Frequency-domain periodic active noise control and equalization

Kuo, Sen M.; Tahernezhadi, M.; Ji, Li

doi:10.1109/89.593309

Cited by 34 publications

(18 citation statements)

References 17 publications

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“…From (1) and (5), the z-transform of the new cancellation error signal is (7) When and are small enough, a simple insight into (7) is that the cancellation error is close to zero, i.e., , after the adaptive filter converges. Hence, we can see that is to realize the following transfer function: (8) In some circumstances, the decay envelops of the impulse responses of and are presented on both sides of the maximum output response. For example, has an unsymmetric decay envelop for propagation delay while consists of a lowpass filter of symmetric coefficients for a linear-phase consideration.…”

Section: A the New Fxlms Algorithmmentioning

confidence: 99%

“…Although some adaptive algorithms such as the filtered-X recursive least-squares (FxRLS) [5] and Kalman filter [6] can provide better convergence rate and performance, the FxLMS algorithm is very appealing to researchers because of a relatively lower computational burden. Some variants of the FxLMS algorithms for ANC can be found in the literature, such as the lattice ANC [7], frequency-domain ANC [8], [9], delayless subband ANC [10], [11], etc. The lattice structure filter fails to provide a satisfying convergence rate when the primary noise is broadband.…”

Section: Introductionmentioning

confidence: 99%

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Feedforward Active Noise Control With a New Variable Tap-Length and Step-Size Filtered-X LMS Algorithm

Chang

Chu

2014

IEEE/ACM Trans. Audio Speech Lang. Process.

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The fixed tap-length and step-size filtered-X least mean-square (FxLMS) algorithm is conventionally used in active noise control (ANC) systems. A tradeoff between the performance and the convergence rate is a well-known problem due to the choice of the step size. Although the variable-step-size FxLMS algorithms have been proposed for fast convergence, a long tap-length filter is frequently required in order to deal with different environments such that the convergence rate is still subject to a small step size for the long tap length. In this paper, we study a new ANC system with a variable tap-length and step-size FxLMS algorithm. Based on the assumption of an unsymmetric and two-sided exponential decay response model for the ANC control filter, the new FxLMS algorithm has the minimum mean-square deviation for the optimal filter coefficients. In the online secondary path modeling ANC system, simulation results show that the new algorithm with different kind of variable step sizes can provide significant improvements of convergence rate and noise reduction ratio, compared to the fixed-tap-length FxLMS algorithms.Index Terms-Active noise control, exponential decay response, filtered-X LMS, mean-square deviation, noise reduction ratio, secondary path model.

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Section: A the New Fxlms Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Feedforward Active Noise Control With a New Variable Tap-Length and Step-Size Filtered-X LMS Algorithm

Chang

Chu

2014

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…The frequency domain FXLMS algorithm was developed to reduce the computational complexity [10][11][12][13][14]. One major disad vantage of such frequency domain algorithm is the delay that is introduced into the signal filtering path, due to the buffering pro cess converting signal samples into signal vectors.…”

Section: Introductionmentioning

confidence: 99%

A Computationally Efficient Multichannel Active Road Noise Control System

Duan

Lim

et al. 2014

Journal of Dynamic Systems, Measurement, and Control

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A multichannel active noise control (ANC) system has been developed for a vehicle appli cation, which employs loudspeakers to reduce the low-frequency road noise. Six acceler ometers were attached to the vehicle structure to provide the reference signal for the feedforward control strategy, and two loudspeakers and two microphones were applied to attenuate acoustic noise near the headrest of the driver's seat. To avoid large compu tational burden caused by the conventional time-domain filtered-x least mean square (FXLMS) algorithm, a time-frequency domain FXLMS (TF-FXLMS) algorithm is pro posed. The proposed algorithm calculates the gradient estimate and filtered reference signal in the frequency domain to reduce the computational requirement, while also updates the control signals in the time domain to avoid delay. A comprehensive computa tional complexity analysis is conducted to demonstrate that the proposed algorithm requires significantly lower computational cost as compared to the conventional FXLMS algorithm.

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“…The gradient algorithm has also less computational complexity over the non-gradient algorithms. Another way of having active noise cancellation is to employ adaptive filter to characterize the transfer function between error mic to control noise in frequency domain and it is described in the [25]. The least mean square (LMS) algorithm was applied in each band of frequency decomposition using DFT to model the control signal.…”

Section: Active Noise Cancellationmentioning

confidence: 99%

Speech Enhancement Techniques: Quality vs. Intelligibility

Nemade¹,

Shah²

2014

IJFCC

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Abstract-With the rapid advancements in industrial and technology applications, the demand from consumer for transmission and manipulation of data, primarily -speech, audio and images, are at its peak. The speech, being a fundamental way of communication for the humans, has been embedded in various essential applications like speech recognition, voice-distance-talk and other forms of personal communications. There are so many applications of speech still to be far from reality just because of lack of efficient and reliable noise removal mechanism and preserving or improving the intelligibility for the speech signals. The broad categories of speech enhancement techniques can be listed as speech filtering techniques, beam forming techniques and active noise cancellation methods. In this paper, an attempt has been stepped towards surveying the methodologies for speech improvement. It is also interesting to investigate, how these techniques affect the performance of various application systems like speech recognition and speech communication. Essentially, we also discuss about the types and sources of noise that can be considered in speech enhancements. Index Terms-Enhancement, beamforming and active noise cancellation, single channel enhancement I. INTRODUCTIONSpeech is the fundamental and common medium, hence important for us, to communicate and most effective and reliable means for expressing oneself for personal communication. With advancement in hardware technologies, there are so many electronic and mobile personal communication based devices available, today in market and that too in cheaper cost and with easy availability. The applications like speech recognition, mobile and personal communication, public address system are few of the applications from long list of speech based systems. However, undesired noises in environment like sound from heavy machines, vehicles are also present in one or other form everywhere. These noises cause undesired effects in speech transmission and acquiring systems. Recently, restricted or usable vicinity of applications is moving from one place and close room to more open and multiple locations, leading to several of types undesired signals of mixing with desired speech signal making speech more corrupt with noise. Not only human communications but intelligent machines which Manuscript received September 14, 2013; revised November 12, 2013 trying to automate the things and sometimes also takes decision based on what it receives as a speech, also suffers from the degraded performance.Since last five decades, various approaches for noise reduction and speech enhancements have been investigated and developed. Among, very early and fundamental approach of noise reduction was introduced to use the theory of the optimum Wiener filter. Given a desired signal and an input signal, the Wiener filter produces an estimate of the desired signal that is optimal, i.e. the squared mean error or difference between the signals is minimized. The Wiener filter can also be adaptively estimated us...

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Frequency-domain periodic active noise control and equalization

Cited by 34 publications

References 17 publications

Feedforward Active Noise Control With a New Variable Tap-Length and Step-Size Filtered-X LMS Algorithm

Feedforward Active Noise Control With a New Variable Tap-Length and Step-Size Filtered-X LMS Algorithm

A Computationally Efficient Multichannel Active Road Noise Control System

Speech Enhancement Techniques: Quality vs. Intelligibility

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