127 citations · 274 across the 15 of their papers we have counts for
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Effects of Lombard Reflex on the Performance of Deep-Learning-Based Audio-Visual Speech Enhancement Systems
Daniel Michelsanti, Zheng-Hua Tan, Sigurdur Sigurdsson +1
Humans tend to change their way of speaking when they are immersed in a noisy environment, a reflex known as Lombard effect. Current speech enhancement systems based on deep learni…
On Training Targets and Objective Functions for Deep-Learning-Based Audio-Visual Speech Enhancement
Daniel Michelsanti, Zheng-Hua Tan, Sigurdur Sigurdsson +1
Audio-visual speech enhancement (AV-SE) is the task of improving speech quality and intelligibility in a noisy environment using audio and visual information from a talker. Recentl…
On the Relationship Between Short-Time Objective Intelligibility and Short-Time Spectral-Amplitude Mean-Square Error for Speech Enhancement
Morten Kolbæk, Zheng-Hua Tan, Jesper Jensen
The majority of deep neural network (DNN) based speech enhancement algorithms rely on the mean-square error (MSE) criterion of short-time spectral amplitudes (STSA), which has no a…
Information Loss in the Human Auditory System
Mohsen Zareian Jahromi, Adel Zahedi, Jesper Jensen +1
From the eardrum to the auditory cortex, where acoustic stimuli are decoded, there are several stages of auditory processing and transmission where information may potentially get…
Monaural Speech Enhancement using Deep Neural Networks by Maximizing a Short-Time Objective Intelligibility Measure
Morten Kolbæk, Zheng-Hua Tan, Jesper Jensen
In this paper we propose a Deep Neural Network (DNN) based Speech Enhancement (SE) system that is designed to maximize an approximation of the Short-Time Objective Intelligibility…