Elephant Sound Classification Using Deep Learning Optimization
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 classificatio...
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| Language: | English |
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/352 |
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| author | Hiruni Dewmini Dulani Meedeniya Charith Perera |
| author_facet | Hiruni Dewmini Dulani Meedeniya Charith Perera |
| author_sort | Hiruni Dewmini |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8ea6e4e842ad45d3bd86fd61da64506d |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-8ea6e4e842ad45d3bd86fd61da64506d2025-01-24T13:48:37ZengMDPI AGSensors1424-82202025-01-0125235210.3390/s25020352Elephant Sound Classification Using Deep Learning OptimizationHiruni Dewmini0Dulani Meedeniya1Charith Perera2Department of Computer Science and Engineering, University of Moratuwa, Moratuwa 10400, Sri LankaDepartment of Computer Science and Engineering, University of Moratuwa, Moratuwa 10400, Sri LankaSchool of Computer Science and Informatics, Cardiff University, Cardiff CF24 3AA, UKElephant 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.https://www.mdpi.com/1424-8220/25/2/352artificial intelligenceaudio processingdeep learningelephant vocalizationoptimizationresource constrained |
| spellingShingle | Hiruni Dewmini Dulani Meedeniya Charith Perera Elephant Sound Classification Using Deep Learning Optimization Sensors artificial intelligence audio processing deep learning elephant vocalization optimization resource constrained |
| title | Elephant Sound Classification Using Deep Learning Optimization |
| title_full | Elephant Sound Classification Using Deep Learning Optimization |
| title_fullStr | Elephant Sound Classification Using Deep Learning Optimization |
| title_full_unstemmed | Elephant Sound Classification Using Deep Learning Optimization |
| title_short | Elephant Sound Classification Using Deep Learning Optimization |
| title_sort | elephant sound classification using deep learning optimization |
| topic | artificial intelligence audio processing deep learning elephant vocalization optimization resource constrained |
| url | https://www.mdpi.com/1424-8220/25/2/352 |
| work_keys_str_mv | AT hirunidewmini elephantsoundclassificationusingdeeplearningoptimization AT dulanimeedeniya elephantsoundclassificationusingdeeplearningoptimization AT charithperera elephantsoundclassificationusingdeeplearningoptimization |