# A Fully Convolutional Neural Network for Speech Enhancement

arXiv: https://arxiv.org/abs/1609.07132 · HTML: https://mlsanity.com/paper/1609.07132/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2016.

## Built on by (most cited first, 23 of 23)

- [Look Once to Hear: Target Speech Hearing with Noisy Examples](https://mlsanity.com/paper/2405.06289/) (2024)
- [BirdSoundsDenoising: Deep Visual Audio Denoising for Bird Sounds](https://mlsanity.com/paper/2210.10196/) (2022)
- [Noise Reduction in X-ray Photon Correlation Spectroscopy with Convolutional Neural Networks Encoder-Decoder Models](https://mlsanity.com/paper/2102.03877/) (2021)
- [Complex Image Generation SwinTransformer Network for Audio Denoising](https://mlsanity.com/paper/2310.16109/) (2023)
- [Linguistic Knowledge Transfer Learning for Speech Enhancement](https://mlsanity.com/paper/2503.07078/) (2025)
- [Speech Enhancement for Wake-Up-Word detection in Voice Assistants](https://mlsanity.com/paper/2101.12732/) (2021)
- [DPATD: Dual-Phase Audio Transformer for Denoising](https://mlsanity.com/paper/2310.19588/) (2023)
- [Advancing Additive Manufacturing through Deep Learning: A Comprehensive Review of Current Progress and Future Challenges](https://mlsanity.com/paper/2403.00669/) (2024)
- [Denoising Induction Motor Sounds Using an Autoencoder](https://mlsanity.com/paper/2208.04462/) (2022)
- [SECP: A Speech Enhancement-Based Curation Pipeline For Scalable Acquisition Of Clean Speech](https://mlsanity.com/paper/2402.12482/) (2024)
- [Diffusion Gaussian Mixture Audio Denoise](https://mlsanity.com/paper/2406.09154/) (2024)
- [Complex Image-Generative Diffusion Transformer for Audio Denoising](https://mlsanity.com/paper/2406.09161/) (2024)
- [Effective Noise-aware Data Simulation for Domain-adaptive Speech Enhancement Leveraging Dynamic Stochastic Perturbation](https://mlsanity.com/paper/2409.01545/) (2024)
- [Are Modern Speech Enhancement Systems Vulnerable to Adversarial Attacks?](https://mlsanity.com/paper/2509.21087/) (2025)
- [Improving multichannel speech enhancement through accurate room-acoustic simulations](https://mlsanity.com/paper/2606.31552/) (2026)
- [Stock Trend Prediction: A Semantic Segmentation Approach](https://mlsanity.com/paper/2303.09323/) (2023)
- [Efficient Monaural Speech Enhancement using Spectrum Attention Fusion](https://mlsanity.com/paper/2308.02263/) (2023)
- [Effects of Dataset Sampling Rate for Noise Cancellation through Deep Learning](https://mlsanity.com/paper/2405.20884/) (2024)
- [A Dual-Branch Parallel Network for Speech Enhancement and Restoration](https://mlsanity.com/paper/2409.08702/) (2024)
- [Passive Underwater Acoustic Signal Separation based on Feature Decoupling Dual-path Network](https://mlsanity.com/paper/2504.08371/) (2025)
- [Unsupervised Speech Enhancement using Data-defined Priors](https://mlsanity.com/paper/2509.22942/) (2025)
- [Universal Robust Speech Adaptation for Cross-Domain Speech Recognition and Enhancement](https://mlsanity.com/paper/2602.04307/) (2026)
- [QC-GAN: A Parameter-Efficient Quaternion Conformer GAN for High-Fidelity Speech Enhancement](https://mlsanity.com/paper/2606.18611/) (2026)

## Abstract (arXiv)

In hearing aids, the presence of babble noise degrades hearing intelligibility of human speech greatly. However, removing the babble without creating artifacts in human speech is a challenging task in a low SNR environment. Here, we sought to solve the problem by finding a `mapping' between noisy speech spectra and clean speech spectra via supervised learning. Specifically, we propose using fully Convolutional Neural Networks, which consist of lesser number of parameters than fully connected networks. The proposed network, Redundant Convolutional Encoder Decoder (R-CED), demonstrates that a convolutional network can be 12 times smaller than a recurrent network and yet achieves better performance, which shows its applicability for an embedded system: the hearing aids.