Complex Spectral Mapping With Attention Based Convolution Recurrent Neural Network for Speech Enhancement
This is an incremental improvement for speech enhancement systems, addressing a specific architectural limitation in existing models.
The paper tackles speech enhancement by proposing an attention-based skip connection in a complex spectral mapping convolution recurrent neural network (CARN), which improved metrics like PESQ by over 10% relative to prior models and outperformed the top model in the DNS Challenge 2020.
Speech enhancement has benefited from the success of deep learning in terms of intelligibility and perceptual quality. Conventional time-frequency (TF) domain methods focus on predicting TF-masks or speech spectrum,via a naive convolution neural network or recurrent neural network.Some recent studies were based on Complex spectral Mapping convolution recurrent neural network (CRN) . These models skiped directly from encoder layers' output and decoder layers' input ,which maybe thoughtless. We proposed an attention mechanism based skip connection between encoder and decoder layers,namely Complex Spectral Mapping With Attention Based Convolution Recurrent Neural Network (CARN).Compared with CRN model,the proposed CARN model improved more than 10% relatively at several metrics such as PESQ,CBAK,COVL,CSIG and son,and outperformed the place 1st model in both real time and non-real time track of the DNS Challenge 2020 at these metrics.