  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.
