Browsing by Author "Luphade, Nonhlanhla"

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    Bioacoustic classification of Hainan gibbon call types using deep learning
    (2023) Luphade, Nonhlanhla; Durbach, Ian; Britz, Stefan; Dufourq, Emmanuel
    In Bawangling National Nature Reserve (BNNR), Hainan, China, there exists a critically endangered primate known as the Hainan gibbon Nomascus hainanus. Many species, including the Hainan gibbon, are at high risk of extinction due to many factors such as unsustainable hunting, climate change, and deforestation. The Hainan gibbons live in social groups and the ability to discriminate between the group is useful for tracking migration patterns, population management, and identification of new groups. Currently, there has not been any study which attempts to distinguish between the groups. More recently, researchers have begun using deep learning to answer ecological questions, in a similar way that deep learning has successfully been used in computer vision and audio classification tasks. This study is the first attempt at investigating how deep learning can be used to distinguish between the Hainan gibbon social groups using only the acoustic data recorded in BNNR. Two convolutional neural networks (CNNs) were developed, the first was a binary classification model to detect gibbon calls from non-gibbon calls, and the second was a group classifier to distinguish between the social groups in BNNR. The audio data was converted into mel-scale spectrograms, resulting in images used as input to train the CNNs. Two steps were taken to train reliable models. Firstly, data augmentation techniques were explored to increase the amount of data as a means to train reliable models, and secondly, hyperparameter tuning was conducted. The binary classifier obtained a testing accuracy of 86%. The findings reveal that the model is able to distinguish between gibbon calls and non-gibbon calls. The social group model was not able to distinguish between the social groups as the model predicted the majority of the calls as one group. The result of this study demonstrates the usefulness of deep learning in addressing ecological questions that would be otherwise very challenging for a human to achieve.