Deep learning for monaural speech separation
Po Sen Huang, Minje Kim, Mark Hasegawa‐Johnson, Paris Smaragdis · 2014
Monaural source separation is useful for many real-world applications 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 constraint. Moreover, we explore a discriminative training criterion 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 compared to NMF models, while maintaining better SDRs and SARs.