Multi-Scale Residual Convolutional Encoder Decoder with Bidirectional Long Short-Term Memory for Single Channel Speech Enhancement

TL;DR: In this paper, a multi-scale convolutional bidirectional long short-term memory (BLSTM) recurrent neural network was proposed for end-to-end single channel speech enhancement.

Abstract: The existing convolutional neural network (CNN) based methods still have limitations in model accuracy, latency and computational cost for single channel speech enhancement. In order to address these limitations, we propose a multi-scale convolutional bidirectional long short-term memory (BLSTM) recurrent neural network, which is named as McbNet, a deep learning framework for end-to-end single channel speech enhancement. The proposed McbNet enlarges the receptive fields in two aspects. Firstly, every convolutional layer employs filters with varied dimensions to capture local and global information. Secondly, the BLSTM is applied to evaluate the interdependency of past, current and future temporal frames. The experimental results confirm the proposed McbNet offers consistent improvement over the state-of-the-art methods and public datasets.