Audio Noise Filter using Cycle Consistent Adversarial Network - CycleGAN ANF
Nam Son Nguyen, Tengpeng Li, Xiaoqian Zhang, Bo Sheng, Teng Wang, Jiayin Wang · 2019
Speech enhance methods base on traditional digital signal processing (DSP) algorithms or adaptive filters can effectively suppress stationary noises. However, they don't provide viable solution for the variety of non-stationary noises that exist in our everyday life. Smart voice assistants such as Google Home and Alexa deteriorate their performance mostly due to non-stationary noises. In this paper we introduce CycleGAN ANF, a neural network approach that can learn to reduce both stationary and non-stationary noises, totally unsupervised. CycleGAN ANF is capable of reducing undesired interference by reading in a raw audio sample from a set X (speech mixed with noises) and transforming it so that it sound as if it belongs in set Y (clean speech). Our experiments demonstrate that without labels and when trained on unparalleled; relatively small vocabulary of speech datasets, CycleGAN ANF can achieve significant improvements without the ground assumptions of nature and form of the noise.