# Joint Noise Reduction and Listening Enhancement for Full-End Speech Enhancement

arXiv: https://arxiv.org/abs/2203.11500 · HTML: https://mlsanity.com/paper/2203.11500/

## Key facts

- Cited by 1 papers in the map (data through 19 Oct 2026).
- Placed in Speech enhancement and noise suppression, one of 11 directions in Speech and audio language models.
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 22 Mar 2022.

## Placed in

- Speech and audio language models / [Speech enhancement and noise suppression](https://mlsanity.com/speech/speech-enhancement-and-noise-suppression/)

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://mlsanity.com/paper/1809.07454/) (2018)
- [MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement](https://mlsanity.com/paper/1905.04874/) (2019)
- [ViSQOL v3: An Open Source Production Ready Objective Speech and Audio Metric](https://mlsanity.com/paper/2004.09584/) (2020)
- [Noise Tokens: Learning Neural Noise Templates for Environment-Aware Speech Enhancement](https://mlsanity.com/paper/2004.04001/) (2020)
- [An instrumental intelligibility metric based on information theory](https://mlsanity.com/paper/1708.05132/) (2017)
- [Joint Far- and Near-End Speech Intelligibility Enhancement based on the Approximated Speech Intelligibility Index](https://mlsanity.com/paper/2111.07759/) (2021)
- [Multi-Metric Optimization using Generative Adversarial Networks for Near-End Speech Intelligibility Enhancement](https://mlsanity.com/paper/2104.08499/) (2021)

## Built on by (most cited first, 1 of 1)

- [Joint Minimum Processing Beamforming and Near-end Listening Enhancement](https://mlsanity.com/paper/2309.11243/) (2023)

## Abstract (arXiv)

Speech enhancement (SE) methods mainly focus on recovering clean speech from noisy input. In real-world speech communication, however, noises often exist in not only speaker but also listener environments. Although SE methods can suppress the noise contained in the speaker's voice, they cannot deal with the noise that is physically present in the listener side. To address such a complicated but common scenario, we investigate a deep learning-based joint framework integrating noise reduction (NR) with listening enhancement (LE), in which the NR module first suppresses noise and the LE module then modifies the denoised speech, i.e., the output of the NR module, to further improve speech intelligibility. The enhanced speech can thus be less noisy and more intelligible for listeners. Experimental results show that our proposed method achieves promising results and significantly outperforms the disjoint processing methods in terms of various speech evaluation metrics.