Performance Analysis of a Series of Adaptive Filters in Non-Stationary Environment for Noise Cancelling Setup
One of the essential components of much of DSP
application is noise cancellation. Changes in real time signals are
quite rapid and swift. In noise cancellation, a reference signal which
is an approximation of noise signal (that corrupts the original
information signal) is obtained and then subtracted from the noise
bearing signal to obtain a noise free signal. This approximation of
noise signal is obtained through adaptive filters which are self
adjusting. As the changes in real time signals are abrupt, this needs
adaptive algorithm that converges fast and is stable. Least mean
square (LMS) and normalized LMS (NLMS) are two widely used
algorithms because of their plainness in calculations and
implementation. But their convergence rates are small. Adaptive
averaging filters (AFA) are also used because they have high
convergence, but they are less stable. This paper provides the
comparative study of LMS and Normalized NLMS, AFA and new
enhanced average adaptive (Average NLMS-ANLMS) filters for noise
cancelling application using speech signals.
[1] Simon Haykin, "Adaptive filter theory", 4th Ed, Printic Hall, 1996.
[2] Sayed. A. Hadei, "A Family of Adaptive Filter Algorithms in Noise
Cancellation for Speech Enhancement", International Journal of
Computer and Electrical Engineering, Vol. 2, No. 2, 1793-8163, 2010.
[3] V. R. Vijavkumar, P. T. Vanathi, "Modified Adaptive Filtering
Algorithms for Noise Cancellation in Speech Signals", Electronics and
Electrical Engineering ISSN 1392-1215, 2007.
[4] Moshe Tarrab, ArieFeuer, "Convergence and performance analysis of
the normalized algorithm with uncorrelated gaussian data", IEEE Trans.
Vol. 34, Israel, July 1988.
[5] Bernard Widrow, John R. Glover, John M. McCool, John Kaunitz,
Charles S. Williasm, Robert H. Hearn, James R.Zeidler, Eugene Dong,
Robert C. Goodlin, "Adaptive noise cancelling: principles and
applications", IEEE Proc, vol. 63 December 1975.
[6] Markus Rupp, Rudi Frenze, "Analysis of lms and nlms with delayed
coefficient update under the presence of spherically invariant process",
IEEE Trans, vol. 42, March 1994.
[7] Markus Rupp, "The behavior of lms and nlms algorithms in the presence
of invariant process", IEEE Trans, vol. 41,March 1993.
[1] Simon Haykin, "Adaptive filter theory", 4th Ed, Printic Hall, 1996.
[2] Sayed. A. Hadei, "A Family of Adaptive Filter Algorithms in Noise
Cancellation for Speech Enhancement", International Journal of
Computer and Electrical Engineering, Vol. 2, No. 2, 1793-8163, 2010.
[3] V. R. Vijavkumar, P. T. Vanathi, "Modified Adaptive Filtering
Algorithms for Noise Cancellation in Speech Signals", Electronics and
Electrical Engineering ISSN 1392-1215, 2007.
[4] Moshe Tarrab, ArieFeuer, "Convergence and performance analysis of
the normalized algorithm with uncorrelated gaussian data", IEEE Trans.
Vol. 34, Israel, July 1988.
[5] Bernard Widrow, John R. Glover, John M. McCool, John Kaunitz,
Charles S. Williasm, Robert H. Hearn, James R.Zeidler, Eugene Dong,
Robert C. Goodlin, "Adaptive noise cancelling: principles and
applications", IEEE Proc, vol. 63 December 1975.
[6] Markus Rupp, Rudi Frenze, "Analysis of lms and nlms with delayed
coefficient update under the presence of spherically invariant process",
IEEE Trans, vol. 42, March 1994.
[7] Markus Rupp, "The behavior of lms and nlms algorithms in the presence
of invariant process", IEEE Trans, vol. 41,March 1993.
@article{"International Journal of Electrical, Electronic and Communication Sciences:63229", author = "Anam Rafique and Syed Sohail Ahmed", title = "Performance Analysis of a Series of Adaptive Filters in Non-Stationary Environment for Noise Cancelling Setup", abstract = "One of the essential components of much of DSP
application is noise cancellation. Changes in real time signals are
quite rapid and swift. In noise cancellation, a reference signal which
is an approximation of noise signal (that corrupts the original
information signal) is obtained and then subtracted from the noise
bearing signal to obtain a noise free signal. This approximation of
noise signal is obtained through adaptive filters which are self
adjusting. As the changes in real time signals are abrupt, this needs
adaptive algorithm that converges fast and is stable. Least mean
square (LMS) and normalized LMS (NLMS) are two widely used
algorithms because of their plainness in calculations and
implementation. But their convergence rates are small. Adaptive
averaging filters (AFA) are also used because they have high
convergence, but they are less stable. This paper provides the
comparative study of LMS and Normalized NLMS, AFA and new
enhanced average adaptive (Average NLMS-ANLMS) filters for noise
cancelling application using speech signals.", keywords = "AFA, ANLMS, LMS, NLMS.", volume = "7", number = "2", pages = "161-4", }