Adaptive noise control algorithms for active headrest system

Pawełczyk, Marek

doi:10.1016/j.conengprac.2003.11.006

Cited by 84 publications

(47 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are some successful cases of applying this virtual microphone technique in the lab [4][5][6][7][8]. Pawelczyk proposed a prototype of an active headrest system by using FXLMS and the filtered-X least mean squares virtual microphone (FXLMS-VM) algorithm [7]. The noises around the observer's ear were successfully controlled by his proposed ANC system, and the distribution of the quiet zone is discussed.…”

Section: Introductionmentioning

confidence: 99%

“…With improvements in computer science, the application of complicated ANC algorithms has become feasible. Many researchers have attempted to improve the performance of ANC algorithms for real applications [2][3][4][5][6][7][8][9][10]. Among these ANC algorithms, the filtered-X least mean squares (FXLMS) algorithm is the most popular one.…”

Section: Introductionmentioning

confidence: 99%

“…Amongst many existing virtual microphone techniques, the remote microphone technique is widely used, which uses an additional filter to estimate virtual error signal from error signal of the physical microphone. There are some successful cases of applying this virtual microphone technique in the lab [4][5][6][7][8]. Pawelczyk proposed a prototype of an active headrest system by using FXLMS and the filtered-X least mean squares virtual microphone (FXLMS-VM) algorithm [7].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Study of the Virtual Microphone Algorithm for ANC System Working in Audio Interference Environment

Wang

Lee

Qiu

2014

Journal of Low Frequency Noise, Vibration and Active Control

View full text Add to dashboard Cite

Two kinds of anti-audio interference active noise control (ANC) algorithms are proposed based on a conventional filtered-X least mean squares virtual microphone (FXLMS-VM) algorithm. The proposed filtered-X least mean squares virtual microphone and anti-audio interference (FXLMS-VM-AI) algorithm employs an extra audio path to compensate for the influence of the audio interference phenomenon on the ANC adaptive filter. The proposed time frequency mixed filtered-X least mean squares virtual microphone and antiaudio interference (TFM-FXLMS-VM-AI) algorithm can overcomes this audio interference effect by calculating the coefficients of the ANC adaptive filter in the frequency domain. The time domain acoustic wave field simulation method is adopted to test the performance of the proposed ANC algorithms in an audio interference environment. By analyzing the simulation results, both proposed algorithms show better convergence accuracy in terms of the adaptive filter coefficients and better audio signal performance than the conventional algorithm. The proposed FXLMS-VM-AI algorithm has a faster convergence speed for the ANC adaptive filter than the proposed TFM-FXLMS-VM-AI algorithm. The proposed TFM-FXLMS-VM-AI algorithm has a lower computational complexity than the conventional algorithm and the proposed TFM-FXLMS-VM-AI algorithm.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Study of the Virtual Microphone Algorithm for ANC System Working in Audio Interference Environment

Wang

Lee

Qiu

2014

Journal of Low Frequency Noise, Vibration and Active Control

View full text Add to dashboard Cite

show abstract

“…The acoustic compensation wave has to have in the point of space (point of observation) the same amplitude as the acoustic noise wave and opposite phase. The main issue in the application of ANR systems in hearing protectors is ensuring stability of the ANR system operation and at the same time a highly effective active noise reduction (Pawełczyk, 2004). The ANR system should analyze a noise signal and generate adequate compensating signal taking into account transmittances of the electroacoustic path, including phase shift results from different distances between sources and point of observation (Morzyński, Makarewicz, 2003;Krukowicz, 2010).…”

Section: Introductionmentioning

confidence: 99%

Active Noise Reduction Algorithm Based on NOTCH Filter and Genetic Algorithm

Górski¹,

Morzyński²

2013

Archives of Acoustics

View full text Add to dashboard Cite

Application of active noise reduction (ANR) systems in hearing protectors requires the use of control algorithms to ensure stability of the ANR system and at the same time highly effective active noise reduction. A control algorithm based on NOTCH filters is an example of solutions that meet these criteria. Their disadvantage is operation over a narrow frequency band and a need for prior determination of frequencies to be reduced. This paper presents a solution of the ANR system for hearing protectors which is controlled with the use of modified NOTCH filters with parameters determined by a genetic algorithm. Application of a genetic algorithm allows to change the NOTCH filter reference signal frequency, and thus, adapt the filter to the reduced signal frequency.

show abstract

“…Rapid progress in digital signal processors (DSPs) and sensor technologies as well as new real-time adaptive control algorithm designs [1][2][3] has opened the door to new approaches in ANC. Active control of sinusoidal signals has been an important research and development topic [4] and can lead to different system architectures [5] and design simplifications [6].…”

Section: Introductionmentioning

confidence: 99%

Sequentially Adapted Parallel Feedforward Active Noise Control of Noisy Sinusoidal Signals

Kannan

Panahi

Briggs

2009

Advances in Acoustics and Vibration

View full text Add to dashboard Cite

A large class of acoustic noise sources has an underlying periodic process that generates a periodic noise component, and thus their acoustic noise can in general be modeled as the sum of a periodic signal and a randomly fluctuating signal (usually a broadband background noise). Active control of periodic noise (i.e., for a mixture of sinusoids) is more effective than that of random noise. For mixtures of sinusoids in a background broadband random noise, conventional FXLMS-based single filter method does not reach the maximum achievable Noise Attenuation Level (NAL max ). In this paper, an alternative approach is taken and the idea of a parallel active noise control (ANC) architecture for cancelling mixtures of periodic and random signals is presented. The proposed ANC system separates the noise into periodic and random components and generates corresponding antinoises via separate noise cancelling filters, and tends to reach NAL max consistently. The derivation of NAL max is presented. Both the separation and noise cancellation are based on adaptive filtering. Experimental results verify the analytical development by showing superior performance of the proposed method, over the single-filter approach, for several cases of sinusoids in white noise.

show abstract

Adaptive noise control algorithms for active headrest system

Cited by 84 publications

References 3 publications

A Study of the Virtual Microphone Algorithm for ANC System Working in Audio Interference Environment

A Study of the Virtual Microphone Algorithm for ANC System Working in Audio Interference Environment

Active Noise Reduction Algorithm Based on NOTCH Filter and Genetic Algorithm

Sequentially Adapted Parallel Feedforward Active Noise Control of Noisy Sinusoidal Signals

Contact Info

Product

Resources

About