Processing and classifying bird songs using wavelet techniques and supervised learning
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: 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.
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BibTeX
@misc{barrios2026processing,
title = {{Processing and classifying bird songs using wavelet techniques and supervised learning}},
author = {Laura Lucia Dominguez Barrios and Fidel Aniano Causil Barrios and Alex Rodrigo dos Santos Sousa and Mariana Rodrigues Motta},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.10826},
eprint = {2609.10826},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.10826},
}
RIS
TY - UNPB
TI - Processing and classifying bird songs using wavelet techniques and supervised learning
AU - Laura Lucia Dominguez Barrios
AU - Fidel Aniano Causil Barrios
AU - Alex Rodrigo dos Santos Sousa
AU - Mariana Rodrigues Motta
PY - 2026
DA - 2026/09/28
PB - arXiv
DO - 10.48550/arxiv.2609.10826
UR - https://arxiv.org/abs/2609.10826
AB - 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.
ER -