基于鸣声的濒危鸟类保护分类技术
Song-based Classification techniques for Endangered Bird Conservation
摘要：
本文提出的工作是一个全球框架的一部分，其长期目标是设计一个无线传感器网络，能够支持濒危鸟类种群的观察。我们提出的第一阶段，我们已经进行了一个知识发现的方法，声学数据的样本。我们使用MFCC特征提取的鸟鸣，我们利用两个知识发现技术。一个依赖于基于聚类的方法，突出了物种歌曲的同质性。另一种是基于预测建模的方法，它展示了各种机器学习技术在识别过程中的良好性能。知识引出提供了有希望的结果，考虑广泛的研究，并引出设计的第一个版本的自动方法的数据汇集的基础上声学传感器的准则。
英文摘要：
The work presented in this paper is part of a global framework which long term goal is to design a wireless sensor network able to support the observation of a population of endangered birds. We present the first stage for which we have conducted a knowledge discovery approach on a sample of acoustical data. We use MFCC features extracted from bird songs and we exploit two knowledge discovery techniques. One that relies on clustering-based approaches, that highlights the homogeneity in the songs of the species. The other, based on predictive modeling, that demonstrates the good performances of various machine learning techniques for the identification process. The knowledge elicited provides promising results to consider a widespread study and to elicit guidelines for designing a first version of the automatic approach for data collection based on acoustic sensors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
