Name
Abstract | ||
|---|---|---|
A new voice activity detection algorithm based on long-term pitch divergence is presented. The long-term pitch divergence not only decomposes speech signals with a bionic decomposition but also makes full use of long-term information. It is more discriminative comparing with other feature sets, such as long-term spectral divergence. Experimental results show that among six analyzed algorithms, the proposed algorithm is the best one with the highest non-speech hit rate and a reasonably high speech hit rate. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1186/s13636-016-0092-y | EURASIP J. Audio, Speech and Music Processing |
Keywords | Field | DocType |
Voice activity detection, Non-stationary noise, Long-term pitch envelop, Long-term pitch divergence | Hit rate,Non stationary noise,Divergence,Audio time-scale/pitch modification,Pattern recognition,Computer science,Voice activity detection,Algorithm,Speech recognition,Artificial intelligence,Discriminative model,Pitch detection algorithm | Journal |
Volume | Issue | ISSN |
2016 | 1 | 1687-4722 |
Citations | PageRank | References |
3 | 0.39 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xu-Kui Yang | 1 | 15 | 2.69 |
| Liang He | 2 | 67 | 17.35 |
| Dan Qu | 3 | 17 | 3.77 |
| Wei-Qiang Zhang | 4 | 136 | 31.22 |