Name
Title | ||
|---|---|---|
A Novel Unified Framework for Speech Enhancement and Bandwidth Extension Based on Jointly Trained Neural Networks |
Abstract | ||
|---|---|---|
In this paper, we propose a unified framework for speech enhancement and bandwidth extension. The speech bandwidth extension (BWE) is investigated in noisy environment. Firstly, a Bidirectional Long Short-Term Memory Recurrent Neural Networks (BLSTM-RNN) is trained to map the noisy to clean speech features. Secondly, the BWE is also a BLSTM-RNN model. The feature enhancement neural network serves as a noise normalization module which aimed at explicitly generating the clean features which are easier to BWE by the following neural network. We combined Griffin-Lim algorithm with proposed jointly model to reconstruct wideband speech. To reduce the size of model while maintaining a similar performance, multi-task transfer learning solution is proposed. Experimental results demonstrate that the proposed framework can achieve significant improvements in both objective and subjective measures over the different baseline methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ISCSLP.2018.8706607 | 2018 11th International Symposium on Chinese Spoken Language Processing (ISCSLP) |
Keywords | Field | DocType |
Feature extraction,Wideband,Narrowband,Neural networks,Training,Speech enhancement | Speech enhancement,Wideband,Narrowband,Wideband audio,Pattern recognition,Computer science,Bandwidth extension,Recurrent neural network,Speech recognition,Feature extraction,Artificial intelligence,Artificial neural network | Conference |
ISBN | Citations | PageRank |
978-1-5386-5627-3 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bin Liu | 1 | 191 | 35.02 |
| Jianhua Tao | 2 | 848 | 138.00 |
| Yibin Zheng | 3 | 38 | 15.13 |