Name
Title | ||
|---|---|---|
DF-Conformer: Integrated Architecture of Conv-Tasnet and Conformer Using Linear Complexity Self-Attention for Speech Enhancement |
Abstract | ||
|---|---|---|
Single-channel speech enhancement (SE) is an important task in speech processing. A widely used framework combines an anal-ysis/synthesis filterbank with a mask prediction network, such as the Conv-TasNet architecture. In such systems, the denoising performance and computational efficiency are mainly affected by the structure of the mask prediction network. In this study, we aim to improve the seq... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/WASPAA52581.2021.9632794 | 2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) |
Keywords | DocType | ISSN |
Convolution,Computational modeling,Computer architecture,Speech enhancement,Predictive models,Data models,Task analysis | Conference | 1931-1168 |
ISBN | Citations | PageRank |
978-1-6654-4870-3 | 1 | 0.36 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Koizumi Yuma | 1 | 41 | 11.75 |
| Shigeki Karita | 2 | 1 | 0.36 |
| Scott Wisdom | 3 | 2 | 1.06 |
| Hakan Erdogan | 4 | 2 | 0.72 |
| John R. Hershey | 5 | 1 | 0.36 |
| Llion Jones | 6 | 523 | 11.93 |
| Michiel Bacchiani | 7 | 1 | 0.36 |