期刊
IEEE SIGNAL PROCESSING LETTERS
卷 29, 期 -, 页码 234-238出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2021.3130023
关键词
Adaptation models; Signal processing algorithms; Actuators; Training; Mathematical models; Deep learning; Time-domain analysis; Active noise control; deep learning; nonlinear secondary path; adaptive filter
资金
- National Key Research and Development Program [2020YFC2004100]
- National Natural Science Foundation of China [11911540067]
This letter proposes a secondary path-decoupled ANC algorithm based on deep learning, which generates control signals by optimizing an adaptive filter to eliminate noise.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据