Recent studies in deep learning-based speech separation have proven the superiority of time-domain approaches to conventional time-frequency-based methods. Unlike the time-frequency domain approaches, the time-domain separation systems often receive input sequences consisting of a huge number of time steps, which introduces challenges for modeling extremely long sequences. Conventional recurrent neural networks (RNNs) are not effective for modeling such long sequences due to optimization difficulties, while one-dimensional convolutional neural networks (1-D CNNs) cannot perform utterance-level sequence modeling when its receptive field is smaller than the sequence length. In this paper, we propose dual-path recurrent neural network (DPRNN), a simple yet effective method for organizing RNN layers in a deep structure to model extremely long sequences. DPRNN splits the long sequential input into smaller chunks and applies intra- and inter-chunk operations iteratively, where the input length can be made proportional to the square root of the original sequence length in each operation. Experiments show that by replacing 1-D CNN with DPRNN and apply sample-level modeling in the time-domain audio separation network (TasNet), a new state-of-the-art performance on WSJ0-2mix is achieved with a 20 times smaller model than the previous best system.

本文提出了一种名为 DPRNN 的新型深度循环神经网络机制，它通过将输入序列分成较小的块并迭代应用区域内和区域间操作来模拟极长序列。实验结果表明，在使用 DPRNN 和在时域中应用样本级建模的 TasNet 中，相对于之前的最佳系统，使用比原来小 20 倍的模型在 WSJ0-2mix 上实现了新的最先进性能。

双通道RNN：适用于时域单声道语音分离的高效长序列建模