ESC-NAS: Environment sound classification using hardware-aware neural architecture search for the edge

Ranmal, Dakshina, Ranasinghe, Piumini, Paranayapa, Thivindu, Meedeniya, Dulani and Perera, Charith ORCID: https://orcid.org/0000-0002-0190-3346 2024. ESC-NAS: Environment sound classification using hardware-aware neural architecture search for the edge. Sensors 24 (12) , 3749. 10.3390/s24123749

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Abstract

The combination of deep-learning and IoT plays a significant role in modern smart solutions, providing the capability of handling task-specific real-time offline operations with improved accuracy and minimised resource consumption. This study provides a novel hardware-aware neural architecture search approach called ESC-NAS, to design and develop deep convolutional neural network architectures specifically tailored for handling raw audio inputs in environmental sound classification applications under limited computational resources. The ESC-NAS process consists of a novel cell-based neural architecture search space built with 2D convolution, batch normalization, and max pooling layers, and capable of extracting features from raw audio. A black-box Bayesian optimization search strategy explores the search space and the resulting model architectures are evaluated through hardware simulation. The models obtained from the ESC-NAS process achieved the optimal trade-off between model performance and resource consumption compared to the existing literature. The ESC-NAS models achieved accuracies of 85.78%, 81.25%, 96.25%, and 81.0% for the FSC22, UrbanSound8K, ESC-10, and ESC-50 datasets, respectively, with optimal model sizes and parameter counts for edge deployment.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Additional Information:	License information from Publisher: LICENSE 1: URL: https://creativecommons.org/licenses/by/4.0/, Type: open-access
Publisher:	MDPI
Date of First Compliant Deposit:	11 July 2024
Date of Acceptance:	7 June 2024
Last Modified:	11 Jul 2024 10:46
URI:	https://orca.cardiff.ac.uk/id/eprint/170498

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