Embedding Recurrent Layers with Dual-Path Strategy in a Variant of Convolutional Network for Speaker-Independent Speech Separation

TL;DR

This paper introduces a novel neural network architecture combining RNNs and a variant of convolutional networks with a dual-path strategy, achieving effective speaker-independent speech separation while balancing performance and computational efficiency.

Contribution

It proposes embedding RNNs into a convolutional network variant using a dual-path strategy, enabling better local and global feature learning for speech separation.

Findings

Effective separation performance on various datasets

Achieves a good balance between accuracy and computational efficiency

Gradual separation at multiple scales improves results

Abstract

Speaker-independent speech separation has achieved remarkable performance in recent years with the development of deep neural network (DNN). Various network architectures, from traditional convolutional neural network (CNN) and recurrent neural network (RNN) to advanced transformer, have been designed sophistically to improve separation performance. However, the state-of-the-art models usually suffer from several flaws related to the computation, such as large model size, huge memory consumption and computational complexity. To find the balance between the performance and computational efficiency and to further explore the modeling ability of traditional network structure, we combine RNN and a newly proposed variant of convolutional network to cope with speech separation problem. By embedding two RNNs into basic block of this variant with the help of dual-path strategy, the proposed network can effectively learn the local information and global dependency. Besides, a four-staged structure enables the separation procedure to be performed gradually at finer and finer scales as the feature dimension increases. The experimental results on various datasets have proven the effectiveness of the proposed method and shown that a trade-off between the separation performance and computational efficiency is well achieved.