255 citations · 780 across the 58 of their papers we have counts for
6 papers · 1 filter
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…
The Importance of Context When Recommending TV Content: Dataset and Algorithms
Miklas S. Kristoffersen, Sven E. Shepstone, Zheng-Hua Tan
Home entertainment systems feature in a variety of usage scenarios with one or more simultaneous users, for whom the complexity of choosing media to consume has increased rapidly o…
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…
A Parallel/Distributed Algorithmic Framework for Mining All Quantitative Association Rules
Ioannis T. Christou, Emmanouil Amolochitis, Zheng-Hua Tan
We present QARMA, an efficient novel parallel algorithm for mining all Quantitative Association Rules in large multidimensional datasets where items are required to have at least a…
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…