Name
Abstract | ||
|---|---|---|
This paper introduces two different ways of time-frequency representations for voice activity detection (VAD). The first method is based on the chirp-based spectral representation of the signal, while the second method is based on wavelet decomposition. Not only this is the first implementation of the Fan-Chirp Transform for VAD, but the method based on Discrete Wavelet Transform is also one of the few multidimensional approaches in the field. The paper addresses the performance of both methods with clean speech and speech in noisy conditions, and discusses their limitations. |
Year | Venue | Keywords |
|---|---|---|
2006 | SPPRA | different way,fan-chirp transform,time-frequency analysis,time-frequency representation,noisy condition,discrete wavelet transform,wavelet decomposition,multidimensional approach,chirp-based spectral representation,voice activity detection,clean speech,time frequency analysis |
Field | DocType | ISBN |
Wavelet decomposition,Pattern recognition,Voice activity detection,Computer science,Speech recognition,Second-generation wavelet transform,Artificial intelligence,Chirp,Time–frequency analysis,Discrete wavelet transform,Stationary wavelet transform,Wavelet packet decomposition | Conference | 0-88986-580-9 |
Citations | PageRank | References |
1 | 0.35 | 10 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tuan Van Pham | 1 | 34 | 6.66 |
| Marián Képesi | 2 | 72 | 8.59 |
| Gernot Kubin | 3 | 197 | 38.65 |
| Luis Weruaga | 4 | 108 | 15.29 |
| Milan Sigmund | 5 | 33 | 5.96 |
| Tomas Dostál | 6 | 13 | 4.33 |