用声音识别四种昆虫，助力生态监测与害虫防控。
Sound Classification of Four Insect Classes
- 结合梅尔频率倒谱系数与数据增强提升模型泛化能力
- 随机森林表现最佳，准确率达97.5%且有效缓解过拟合
- 适合对声学分类与生态智能监控感兴趣的读者
本项目旨在对四种昆虫声音——蝉、甲虫、白蚁和蟋蟀进行分类，应用前景包括害虫监控与生态系统保护。研究采用数据增强技术（如音高偏移与速度变化），生成6种增强版本数据，以扩充原始声音数据集。实验对比了决策树、随机森林、SVM-RBF、XGBoost和k-NN等模型在梅尔频率倒谱系数（MFCC）特征上的表现。结果显示，随机森林模型在测试集上达到97.5%的准确率，显著优于其他模型，并有效缓解了过拟合问题。该研究通过系统性数据增强策略提升了分类性能，具有潜在应用价值。
原文摘要 · Abstract (English)
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.
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