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**Sample text**

Thus modiﬁed-Sun’s algorithm for ANC of impulse noise is proposed as w (n + 1) = w (n ) + μ w e (n )xˆ s (n ). (21) In order to further improve the robustness of the Sun’s algorithm; instead of ignoring the large amplitude sample; we may clip the sample by a threshold value, and thus the reference signal is modiﬁed as ⎧ ⎨ c1 , x ( n ) ≤ c1 x (n ) = c , x ( n ) ≥ c2 (22) ⎩ 2 x (n ), otherwise As stated earlier, ignoring (or even clipping) the peaky samples in the update of FxLMS algorithm does not mean that peaky samples will not appear in the residual error e(n ).

2008) and references there in. , 1997). , 2008). In this case, the error microphone cannot be placed at the ears of the bed partner, where maximum cancellation is required, and hence an efﬁcient virtual sensing technique is required to improve the noise reduction around ears using error microphones installed on the headboard. There has been a very little research on active control of moving noise sources. It is obvious that acoustic paths will be highly time varying in such cases, and hence the optimal solution for ANC would also vary when the positions of primary noise source change (Guo & Pan, 2000).

It is worth mentioning that the feedforward ANC provides wider control bandwidth within moderate controller gain than the feedback ANC, whereas feedback ANC gives signiﬁcant performance for narrowband or predictable noise sources. 3 Review on signal processing challenges The FxLMS algorithm appears to be very tolerant of errors made in the modeling of S (z) by the ﬁlter Sˆ (z). As shown in (Elliott et. , 1987; Morgan, 1980), with in the limit of slow 1 This is why FxLMS algorithm for feedback ANC systems is sometimes referred as internal model control (Kuo & Morgan, 1996) Applications ofFiltering: Adaptive Recent Advancements in Active Noise Control Applications of Adaptive Recent Filtering: Advancements in Active Noise Control 255 adaptation, the algorithm will converge with nearly 90◦ of phase error between Sˆ(z) and S (z).