GSC Based Speech Enhancement with Generative Adversarial Network

Abstract

At present, the technology of using microphone arrays for speech enhancement has been widely concerned, and the enhancement effect is excellent. The widely used Generalized Sidelobe Canceller (GSC) method can achieve good noise reduction for noisy speech in the additive noise acoustic environment, and achieve better intelligibility improvement. But there are also areas for improvement. In the lower branch of GSC, signal leakage caused by the estimation of the incident angle or the slight change of the position of the microphone array may cause the self-cancellation of target speech signal, thereby the severe speech distortion is caused. In this paper, the Generative Adversarial Network (GAN), which has broad application prospects in deep learning technology, replaces the lower branch of the traditional GSC structure, thus the self-cancellation of speech signals is avoided and improving the anti-error ability of the enhancement system is improved effectively.