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Identifying Distinguishing Acoustic Features in Felid Vocalizations Based on Call Type and Species Classification

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Abstract

The cat family Felidae is one of the most successful carnivore lineages today. However, the study of acoustic communication between felids remains a challenge due to the lack of fossils, the limited availability of audio recordings because of their largely solitary and secretive behaviour, and the underdevelopment of computational models and methods needed to address these questions. This study attempts to develop a machine learning-based approach which can be used to identify acoustic features that distinguish felid call types and species from one another through the optimization of classification tasks on these call types and species. A felid call dataset was developed by extracting audio clips from diverse sources. Due to the limited availability of samples, this study focused on the Pantherinae subfamily. The audio clips were manually annotated for call type and species. Time–frequency features were then extracted from the dataset. Finally, several multi-class classification algorithms were applied to the resulting data for classifying species and call types. We found that duration, mean mel spectrogram, frequency range, and amplitude range were among the most distinguishing features for the classifications.

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Data availability

The data that support the findings of this study are available on request from the corresponding author, DB. The data are not publicly available due to copyright limitations.

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Acknowledgements

The authors would like to acknowledge the contributions by: Sai Greeshma Saladi, Rohindraj Kandasamy, Nicholas Furey, Tianyu Yang to the data preprocessing and Brendan Smith, Rebecca Buonopane for assisting in audio annotations. We are grateful for the audio files provided by Dr. Gustav Peters, former Curator of Mammals, Zoological Research Museum, Bonn, and Dr. Karl-Heinz Frommolt, Scientific Head of the Animal Sound Archive (Museum für Naturkunde Berlin). We would also like to thank the anonymous reviewers for their helpful comments that helped to improve this paper.

Funding

This work was supported by the Fairfield University Science Institute (AB and MP), the Fredrickson Family Innovation Lab Grant (AB and MP), Fairfield University Computer Science Start-up grant (DB), and Georgia State University Computer Science Start-up Grant (MP).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Fairfield University, Fairfield, CT, 06824, USA

    Danushka Bandara & Karen Exantus

  2. Department of Biology, Fairfield University, Fairfield, CT, 06824, USA

    Cristian Navarro-Martinez & Ashley Byun

  3. Department of Computer Science, Georgia State University, Atlanta, GA, 30303, USA

    Murray Patterson

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  1. Danushka Bandara
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Correspondence to Danushka Bandara.

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Bandara, D., Exantus, K., Navarro-Martinez, C. et al. Identifying Distinguishing Acoustic Features in Felid Vocalizations Based on Call Type and Species Classification. Acoust Aust 51, 345–357 (2023). https://doi.org/10.1007/s40857-023-00298-5

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  • DOI: https://doi.org/10.1007/s40857-023-00298-5

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