Dual-microphone voice activity detection based on using optimally weighted maximum a posteriori probabilities
- Authors
- Huang, Seng Hyun; Park, Jihwan; Chang, Joon-Hyuk
- Issue Date
- May-2016
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- discriminative weight training; dual-microphone; minimum classification error; Voice activity detection
- Citation
- ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, v.2016-May, pp.5360 - 5364
- Indexed
- SCOPUS
- Journal Title
- ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
- Volume
- 2016-May
- Start Page
- 5360
- End Page
- 5364
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/23086
- DOI
- 10.1109/ICASSP.2016.7472701
- ISSN
- 0736-7791
- Abstract
- In this paper, we propose to improve the dual-microphone voice activity detection (VAD) technique for which a discriminative weight training is applied to achieve optimally weighted spatial features. In our approach, we first derive the maximum a posteriori (MAP) probabilities from the spatial features such as the power level difference ratio (PLDR), phase vector, and coherence. Then, we combine each MAP probability within the minimum classification error (MCE) framework to offer an optimal VAD decision in a spectral domain. Experimental results show that the proposed dual-microphone VAD algorithm shows better performances than the conventional dual-microphone VAD methods, which solely utilize the PLDR, phase, and spectral coherence.
- Files in This Item
-
Go to Link
- Appears in
Collections - 서울 공과대학 > 서울 융합전자공학부 > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/23086)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.