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

Processing and classifying bird songs using wavelet techniques and supervised learning

Read the original on arXiv Machine Learning →

The paper presents an integrated framework for processing and classifying invasive bird species vocalizations in noisy natural soundscapes. It uses Bayesian wavelet shrinkage with an Epanechnikov kernel prior to denoise signals, then extracts features such as MFCCs and spectral indices. Supervised models—including Random Forest, Multinomial Logistic Regression, and SVM—are evaluated, with the SVM achieving the highest accuracy (0.9398) on a 10‑dimensional MFCC set.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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