# Raw Waveform-based Speech Enhancement by Fully Convolutional Networks

arXiv: https://arxiv.org/abs/1703.02205 · HTML: https://mlsanity.com/paper/1703.02205/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2017.

## Built on by (most cited first, 24 of 24)

- [Speech Enhancement and Dereverberation with Diffusion-based Generative Models](https://mlsanity.com/paper/2208.05830/) (2022)
- [Interspeech 2021 Deep Noise Suppression Challenge](https://mlsanity.com/paper/2101.01902/) (2021)
- [Deep Learning-based Non-Intrusive Multi-Objective Speech Assessment Model with Cross-Domain Features](https://mlsanity.com/paper/2111.02363/) (2021)
- [Unsupervised Speech Enhancement using Dynamical Variational Auto-Encoders](https://mlsanity.com/paper/2106.12271/) (2021)
- [Deep Impulse Responses: Estimating and Parameterizing Filters with Deep Networks](https://mlsanity.com/paper/2202.03416/) (2022)
- [Robust Time Series Denoising with Learnable Wavelet Packet Transform](https://mlsanity.com/paper/2206.06126/) (2022)
- [InQSS: a speech intelligibility and quality assessment model using a multi-task learning network](https://mlsanity.com/paper/2111.02585/) (2021)
- [ECG Artifact Removal from Single-Channel Surface EMG Using Fully Convolutional Networks](https://mlsanity.com/paper/2210.13271/) (2022)
- [MTI-Net: A Multi-Target Speech Intelligibility Prediction Model](https://mlsanity.com/paper/2204.03310/) (2022)
- [TrustEMG-Net: Using Representation-Masking Transformer with U-Net for Surface Electromyography Enhancement](https://mlsanity.com/paper/2410.03843/) (2024)
- [DPSNN: Spiking Neural Network for Low-Latency Streaming Speech Enhancement](https://mlsanity.com/paper/2408.07388/) (2024)
- [Removing Noise from Extracellular Neural Recordings Using Fully Convolutional Denoising Autoencoders](https://mlsanity.com/paper/2109.08945/) (2021)
- [CleanUNet 2: A Hybrid Speech Denoising Model on Waveform and Spectrogram](https://mlsanity.com/paper/2309.05975/) (2023)
- [Pre-training Feature Guided Diffusion Model for Speech Enhancement](https://mlsanity.com/paper/2406.07646/) (2024)
- [NeuroAMP: A Novel End-to-end General Purpose Deep Neural Amplifier for Personalized Hearing Aids](https://mlsanity.com/paper/2502.10822/) (2025)
- [Towards Intelligibility-Oriented Audio-Visual Speech Enhancement](https://mlsanity.com/paper/2111.09642/) (2021)
- [A Novel Speech Intelligibility Enhancement Model based on CanonicalCorrelation and Deep Learning](https://mlsanity.com/paper/2202.05756/) (2022)
- [Multi-Task Pseudo-Label Learning for Non-Intrusive Speech Quality Assessment Model](https://mlsanity.com/paper/2308.09262/) (2023)
- [DeepExtractor: Time-domain reconstruction of signals and glitches in gravitational wave data with deep learning](https://mlsanity.com/paper/2501.18423/) (2025)
- [SEOFP-NET: Compression and Acceleration of Deep Neural Networks for Speech Enhancement Using Sign-Exponent-Only Floating-Points](https://mlsanity.com/paper/2111.04436/) (2021)
- [EMGSE: Acoustic/EMG Fusion for Multimodal Speech Enhancement](https://mlsanity.com/paper/2202.06507/) (2022)
- [Objective and subjective evaluation of speech enhancement methods in the UDASE task of the 7th CHiME challenge](https://mlsanity.com/paper/2402.01413/) (2024)
- [TokenSE: a Mamba-based discrete token speech enhancement framework for cochlear implants](https://mlsanity.com/paper/2604.12246/) (2026)
- [DriftSE: Speech Enhancement with Generative Drifting](https://mlsanity.com/paper/2609.12252/) (2026)

## Abstract (arXiv)

This study proposes a fully convolutional network (FCN) model for raw waveform-based speech enhancement. The proposed system performs speech enhancement in an end-to-end (i.e., waveform-in and waveform-out) manner, which dif-fers from most existing denoising methods that process the magnitude spectrum (e.g., log power spectrum (LPS)) only. Because the fully connected layers, which are involved in deep neural networks (DNN) and convolutional neural networks (CNN), may not accurately characterize the local information of speech signals, particularly with high frequency components, we employed fully convolutional layers to model the waveform. More specifically, FCN consists of only convolutional layers and thus the local temporal structures of speech signals can be efficiently and effectively preserved with relatively few weights. Experimental results show that DNN- and CNN-based models have limited capability to restore high frequency components of waveforms, thus leading to decreased intelligibility of enhanced speech. By contrast, the proposed FCN model can not only effectively recover the waveforms but also outperform the LPS-based DNN baseline in terms of short-time objective intelligibility (STOI) and perceptual evaluation of speech quality (PESQ). In addition, the number of model parameters in FCN is approximately only 0.2% compared with that in both DNN and CNN.