2008
DOI: 10.1109/tasl.2008.2002757
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Optimal Variable Step-Size NLMS Algorithms With Auxiliary Noise Power Scheduling for Feedforward Active Noise Control

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Cited by 74 publications
(51 citation statements)
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“…All these components of secondary path introduce signal delay and also add noise to the filter output which deteriorates system performance. Various methods were proposed in the past for tackling this problem [8] - [17]. In this paper, Filtered-X NLMS algorithm is used for estimating the secondary path transfer function.…”
Section: Technique Of Active Noise Controlmentioning
confidence: 99%
“…All these components of secondary path introduce signal delay and also add noise to the filter output which deteriorates system performance. Various methods were proposed in the past for tackling this problem [8] - [17]. In this paper, Filtered-X NLMS algorithm is used for estimating the secondary path transfer function.…”
Section: Technique Of Active Noise Controlmentioning
confidence: 99%
“…The error signal is used to update the coefficients of adaptive filters that drive the secondary sources. Adaptive algorithms, such as least mean square (LMS), filtered-x LMS (FxLMS) [3] and their extensions [4,5], are capable to catch up the variations of the noisy acoustic field and, consequently, to minimize the variance of the residual acoustic signal. Real-time ANC systems can be effectively exploited in scenarios where the noise can be modeled as a stationary or slowly varying process with respect to the reaction time of the ANC systems [6,7,8].…”
Section: Introductionmentioning
confidence: 99%
“…Nevertheless, the fixed power auxiliary noise contributes to the residual error, which we want to minimize, and leads to the performance degradation of the ANC system in the steady state. To remedy this problem, various auxiliary noise power scheduling strategies have been proposed [20][21][22][23][24][25]. However, it should be noted that most of the above mentioned strategies were designed for broadband active noise control (BANC).…”
Section: Introductionmentioning
confidence: 99%