# Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation

arXiv: https://arxiv.org/abs/1910.06379 · HTML: https://mlsanity.com/paper/1910.06379/

## Key facts

- Cited by 182 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2019.

## Built on by (most cited first, 30 of 182)

- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [Hybrid Spectrogram and Waveform Source Separation](https://mlsanity.com/paper/2111.03600/) (2021)
- [Proactive Detection of Voice Cloning with Localized Watermarking](https://mlsanity.com/paper/2401.17264/) (2024)
- [Music Source Separation with Band-split RNN](https://mlsanity.com/paper/2209.15174/) (2022)
- [LEAF: A Learnable Frontend for Audio Classification](https://mlsanity.com/paper/2101.08596/) (2021)
- [Open-Source Conversational AI with SpeechBrain 1.0](https://mlsanity.com/paper/2407.00463/) (2024)
- [Efficient Neural Music Generation](https://mlsanity.com/paper/2305.15719/) (2023)
- [Parallelizing non-linear sequential models over the sequence length](https://mlsanity.com/paper/2309.12252/) (2023)
- [Dual-path Mamba: Short and Long-term Bidirectional Selective Structured State Space Models for Speech Separation](https://mlsanity.com/paper/2403.18257/) (2024)
- [MambaMixer: Efficient Selective State Space Models with Dual Token and Channel Selection](https://mlsanity.com/paper/2403.19888/) (2024)
- [An efficient encoder-decoder architecture with top-down attention for speech separation](https://mlsanity.com/paper/2209.15200/) (2022)
- [TSTNN: Two-stage Transformer based Neural Network for Speech Enhancement in the Time Domain](https://mlsanity.com/paper/2103.09963/) (2021)
- [MossFormer: Pushing the Performance Limit of Monaural Speech Separation using Gated Single-Head Transformer with Convolution-Augmented Joint Self-Attentions](https://mlsanity.com/paper/2302.11824/) (2023)
- [MESH2IR: Neural Acoustic Impulse Response Generator for Complex 3D Scenes](https://mlsanity.com/paper/2205.09248/) (2022)
- [Speech Slytherin: Examining the Performance and Efficiency of Mamba for Speech Separation, Recognition, and Synthesis](https://mlsanity.com/paper/2407.09732/) (2024)
- [Real-Time Target Sound Extraction](https://mlsanity.com/paper/2211.02250/) (2022)
- [Temporal Modeling Matters: A Novel Temporal Emotional Modeling Approach for Speech Emotion Recognition](https://mlsanity.com/paper/2211.08233/) (2022)
- [End-to-End Integration of Speech Recognition, Speech Enhancement, and Self-Supervised Learning Representation](https://mlsanity.com/paper/2204.00540/) (2022)
- [SepIt: Approaching a Single Channel Speech Separation Bound](https://mlsanity.com/paper/2205.11801/) (2022)
- [Compute and memory efficient universal sound source separation](https://mlsanity.com/paper/2103.02644/) (2021)
- [Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution](https://mlsanity.com/paper/2301.02068/) (2023)
- [Separate And Diffuse: Using a Pretrained Diffusion Model for Improving Source Separation](https://mlsanity.com/paper/2301.10752/) (2023)
- [Annealed Multiple Choice Learning: Overcoming limitations of Winner-takes-all with annealing](https://mlsanity.com/paper/2407.15580/) (2024)
- [Many-Speakers Single Channel Speech Separation with Optimal Permutation Training](https://mlsanity.com/paper/2104.08955/) (2021)
- [Discretization and Re-synthesis: an alternative method to solve the Cocktail Party Problem](https://mlsanity.com/paper/2112.09382/) (2021)
- [Resource-Efficient Separation Transformer](https://mlsanity.com/paper/2206.09507/) (2022)
- [IIANet: An Intra- and Inter-Modality Attention Network for Audio-Visual Speech Separation](https://mlsanity.com/paper/2308.08143/) (2023)
- [Look Once to Hear: Target Speech Hearing with Noisy Examples](https://mlsanity.com/paper/2405.06289/) (2024)
- [TIGER: Time-frequency Interleaved Gain Extraction and Reconstruction for Efficient Speech Separation](https://mlsanity.com/paper/2410.01469/) (2024)
- [Sandglasset: A Light Multi-Granularity Self-attentive Network For Time-Domain Speech Separation](https://mlsanity.com/paper/2103.00819/) (2021)

## Abstract (arXiv)

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.