Song-based Classification techniques for Endangered Bird Conservation

TL;DR

This paper explores machine learning and clustering techniques on bird song data to develop automatic identification methods, supporting endangered bird conservation efforts through acoustic sensor data analysis.

Contribution

It introduces a novel approach combining clustering and predictive modeling for bird song classification, aiding conservation with automated acoustic data analysis.

Findings

Clustering reveals homogeneity in species-specific songs.

Predictive models achieve high accuracy in bird identification.

Promising results support further development of automated acoustic monitoring.

Abstract

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.