NDLI: Boosting contextual information for deep neural network based voice activity detection

Content Provider	ACM Digital Library
Author	Zhang, Xiao-Lei Wang, DeLiang
Copyright Year	2016
Abstract	Voice activity detection (VAD) is an important topic in audio signal processing. Contextual information is important for improving the performance of VAD at low signal-to-noise ratios. Here we explore contextual information by machine learning methods at three levels. At the top level, we employ an ensemble learning framework, named multi-resolution stacking (MRS), which is a stack of ensemble classifiers. Each classifier in a building block inputs the concatenation of the predictions of its lower building blocks and the expansion of the raw acoustic feature by a given window (called a resolution). At the middle level, we describe a base classifier in MRS, named boosted deep neural network (bDNN). bDNN first generates multiple base predictions from different contexts of a single frame by only one DNN and then aggregates the base predictions for a better prediction of the frame, and it is different from computationally-expensive boosting methods that train ensembles of classifiers for multiple base predictions. At the bottom level, we employ the multi-resolution cochleagram feature, which incorporates the contextual information by concatenating the cochleagram features at multiple spectrotemporal resolutions. Experimental results show that the MRS-based VAD outperforms other VADs by a considerable margin. Moreover, when trained on a large amount of noise types and a wide range of signal-to-noise ratios, the MRS-based VAD demonstrates surprisingly good generalization performance on unseen test scenarios, approaching the performance with noise-dependent training.
Starting Page	252
Ending Page	264
Page Count	13
File Format	PDF
ISSN	23299290
e-ISSN	23299304
DOI	10.1109/TASLP.2015.2505415
Volume Number	24
Issue Number	2
Journal	IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-02-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Cochleagram Deep neural network Ensemble learning Multi-resolution stacking Noise-independent training Voice activity detection
Content Type	Text
Resource Type	Article
Subject	Instrumentation Computational Mathematics Signal Processing Electrical and Electronic Engineering Acoustics and Ultrasonics Speech and Hearing Media Technology

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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