# HGCN: Harmonic gated compensation network for speech enhancement

arXiv: https://arxiv.org/abs/2201.12755 · HTML: https://mlsanity.com/paper/2201.12755/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2022.

## Built on by (most cited first, 4 of 4)

- [Mamba-SEUNet: Mamba UNet for Monaural Speech Enhancement](https://mlsanity.com/paper/2412.16626/) (2024)
- [IMAN: An Adaptive Network for Robust NPC Mortality Prediction with Missing Modalities](https://mlsanity.com/paper/2410.18551/) (2024)
- [MBTFNet: Multi-Band Temporal-Frequency Neural Network For Singing Voice Enhancement](https://mlsanity.com/paper/2310.04369/) (2023)
- [Speech Enhancement with Perceptually-motivated Optimization and Dual Transformations](https://mlsanity.com/paper/2209.11905/) (2022)

## Abstract (arXiv)

Mask processing in the time-frequency (T-F) domain through the neural network has been one of the mainstreams for single-channel speech enhancement. However, it is hard for most models to handle the situation when harmonics are partially masked by noise. To tackle this challenge, we propose a harmonic gated compensation network (HGCN). We design a high-resolution harmonic integral spectrum to improve the accuracy of harmonic locations prediction. Then we add voice activity detection (VAD) and voiced region detection (VRD) to the convolutional recurrent network (CRN) to filter harmonic locations. Finally, the harmonic gating mechanism is used to guide the compensation model to adjust the coarse results from CRN to obtain the refinedly enhanced results. Our experiments show HGCN achieves substantial gain over a number of advanced approaches in the community.