Inversion-Based Feedforward Approach to Broadband Acoustic Noise Reduction

2008 ◽  
Vol 130 (5) ◽  
Author(s):  
Tom C. Waite ◽  
Qingze Zou ◽  
Atul Kelkar

In this article, an inversion-based feedforward control approach to achieve broadband active-noise control is investigated. Broadband active-noise control is needed in many areas, from heating, ventilation and air conditioning (HVAC) ducts to aircraft cabins. Achieving broadband active-noise control, however, is very challenging due to issues such as the complexity of acoustic dynamics (which has no natural roll-off at high frequency, and is often nonminimum phase), the wide frequency spectrum of the acoustic noise, and the critical requirement to overcome the delay of the control input relative to the noise signal. These issues have limited the success of existing feedforward control techniques to the low-frequency range of [0,1]kHz. The modeling issues in capturing the complex acoustic dynamics coupled with its nonminimum-phase characteristic also prevent the use of high-gain feedback methods, making the design of an effective controller to combat broadband noises challenging. In this article, we explore, through experiments, the potential of inversion-based feedforward control approach for noise control over the 1kHz low-frequency range limit. Then we account for the effect of modeling errors on the feedforward input by a recently developed inversion-based iterative control technique. Experimental results presented show that noise reduction of over 10–15dB can be achieved in a broad frequency range of 5kHz by using the inversion-based feedforward control technique.

2001 ◽  
Vol 124 (1) ◽  
pp. 100-104 ◽  
Author(s):  
Zhang Qizhi ◽  
Jia Yongle

The nonlinear active noise control (ANC) is studied. The nonlinear ANC system is approximated by an equivalent model composed of a simple linear sub-model plus a nonlinear sub-model. Feedforward neural networks are selected to approximate the nonlinear sub-model. An adaptive active nonlinear noise control approach using a neural network enhancement is derived, and a simplified neural network control approach is proposed. The feedforward compensation and output error feedback technology are utilized in the controller designing. The on-line learning algorithm based on the error gradient descent method is proposed, and local stability of closed loop system is proved based on the discrete Lyapunov function. A nonlinear simulation example shows that the adaptive active noise control method based on neural network compensation is very effective to the nonlinear noise control, and the convergence of the NNEH control is superior to that of the NN control.


2018 ◽  
Vol 8 (11) ◽  
pp. 2291 ◽  
Author(s):  
Kenta Iwai ◽  
Satoru Hase ◽  
Yoshinobu Kajikawa

In this paper, we propose a multichannel active noise control (ANC) system with an optimal reference microphone selector based on the time difference of arrival (TDOA). A multichannel feedforward ANC system using upstream reference signals can reduce various noises such as broadband noise by arranging reference microphones close to noise sources. However, the noise reduction performance of an ANC system degrades when the noise environment changes, such as the arrival direction. This is because some reference microphones do not satisfy the causality constraint that the unwanted noise propagates to the control point faster than the anti-noise used to cancel the unwanted noise. To solve this problem, we propose a multichannel ANC system with an optimal reference microphone selector. This selector chooses the reference microphones that satisfy the causality constraint based on the TDOA. Some experimental results demonstrate that the proposed system can choose the optimal reference microphones and effectively reduce unwanted acoustic noise.


2009 ◽  
Vol 2009 ◽  
pp. 1-13 ◽  
Author(s):  
Govind Kannan ◽  
Issa M. S. Panahi ◽  
Richard W. Briggs

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 (NALmax⁡). 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 NALmax⁡ consistently. The derivation of NALmax⁡ 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.


2011 ◽  
Vol 2011 (0) ◽  
pp. _628-1_-_628-8_
Author(s):  
Xnn WANG ◽  
Shinya KIJIMOTO ◽  
Yosuke KOBA

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