Title
Model-Based Voice Activity Detection in Wireless Acoustic Sensor Networks.
Abstract
One of the major challenges in wireless acoustic sensor networks (WASN) based speech enhancement is robust and accurate voice activity detection (VAD). VAD is widely used in speech enhancement, speech coding, speech recognition, etc. In speech enhancement applications, VAD plays an important role, since noise statistics can be updated during non-speech frames to ensure efficient noise reduction and tolerable speech distortion. Although significant efforts have been made in single channel VAD, few solutions can be found in the multichannel case, especially in WASN. In this paper, we introduce a distributed VAD by using model-based noise power spectral density (PSD) estimation. For each node in the network, the speech PSD and noise PSD are first estimated, then a distributed detection is made by applying the generalized likelihood ratio test (GLRT). The proposed global GLRT based VAD has a quite general form. Indeed, we can judge whether the speech is present or absent by using the current time frame and frequency band observation or by taking into account the neighbouring frames and bands. Finally, the distributed GLRT result is obtained by using a distributed consensus method, such as random gossip, i.e., the whole detection system does not need any fusion center. With the model-based noise estimation method, the proposed distributed VAD performs robustly under non-stationary noise conditions, such as babble noise. As shown in experiments, the proposed method outperforms traditional multichannel VAD methods in terms of detection accuracy.
Year
DOI
Venue
2018
10.23919/EUSIPCO.2018.8553457
European Signal Processing Conference
Keywords
Field
DocType
Wireless acoustic sensor networks,noise PSD estimation,distributed voice activity detection
Consensus,Noise reduction,Speech enhancement,Noise power,Speech coding,Likelihood-ratio test,Computer science,Voice activity detection,Speech recognition,Fusion center
Conference
ISSN
Citations 
PageRank 
2076-1465
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Yingke Zhao101.01
Jesper Kjær Nielsen25713.07
Mads Grísbøll Christensen376176.48
Jinzdona Chen400.34