Name
Abstract | ||
|---|---|---|
Speech enhancement methods based on deep learning have surpassed traditional methods. While many of these new approaches are operating on the wideband (16kHz) sample rate, a new fullband (48kHz) speech enhancement system is proposed in this paper. Compared to the existing fullband systems that utilizes perceptually motivated features to train the fullband speech enhancement using a single network structure, the proposed system is a two-step system ensuring good fullband speech enhancement quality while backward compatible to the existing wideband systems. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ICASSP43922.2022.9747405 | IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xu Zhang | 1 | 0 | 0.34 |
| Lianwu Chen | 2 | 0 | 1.01 |
| Xiguang Zheng | 3 | 0 | 1.01 |
| Xinlei Ren | 4 | 0 | 1.35 |
| Chen Zhang | 5 | 112 | 41.68 |
| Liang Guo | 6 | 0 | 0.68 |
| Bing Yu | 7 | 0 | 0.68 |