Please use this identifier to cite or link to this item:
http://hdl.handle.net/1893/36632
Appears in Collections: | Computing Science and Mathematics Journal Articles |
Peer Review Status: | Refereed |
Title: | Canonical cortical graph neural networks and its application for speech enhancement in audio-visual hearing aids |
Author(s): | Passos, Leandro A Papa, João Paulo Hussain, Amir Adeel, Ahsan |
Contact Email: | ahsan.adeel1@stir.ac.uk |
Keywords: | Cortical circuits Canonical correlation analysis Multimodal learning Graph neural network Prior frames neighbourhood Positional encoding |
Issue Date: | 28-Mar-2023 |
Date Deposited: | 28-Feb-2025 |
Citation: | Passos LA, Papa JP, Hussain A & Adeel A (2023) Canonical cortical graph neural networks and its application for speech enhancement in audio-visual hearing aids. <i>Neurocomputing</i>, 527, pp. 196-203. https://doi.org/10.1016/j.neucom.2022.11.081 |
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. |
DOI Link: | 10.1016/j.neucom.2022.11.081 |
Rights: | This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. You are not required to obtain permission to reuse this article. |
Licence URL(s): | http://creativecommons.org/licenses/by/4.0/ |
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