Model-Based Voice Activity Detection in Wireless Acoustic Sensor Networks

Yingke Zhao, Jesper Kjær Nielsen, Mads Græsbøll Christensen, Jingdong Chen

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

1 Citation (Scopus)
130 Downloads (Pure)

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.
Original languageEnglish
Title of host publication2018 26th European Signal Processing Conference (EUSIPCO)
Number of pages5
PublisherIEEE
Publication dateSep 2018
Pages425-429
Article number8553457
ISBN (Print)978-90-827970-0-8, 978-1-5386-3736-4
ISBN (Electronic)978-9-0827-9701-5
DOIs
Publication statusPublished - Sep 2018
Event26th European Signal Processing Conference (EUSIPCO 2018) - Rome, Italy
Duration: 3 Sep 20187 Sep 2018
Conference number: 26
http://www.eusipco2018.org

Conference

Conference26th European Signal Processing Conference (EUSIPCO 2018)
Number26
CountryItaly
CityRome
Period03/09/201807/09/2018
Internet address
SeriesProceedings of the European Signal Processing Conference
ISSN2076-1465

Cite this