Enhancement of noise reduction efficiency based on compensation in the ANC headset using fixed-point DSP

Kim, Cholnam; Jong, Chol; Zhang, Xiaolin; Wang, Peng; Kim, Jinhyok

doi:10.1016/j.apacoust.2016.09.031

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…The multiple harmonics are reduced by the extension of noise sources. The signals are sampled at the beginning, and then, processed by a digital signal processing system; in which anti-noise is generated by the loudspeakers [4]. The FxLMS algorithm is used to compute the acoustic delay and transducer transfer function.…”

Section: Introductionmentioning

confidence: 99%

Active Noise Control Using Modified FsLMS and Hybrid PSOFF Algorithm

Walia

Ghosh

2018

Applied Sciences

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Active noise control is an efficient technique for noise cancellation of the system, which has been defined in this paper with the aid of Modified Filtered-s Least Mean Square (MFsLMS) algorithm. The Hybrid Particle Swarm Optimization and Firefly (HPSOFF) algorithm are used to identify the stability factor of the MFsLMS algorithm. The computational difficulty of the modified algorithm is reduced when compared with the original Filtered-s Least Mean Square (FsLMS) algorithm. The noise sources are removed from the signal and it is compared with the existing FsLMS algorithm. The performance of the system is established with the normalized mean square error for two different types of noises. The proposed method has also been compared with the existing algorithms for the same purposes.

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Section: Introductionmentioning

confidence: 99%

Active Noise Control Using Modified FsLMS and Hybrid PSOFF Algorithm

Walia

Ghosh

2018

Applied Sciences

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“…However, such method requires a specialized measurement of the devices in the control system. In addition, the round-off and quantization errors will change with the programmer's code implementation, and the correct values of the phase and amplitude compensation factors need to be determined [204]. An improvement of fixed-point DSP method Feedback ANC for headphone [202] was proposed in [204], which added feedback and communication error 2 Considering the implementation of different structures in fixed-point DSP, a detailed performance comparison of FxLMS, FxNLMS, IIR, and subband ANC algorithms was presented in [205].…”

Section: Anc Implementationmentioning

confidence: 99%

“…In addition, the round-off and quantization errors will change with the programmer's code implementation, and the correct values of the phase and amplitude compensation factors need to be determined [204]. An improvement of fixed-point DSP method Feedback ANC for headphone [202] was proposed in [204], which added feedback and communication error 2 Considering the implementation of different structures in fixed-point DSP, a detailed performance comparison of FxLMS, FxNLMS, IIR, and subband ANC algorithms was presented in [205]. The research results showed that the FxLMS and its variant are more suitable for ANC implementation and the fixed-point DSP has about 2dB degradation as compared with its floating-point counterpart.…”

Section: Anc Implementationmentioning

confidence: 99%

A survey on active noise control in the past decade–Part II: Nonlinear systems

Yin

Lamare

et al. 2021

Signal Processing

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“…Luo proposed a Volterra recursive even mirror Fourier nonlinear filter for the nonlinear ANC system with acoustic feedback, showing that it can achieve global noise control in closed space [10]. Kim proposed a new algorithm to enhance the convergence rate and noise reduction efficiency of ANC headsets with fixed-point DSP, reducing the broadband noise by 24 dB [11]. We notice that the FxLMS and FxRLS-based adaptive algorithms are the two of the most widely used in ANC systems.…”

Section: Introductionmentioning

confidence: 96%

A New Proportionate Filtered-x RLS Algorithm for Active Noise Control System

Liang

Yao

Luo

et al. 2022

Sensors

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The filtered-x recursive least square (FxRLS) algorithm is widely used in the active noise control system and has achieved great success in some complex de-noising environments, such as the cabin in vehicles and aircraft. However, its performance is sensitive to some user-defined parameters such as the forgetting factor and initial gain. Once these parameters are not selected properly, the de-noising effect of FxRLS will deteriorate. Moreover, the tracking performance of FxRLS for mutation is still restricted to a certain extent. To solve the above problems, this paper proposes a new proportional FxRLS (PFxRLS) algorithm. The forgetting factor and initial gain sensitivity are successfully reduced without introducing new turning parameters. The de-noising level and tracking performance have also been improved. Moreover, the momentum technique is introduced in PFxRLS to further improve its robustness and de-noising level. To ensure stability, its convergence condition is also discussed in this paper. The effectiveness of the proposed algorithms is illustrated by simulations and experiments with different user-defined parameters and time-varying noise environments.

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Enhancement of noise reduction efficiency based on compensation in the ANC headset using fixed-point DSP

Cited by 8 publications

References 8 publications

Active Noise Control Using Modified FsLMS and Hybrid PSOFF Algorithm

Active Noise Control Using Modified FsLMS and Hybrid PSOFF Algorithm

A survey on active noise control in the past decade–Part II: Nonlinear systems

A New Proportionate Filtered-x RLS Algorithm for Active Noise Control System

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