Active noise control: compensating non-stationary acoustic signals

Alippi, Chiara; Cogliati, D.

doi:10.1109/cimsa.2005.1522884

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Based on the principle of superposition the primary noise ( ) is canceled by the secondary noise of equal amplitude but opposite phase [2]. In most of real active noise control systems not only the primary noise has a non-stationary nature but also the surrounding environment is time varying and prone to change [3]- [5]. This necessitates development of an adaptive mechanism to cope with these two types of changes.…”

Section: Introductionmentioning

confidence: 99%

Delayless Identification in ANC Systems Using Subbband Adaptive Techniques

Karna

Montazeri

2016

IJSPS

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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.

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Section: Introductionmentioning

confidence: 99%

Delayless Identification in ANC Systems Using Subbband Adaptive Techniques

Karna

Montazeri

2016

IJSPS

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“…Based on the principle of superposition the primary noise is canceled by the secondary noise of equal amplitude but opposite phase . In most of real active noise control systems not only the primary noise has a non-stationary nature but also the surrounding environment is time varying and prone to change [3,4,5]. One of the commonly used adaptive algorithms to cope these problems is the so called Filtered-x least mean square Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Adaptive frequency domain identification for ANC systems using non-stationary signals

Montazeri

Karna

2015

2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

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The problem of identification of secondary path in active noise control applications is dealt with fundamentally using time-domain adaptive filters. The use of adaptive frequency domain subband identification as an alternative has some significant advantages which are overlooked in such applications. In this paper two different delayless subband adaptive algorithms for identification of an unknown secondary path in an ANC framework are utilized and compared. Despite of reduced computational complexity and increase convergence rate this approach allows us to use non-stationary audio signals as the excitation input to avoid injection of annoying white noise. For this purpose two non-stationary music and speech signals are used for identification. The performances of the algorithms are measured in terms of minimum mean square error and convergence speed. The results are also compared to the time domain NLMS algorithm for the same scenario. The proposed delayless algorithms have a closed loop structure with DFT filterbanks as the analysis filter. To eliminate the delay in the signal path two different weights transformation schemes are compared.

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Active noise control: compensating non-stationary acoustic signals

Cited by 2 publications

References 7 publications

Delayless Identification in ANC Systems Using Subbband Adaptive Techniques

Delayless Identification in ANC Systems Using Subbband Adaptive Techniques

Adaptive frequency domain identification for ANC systems using non-stationary signals

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