Paper Info
Title
Implementation of Bayesian recursive state-space Kalman filter for noise reduction of speech signal
Abstract
Noise reduction of speech signals plays an important role in telecommunication systems. Various types of speech additive noise can be introduced such as babble, crowd, large city, and highway which are the main factor of degradation in perceived speech quality. There are some cases on the receiver side of telecommunication systems, where the direct value of interfering noise is not available and there is just access to noisy speech. In these cases the noise cannot be cancelled totally but it may be possible to reduce the noise in a sensible way by utilizing the statistics of the noise and speech signal. In this paper the proposed method for noise reduction is Bayesian recursive state-space Kalman filter, which is a method for estimation of a speech signal from its noisy version. It utilizes the prior probability distributions of the signal and noise processes, which are assumed to be zero-mean Gaussian processes. The function of Kalman filter is assessed for different types of noise such as babble, crowd, large city, and highway. The noise cancellation is implemented for each of aforementioned noises which their powers vary in a range of values. This method of noise reduction yields better speech perceived quality and efficient results compared to Wiener filter.
Year
DOI
Venue
2014
10.1109/CCECE.2014.6900916
Electrical and Computer Engineering
Keywords
Field
DocType
Bayes methods,Gaussian processes,Kalman filters,signal denoising,speech processing,statistical distributions,Bayesian recursive state-space Kalman filter,noise cancellation,perceived speech quality,probability distributions,speech additive noise,speech signal noise reduction,telecommunication systems,zero-mean Gaussian process,Bayesian method,Kalman filter,noise reduction,speech enhancement,state-space model
Value noise,Median filter,Noise (signal processing),Noise measurement,Computer science,Salt-and-pepper noise,Electronic engineering,Speech recognition,Gaussian noise,Noise,Gradient noise
Conference
ISSN
ISBN
Citations 
0840-7789
978-1-4799-3099-9
0
PageRank 
References 
Authors
0.34
2
2
Name
Order
Citations
PageRank
Sarafnia, A.101.01
Seyed Ghorshi2175.17