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eess.AS2024

EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation

Julius Richter, Yi-Chiao Wu, Steven Krenn +5

We release the EARS (Expressive Anechoic Recordings of Speech) dataset, a high-quality speech dataset comprising 107 speakers from diverse backgrounds, totaling in 100 hours of cle…

eess.AS2024

The PESQetarian: On the Relevance of Goodhart's Law for Speech Enhancement

Danilo de Oliveira, Simon Welker, Julius Richter +1

To obtain improved speech enhancement models, researchers often focus on increasing performance according to specific instrumental metrics. However, when the same metric is used in…

eess.AS2023

A Flexible Online Framework for Projection-Based STFT Phase Retrieval

Tal Peer, Simon Welker, Johannes Kolhoff +1

Several recent contributions in the field of iterative STFT phase retrieval have demonstrated that the performance of the classical Griffin-Lim method can be considerably improved…

eess.AS2023

Speech Signal Improvement Using Causal Generative Diffusion Models

Julius Richter, Simon Welker, Jean-Marie Lemercier +3

In this paper, we present a causal speech signal improvement system that is designed to handle different types of distortions. The method is based on a generative diffusion model w…

eess.AS2023

Reducing the Prior Mismatch of Stochastic Differential Equations for Diffusion-based Speech Enhancement

Bunlong Lay, Simon Welker, Julius Richter +1

Recently, score-based generative models have been successfully employed for the task of speech enhancement. A stochastic differential equation is used to model the iterative forwar…