Automated Detection of Capuchinbird Calls in Tropical Forest Recordings Using Deep Learning-Based Spectrogram Analysis

This research paper presents an advanced methodology for the automated detection of Capuchinbird (Perissocephalus tricolor) calls in tropical forest recordings, utilizing deep learning-based spectrogram analysis. The primary aim is to enhance biodiversity monitoring by accurately identifying the vocalizations of this elusive species within complex acoustic environments. The proposed system leverages convolutional neural networks (CNNs) to analyze spectrograms derived from audio recordings, effectively distinguishing Capuchinbird calls from background noise and other avian species. The dataset used for training and validation includes a diverse array of recordings collected from various tropical forest locations, ensuring robustness and generalizability of the model. Performance metrics indicate high precision and recall rates, demonstrating the efficacy of the deep learning approach in handling the intricate acoustic patterns of tropical forests. This automated detection framework offers significant improvements over traditional manual and semi-automated methods, providing a scalable and efficient tool for ornithologists and conservationists to monitor Capuchinbird populations and, by extension, the health of tropical ecosystems. The implications of this research extend to broader applications in bioacoustics, contributing to the preservation of biodiversity through advanced technological integration.