingscheidt arXiv preprint arXiv:2103.17189, 2021 | 5 | 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 | 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 |
| 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 | 3 | 2019 |
| 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 | 1 | 2022 |
| 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 arXiv preprint arXiv:2205.02085, 2022 | | 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, ... | | 2021 |
Tim FingscheidtTechnische Universität BraunschweigVerified email at tu-bs.de
