scholarly journals Rapidly converging adaptive state‐space‐based multichannel active noise control algorithm for reduction of broadband noise

2007 ◽  
Vol 121 (5) ◽  
pp. 3179-3179
Author(s):  
Arthur P. Berkhoff ◽  
Johan M. Wesselink
Keyword(s):  
State Space ◽  
Control Algorithm ◽  
Noise Control ◽  
Active Noise Control ◽  
Broadband Noise ◽  
Active Noise

A Secondary Path-decoupled Active Noise Control Algorithm Based on Deep Learning

2021 ◽  
pp. 1-1
Author(s):  
Daocheng Chen ◽  
Longbiao Cheng ◽  
Dingding Yao ◽  
Junfeng Li ◽  
Yonghong Yan
Keyword(s):  
Deep Learning ◽  
Control Algorithm ◽  
Noise Control ◽  
Active Noise Control ◽  
Active Noise

scholarly journals Multichannel Feedforward Active Noise Control System with Optimal Reference Microphone SelectorBased on Time Difference of Arrival

2018 ◽  
Vol 8 (11) ◽  
pp. 2291 ◽  
Author(s):  
Kenta Iwai ◽  
Satoru Hase ◽  
Yoshinobu Kajikawa
Keyword(s):  
Noise Control ◽  
Acoustic Noise ◽  
Control Point ◽  
Active Noise Control ◽  
Broadband Noise ◽  
Time Difference ◽  
Arrival Direction ◽  
Noise Sources ◽  
Time Difference Of Arrival ◽  
Active Noise

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.

scholarly journals Adaptive Active Noise Suppression Using Multiple Model Switching Strategy

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Quanzhen Huang ◽  
Suxia Chen ◽  
Mingming Huang ◽  
Zhuangzhi Guo
Keyword(s):  
Control System ◽  
Control Algorithm ◽  
Noise Suppression ◽  
Noise Control ◽  
Active Noise Control ◽  
System Response ◽  
Multiple Model ◽  
Switching Strategy ◽  
Online Identification ◽  
Active Noise

Active noise suppression for applications where the system response varies with time is a difficult problem. The computation burden for the existing control algorithms with online identification is heavy and easy to cause control system instability. A new active noise control algorithm is proposed in this paper by employing multiple model switching strategy for secondary path varying. The computation is significantly reduced. Firstly, a noise control system modeling method is proposed for duct-like applications. Then a multiple model adaptive control algorithm is proposed with a new multiple model switching strategy based on filter-u least mean square (FULMS) algorithm. Finally, the proposed algorithm was implemented on Texas Instruments digital signal processor (DSP) TMS320F28335 and real time experiments were done to test the proposed algorithm and FULMS algorithm with online identification. Experimental verification tests show that the proposed algorithm is effective with good noise suppression performance.

Active Noise Control Using Multi-Layered Perceptron Neural Networks

1997 ◽  
Vol 16 (2) ◽  
pp. 109-144 ◽  
Author(s):  
M.O. Tokhi ◽  
R. Wood
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Noise Control ◽  
Learning Algorithm ◽  
Free Field ◽  
Active Noise Control ◽  
Three Dimensional ◽  
Broadband Noise ◽  
Active Noise

This paper presents the development of a neuro-adaptive active noise control (ANC) system. Multi-layered perceptron neural networks with a backpropagation learning algorithm are considered in both the modelling and control contexts. The capabilities of the neural network in modelling dynamical systems are investigated. A feedforward ANC structure is considered for optimum cancellation of broadband noise in a three-dimensional propagation medium. An on-line adaptation and training mechanism allowing a neural network architecture to characterise the optimal controller within the ANC system is developed. The neuro-adaptive ANC algorithm thus developed is implemented within a free-field environment and simulation results verifying its performance are presented and discussed.

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