Feedforward Algorithms With Simplified Plant Model for Active Noise Control

Pawełczyk, Marek

doi:10.1006/jsvi.2001.4141

Cited by 7 publications

(10 citation statements)

References 8 publications

(8 reference statements)

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“…When |S(f )| ≥ 90 The idea in this paper for control of tonal or narrowband noise is defined in the following steps: a) apply a simplified version of the FXLMS algorithm -the Delayed-x LMS algorithm (Pawelczyk, 2002), i.e.…”

Section: Modification To the Fxlms Algorithmmentioning

confidence: 99%

“…For that algorithm the secondary path delay needs only to be known, instead of a full model. For wideband noise that algorithm was succesfully used in a multi-bank structure by Pawelczyk (2002).…”

Section: Introductionmentioning

confidence: 99%

“…Then, the step size should be greater than 0 but smaller than a certain upper bound [10]. In this paper the authors motivated by the research of Zhou and DeBrunner (2007), are developing further that modification to the FXLMS algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Active Noise Control Using a Fuzzy Inference System Without Secondary Path Modelling

Kurczyk¹,

Pawełczyk²

2015

Archives of Acoustics

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For many adaptive noise control systems the Filtered-Reference LMS, known as the FXLMS algorithm is used to update parameters of the control filter. Appropriate adjustment of the step size is then important to guarantee convergence of the algorithm, obtain small excess mean square error, and react with required rate to variation of plant properties or noise nonstationarity. There are several recipes presented in the literature, theoretically derived or of heuristic origin.This paper focuses on a modification of the FXLMS algorithm, were convergence is guaranteed by changing sign of the algorithm steps size, instead of using a model of the secondary path. A TakagiSugeno-Kang fuzzy inference system is proposed to evaluate both the sign and the magnitude of the step size. Simulation experiments are presented to validate the algorithm and compare it to the classical FXLMS algorithm in terms of convergence and noise reduction.

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Section: Modification To the Fxlms Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Active Noise Control Using a Fuzzy Inference System Without Secondary Path Modelling

Kurczyk¹,

Pawełczyk²

2015

Archives of Acoustics

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“…With regard to the estimation of the secondary path, there are techniques that are based on the hypothesis that the secondary path model does not have to be accurate and can be represented by a delay (delayed-x LMS) [37]. These techniques are used in applications with variable systems in which rapid reaction is also of utmost interest.…”

Section: Description Of the Algorithmsmentioning

confidence: 99%

GPU Implementation of Multichannel Adaptive Algorithms for Local Active Noise Control

Lorente

Ferrer

Diego

et al. 2014

IEEE/ACM Trans. Audio Speech Lang. Process.

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Abstract-Multichannel active noise control (ANC) systems are commonly based on adaptive signal processing algorithms that require high computational capacity, which constrains their practical implementation. Graphics Processing Units (GPUs) are well known for their potential for highly parallel data processing. Therefore, GPUs seem to be a suitable platform for multichannel scenarios. However, efficient use of parallel computation in the adaptive filtering context is not straightforward due to the feedback loops. This paper compares two GPU implementations of a multichannel feedforward local ANC system working as a real-time prototype. Both GPU implementations are based on the filtered-x Least Mean Square algorithms; one is based on the conventional filtered-x scheme and the other is based on the modified filtered-x scheme. Details regarding the parallelization of the algorithms are given. Finally, experimental results are presented to compare the performance of both multichannel ANC GPU implementations. The results show the usefulness of many-core devices for developing versatile, scalable, and low-cost multichannel ANC systems.

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“…Rapid progress in digital signal processors (DSPs) and sensor technologies as well as new real-time adaptive control algorithm designs [1][2][3] has opened the door to new approaches in ANC. Active control of sinusoidal signals has been an important research and development topic [4] and can lead to different system architectures [5] and design simplifications [6]. The standard approach to realize an ANC system is to employ the FXLMS algorithm in feedforward configuration [4,7] as portrayed in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Sequentially Adapted Parallel Feedforward Active Noise Control of Noisy Sinusoidal Signals

Kannan

Panahi

Briggs

2009

Advances in Acoustics and Vibration

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A large class of acoustic noise sources has an underlying periodic process that generates a periodic noise component, and thus their acoustic noise can in general be modeled as the sum of a periodic signal and a randomly fluctuating signal (usually a broadband background noise). Active control of periodic noise (i.e., for a mixture of sinusoids) is more effective than that of random noise. For mixtures of sinusoids in a background broadband random noise, conventional FXLMS-based single filter method does not reach the maximum achievable Noise Attenuation Level (NAL max ). In this paper, an alternative approach is taken and the idea of a parallel active noise control (ANC) architecture for cancelling mixtures of periodic and random signals is presented. The proposed ANC system separates the noise into periodic and random components and generates corresponding antinoises via separate noise cancelling filters, and tends to reach NAL max consistently. The derivation of NAL max is presented. Both the separation and noise cancellation are based on adaptive filtering. Experimental results verify the analytical development by showing superior performance of the proposed method, over the single-filter approach, for several cases of sinusoids in white noise.

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Feedforward Algorithms With Simplified Plant Model for Active Noise Control

Cited by 7 publications

References 8 publications

Active Noise Control Using a Fuzzy Inference System Without Secondary Path Modelling

Active Noise Control Using a Fuzzy Inference System Without Secondary Path Modelling

GPU Implementation of Multichannel Adaptive Algorithms for Local Active Noise Control

Sequentially Adapted Parallel Feedforward Active Noise Control of Noisy Sinusoidal Signals

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