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Canonical cortical graph neural networks and its application for speech enhancement in audio-visual hearing aids

Passos, Leandro A.; Papa, João Paulo; Hussain, Amir; Adeel, Ahsan

Authors

Leandro A. Passos

João Paulo Papa

Ahsan Adeel



Abstract

Despite the recent success of machine learning algorithms, most models face drawbacks when considering more complex tasks requiring interaction between different sources, such as multimodal input data and logical time sequences. On the other hand, the biological brain is highly sharpened in this sense, empowered to automatically manage and integrate such streams of information. In this context, this work draws inspiration from recent discoveries in brain cortical circuits to propose a more biologically plausible self-supervised machine learning approach. This combines multimodal information using intra-layer modulations together with Canonical Correlation Analysis, and a memory mechanism to keep track of temporal data, the overall approach termed Canonical Cortical Graph Neural networks. This is shown to outperform recent state-of-the-art models in terms of clean audio reconstruction and energy efficiency for a benchmark audio-visual speech dataset. The enhanced performance is demonstrated through a reduced and smother neuron firing rate distribution. suggesting that the proposed model is amenable for speech enhancement in future audio-visual hearing aid devices.

Journal Article Type Article
Acceptance Date Nov 21, 2022
Online Publication Date Dec 8, 2022
Publication Date 2023-03
Deposit Date Mar 2, 2023
Publicly Available Date Mar 2, 2023
Journal Neurocomputing
Print ISSN 0925-2312
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 527
Pages 196-203
DOI https://doi.org/10.1016/j.neucom.2022.11.081
Keywords Cortical circuits, Canonical correlation analysis, Multimodal learning, Graph neural network, Prior frames neighborhood, Positional encoding

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