# DPCRN: Dual-Path Convolution Recurrent Network for Single Channel Speech Enhancement

arXiv: https://arxiv.org/abs/2107.05429 · HTML: https://mlsanity.com/paper/2107.05429/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2021.

## Built on by (most cited first, 14 of 14)

- [Noise robust speech emotion recognition with signal-to-noise ratio adapting speech enhancement](https://mlsanity.com/paper/2309.01164/) (2023)
- [Dynamic Gated Recurrent Neural Network for Compute-efficient Speech Enhancement](https://mlsanity.com/paper/2408.12425/) (2024)
- [A Composite Predictive-Generative Approach to Monaural Universal Speech Enhancement](https://mlsanity.com/paper/2505.24576/) (2025)
- [A Mask Free Neural Network for Monaural Speech Enhancement](https://mlsanity.com/paper/2306.04286/) (2023)
- [A Survey of Deep Learning for Complex Speech Spectrograms](https://mlsanity.com/paper/2505.08694/) (2025)
- [Multi-Loss Convolutional Network with Time-Frequency Attention for Speech Enhancement](https://mlsanity.com/paper/2306.08956/) (2023)
- [A Two-Stage Hierarchical Deep Filtering Framework for Real-Time Speech Enhancement](https://mlsanity.com/paper/2506.01023/) (2025)
- [A Closer Look at Wav2Vec2 Embeddings for On-Device Single-Channel Speech Enhancement](https://mlsanity.com/paper/2403.01369/) (2024)
- [GAP-URGENet: A Generative-Predictive Fusion Framework for Universal Speech Enhancement](https://mlsanity.com/paper/2604.01832/) (2026)
- [Adaptive Speech Quality Aware Complex Neural Network for Acoustic Echo Cancellation with Supervised Contrastive Learning](https://mlsanity.com/paper/2210.16791/) (2022)
- [A Lightweight and Real-Time Binaural Speech Enhancement Model with Spatial Cues Preservation](https://mlsanity.com/paper/2409.12444/) (2024)
- [UL-UNAS: Ultra-Lightweight U-Nets for Real-Time Speech Enhancement via Network Architecture Search](https://mlsanity.com/paper/2503.00340/) (2025)
- [Leveraging Local and Global Knowledge Integration with Time-Frequency Calibrated Distillation for Speech Enhancement](https://mlsanity.com/paper/2506.13127/) (2025)
- [StuPASE: Towards Low-Hallucination Studio-Quality Generative Speech Enhancement](https://mlsanity.com/paper/2603.09234/) (2026)

## Abstract (arXiv)

The dual-path RNN (DPRNN) was proposed to more effectively model extremely long sequences for speech separation in the time domain. By splitting long sequences to smaller chunks and applying intra-chunk and inter-chunk RNNs, the DPRNN reached promising performance in speech separation with a limited model size. In this paper, we combine the DPRNN module with Convolution Recurrent Network (CRN) and design a model called Dual-Path Convolution Recurrent Network (DPCRN) for speech enhancement in the time-frequency domain. We replace the RNNs in the CRN with DPRNN modules, where the intra-chunk RNNs are used to model the spectrum pattern in a single frame and the inter-chunk RNNs are used to model the dependence between consecutive frames. With only 0.8M parameters, the submitted DPCRN model achieves an overall mean opinion score (MOS) of 3.57 in the wide band scenario track of the Interspeech 2021 Deep Noise Suppression (DNS) challenge. Evaluations on some other test sets also show the efficacy of our model.