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.

本研究着眼于设计一种无线传感器网络来支持对濒危鸟类族群的观测。在对样本的声学数据进行知识发现的初步阶段，我们使用从鸟歌中提取的MFCC特征和两种知识发现技术（基于聚类的方法和基于预测建模的方法）来分析数据。研究结果表明，机器学习技术在鸟类种类识别过程中具有良好的表现，并为基于声学传感器的自动数据收集方法的设计提供了有希望的结果和指导建议。

基于歌声的濒危鸟类分类技术在保护中的应用