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Delayless identification in ANC systems using Subband adaptive techniques

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Delayless identification in ANC systems using Subband adaptive techniques. / Karna, Saurav; Montazeri, Allahyar.
In: International Journal of Signal Processing Systems, Vol. 4, No. 6, 12.2016, p. 459-464.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Karna, S & Montazeri, A 2016, 'Delayless identification in ANC systems using Subband adaptive techniques', International Journal of Signal Processing Systems, vol. 4, no. 6, pp. 459-464. https://doi.org/10.18178/ijsps.4.6.459-464

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Vancouver

Karna S, Montazeri A. Delayless identification in ANC systems using Subband adaptive techniques. International Journal of Signal Processing Systems. 2016 Dec;4(6):459-464. doi: 10.18178/ijsps.4.6.459-464

Author

Karna, Saurav ; Montazeri, Allahyar. / Delayless identification in ANC systems using Subband adaptive techniques. In: International Journal of Signal Processing Systems. 2016 ; Vol. 4, No. 6. pp. 459-464.

Bibtex

@article{87eae0f66da1434d9007b6a8c91ea54e,
title = "Delayless identification in ANC systems using Subband adaptive techniques",
abstract = "Subband techniques have been developed to use low order subfilters instead of full band higher order filters and consequently reduce the complexity and increase the convergence speed of the adaptive algorithm. In this paper the performance of two delayless subband adaptive algorithms for identification of an unknown system in an active noise control scheme are compared. This is carried out by using a common speech signal as the excitation input for identification of the secondary path model. The performances of the algorithms are measured in terms of the achieved minimum mean square error and misalignment error. The results are also compared to the time domain NLMS algorithm. The compared delayless structures are working in the closed loop form with DFT analysis filterbanks. Adaptation in the auxiliary loop and with help of weight transformation eliminates signal path delay and hence the unknown secondary path can be modelled accurately.",
keywords = "Active Noise Control (ANC), Delayless subband adaptive filter, Frequency domain adaptive filter",
author = "Saurav Karna and Allahyar Montazeri",
year = "2016",
month = dec,
doi = "10.18178/ijsps.4.6.459-464",
language = "English",
volume = "4",
pages = "459--464",
journal = "International Journal of Signal Processing Systems",
issn = "2315-4535",
number = "6",

}

RIS

TY - JOUR

T1 - Delayless identification in ANC systems using Subband adaptive techniques

AU - Karna, Saurav

AU - Montazeri, Allahyar

PY - 2016/12

Y1 - 2016/12

N2 - Subband techniques have been developed to use low order subfilters instead of full band higher order filters and consequently reduce the complexity and increase the convergence speed of the adaptive algorithm. In this paper the performance of two delayless subband adaptive algorithms for identification of an unknown system in an active noise control scheme are compared. This is carried out by using a common speech signal as the excitation input for identification of the secondary path model. The performances of the algorithms are measured in terms of the achieved minimum mean square error and misalignment error. The results are also compared to the time domain NLMS algorithm. The compared delayless structures are working in the closed loop form with DFT analysis filterbanks. Adaptation in the auxiliary loop and with help of weight transformation eliminates signal path delay and hence the unknown secondary path can be modelled accurately.

AB - Subband techniques have been developed to use low order subfilters instead of full band higher order filters and consequently reduce the complexity and increase the convergence speed of the adaptive algorithm. In this paper the performance of two delayless subband adaptive algorithms for identification of an unknown system in an active noise control scheme are compared. This is carried out by using a common speech signal as the excitation input for identification of the secondary path model. The performances of the algorithms are measured in terms of the achieved minimum mean square error and misalignment error. The results are also compared to the time domain NLMS algorithm. The compared delayless structures are working in the closed loop form with DFT analysis filterbanks. Adaptation in the auxiliary loop and with help of weight transformation eliminates signal path delay and hence the unknown secondary path can be modelled accurately.

KW - Active Noise Control (ANC)

KW - Delayless subband adaptive filter

KW - Frequency domain adaptive filter

U2 - 10.18178/ijsps.4.6.459-464

DO - 10.18178/ijsps.4.6.459-464

M3 - Journal article

VL - 4

SP - 459

EP - 464

JO - International Journal of Signal Processing Systems

JF - International Journal of Signal Processing Systems

SN - 2315-4535

IS - 6

ER -