Cabin Noise Separation Using an Improved Convolutional Time Domain Noise Separation Networks
Qunyi He, Haitao Wang, Xiangyang Zeng, Kean Chen, Ye Lei, Shuwei Ren · 2023
Traditional acoustic signal separation techniques usually have high requirements for experimental conditions and economic costs. In this paper, an improved convolutional time domain noise separation networks is proposed for noise separation in the cabin environment based on the classical CONY-TasNet. Parallel dilated convolutions is designed in the separation layer to achieving relatively long time signal processing. Then, more envelope information can be obtained which is beneficial for enhancing the processing effect of cabin low-frequency noise and harmonic noise, and also reducing the loss of local noise information. Compared with other commonly used deep networks, the proposed networks has better separation results. It not only restores a more accurate spectrum structure, but also obtain smaller distortions.