# Classification of Anuran Frog Species Using Machine Learning

arXiv: https://arxiv.org/abs/2112.05148 · HTML: https://mlsanity.com/paper/2112.05148/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 9 Dec 2021.

## Builds on (most cited first, 5 of 5)

- [A Vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees](https://mlsanity.com/paper/2104.06856/) (2021)
- [Mobile Sensing for Multipurpose Applications in Transportation](https://mlsanity.com/paper/2106.10733/) (2021)
- [Machine learning approach for text and document mining](https://mlsanity.com/paper/1406.1580/) (2014)
- [Developing a Machine Learning Algorithm-Based Classification Models for the Detection of High-Energy Gamma Particles](https://mlsanity.com/paper/2111.09496/) (2021)
- [Identifying the Factors that Influence Urban Public Transit Demand](https://mlsanity.com/paper/2111.09126/) (2021)

## Built on by (most cited first, 1 of 1)

- [Mixture of Mixups for Multi-label Classification of Rare Anuran Sounds](https://mlsanity.com/paper/2403.09598/) (2024)

## Abstract (arXiv)

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.