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Within the ambit of VoIP (Voice over Internet Protocol) telecommunications,the complexities introduced by acoustic transformations merit rigorousanalysis. This research, rooted in the exploration of proprietary sender-sidedenoising effects, meticulously evaluates platforms such as Google Meets andZoom. The study draws upon the Deep Noise Suppression (DNS) 2020 dataset,ensuring a structured examination tailored to various denoising settings andreceiver interfaces. A methodological novelty is introduced via the Oaxacadecomposition, traditionally an econometric tool, repurposed herein to analyzeacoustic-phonetic perturbations within VoIP systems. To further ground theimplications of these transformations, psychoacoustic metrics, specificallyPESQ and STOI, were harnessed to furnish a comprehensive understanding ofspeech alterations. Cumulatively, the insights garnered underscore theintricate landscape of VoIP-influenced acoustic dynamics. In addition to theprimary findings, a multitude of metrics are reported, extending the researchpurview. Moreover, out-of-domain benchmarking for both time and time-frequencydomain speech enhancement models is included, thereby enhancing the depth andapplicability of this inquiry. Repository:github.com/deepology/VoIP-DNS-Challenge

Psychoacoustic Challenges Of Speech Enhancement On VoIP Platforms