On Learning to Classify Meerkat Calls
| Type of publication: | Idiap-Com |
| Citation: | BenMahmoud_Idiap-Com-01-2024 |
| Number: | Idiap-Com-01-2024 |
| Year: | 2024 |
| Month: | 5 |
| Institution: | Idiap |
| Abstract: | This thesis focuses on the classification of meerkat vocalizations using machine learning techniques. Meerkats have a complex social structure and a diverse communication system. They communicate through vocalizations, also called calls, which serve multifaceted purposes and can be classified into distinct categories. Even though several studies have been published analyzing their intriguing acoustic signals, the task of classification of the calls has been barely explored so far. It is a task that is still manually performed by human experts, hence the need for a computational method. This research explores feature extraction methods and classification algorithms to effectively categorize meerkat call types utilizing three distinct datasets. The experimental results demonstrate the effectiveness of the support vector machine algorithm applied to convolution neural network crafted features, achieving the best performance across the three datasets. Furthermore, the thesis explores the generalization capability of a trained end-to-end convolution neural network, emphasizing the importance of re-adapting the filter stage of the convolutional neural network to the new dataset for improved classification performance. |
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| Projects |
Idiap |
| Authors | |
| Added by: | [ADM] |
| Total mark: | 0 |
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