Name
Title | ||
|---|---|---|
Closing the Gap Between Time-Domain Multi-Channel Speech Enhancement on Real and Simulation Conditions |
Abstract | ||
|---|---|---|
The deep learning based time-domain models, e.g. Conv-TasNet, have shown great potential in both single-channel and multi-channel speech enhancement. However, many experiments on the time-domain speech enhancement model are done in simulated conditions, and it is not well studied whether the good performance can generalize to real-world scenarios. In this paper, we aim to provide an insightful inv... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/WASPAA52581.2021.9632720 | 2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) |
Keywords | DocType | ISSN |
Training,Deep learning,Array signal processing,Conferences,Speech recognition,Speech enhancement,Data models | Conference | 1931-1168 |
ISBN | Citations | PageRank |
978-1-6654-4870-3 | 1 | 0.35 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wangyou Zhang | 1 | 1 | 0.35 |
| Jing Shi | 2 | 5 | 5.80 |
| Chenda Li | 3 | 1 | 0.35 |
| Shinji Watanabe | 4 | 1158 | 139.38 |
| Yanmin Qian | 5 | 295 | 44.44 |