Abstract
This paper explores the possibility of classifying elephant vocalizations as per their associated contexts. While elephants produce a variety of calls, trumpet calls only were explored to distinguish between two contexts: interaction with other elephants and interaction with their human caretakers (mahouts). For this study, we collected task-specific elephant vocalization data through fieldwork. A support vector machine based classifier is developed on openSMILE features for the said classification. The classification accuracy in categorizing elephant directed and human directed trumpet calls was found to be 81.43%. A detailed analysis of the employed acoustic features revealed that loudness, the spectral slope of 500–1500 Hz band, and spectral flux were found to be maximally contributing to the said categorization.
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Acknowledgments
The authors thank the Assam Forest Department for their permission to collect the data. The first author would also like to thank all the forest guards and elephant handlers in Kaziranga National Park and Tiger Reserve for their help in conducting the fieldwork.
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Lokhandwala, S., Sarmah, P., Sinha, R. (2022). Classifying Mahout and Social Interactions of Asian Elephants Based on Trumpet Calls. In: Prasanna, S.R.M., Karpov, A., Samudravijaya, K., Agrawal, S.S. (eds) Speech and Computer. SPECOM 2022. Lecture Notes in Computer Science(), vol 13721. Springer, Cham. https://doi.org/10.1007/978-3-031-20980-2_37
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