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 |
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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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