# Sound Classification of Four Insect Classes

arXiv: https://arxiv.org/abs/2412.12395 · HTML: https://mlsanity.com/paper/2412.12395/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 16 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [Flying Insect Classification with Inexpensive Sensors](https://mlsanity.com/paper/1403.2654/) (2014)
- [Continuous Authentication of Wearable Device Users from Heart Rate, Gait, and Breathing Data](https://mlsanity.com/paper/2008.10779/) (2020)
- [Transfer Learning to Detect COVID-19 Coughs with Incremental Addition of Patient Coughs to Healthy People's Cough Detection Models](https://mlsanity.com/paper/2311.06707/) (2023)
- [Mitigating Sex Bias in Audio Data-driven COPD and COVID-19 Breathing Pattern Detection Models](https://mlsanity.com/paper/2409.10677/) (2024)

## Abstract (arXiv)

The goal of this project is to classify four different insect sounds: cicada, beetle, termite, and cricket. One application of this project is for pest control to monitor and protect our ecosystem. Our project leverages data augmentation, including pitch shifting and speed changing, to improve model generalization. This project will test the performance of Decision Tree, Random Forest, SVM RBF, XGBoost, and k-NN models, combined with MFCC feature. A potential novelty of this project is that various data augmentation techniques are used and created 6 data along with the original sound. The dataset consists of the sound recordings of these four insects. This project aims to achieve a high classification accuracy and to reduce the over-fitting problem.