| Sergan: Speech enhancement using relativistic generative adversarial networks with gradient penalty D Baby, S Verhulst ICASSP 2019-2019 IEEE international conference on acoustics, speech and …, 2019 | 129 | 2019 |
| A convolutional neural-network model of human cochlear mechanics and filter tuning for real-time applications D Baby, A Van Den Broucke, S Verhulst Nature machine intelligence 3 (2), 134-143, 2021 | 51 | 2021 |
| Coupled dictionaries for exemplar-based speech enhancement and automatic speech recognition D Baby, T Virtanen, JF Gemmeke IEEE/ACM transactions on audio, speech, and language processing 23 (11 …, 2015 | 41 | 2015 |
| A convolutional neural-network framework for modelling auditory sensory cells and synapses F Drakopoulos, D Baby, S Verhulst Communications Biology 4 (1), 827, 2021 | 28 | 2021 |
| Exemplar-based speech enhancement for deep neural network based automatic speech recognition D Baby, J Gemmeke, T Virtanen, H Van hamme IEEE ICASSP 2015, 2015 | 24 | 2015 |
| Coupled dictionary training for exemplar-based speech enhancement D Baby, T Virtanen, T Barker, H Van hamme Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International …, 2014 | 23 | 2014 |
| Recurrent neural networks (rnns) J Nabi Towards Data Science, 2019 | 16 | 2019 |
| Biophysically-inspired features improve the generalizability of neural network-based speech enhancement systems D Baby, S Verhulst INTERSPEECH, 2018 | 16 | 2018 |
| Real-time audio processing on a Raspberry Pi using deep neural networks F Drakopoulos, D Baby, S Verhulst 23rd International Congress on Acoustics (ICA 2019), 2827-2834, 2019 | 15 | 2019 |
| Investigating modulation spectrogram features for deep neural network-based automatic speech recognition D Baby, H Van hamme Proceedings Interspeech 2015, 2479-2483, 2015 | 14 | 2015 |
| Ordered Orthogonal Matching Pursuit D Baby, SRB Pillai Communications (NCC), 2012 National Conference on, 2012 | 13* | 2012 |
| Supervised speech dereverberation in noisy environments using exemplar-based sparse representations D Baby, H Van hamme Accoustics, Speech and Signal Processing, 2016 IEEE International Conference on, 2016 | 12 | 2016 |
| Joint Denoising and Dereverberation Using Exemplar-Based Sparse Representations and Decaying Norm Constraint D Baby, H Van hamme IEEE/ACM Transactions on Audio, Speech, and Language Processing 25 (10 …, 2017 | 11 | 2017 |
| isegan: Improved speech enhancement generative adversarial networks D Baby arXiv preprint arXiv:2002.08796, 2020 | 9 | 2020 |
| Incremental learning for RNN-Transducer based speech recognition models D Baby, P D'Alterio, V Mendelev | 8 | 2022 |
| Hearing-impaired bio-inspired cochlear models for real-time auditory applications A Van Den Broucke, D Baby, S Verhulst 21st Annual Conference of the International Speech Communication Association …, 2020 | 8 | 2020 |
| Speech dereverberation using variational autoencoders D Baby, H Bourlard ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 7 | 2021 |
| Automated speech analysis to improve TMS-based language mapping: Algorithm and proof of concept L Seynaeve, D Baby, S De Vleeschouwer, P Dupont, W Van Paesschen Brain Stimulation: Basic, Translational, and Clinical Research in …, 2020 | 6 | 2020 |
| Exemplar-based noise robust automatic speech recognition using modulation spectrogram features D Baby, T Virtanen, J Gemmeke, T Barker, H Van hamme Proceedings SLT 2014, 1-6, 2014 | 6 | 2014 |
| NEURAL NETWORK MODEL FOR COCHLEAR MECHANICS AND PROCESSING D Baby, S Verhulst, F Drakpoulos, A Van Den Broucke US Patent US 11,800,301, 2023 | 5* | 2023 |
Sarah VerhulstFull ProfessorVerified email at ugent.be
