A simplified adaptive feedback active noise control system

Wu, Lifu; Qiu, Xiaojun; Guo, Yecai

doi:10.1016/j.apacoust.2014.02.006

Cited by 46 publications

(41 citation statements)

References 12 publications

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“…Comparing with feedforward ANC, the feedback ANC is not involved of reference microphone and only error microphone is required, it reduces the number of system components and cuts back the cost greatly [5][6][7][8][9]. Because there is no reference microphone, so the problem caused by secondary source (loudspeaker) output getting back to the reference microphone and affecting the performance of ANC system can be avoided.…”

Section: Introductionmentioning

confidence: 99%

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A novel feedback active noise control for broadband chaotic noise and random noise

2017

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The feedback active noise control (ANC) can be seen as a predictor, the conventional method based on filteredx least mean square (FXLMS) algorithm can only be useful for linear and tonal noise, but for nonlinear and broadband noise, it is useless. The feedback ANC using functional link artificial neural networks (FLANN) based on filtered-s least mean square (FSLMS) algorithm can reduce some nonlinear noise such as chaotic noise, but the noise cancellation performance is not very well, at the same time, it is not useful to random noise. To solve the problem above, a new feedback ANC using wavelet packet FXLMS (WPFXLMS) algorithm is proposed in this paper. By decomposing the broadband noise into several band-limited parts which are predictable and each part is controlled independently, the proposed algorithm can not only suppress the chaotic noise, but also migrate the random noise. Compared with FXLMS and FSLMS algorithms, proposed WPFXLMS algorithm also holds the best performance on noise cancellation. Numerous simulations are conducted to demonstrate the effectiveness of the proposed WPFXLMS algorithm.

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Section: Introductionmentioning

confidence: 99%

“…Feedback ANC system has been studied in many paper, most of them are used to cancel linear and tonal noise, which are predictable [5][6][7][8][9][10][11]. But for wideband noise like random noise and chaotic noise, less of them are involved.…”

Section: Introductionmentioning

confidence: 99%

A novel feedback active noise control for broadband chaotic noise and random noise

2017

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“…The ANC applications often use the high-speed DSPs to carry out the active noise cancellation systems. For example, an active noise cancellation system in a long duct was realized on DSP platform to achieve real-time performance in attenuating the narrowband noise at low-frequencies [1], [2]. The adaptive feedback ANC control algorithm and its variations have been proposed for the ANC headphones to cancel out the narrowband noise at low-frequencies [3]- [5].…”

Section: Introductionmentioning

confidence: 99%

A 6.42 mW low-power feed-forward FxLMS ANC VLSI design for in-ear headphones

Chen

Sun

et al. 2015

2015 IEEE International Symposium on Circuits and Systems (ISCAS)

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Conventional active noise cancellation (ANC) applications often use high-speed digital signal processors (DSPs) to cancel annoying noise, which result in an expensive cost and high-power dissipation for modern portable devices. To achieve both high-performance and low-power consumption requirements, this paper proposes a dedicated feed-forward filtered-x least mean square (FxLMS) ANC VLSI design for inear headphones. The validity of the proposed design is then verified through adopting experiments in an anechoic chamber. Experimental results show that the proposed design can attenuate 15 dB for broadband pink noise with a bandwidth of 50-1500 Hz when operated at 10 MHz clock frequency, at the costs of 84.2 k gates and power consumption of 6.42 mW only. Keywords-in-ear headphone, active noise cancellation, feedforward FxLMS, VLSI design.978-1-4799-8391-9/15/$31.00 ©2015 IEEE

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“…( ) W z is a FIR filter as [11,12]), and analyze the computational complexities of 1) a linear FIR filter control with FxLMS scheme (will be presented in Section 3.1); 2) nonlinear NN control (8) with the LMS algorithm (17) and (22) (as Section 2.2); 3) nonlinear NN control (8) with the VSS-LMS algorithm (17), (23) and (25) (as Section 2.3). [11,12] In this case, an adaptive filter is used as the control, where the output of adaptive control ( ) W z can be written as…”

Section: Comparisonsmentioning

confidence: 99%

“…To solve this problem, feedback ANC system (FANC) was developed, which uses only an error signal in the summing junction. Thus, feedback ANC strategies can address the unforeseen noise sources [12,13]. Some available results show that feedback ANC systems based on adaptive finite impulse response (FIR) filter and filtered-x least-mean-square (FxLMS) algorithm work well for the cases with linear paths [14,15].…”

Section: Introductionmentioning

confidence: 99%