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433–437. Eriksson, L. J. & Allie, M. C. (1989). Use of random noise for on-line transducer modeling in an adaptive active attenuation system. Journal of Acoustical Society of Amerrica, Vol. 85, No. 2, Feb. 1989, pp. 797–802. Esmailzadeh, E. & Alasty, A. & Ohadi, A. R. (2002). Hybrid active noise control of a one-dimensional acosutic duct. Transactions of the ASME, Vol. 124, 2002, pp. 10–18. Gan, W. S. & Kuo, S. M. (2002). An integrated audio and active noise control headsets. IEEE Transactions Consumer Electronics, Vol.

13, No. 5, 2005, pp. 1082–1088. Akhtar, M. T. & Abe, M. & Kawamata, M. (2006). A new variable step size LMS algorithm-based method for improved online secondary path modelling in active noise control systems. IEEE Transactions Audio Speech Language Processing, Vol. 14, No. 2, 2006, pp. 720–726. Akhtar, M. T. & Abe, M. & Kawamata, M. (2007). On active noise control systems with online acoustic feedback path modeling. IEEE Transactions Audio Speech Language Processing, Vol. 15, No. 3, 2007, pp. 593–600.

X (n ), and the B (z) is excited by an internally generated reference signal u (n ). Both ANC filters W (z) and B (z) are adapted by FxLMS algorithms. The residual error signal eo (n ) is given in Eq. (41) and as explained earlier, the first term is desired error signal for the adaptation of W (z) and second term is desired error signal for B (z). To achieve cancellation [ideally eo (n ) = 0], W (z) needs to be excited by the input correlated with d(n ) [the reference signal x (n ) is indeed that input], and B (z) needs to be excited by the input correlated with v(n ) [such input is not available directly and needs to be generated internally].

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