DNoiseNet: Deep learning-based feedback active noise control in various noisy environments

Abstract

The use of active noise control/cancelation (ANC) has increased because of the availability of efficient circuits and computational power. However, most ANC systems are based on traditional linear filters with limited efficiency due to the highly nonlinear and nonstationary nature of various noises. This paper proposes an advanced deep learning–based feedback ANC named DNoiseNet that overcomes the limitations of traditional ANCs and addresses primary and secondary path effects, including acoustic delay. Mathematical operators (i.e., atrous convolution, pointwise convolution, nonlinear activation filters, and recurrent neural networks) learn multilevel temporal features under different noises in various environments, such as construction sites, vehicle interiors, and airplane cockpits. Due to the nature of feedback control using a single error sensor, an estimation of the reference noise signal must be regenerated. In this paper, a multilayer perceptron (MLP) neural network–based secondary path estimator is also proposed to improve the performance of DNoiseNet. In extensive parametric and comparative studies, the DNoiseNet with the MLP secondary path estimator exhibited the best performance in root mean square error and noise attenuation metrics.