Name
Abstract | ||
|---|---|---|
This paper presents a novel artificial bandwidth extension (ABE) framework based on deep neural networks (DNNs) with a multiple-layer's deep architecture. It demonstrates the suitability of DNNs for modeling log power spectra of speech signals using the application of ABE. The DNN is used to estimate the log power spectra in the high-band. Two strategies are proposed to improve the performances of the proposed ABE system. First, global variance equalization is proposed to alleviate the over-smoothing issue in generated log spectra. Second, rich acoustic features in the low-band are considered to improve the construction of the log power spectra in the high-band. Experimental results demonstrate that the proposed framework can achieve significant improvements in both objective and subjective measures over the different baseline methods. |
Year | Venue | Keywords |
|---|---|---|
2015 | 16TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2015), VOLS 1-5 | deep neural networks, artificial bandwidth extension, rich acoustic features, global variance equalization |
Field | DocType | Citations |
Architecture,Computer science,Bandwidth extension,Speech recognition | Conference | 3 |
PageRank | References | Authors |
0.39 | 11 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bin Liu | 1 | 5 | 2.45 |
| Jianhua Tao | 2 | 848 | 138.00 |
| Zhengqi Wen | 3 | 86 | 24.41 |
| Ya Li | 4 | 36 | 11.21 |
| Danish Bukhari | 5 | 3 | 0.39 |