Development of Frequency Domain Block Filtered-s LMS (FBFSLMS) Algorithm for Active Noise Control System

Panda,; Nayak, Richi

doi:10.1109/icassp.2006.1661269

Cited by 14 publications

(16 citation statements)

References 5 publications

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“…As is evident from the literature, ANC can be implemented either in the time-domain (TD) or frequency-domain (FD). The latter approach has an edge over the former in terms of the computational efficiency, especially for larger block sizes of input [3]. Larger block sizes (which are typically of the order of 512-points or more) imply the need for computing higher order DFTs.…”

Section: Introductionmentioning

confidence: 98%

“…Besides, ANC algorithms require several such FFT blocks. As the DFT is implemented on a digital signal processor (DSP), the computational burden on the processor poses problems in real-time implementations, such as in frequency-domain block filtered-s least-mean-square (FBFSLMS) algorithm [3] and RDL-FBFXLMS algorithm. Further, the conventional method is not processor-efficient as the processor is idle during the sampling instants other than the N th , 2N th , 3N th ,.., sampling instants.…”

Section: Introductionmentioning

confidence: 99%

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Processor-efficient FFT Implementation Scheme for Active Noise Control Applications

Kumar

Prabhu

Das

2010

2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems

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Most of the frequency-domain (FD)-based activenoise control (ANC) applications involve the computation of several discrete Fourier transforms (DFTs). Conventionally, an N-point DFT of a sequentially arriving data is computed only after the arrival of the N th sample. For applications involving ANC, such an approach will overload the processor. In this paper, an alternative method to compute the DFT is proposed, which distributes the computations over a span of several sampling instants. As an example to prove the efficiency of the proposed algorithm, it is applied to the reduced delay-less frequency-domain block filtered-x least-mean-square (RD-FBFXLMS) algorithm, wherein about 24% (for a block length of 1024 samples) of the multiplications and about 29% of additions (which were supposed to have been done at the last sampling instant of each block) are shifted to earlier sampling instants during which the processor is idle. The percentage of computational redistribution will be higher for multichannel non-linear systems.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Processor-efficient FFT Implementation Scheme for Active Noise Control Applications

Kumar

Prabhu

Das

2010

2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems

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“…Unlike the previously proposed block frequency-domain algorithms [19][20][21], this new algorithm provides an opportunity to use higher order filters in the ANC controller. Appending zeros to the shorter estimated acoustic path filters so that they are the same length as the ANC filter (to make N L = ), as is used in [19][20][21], unnecessarily introduces extra computational complexity. The major advantage of the proposed algorithm is that it is more power efficient due to its lower computational effort as the average power consumption is related to the total number of computations over a period of time.…”

Section: Experiment-3: Delayed Adaptationmentioning

confidence: 99%

“…In addition to this, the block algorithm proposed in this paper does not require that the length of the ANC filter and the length of the estimated acoustic paths to be equal, unlike the algorithms in [19][20][21]. Detailed computational complexity analysis is carried out to demonstrate the advantages of the proposed algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…A number of researchers have shown interest in developing computationally efficient algorithms, otherwise known as fast ANC algorithms [10][11][12][13][14][15][16][17][18]. A class of fast ANC algorithms known as the block algorithm, which exploits frequency-domain convolution and correlation operations, has been shown to achieve a significant computational advantage over its conventional time-domain counterpart [19][20][21]. One disadvantage of the block implementation is however, the inherent delay equal to the size of the block length.…”

Section: Introductionmentioning

confidence: 99%

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A computationally efficient frequency-domain filtered-X LMS algorithm for virtual microphone

Das

Moreau

Cazzolato

2013

Mechanical Systems and Signal Processing

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The computational complexity of the virtual FXLMS algorithm is higher than that of the conventional FXLMS algorithm. The additional complexity comes from computation of three secondary path transfer functions (as opposed to one) and a transfer function between the physical and the virtual microphones. The order of these transfer functions may be very high in practical situations where the acoustic damping is low. The high computational complexity of the virtual FXLMS algorithm imposes issues like high power consumption, making it difficult to implement the algorithm in battery operated ANC devices such as active headsets. In addition, the operating sampling frequency of the algorithm is limited and this in turn restricts its operation to relatively low frequency applications. In this paper, a new virtual FXLMS algorithm is derived by implementing all of the secondary path transfer functions in the frequency domain. The algorithm is simulated using measured transfer functions in a duct with low acoustic damping. Implementation schemes are proposed for the new frequency-domain virtual FXLMS algorithm, citing its advantages for use as an efficient real-time active noise control algorithm.Index Terms: Active noise control, adaptive filter, frequency-domain adaptive filter, computational complexity, virtual ANC. 2

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A dual filtering scheme for nonlinear active noise control

Delvecchio

Piroddi

2013

Adaptive Control & Signal

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Active noise control problems are often affected by nonlinear effects such as distortion and saturation of measurement and actuation devices, which call for suitable nonlinear models and algorithms. The active noise control problem can be interpreted as an indirect model identification problem, due to the secondary path dynamics that follow the control filter block. This complicates the weight update mechanism in the nonlinear case, in that the error gradient depends on the secondary path gradient through nonlinear recursions. A simpler and computationally less demanding approach is here proposed that employs the updating scheme of the standard filtered-x least mean squares (LMS) or filtered-u LMS algorithm. As in those schemes, the calculation of the error gradient requires a signal filtering through an auxiliary system, here obtained through a secondary adaptation loop. The resulting dual filtering LMS algorithm performs the adaptation of the controller parameters in a direct identification mode and can therefore be easily coupled with adaptive model structure selection schemes to provide online tuning of the model structure, for improved model robustness.

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Development of Frequency Domain Block Filtered-s LMS (FBFSLMS) Algorithm for Active Noise Control System

Cited by 14 publications

References 5 publications

Processor-efficient FFT Implementation Scheme for Active Noise Control Applications

Processor-efficient FFT Implementation Scheme for Active Noise Control Applications

A computationally efficient frequency-domain filtered-X LMS algorithm for virtual microphone

A dual filtering scheme for nonlinear active noise control

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