Classification of Anuran Frog Species Using Machine Learning
This work addresses the need for automated frog species identification in ecological research, but it is incremental as it applies existing methods like PCA and ICA to this domain.
The study tackled the problem of classifying anuran frog species from audio recordings by using machine learning on time-frequency representations and data reduction techniques, achieving better classification accuracy with PCA-extracted features compared to ICA.
Acoustic classification of frogs has gotten a lot of attention recently due to its potential applicability in ecological investigations. Numerous studies have been presented for identifying frog species, although the majority of recorded species are thought to be monotypic. The purpose of this study is to demonstrate a method for classifying various frog species using an audio recording. To be more exact, continuous frog recordings are cut into audio snippets first (10 seconds). Then, for each ten-second recording, several time-frequency representations are constructed. Following that, rather than using manually created features, Machine Learning methods are employed to classify the frog species. Data reduction techniques; Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are used to extract the most important features before classification. Finally, to validate our classification accuracy, cross validation and prediction accuracy are used. Experimental results show that PCA extracted features that achieved better classification accuracy both with cross validation and prediction accuracy.