Name
Abstract | ||
|---|---|---|
Recent progress in singing voice separation has primarily focused on supervised deep learning methods. However, the scarcity of ground-truth data with clean musical sources has been a problem for long. Given a limited set of labeled data, we present a method to leverage a large volume of unlabeled data to improve the model’s performance. Following the noisy self-training framework, we first train ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICASSP39728.2021.9413723 | ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
Keywords | DocType | ISBN |
Training,Deep learning,Tracking loops,Solid modeling,Filtering,Music,Detectors | Conference | 978-1-7281-7605-5 |
Citations | PageRank | References |
3 | 0.44 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhepei Wang | 1 | 3 | 0.78 |
| Ritwik Giri | 2 | 164 | 12.93 |
| Umut Isik | 3 | 10 | 3.33 |
| J.-M. Valin | 4 | 740 | 66.29 |
| Arvindh Krishnaswamy | 5 | 12 | 3.37 |