A comparative study of preprocessing and model compression techniques in deep learning for forest sound classification

Paranayapa, Thivindu, Ranasinghe, Piumini, Ranmal, Dakshina, Meedeniya, Dulani and Perera, Charith ORCID: https://orcid.org/0000-0002-0190-3346 2024. A comparative study of preprocessing and model compression techniques in deep learning for forest sound classification. Sensors 24 (4) , 1149. 10.3390/s24041149

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Abstract

Deep-learning models play a significant role in modern software solutions, with the capabilities of handling complex tasks, improving accuracy, automating processes, and adapting to diverse domains, eventually contributing to advancements in various industries. This study provides a comparative study on deep-learning techniques that can also be deployed on resource-constrained edge devices. As a novel contribution, we analyze the performance of seven Convolutional Neural Network models in the context of data augmentation, feature extraction, and model compression using acoustic data. The results show that the best performers can achieve an optimal trade-off between model accuracy and size when compressed with weight and filter pruning followed by 8-bit quantization. In adherence to the study workflow utilizing the forest sound dataset, MobileNet-v3-small and ACDNet achieved accuracies of 87.95% and 85.64%, respectively, while maintaining compact sizes of 243 KB and 484 KB, respectively. Henceforth, this study concludes that CNNs can be optimized and compressed to be deployed in resource-constrained edge devices for classifying forest environment sounds.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Publisher:	MDPI Publishing
ISSN:	1424-8220
Date of First Compliant Deposit:	7 March 2024
Date of Acceptance:	7 February 2024
Last Modified:	08 Mar 2024 14:30
URI:	https://orca.cardiff.ac.uk/id/eprint/166974

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