发表机构
State University of Campinas（坎皮纳斯州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于贝叶斯小波去噪和多种监督学习模型的框架，用于分类入侵鸟类鸣声，其中SVM在10维MFCC下达到最高准确率0.9398，为生态监测提供稳健工具。
AI 中文摘要
本研究提出了一个集成框架，用于在具有高水平环境噪声的自然声景中处理和分类入侵鸟类物种的鸣声。我们通过采用基于Epanechnikov核先验的贝叶斯小波收缩方法来解决信号退化问题，该方法提供了封闭形式的决策规则和高计算效率，适用于处理大规模生物声学数据集。该方法应用于从iNaturalist平台获取的三个物种的录音：Euphonia violacea、Leiothrix lutea和Passer domesticus。在信号去噪后，我们提取了一组全面的特征，包括梅尔频率倒谱系数（MFCCs）以及诸如熵和过零率等光谱指数。我们评估了多种监督学习模型：随机森林、多项逻辑回归和支持向量机（SVM），并在不同的特征维度下进行了评估。我们的结果表明，所提出的基于小波的预处理显著提高了分类性能，其中SVM模型在10维MFCC配置下达到了最高准确率（高达0.9398）。这项研究为自动化生态监测和生物入侵管理提供了一个稳健的统计工具。
英文摘要
This study proposes an integrated framework for the processing and classification of invasive bird species vocalizations within natural soundscapes, characterized by high levels of environmental noise. We address the challenge of signal degradation by employing a Bayesian wavelet shrinkage methodology based on the Epanechnikov kernel prior, which offers a closed form decision rule and high computational efficiency for processing large bioacoustic datasets. The methodology was applied to recordings of three species obtained from the iNaturalist platform: Euphonia violacea, Leiothrix lutea, and Passer domesticus. After signal denoising, we extracted a comprehensive set of features, including Mel-Frequency Cepstral Coefficients (MFCCs) and spectral indices such as entropy and zero-crossing rate. Several supervised learning models: Random Forest, Multinomial Logistic Regression and Support Vector Machine (SVM) were evaluated across different feature dimensionalities. Our results demonstrate that the proposed wavelet based preprocessing significantly enhances classification performance, with the SVM model achieving the highest accuracy (up to 0.9398) under a 10-dimensional MFCC configuration. This research provides a robust statistical tool for automated ecological monitoring and the management of biological invasions.
