4 citations · 6 across the 4 of their papers we have counts for
4 papers
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…
Dynamic Processing Neural Network Architecture For Hearing Loss Compensation
Szymon Drgas, Lars Bramsløw, Archontis Politis +2
This paper proposes neural networks for compensating sensorineural hearing loss. The aim of the hearing loss compensation task is to transform a speech signal to increase speech in…
Deep neural network based speech separation optimizing an objective estimator of intelligibility for low latency applications
Gaurav Naithani, Joonas Nikunen, Lars Bramsløw +1
Mean square error (MSE) has been the preferred choice as loss function in the current deep neural network (DNN) based speech separation techniques. In this paper, we propose a new…