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Processing and classifying bird songs using wavelet techniques and supervised learning — arXiv:2609.10826

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.10826
License (as archived): CC0-1.0
Archived: 2026-10-16T05:54:48.455Z
SHA-256 of the archived text: d4a7b835892b2bd2cf764993731af0d16fc289139b1faed192d3db278a0d2c59
Stable URL of this page: https://metraipolis.com/documento/dc_0ffbd094df75

How to cite

Processing and classifying bird songs using wavelet techniques and supervised learning — arXiv:2609.10826 — arXiv (en), https://arxiv.org/abs/2609.10826 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_0ffbd094df75 (Metraipolis archive dc_0ffbd094df75, sha256 d4a7b835892b2bd2cf764993731af0d16fc289139b1faed192d3db278a0d2c59)

BibTeX

@misc{dc0ffbd094df75,
  title = {Processing and classifying bird songs using wavelet techniques and supervised learning — arXiv:2609.10826},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.10826},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_0ffbd094df75; sha256 d4a7b835892b2bd2cf764993731af0d16fc289139b1faed192d3db278a0d2c59},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.10826. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-11 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG) ; Methodology (stat.ME).
Pagina del lavoro: https://arxiv.org/abs/2609.10826 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T05:44:24.949Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-11?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).