Processing and classifying bird songs using wavelet techniques and supervised learning — Paper

Abstract

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: \textit{Euphonia violacea}, \textit{Leiothrix lutea}, and \textit{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.

Authors 4

Laura Lucia Dominguez Barrios, Fidel Aniano Causil Barrios, Alex Rodrigo dos Santos Sousa, Mariana Rodrigues Motta

Specification

Official page: arxiv.org/abs/2609.10826
Source:arXiv (Atom API + RSS)T1observed 29 min agohigh
Arxiv announce type: new
Source:arXiv (Atom API + RSS)T1observed 29 min agohigh
arXiv id: 2609.10826
Source:arXiv (Atom API + RSS)T1observed 29 min agohigh
Categories: cs.LG, stat.ME
Source:arXiv (Atom API + RSS)T1observed 29 min agohigh
PDF: arxiv.org/pdf/2609.10826
Source:arXiv (Atom API + RSS)T1observed 29 min agohigh
Primary category: cs.LG
Source:arXiv (Atom API + RSS)T1observed 29 min agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 29 min agohigh

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9

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T19

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29 min ago

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Authors: Laura Lucia Dominguez Barrios, Fidel Aniano Causil Barrios, Alex Rodrigo dos Santos Sousa

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Value	Valid from → to	Status	Source	Confidence	Extractor
https://arxiv.org/pdf/2609.10826	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperProcessing and classifying bird songs using wavelet techniques and supervised learning
New paper: Processing and classifying bird songs using wavelet techniques and supervised learning
arxiv
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Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.LG	feed	T1· Official	29 min ago	1

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