  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.
