# Joint Minimum Processing Beamforming and Near-end Listening Enhancement

arXiv: https://arxiv.org/abs/2309.11243 · HTML: https://mlsanity.com/paper/2309.11243/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Speech enhancement and noise suppression, one of 11 directions in Speech and audio language models.
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 20 Sep 2023.

## 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, 3 of 3)

- [Joint Far- and Near-End Speech Intelligibility Enhancement based on the Approximated Speech Intelligibility Index](https://mlsanity.com/paper/2111.07759/) (2021)
- [Joint Noise Reduction and Listening Enhancement for Full-End Speech Enhancement](https://mlsanity.com/paper/2203.11500/) (2022)
- [Minimum Processing Near-end Listening Enhancement](https://mlsanity.com/paper/2210.17154/) (2022)

## Abstract (arXiv)

We consider speech enhancement for signals picked up in one noisy environment that must be rendered to a listener in another noisy environment. For both far-end noise reduction and near-end listening enhancement, it has been shown that excessive focus on noise suppression or intelligibility maximization may lead to excessive speech distortions and quality degradations in favorable noise conditions, where intelligibility is already at ceiling level. Recently [1,2] propose to remedy this with a minimum processing framework that either reduces noise or enhances listening a minimum amount given that a certain intelligibility criterion is still satisfied Additionally, it has been shown that joint consideration of both environments improves speech enhancement performance. In this paper, we formulate a joint far- and near-end minimum processing framework, that improves intelligibility while limiting speech distortions in favorable noise conditions. We provide closed-form solutions to specific boundary scenarios and investigate performance for the general case using numerical optimization. We also show concatenating existing minimum processing far- and near-end enhancement methods preserves the effects of the initial methods. Results show that the joint optimization can further improve performance compared to the concatenated approach.