19 citations · 39 across the 14 of their papers we have counts for
5 papers · 1 filter
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…
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…
Self-supervised Pretraining for Robust Personalized Voice Activity Detection in Adverse Conditions
Holger Severin Bovbjerg, Jesper Jensen, Jan Østergaard +1
In this paper, we propose the use of self-supervised pretraining on a large unlabelled data set to improve the performance of a personalized voice activity detection (VAD) model in…
Diffusion-Based Speech Enhancement in Matched and Mismatched Conditions Using a Heun-Based Sampler
Philippe Gonzalez, Zheng-Hua Tan, Jan Østergaard +3
Diffusion models are a new class of generative models that have recently been applied to speech enhancement successfully. Previous works have demonstrated their superior performanc…