Paper Info
Title
Linear filtering for noise reduction and interference rejection
Abstract
We study the linearly constrained minimum variance (LCMV) and the minimum variance distortionless response (MVDR) filters when multiple interferers and unknown (ambient) noise coexist with a target speech signal. Precisely, the LCMV is designed to remove all the interference signals while preserving the desired speech and attempting to reduce the ambient noise components. The MVDR is simply formulated such that the overall ambient-noise-plus-interference are reduced while satisfying a distortionless constraint. We provide simplified expressions for both beamformers and show their relationship. Furthermore, we underline the limitations of the LCMV when the ambient noise is present. When the latter is absent, we also prove that the MVDR degenerates to the LCMV. Numerical examples are provided to support our study.
Year
DOI
Venue
2010
10.1109/ICASSP.2010.5496184
Acoustics Speech and Signal Processing
Keywords
Field
DocType
acoustic filters,acoustic signal processing,filtering theory,noise abatement,speech,speech processing,interference rejection,interference signals,linear filtering,linearly constrained minimum variance filters,minimum variance distortionless response filters,noise reduction,target speech signal,Noise reduction,beamforming,interference rejection,linearly constrained minimum variance (LCMV),microphone array,minimum variance distortionless response (MVDR)
Speech enhancement,Noise reduction,Speech processing,Ambient noise level,Computer science,Noise control,Artificial intelligence,Beamforming,Pattern recognition,Linear filter,Signal-to-noise ratio,Algorithm,Speech recognition
Conference
ISSN
ISBN
Citations 
1520-6149 E-ISBN : 978-1-4244-4296-6
978-1-4244-4296-6
0
PageRank 
References 
Authors
0.34
11
3
Name
Order
Citations
PageRank
M. Souden1965.91
Jacob Benesty211.38
Sofiène Affes3212.43