用声音识别蝉、甲虫、白蚁和蟋蟀，提升自动监测精度。
Audio-Based Classification of Insect Species Using Machine Learning Models: Cicada, Beetle, Termite, and Cricket
- 基于MFCC等声学特征，结合XGBoost等模型分类
- 通过捕捉细微声纹差异实现高准确率识别
- 适合生态监测与害虫管理领域的自动化系统
本研究针对蝉、甲虫、白蚁和蟋蟀的物种分类问题，利用声音记录进行识别。准确的物种鉴定对生态监测和害虫管理至关重要。我们采用XGBoost、随机森林和K近邻（KNN）等机器学习模型分析音频特征，包括梅尔频率倒谱系数（MFCC）。研究创新点在于整合多种音频特征与机器学习模型，专门捕捉不同物种间微妙的声学差异，这些差异在以往研究中未被充分挖掘。数据集来自多个公开来源，预计可实现高分类准确率，有助于提升自动化昆虫检测系统的性能。
原文摘要 · Abstract (English)
This project addresses the challenge of classifying insect species: Cicada, Beetle, Termite, and Cricket using sound recordings. Accurate species identification is crucial for ecological monitoring and pest management. We employ machine learning models such as XGBoost, Random Forest, and K Nearest Neighbors (KNN) to analyze audio features, including Mel Frequency Cepstral Coefficients (MFCC). The potential novelty of this work lies in the combination of diverse audio features and machine learning models to tackle insect classification, specifically focusing on capturing subtle acoustic variations between species that have not been fully leveraged in previous research. The dataset is compiled from various open sources, and we anticipate achieving high classification accuracy, contributing to improved automated insect detection systems.
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