DEEP LEARNING FOR MONAURAL SPEECH SEPARATION

Monaural source separation is useful for many real-world ap-plications though it is a challenging problem. In this paper, we study deep learning for monaural speech separation. We propose the joint optimization of the deep learning models (deep neural networks and recurrent neural networks) with an extra masking layer, which enforces a reconstruction con-straint. Moreover, we explore a discriminative training crite-rion for the neural networks to further enhance the separation performance. We evaluate our approaches using the TIMIT speech corpus for a monaural speech separation task. Our proposed models achieve about 3.8⇠4.9 dB SIR gain com-pared to NMF models, while maintaining better SDRs and SARs