# Self-supervised Learning for Speech Enhancement

arXiv: https://arxiv.org/abs/2006.10388 · HTML: https://mlsanity.com/paper/2006.10388/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2020.

## Built on by (most cited first, 9 of 9)

- [Audio Self-supervised Learning: A Survey](https://mlsanity.com/paper/2203.01205/) (2022)
- [LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech](https://mlsanity.com/paper/2309.05472/) (2023)
- [Self-Supervised Speech Quality Estimation and Enhancement Using Only Clean Speech](https://mlsanity.com/paper/2402.16321/) (2024)
- [Efficient Personalized Speech Enhancement through Self-Supervised Learning](https://mlsanity.com/paper/2104.02017/) (2021)
- [Personalized Speech Enhancement through Self-Supervised Data Augmentation and Purification](https://mlsanity.com/paper/2104.02018/) (2021)
- [Coherent, super resolved radar beamforming using self-supervised learning](https://mlsanity.com/paper/2106.13085/) (2021)
- [Leveraging Real Conversational Data for Multi-Channel Continuous Speech Separation](https://mlsanity.com/paper/2204.03232/) (2022)
- [Conditional independence for pretext task selection in Self-supervised speech representation learning](https://mlsanity.com/paper/2104.07388/) (2021)
- [Speech Augmentation Based Unsupervised Learning for Keyword Spotting](https://mlsanity.com/paper/2205.14329/) (2022)

## Abstract (arXiv)

Supervised learning for single-channel speech enhancement requires carefully labeled training examples where the noisy mixture is input into the network and the network is trained to produce an output close to the ideal target. To relax the conditions on the training data, we consider the task of training speech enhancement networks in a self-supervised manner. We first use a limited training set of clean speech sounds and learn a latent representation by autoencoding on their magnitude spectrograms. We then autoencode on speech mixtures recorded in noisy environments and train the resulting autoencoder to share a latent representation with the clean examples. We show that using this training schema, we can now map noisy speech to its clean version using a network that is autonomously trainable without requiring labeled training examples or human intervention.