4 papers
SlimDiffuSE: Towards Efficient Diffusion-Based Speech Enhancement using Slimmable Networks
Nagashree K. S. Rao, Shrishti Saha Shetu, Mohamed Elminshawi +2
Diffusion-based models are emerging in the speech enhancement domain and are achieving state-of-the-art performance across various benchmark datasets. A major downside of diffusion…
A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination
Shrishti Saha Shetu, Emanuël A. P. Habets, Andreas Brendel
In this study, we conduct a comprehensive comparative analysis of generative and discriminative deep learning-based speech enhancement methods, specifically in noise reduction task…
UBGAN: Enhancing Coded Speech with Blind and Guided Bandwidth Extension
Kishan Gupta, Srikanth Korse, Andreas Brendel +2
In practical application of speech codecs, a multitude of factors such as the quality of the radio connection, limiting hardware or required user experience necessitate trade-offs…
On the Design of Diffusion-based Neural Speech Codecs
Pietro Foti, Andreas Brendel
Recently, neural speech codecs (NSCs) trained as generative models have shown superior performance compared to conventional codecs at low bitrates. Although most state-of-the-art N…