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

Autor:	Junfeng Li, Yonghong Yan, Daocheng Chen, Longbiao Cheng, Dingding Yao
Rok vydání:	2022
Předmět:	business.industry Noise (signal processing) Computer science Applied Mathematics Deep learning Residual Signal Adaptive filter Nonlinear system Signal Processing Path (graph theory) Artificial intelligence Electrical and Electronic Engineering business Algorithm Active noise control
Zdroj:	IEEE Signal Processing Letters. 29:234-238
ISSN:	1558-2361 1070-9908
Popis:	Active noise control (ANC) systems are widely used to cancel unwanted noise. However, for high-level noise, the residual error signal cannot be fully eliminated because of the nonlinearity of the secondary path, resulting in the diverging of the adaptive filter. In this letter, we propose a secondary path-decoupled ANC (SPD-ANC) algorithm based on deep learning. Specifically, the secondary path decoupled module consisting of two time-domain convolutional recurrent networks, one for modeling the nonlinear secondary path and the other for modeling the reverse process, is employed to calculate the secondary path-decoupled (SPD) error signal. The control signal is then generated by an adaptive filter that is optimized towards minimizing the SPD error signal. Simulation results indicate that the proposed method outperforms the conventional ANC methods under different conditions.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::60bbab27041fec15af2f9e724850e1a8 https://doi.org/10.1109/lsp.2021.3130023 Zobrazit plný text záznamu