Name
Title | ||
|---|---|---|
Robust distributed multi-speaker voice activity detection using stability selection for sparse non-negative feature extraction. |
Abstract | ||
|---|---|---|
In this paper, we propose a robust multi-speaker voice activity detection approach for wireless acoustic sensor networks (WASN). Each node of the WASN receives a mixture of sound sources. We propose a non-negative feature extraction using stability selection that exploits the sparsity of the speech energy signals. The strongest right singular vectors serve as source-specific features for the subsequent voice activity detection (VAD). To separate active speech frames from silent frames, we propose a robust Mahalanobis classifier that is based on an M-estimator of the covariance matrix. The proposed approach can also be applied to a distributed setting, where no fusion center is available. Highly accurate VAD results are obtained in a challenging WASN of 20 nodes observing 6 sources in a reverberant environment. |
Year | Venue | Field |
|---|---|---|
2017 | European Signal Processing Conference | Wireless,Pattern recognition,Voice activity detection,Computer science,Speech recognition,Mahalanobis distance,Feature extraction,Robustness (computer science),Fusion center,Artificial intelligence,Covariance matrix,Classifier (linguistics) |
DocType | ISSN | Citations |
Conference | 2076-1465 | 1 |
PageRank | References | Authors |
0.36 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| L. Khadidja Hamaidi | 1 | 1 | 0.36 |
| Michael Muma | 2 | 144 | 19.51 |
| Abdelhak M. Zoubir | 3 | 1036 | 148.03 |