AccScience Publishing / IJOCTA / Online First / DOI: 10.36922/IJOCTA026050021
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Enhanced audio classification with quantum-inspired neural layers and exponentially weighted attention fusion

Zahraa Tarek1 ,  Esraa Hasan2*
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1 Department of Computer Engineering and Information, College of Engineering, Wadi Ad Dwaser, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
2 Department of Machine Learning and Information Retrieval, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafr El Sheikh, Egypt
IJOCTA, 026050021 https://doi.org/10.36922/IJOCTA026050021
Received: 31 January 2026 | Revised: 24 February 2026 | Accepted: 3 March 2026 | Published online: 29 April 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Snoring is a common sleep problem and may indicate underlying health conditions, such as obstructive sleep apnea. Accurate detection and classification of snoring sounds are critical for early diagnosis and effective treatment. Traditional snoring detection methods rely on manual analysis or rule-based systems, which are often time-consuming and error-prone. With advances in deep learning and quantum-inspired computing, an opportunity has emerged to develop more powerful and intelligent solutions to automate snoring classification. In this paper, we propose a quantum-inspired attention fusion network for snoring detection, using Mel-Frequency Cepstral Coefficients as the primary representation of acoustic features. The network combines quantum-inspired layers with an attention mechanism to enhance feature learning and improve classification accuracy, offering a novel approach to audio analysis in the healthcare domain. The quantum-inspired layer simulates quantum gates using dense neural layers, enabling the model to detect complex patterns in audio data, while an attention mechanism dynamically weights the most important features to improve classification. The model achieved outstanding performance with an accuracy of 0.997, a prediction precision of 0.997, a recall of 0.997, and an average F1-score of 0.997, demonstrating its ability to generalize well and classify snoring sounds with near-perfect accuracy.

Graphical abstract
Keywords
Snoring detection
Quantum
Fusion
Sleeping disorder
Funding
None.
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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An International Journal of Optimization and Control: Theories & Applications, Electronic ISSN: 2146-5703 Print ISSN: 2146-0957, Published by AccScience Publishing
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