# FRCRN: Boosting Feature Representation using Frequency Recurrence for Monaural Speech Enhancement

arXiv: https://arxiv.org/abs/2206.07293 · HTML: https://mlsanity.com/paper/2206.07293/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2022.

## Built on by (most cited first, 26 of 26)

- [MaskSR: Masked Language Model for Full-band Speech Restoration](https://mlsanity.com/paper/2406.02092/) (2024)
- [Spiking Structured State Space Model for Monaural Speech Enhancement](https://mlsanity.com/paper/2309.03641/) (2023)
- [AnyEnhance: A Unified Generative Model with Prompt-Guidance and Self-Critic for Voice Enhancement](https://mlsanity.com/paper/2501.15417/) (2025)
- [LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement](https://mlsanity.com/paper/2503.00493/) (2025)
- [Ultra Low Complexity Deep Learning Based Noise Suppression](https://mlsanity.com/paper/2312.08132/) (2023)
- [Spatial-Frequency Attention for Image Denoising](https://mlsanity.com/paper/2302.13598/) (2023)
- [Joint Semantic Knowledge Distillation and Masked Acoustic Modeling for Full-band Speech Restoration with Improved Intelligibility](https://mlsanity.com/paper/2409.09357/) (2024)
- [UniSE: A Unified Framework for Decoder-Only Autoregressive LM-Based Speech Enhancement](https://mlsanity.com/paper/2510.20441/) (2025)
- [A Mask Free Neural Network for Monaural Speech Enhancement](https://mlsanity.com/paper/2306.04286/) (2023)
- [Towards Zero-Shot Text-To-Speech for Arabic Dialects](https://mlsanity.com/paper/2406.16751/) (2024)
- [EDNet: A Versatile Speech Enhancement Framework with Gating Mamba Mechanism and Phase Shift-Invariant Training](https://mlsanity.com/paper/2506.16231/) (2025)
- [SenSE: Semantic-Aware High-Fidelity Universal Speech Enhancement](https://mlsanity.com/paper/2509.24708/) (2025)
- [Let SSMs be ConvNets: State-space Modeling with Optimal Tensor Contractions](https://mlsanity.com/paper/2501.13230/) (2025)
- [A Semantic Information-based Hierarchical Speech Enhancement Method Using Factorized Codec and Diffusion Model](https://mlsanity.com/paper/2505.13843/) (2025)
- [MBTFNet: Multi-Band Temporal-Frequency Neural Network For Singing Voice Enhancement](https://mlsanity.com/paper/2310.04369/) (2023)
- [RaD-Net 2: A causal two-stage repairing and denoising speech enhancement network with knowledge distillation and complex axial self-attention](https://mlsanity.com/paper/2406.07498/) (2024)
- [Describe Where You Are: Improving Noise-Robustness for Speech Emotion Recognition with Text Description of the Environment](https://mlsanity.com/paper/2407.17716/) (2024)
- [Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement](https://mlsanity.com/paper/2508.20859/) (2025)
- [Investigating training objective for flow matching-based speech enhancement](https://mlsanity.com/paper/2512.10382/) (2025)
- [aTENNuate: Optimized Real-time Speech Enhancement with Deep SSMs on Raw Audio](https://mlsanity.com/paper/2409.03377/) (2024)
- [Are Deep Speech Denoising Models Robust to Adversarial Noise?](https://mlsanity.com/paper/2503.11627/) (2025)
- [How much to Dereverberate? Low-Latency Single-Channel Speech Enhancement in Distant Microphone Scenarios](https://mlsanity.com/paper/2505.01338/) (2025)
- [Leveraging Local and Global Knowledge Integration with Time-Frequency Calibrated Distillation for Speech Enhancement](https://mlsanity.com/paper/2506.13127/) (2025)
- [Leveraging Multiple Speech Enhancers for Non-Intrusive Intelligibility Prediction for Hearing-Impaired Listeners](https://mlsanity.com/paper/2509.16979/) (2025)
- [RT-SEMamba: Real-Time Speech Enhancement Mamba via Progressive Knowledge Distillation](https://mlsanity.com/paper/2608.12099/) (2026)
- [COT-TTS: Audio Context-Aware Text-to-Speech with Chain-of-Thought Reasoning](https://mlsanity.com/paper/2609.22697/) (2026)

## Abstract (arXiv)

Convolutional recurrent networks (CRN) integrating a convolutional encoder-decoder (CED) structure and a recurrent structure have achieved promising performance for monaural speech enhancement. However, feature representation across frequency context is highly constrained due to limited receptive fields in the convolutions of CED. In this paper, we propose a convolutional recurrent encoder-decoder (CRED) structure to boost feature representation along the frequency axis. The CRED applies frequency recurrence on 3D convolutional feature maps along the frequency axis following each convolution, therefore, it is capable of catching long-range frequency correlations and enhancing feature representations of speech inputs. The proposed frequency recurrence is realized efficiently using a feedforward sequential memory network (FSMN). Besides the CRED, we insert two stacked FSMN layers between the encoder and the decoder to model further temporal dynamics. We name the proposed framework as Frequency Recurrent CRN (FRCRN). We design FRCRN to predict complex Ideal Ratio Mask (cIRM) in complex-valued domain and optimize FRCRN using both time-frequency-domain and time-domain losses. Our proposed approach achieved state-of-the-art performance on wideband benchmark datasets and achieved 2nd place for the real-time fullband track in terms of Mean Opinion Score (MOS) and Word Accuracy (WAcc) in the ICASSP 2022 Deep Noise Suppression (DNS) challenge (https://github.com/modelscope/ClearerVoice-Studio).