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Extra info for Adaptive Filtering Applications
It is evident that for small value of α, the process has a peaky and heavy tailed distribution. e. signals with outliers), one of the following solution may be adopted: 1. A robust optimization criterion may be used to derive the adaptive algorithm. 2. The large amplitude samples may be ignored. 3. The large amplitude samples may be replaced by an appropriate threshold value. The existing algorithms for ANC of impulsive noise are based on the ﬁrst two approaches. In the proposed algorithms, we consider combining these approaches as well as borrow concept of the normalized step size, as explained later in this section.
Kuo, S. M. & Ji, M. J. (1995). Development and analysis of an adaptive noise equalizer. IEEE Transactions Speech Audio Processing, Vol. 3, May 1995, pp. 217–222. Kuo, S. M. & Yang, Y. (1996). Broadband adaptive noise equalizer. IEEE Signal Processing Letters, Vol. 3, No. 8, August 1996, pp. 234–235. Kuo, S. M. & Ji, M. (1996). Passband disturbance reduction in periodic active noise control systems. IEEE Transactions Speech Audio Processing, Vol. 4, No. 2, 1996, pp. 96–103. Kuo, S. M. & Morgan, D.
2, March 2006, pp. 331–335. Applications ofFiltering: Adaptive Recent Advancements in Active Noise Control Applications of Adaptive Recent Filtering: Advancements in Active Noise Control 47 27 Kuo, S. M. & Puvvala, A. B. (2006). Effects of frequency separation in periodic active noise control systems. IEEE Transactions Audio Speech Language Processing, Vol. 14, No. 5, Sept. 2006, pp. 1857–1866. Kuo, S. M. & Gireddy, R. (2007).
Adaptive Filtering Applications by Edited by: Lino Garcia