Filtered-x Error Coded Affine Projection algorithm with Evolving order for active noise control

Rodríguez, SA; Avalos, Juan-Gerardo; Sanchez, Garcia J. C.

doi:10.1109/lascas.2017.7948073

Cited by 1 publication

(1 citation statement)

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“…The authors of [26] presented a multichannel ANC system using variants of AP algorithms with a variable step size and a projection order that evolves. In [27], the error signal of the AP algorithm is encoded and the projection order is also calculated dynamically, the resulting algorithm is applied to a single-channel ANC system; the reported results in both works demonstrate the efficiency of those strategies; nevertheless, it is still necessary to perform matrix inversion to update the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Multichannel Filtered-X Error Coded Affine Projection-Like Algorithm with Evolving Order

Avalos

Aguilar

Martínez

et al. 2017

Shock and Vibration

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Affine projection (AP) algorithms are commonly used to implement active noise control (ANC) systems because they provide fast convergence. However, their high computational complexity can restrict their use in certain practical applications. The Error Coded Affine Projection-Like (ECAP-L) algorithm has been proposed to reduce the computational burden while maintaining the speed of AP, but no version of this algorithm has been derived for active noise control, for which the adaptive structures are very different from those of other configurations. In this paper, we introduce a version of the ECAP-L for single-channel and multichannel ANC systems. The proposed algorithm is implemented using the conventional filtered-x scheme, which incurs a lower computational cost than the modified filtered-x structure, especially for multichannel systems. Furthermore, we present an evolutionary method that dynamically decreases the projection order in order to reduce the dimensions of the matrix used in the algorithm's computations. Experimental results demonstrate that the proposed algorithm yields a convergence speed and a final residual error similar to those of AP algorithms. Moreover, it achieves meaningful computational savings, leading to simpler hardware implementation of real-time ANC applications.

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Section: Introductionmentioning

confidence: 99%