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eess.AS2026

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…

eess.AS2026

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…

eess.AS2026

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…

eess.AS2024

Neural Speech Coding for Real-time Communications using Constant Bitrate Scalar Quantization

Andreas Brendel, Nicola Pia, Kishan Gupta +3

Neural audio coding has emerged as a vivid research direction by promising good audio quality at very low bitrates unachievable by classical coding techniques. Here, end-to-end tra…

eess.AS2024

On Improving Error Resilience of Neural End-to-End Speech Coders

Kishan Gupta, Nicola Pia, Srikanth Korse +3

Error resilient tools like Packet Loss Concealment (PLC) and Forward Error Correction (FEC) are essential to maintain a reliable speech communication for applications like Voice ov…