Adaptive U net: Conditional Neural Network for Monaural Speech Enhancement using Memory Mechanism
While the attention mechanisms have been widely used for speech enhancement, usage of memory mechanisms is less common. In this paper, we investigate the task of monaural speech enhancement in a memory based Adaptive system, namely Adaptive U net (Ada U net) Specifically, this system consists of auxiliary classifier and backbone U net, where the classifier is used to provide the category sensitive high level feature, the U net fetches affine parameters adapted to category attribute through memory mechanism. Moreover, we explore the impact of adaptive targets on noise reduction performance of the proposed system. The experimental results show that the denoise ability achieves substantial improvement when there is no artificial design target is set. To further improve the denoise capability of the system, the waveform angle ratio loss is designed. Compared with typical monaural speech enhancement models, the Ada U net has better performance in multiple speech quality metrics.
2020 IEEE 5th International Conference on Signal and Image Processing (ICSIP)