| Improving music source separation based on deep neural networks through data augmentation and network blending S Uhlich, M Porcu, F Giron, M Enenkl, T Kemp, N Takahashi, Y Mitsufuji International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017 | 234 | 2017 |
| Open-unmix-a reference implementation for music source separation FR Stöter, S Uhlich, A Liutkus, Y Mitsufuji Journal of Open Source Software 4 (41), 1667, 2019 | 222 | 2019 |
| Mixed precision DNNs: All you need is a good parametrization S Uhlich, L Mauch, F Cardinaux, K Yoshiyama, JA García, S Tiedemann, ... ICLR 2020, 2020 | 150* | 2020 |
| Deep neural network based instrument extraction from music S Uhlich, F Giron, Y Mitsufuji 2015 IEEE International Conference on Acoustics, Speech and Signal …, 2015 | 137 | 2015 |
| Music demixing challenge 2021 Y Mitsufuji, G Fabbro, S Uhlich, FR Stöter, A Défossez, M Kim, W Choi, ... Frontiers in Signal Processing 1, 18, 2022 | 45* | 2022 |
| Exploring the best loss function for DNN-based low-latency speech enhancement with temporal convolutional networks Y Koyama, T Vuong, S Uhlich, B Raj arXiv preprint arXiv:2005.11611, 2020 | 43 | 2020 |
| All for one and one for all: Improving music separation by bridging networks R Sawata, S Uhlich, S Takahashi, Y Mitsufuji ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 38 | 2021 |
| Multidimensional localization of multiple sound sources using frequency domain ICA and an extended state coherence transform B Loesch, S Uhlich, B Yang 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, 677-680, 2009 | 34 | 2009 |
| Method, system and artificial neural network F Cardinaux, M Enenkl, F Giron, T Kemp, S Uhlich US Patent 10,564,923, 2020 | 16 | 2020 |
| Improving DNN-based Music Source Separation using Phase Features J Muth, S Uhlich, N Perraudin, T Kemp, F Cardinaux, Y Mitsufuji Joint Workshop on Machine Learning for Music at ICML, IJCAI/ECAI and AAMAS, 2018 | 16 | 2018 |
| Bayes risk reduction of estimators using artificial observation noise S Uhlich IEEE Transactions on Signal Processing 63 (20), 5535-5545, 2015 | 16 | 2015 |
| Iteratively training look-up tables for network quantization F Cardinaux, S Uhlich, K Yoshiyama, JA García, L Mauch, S Tiedemann, ... IEEE Journal of Selected Topics in Signal Processing 14 (4), 860-870, 2020 | 13 | 2020 |
| Multichannel non-negative matrix factorization using banded spatial covariance matrices in wavenumber domain Y Mitsufuji, S Uhlich, N Takamune, D Kitamura, S Koyama, H Saruwatari IEEE/ACM Transactions on Audio, Speech, and Language Processing 28, 49-60, 2019 | 13 | 2019 |
| Signal processing unit employing a blind channel estimation algorithm and method of operating a receiver apparatus B Eitel, J Zinsser, RA Salem, S Uhlich US Patent 9,401,826, 2016 | 12 | 2016 |
| NMF-based blind source separation using a linear predictive coding error clustering criterion X Guo, S Uhlich, Y Mitsufuji 2015 IEEE International Conference on Acoustics, Speech and Signal …, 2015 | 12 | 2015 |
| Robustification and optimization of a Kalman filter with measurement loss using linear precoding R Blind, S Uhlich, B Yang, F Allgower 2009 American Control Conference, 2222-2227, 2009 | 12 | 2009 |
| Neural Network Libraries: A Deep Learning Framework Designed from Engineers' Perspectives T Narihira, J Alonsogarcia, F Cardinaux, A Hayakawa, M Ishii, K Iwaki, ... arXiv preprint arXiv:2102.06725, 2021 | 8 | 2021 |
| Open-unmix for speech enhancement (UMX SE) S Uhlich, Y Mitsufuji May, 2020 | 7 | 2020 |
| MMSE estimation in a linear signal model with ellipsoidal constraints S Uhlich, B Yang 2009 IEEE International Conference on Acoustics, Speech and Signal …, 2009 | 7 | 2009 |
| Automatic music mixing with deep learning and out-of-domain data MA Martínez-Ramírez, WH Liao, G Fabbro, S Uhlich, C Nagashima, ... arXiv preprint arXiv:2208.11428, 2022 | 6 | 2022 |
Thomas KempSonyBestätigte E-Mail-Adresse bei eu.sony.com
