Active noise control system based on the combined CNN-LSTM network

Rongzan Hu; Qinghua Liu

doi:10.1117/12.3015656

16 January 2024 Active noise control system based on the combined CNN-LSTM network

Rongzan Hu, Qinghua Liu

Proceedings Volume 12973, Workshop on Electronics Communication Engineering (WECE 2023); 129730C (2024) https://doi.org/10.1117/12.3015656
Event: Workshop on Electronics Communication Engineering (WECE 2023), 2023, Guilin, China

Abstract

In active noise control system, the noise control effect based on the traditional method is reduced under nonlinear interference from the electronic devices like speakers and microphones. In this paper, a combined CNN-LSTM network active noise control system is proposed, and an experimental scenario for imitating a real auditory system is built using an artificial head. The primary and secondary channel models are estimated using BP neural networks to simulate the nonlinear properties from the electronic devices. Sever type noise data from various scenes are chosen for simulation. As compared with the conventional FxLMS algorithm, FNN, CNN and LSTM, the trials demonstrate that the proposed network can effectively suppress the noise at both low and high frequencies with high convergence speed.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Rongzan Hu and Qinghua Liu "Active noise control system based on the combined CNN-LSTM network", Proc. SPIE 12973, Workshop on Electronics Communication Engineering (WECE 2023), 129730C (16 January 2024); https://doi.org/10.1117/12.3015656

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