23-07-2012, 04:51 PM
A Fast Adaptive Kalman Filtering Algorithm for Speech Enhancement
A Fast Adaptive Kalman Filtering Algorithm(1).pdf (Size: 622.33 KB / Downloads: 72)
Abstract
The speech enhancement is one of the effective
techniques to solve speech degraded by noise. In this paper a
fast speech enhancement method for noisy speech signals is
presented, which is based on improved Kalman filtering. The
conventional Kalman filter algorithm for speech enhancement
needs to calculate the parameters of AR (auto-regressive) model,
and perform a lot of matrix operations, which usually is
non-adaptive. The speech enhancement algorithm proposed in
this paper eliminates the matrix operations and reduces the
calculating time by only constantly updating the first value of
state vector X(n). We design a coefficient factor for adaptive
filtering, to automatically amend the estimation of
environmental noise by the observation data. Simulation results
show that the fast adaptive algorithm using Kalman filtering is
effective for speech enhancement.
I. INTRODUCTION
n the past several years, there were many applications in
speech enhancement based on Kalman filtering algorithm.
Those methods were proposed by [1]-[7]. Most of those
methods need to estimate the parameters of AR model at first,
and then perform the noise suppression using Kalman
filtering algorithm. In this process, the calculations of LPC
(linear prediction coding) coefficient and inverse matrix
greatly increase the computational complexity of the filtering
algorithm. Although these methods can achieve a good
filtering efficiency, the noise suppressed signal may
deteriorate the quality of the speech signal dependent on
estimation accuracy of the parameters of the AR model. [2]
and [3] have been given a simple Kalman filtering algorithm
without calculating LPC coefficient in the AR model, but the
algorithm still contains a large number of redundant data and
matrix inverse operations. In addition, the algorithm is
non-adaptive.
To overcome the drawback of conventional Kalman
filtering for speech enhancement, we propose a fast adaptive
This work was supported in part by Training Program Foundation for the
Talents by Beijing Municipal Party Committee Organization Department
under Grant 20081D0501500169, supported in part by science and
technology project of Beijing Municipal Education Commission under Grant
PXM2011_014204_09_000232.
Quanshen Mai is with Beijing University of Technology, Beijing, 100124
CHINA (e-mail: freedom0mai[at]gmail.com).
Dongzhi He is with Beijing University of Technology, Beijing, 100124
CHINA (e-mail: victor[at]bjut.edu.cn).
Yibin Hou is with Beijing University of Technology, Beijing, 100124
CHINA (e-mail: victor[at]bjut.edu.cn).
Zhangqin Huang is with Beijing University of Technology, Beijing,
100124 CHINA (e-mail: victor[at]bjut.edu.cn).
algorithm of Kalman filtering. This algorithm only constantly
updates the first value of state vector X(n), which eliminates
the matrix operations and reduces the time complexity of the
algorithm. Actually, it is difficult to know what
environmental noise exactly is. And it affects the application
of the Kalman filtering algorithm. So we need a real-time
adaptive algorithm to estimate the ambient noise. We add the
forgetting factor which has been mentioned by [4] and [5] to
amend the estimation of environmental noise by the
observation data automatically, so the algorithm can catch the
real noise. Simulation results show that, compared with the
conventional Kalman filtering algorithm, the fast adaptive
algorithm of Kalman filtering is more effective. At the same
time, it reduced its’ running time without sacrificing quality
of the speech signal. It also has good adaptability to improve
the algorithm robustness.