48 citations · 165 across the 36 of their papers we have counts for
38 papers
Ranking the Impact of Contextual Specialization in Neural Speech Enhancement
Peter Leer, Svend Feldt, Zheng-Hua Tan +2
We systematically investigate neural speech enhancement systems, ranging from very small (10\,k parameters) to medium-large (2-5\,M parameters), which specialize to aco…
The Effect of Training Dataset Size on Discriminative and Diffusion-Based Speech Enhancement Systems
Philippe Gonzalez, Zheng-Hua Tan, Jan Østergaard +3
The performance of deep neural network-based speech enhancement systems typically increases with the training dataset size. However, studies that investigated the effect of trainin…
Higher-order Common Information
Jan Østergaard
Shannon's mutual information quantifies dependence between two random variables. We introduce \emph{higher-order common information} (HCI), a measure of statistical relevance acros…
Deep low-latency joint speech transmission and enhancement over a gaussian channel
Mohammad Bokaei, Jesper Jensen, Simon Doclo +1
Ensuring intelligible speech communication for hearing assistive devices in low-latency scenarios presents significant challenges in terms of speech enhancement, coding and transmi…
How to train your ears: Auditory-model emulation for large-dynamic-range inputs and mild-to-severe hearing losses
Peter Leer, Jesper Jensen, Zheng-Hua Tan +2
Advanced auditory models are useful in designing signal-processing algorithms for hearing-loss compensation or speech enhancement. Such auditory models provide rich and detailed de…
Hearing-Loss Compensation Using Deep Neural Networks: A Framework and Results From a Listening Test
Peter Leer, Jesper Jensen, Laurel H. Carney +3
This article investigates the use of deep neural networks (DNNs) for hearing-loss compensation. Hearing loss is a prevalent issue affecting millions of people worldwide, and conven…