# Multi-Domain Processing via Hybrid Denoising Networks for Speech Enhancement

arXiv: https://arxiv.org/abs/1812.08914 · HTML: https://mlsanity.com/paper/1812.08914/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2018.

## Built on by (most cited first, 2 of 2)

- [CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement](https://mlsanity.com/paper/2209.11112/) (2022)
- [Speech Enhancement using Separable Polling Attention and Global Layer Normalization followed with PReLU](https://mlsanity.com/paper/2105.02509/) (2021)

## Abstract (arXiv)

We present a hybrid framework that leverages the trade-off between temporal and frequency precision in audio representations to improve the performance of speech enhancement task. We first show that conventional approaches using specific representations such as raw-audio and spectrograms are each effective at targeting different types of noise. By integrating both approaches, our model can learn multi-scale and multi-domain features, effectively removing noise existing on different regions on the time-frequency space in a complementary way. Experimental results show that the proposed hybrid model yields better performance and robustness than using each model individually.