# Psychoacoustic Challenges Of Speech Enhancement On VoIP Platforms

arXiv: https://arxiv.org/abs/2310.07161 · HTML: https://mlsanity.com/paper/2310.07161/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 11 Oct 2023.

## Builds on (most cited first, 6 of 6)

- [Real Time Speech Enhancement in the Waveform Domain](https://mlsanity.com/paper/2006.12847/) (2020)
- [The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results](https://mlsanity.com/paper/2005.13981/) (2020)
- [Improving Speech Enhancement through Fine-Grained Speech Characteristics](https://mlsanity.com/paper/2207.00237/) (2022)
- [TAPLoss: A Temporal Acoustic Parameter Loss for Speech Enhancement](https://mlsanity.com/paper/2302.08088/) (2023)
- [PAAPLoss: A Phonetic-Aligned Acoustic Parameter Loss for Speech Enhancement](https://mlsanity.com/paper/2302.08095/) (2023)
- [Improving Perceptual Quality, Intelligibility, and Acoustics on VoIP Platforms](https://mlsanity.com/paper/2303.09048/) (2023)

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

- [aoip.ai: An Open-Source P2P SDK](https://mlsanity.com/paper/2312.14934/) (2023)

## Abstract (arXiv)

Within the ambit of VoIP (Voice over Internet Protocol) telecommunications, the complexities introduced by acoustic transformations merit rigorous analysis. This research, rooted in the exploration of proprietary sender-side denoising effects, meticulously evaluates platforms such as Google Meets and Zoom. The study draws upon the Deep Noise Suppression (DNS) 2020 dataset, ensuring a structured examination tailored to various denoising settings and receiver interfaces. A methodological novelty is introduced via Blinder-Oaxaca decomposition, traditionally an econometric tool, repurposed herein to analyze acoustic-phonetic perturbations within VoIP systems. To further ground the implications of these transformations, psychoacoustic metrics, specifically PESQ and STOI, were used to explain of perceptual quality and intelligibility. Cumulatively, the insights garnered underscore the intricate landscape of VoIP-influenced acoustic dynamics. In addition to the primary findings, a multitude of metrics are reported, extending the research purview. Moreover, out-of-domain benchmarking for both time and time-frequency domain speech enhancement models is included, thereby enhancing the depth and applicability of this inquiry.