Tuning into the city soundscape: Optimizing Convolutional Neural Networks for avian acoustic identification in the neotropics and evaluating their performance against established monitoring approaches.

Name: Tuning into the city soundscape: Optimizing Convolutional Neural Networks for avian acoustic identification in the neotropics and evaluating their performance against established monitoring approaches.
Creator: Melissa Ardila
Published: 2026-06-05T15:03:49.783Z
Keywords: Biodiversity, Urban Ecosystem, Acoustic Monitoring, Bioacoustics, Tropical Bird

Ardila, Melissa

doi:10.17632/5s35783d2t.1

Tuning into the city soundscape: Optimizing Convolutional Neural Networks for avian acoustic identification in the neotropics and evaluating their performance against established monitoring approaches.

Published: 5 June 2026| Version 1 | DOI: 10.17632/5s35783d2t.1

Contributor:

Melissa Ardila

Description

Code used to train, validate, and test the best-performing CNN model (cnn_model.py), generate predictions using the trained model (inference_using_cnn.py and best_model.pt), and calculate acoustic indices (acoustic_indices_calculation.py).

Tuning into the city soundscape: Optimizing Convolutional Neural Networks for avian acoustic identification in the neotropics and evaluating their performance against established monitoring approaches.

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