Elephant sound classification using deep learning optimization

Dewmini, Hiruni, Meedeniya, Dulani and Perera, Charith ORCID: https://orcid.org/0000-0002-0190-3346 2025. Elephant sound classification using deep learning optimization. Sensors 25 (2) , 352. 10.3390/s25020352

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Abstract

Elephant sound identification is crucial in wildlife conservation and ecological research. The identification of elephant vocalizations provides insights into the behavior, social dynamics, and emotional expressions, leading to elephant conservation. This study addresses elephant sound classification utilizing raw audio processing. Our focus lies on exploring lightweight models suitable for deployment on resource-costrained edge devices, including MobileNet, YAMNET, and RawNet, alongside introducing a novel model termed ElephantCallerNet. Notably, our investigation reveals that the proposed ElephantCallerNet achieves an impressive accuracy of 89% in classifying raw audio directly without converting it to spectrograms. Leveraging Bayesian optimization techniques, we fine-tuned crucial parameters such as learning rate, dropout, and kernel size, thereby enhancing the model’s performance. Moreover, we scrutinized the efficacy of spectrogram-based training, a prevalent approach in animal sound classification. Through comparative analysis, the raw audio processing outperforms spectrogram-based methods. In contrast to other models in the literature that primarily focus on a single caller type or binary classification that identifies whether a sound is an elephant voice or not, our solution is designed to classify three distinct caller-types namely roar, rumble, and trumpet.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Additional Information:	License information from Publisher: LICENSE 1: URL: https://creativecommons.org/licenses/by/4.0/, Start Date: 2025-01-09
Publisher:	MDPI
Date of First Compliant Deposit:	21 January 2025
Date of Acceptance:	8 January 2025
Last Modified:	21 Jan 2025 12:15
URI:	https://orca.cardiff.ac.uk/id/eprint/175444

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