3 citations · 3 across the 1 of their papers we have counts for
5 papers · 1 filter
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…
Investigating the Design Space of Diffusion Models for Speech Enhancement
Philippe Gonzalez, Zheng-Hua Tan, Jan Ãstergaard +3
Diffusion models are a new class of generative models that have shown outstanding performance in image generation literature. As a consequence, studies have attempted to apply diff…
The Effect of Training Dataset Size on Discriminative and Diffusion-Based Speech Enhancement Systems
Philippe Gonzalez, Zheng-Hua Tan, Jan Ãstergaard +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…
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…