# A Review of Multi-Objective Deep Learning Speech Denoising Methods

arXiv: https://arxiv.org/abs/2003.12108 · HTML: https://mlsanity.com/paper/2003.12108/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2020.

## Built on by (most cited first, 6 of 6)

- [Speech Denoising Without Clean Training Data: A Noise2Noise Approach](https://mlsanity.com/paper/2104.03838/) (2021)
- [BirdSoundsDenoising: Deep Visual Audio Denoising for Bird Sounds](https://mlsanity.com/paper/2210.10196/) (2022)
- [U-vectors: Generating clusterable speaker embedding from unlabeled data](https://mlsanity.com/paper/2102.03868/) (2021)
- [Advances in Electron Microscopy with Deep Learning](https://mlsanity.com/paper/2101.01178/) (2021)
- [aTENNuate: Optimized Real-time Speech Enhancement with Deep SSMs on Raw Audio](https://mlsanity.com/paper/2409.03377/) (2024)
- [Denoising by neural network for muzzle blast detection](https://mlsanity.com/paper/2508.14919/) (2025)

## Abstract (arXiv)

This paper presents a review of multi-objective deep learning methods that have been introduced in the literature for speech denoising. After stating an overview of conventional, single objective deep learning, and hybrid or combined conventional and deep learning methods, a review of the mathematical framework of the multi-objective deep learning methods for speech denoising is provided. A representative method from each speech denoising category, whose codes are publicly available, is selected and a comparison is carried out by considering the same public domain dataset and four widely used objective metrics. The comparison results indicate the effectiveness of the multi-objective method compared with the other methods, in particular when the signal-to-noise ratio is low. Possible future improvements that can be achieved are also mentioned.