NDLI: Switched Griffiths-Jim beamformer using the affine projection algorithm

Content Provider	IEEE Xplore Digital Library
Author	Yoganathan, V. Moir, T.J.
Copyright Year	2008
Description	Author affiliation: Sch. of Eng. & Adv. Technol., Massey Univ., Auckland (Yoganathan, V.; Moir, T.J.)
Abstract	Speech controlled applications are now becoming more and more practical due to advances in technology. These applications vary from command and control instruments, video conferencing to robotics. However, their performance decreases when the acquired speech signal is corrupted by background noise. Numerous research has been done in the last two decades to improve their performance. The switched Griffiths-Jim beamformer is one of the well known methods used to reduce background noise (or interference). This algorithm makes use of two adaptive filters and a voice activity detector. The first filter is used as a beam-steering filter which is updated during speech signals and the second filter is used as a noise cancellation filter which is updated during a noise-alone signal. A voice activity detector is used to control the updates of these adaptive filters. Generally, the normalised least mean squares (NLMS) algorithm is used for the adaptive filters. However, it is found that the convergence rate of the NLMS deteriorates for coloured noise under certain non-stationary conditions where the correlation matrix is "stiff". It has been found that the affine projection algorithm will perform better under these conditions. Therefore, a comparison of these two adaptive filters in this beamformer structure will be discussed in this paper.
Starting Page	391
Ending Page	396
File Size	2877140
Page Count	6
File Format	PDF
ISBN	9781424437795
DOI	10.1109/MMVIP.2008.4749565
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-02
Publisher Place	New Zealand
Access Restriction	Subscribed
Rights Holder	Intl Journal of Intelligent
Subject Keyword	Command and control systems Instruments Adaptive filters Detectors Speech enhancement Noise cancellation Background noise Projection algorithms Videoconference Robots
Content Type	Text
Resource Type	Article

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