45 citations · 69 across the 12 of their papers we have counts for
6 papers · 1 filter
Towards Environmental Preference Based Speech Enhancement For Individualised Multi-Modal Hearing Aids
Jasper Kirton-Wingate, Shafique Ahmed, Adeel Hussain +6
Since the advent of Deep Learning (DL), Speech Enhancement (SE) models have performed well under a variety of noise conditions. However, such systems may still introduce sonic arte…
Canonical Cortical Graph Neural Networks and its Application for Speech Enhancement in Audio-Visual Hearing Aids
Leandro A. Passos, João Paulo Papa, Amir Hussain +1
Despite the recent success of machine learning algorithms, most models face drawbacks when considering more complex tasks requiring interaction between different sources, such as m…
A Novel Speech Intelligibility Enhancement Model based on CanonicalCorrelation and Deep Learning
Tassadaq Hussain, Muhammad Diyan, Mandar Gogate +4
Current deep learning (DL) based approaches to speech intelligibility enhancement in noisy environments are often trained to minimise the feature distance between noise-free speech…
Multimodal Audio-Visual Information Fusion using Canonical-Correlated Graph Neural Network for Energy-Efficient Speech Enhancement
Leandro Aparecido Passos, João Paulo Papa, Javier Del Ser +2
This paper proposes a novel multimodal self-supervised architecture for energy-efficient audio-visual (AV) speech enhancement that integrates Graph Neural Networks with canonical c…
Towards Robust Real-time Audio-Visual Speech Enhancement
Mandar Gogate, Kia Dashtipour, Amir Hussain
The human brain contextually exploits heterogeneous sensory information to efficiently perform cognitive tasks including vision and hearing. For example, during the cocktail party…
Towards Intelligibility-Oriented Audio-Visual Speech Enhancement
Tassadaq Hussain, Mandar Gogate, Kia Dashtipour +1
Existing deep learning (DL) based speech enhancement approaches are generally optimised to minimise the distance between clean and enhanced speech features. These often result in i…