The vocalizations of birds are more than just sounds—they serve as critical indicators of species behaviour, population health, and environmental conditions. In particular, birds like the Jacobin cuckoo, known for its distinct migratory behaviour and vocal patterns, provide valuable data that reflect changes in their environment. Studying these vocal frequencies and their variations over time can offer insights into how factors like climate change and habitat disturbances affect bird populations. Traditional bird species identification methods—such as visual observations or manual auditory analysis—are often slow, error-prone, and limited by human expertise. This project focuses on automating the process of identifying bird species, especially the Jacobin cuckoo, using vocal frequency recognition and machine learning techniques.
Automated Identification of Jacobin Cuckoo: Develop an automated system using deep learning techniques to identify patterns in Jacobin cuckoo vocal frequency accurately.
Frequency Analysis for Environmental Impact: Analyze how variations in the vocal frequencies of the Jacobin cuckoo over time can indicate changes in environmental conditions such as climate and habitat disruptions.
Spectrogram Generation for Accurate Classification: Implement the use of spectrograms as visual representations of bird vocal frequencies to improve the accuracy of species identification through neural networks like Convolutional Neural Networks (CNNs).
Contribute to Conservation Efforts: Provide valuable data on Jacobin cuckoo population trends and environmental stressors, aiding conservationists and ecologists in monitoring the impact of environmental changes on their behavior and population.
Real-time Monitoring Application: Design a user-friendly, real-time application that can classify bird species based on recorded vocalizations, assisting researchers, birdwatchers, and environmentalists in studying bird populations efficiently.