# Convolutional-Recurrent Neural Networks for Speech Enhancement

arXiv: https://arxiv.org/abs/1805.00579 · HTML: https://mlsanity.com/paper/1805.00579/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2018.

## Built on by (most cited first, 17 of 17)

- [RemixIT: Continual self-training of speech enhancement models via bootstrapped remixing](https://mlsanity.com/paper/2202.08862/) (2022)
- [Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database-HF_Lung_V1](https://mlsanity.com/paper/2102.03049/) (2021)
- [Resource-Efficient Separation Transformer](https://mlsanity.com/paper/2206.09507/) (2022)
- [ConvRNN-T: Convolutional Augmented Recurrent Neural Network Transducers for Streaming Speech Recognition](https://mlsanity.com/paper/2209.14868/) (2022)
- [Speech Enhancement Using Continuous Embeddings of Neural Audio Codec](https://mlsanity.com/paper/2502.16240/) (2025)
- [Perceptual Contrast Stretching on Target Feature for Speech Enhancement](https://mlsanity.com/paper/2203.17152/) (2022)
- [Improving Speech Enhancement through Fine-Grained Speech Characteristics](https://mlsanity.com/paper/2207.00237/) (2022)
- [Complex Image Generation SwinTransformer Network for Audio Denoising](https://mlsanity.com/paper/2310.16109/) (2023)
- [Improving Character Error Rate Is Not Equal to Having Clean Speech: Speech Enhancement for ASR Systems with Black-box Acoustic Models](https://mlsanity.com/paper/2110.05968/) (2021)
- [PAAPLoss: A Phonetic-Aligned Acoustic Parameter Loss for Speech Enhancement](https://mlsanity.com/paper/2302.08095/) (2023)
- [NeuroAMP: A Novel End-to-end General Purpose Deep Neural Amplifier for Personalized Hearing Aids](https://mlsanity.com/paper/2502.10822/) (2025)
- [A Training and Inference Strategy Using Noisy and Enhanced Speech as Target for Speech Enhancement without Clean Speech](https://mlsanity.com/paper/2210.15368/) (2022)
- [Effective Noise-aware Data Simulation for Domain-adaptive Speech Enhancement Leveraging Dynamic Stochastic Perturbation](https://mlsanity.com/paper/2409.01545/) (2024)
- [Speech Enhancement for Virtual Meetings on Cellular Networks](https://mlsanity.com/paper/2302.00868/) (2023)
- [Objective and subjective evaluation of speech enhancement methods in the UDASE task of the 7th CHiME challenge](https://mlsanity.com/paper/2402.01413/) (2024)
- [Masked Autoencoders as Universal Speech Enhancer](https://mlsanity.com/paper/2602.02413/) (2026)
- [Universal Robust Speech Adaptation for Cross-Domain Speech Recognition and Enhancement](https://mlsanity.com/paper/2602.04307/) (2026)

## Abstract (arXiv)

We propose an end-to-end model based on convolutional and recurrent neural networks for speech enhancement. Our model is purely data-driven and does not make any assumptions about the type or the stationarity of the noise. In contrast to existing methods that use multilayer perceptrons (MLPs), we employ both convolutional and recurrent neural network architectures. Thus, our approach allows us to exploit local structures in both the frequency and temporal domains. By incorporating prior knowledge of speech signals into the design of model structures, we build a model that is more data-efficient and achieves better generalization on both seen and unseen noise. Based on experiments with synthetic data, we demonstrate that our model outperforms existing methods, improving PESQ by up to 0.6 on seen noise and 0.64 on unseen noise.