A CNN Based Bird Species Classification from Their Unique Acoustic Signature
Year: 2026
Type: conference-paper
Abstract: Diverse Bird species are essential parts of our ecology. They played a crucial role in understanding biodiversity and preserving nature. To maintain our ecosystem we must understand it and that's why identification of different bird species is important. Identifying by spotting a bird is difficult as it needs experience, knowledge and time. Most of the time birds are unseen but we can hear their tweets. This study presents a deep learning based approach for classifying bird species based on their vocalizations. It is observed that each bird species has a unique voice pattern that is visible on the mel spectrum. So here the bird calls are recorded and converted into spectrograms. Those spectrograms feed to train the Convolutional Neural Network (CNN) model. The trained CNN model then recognizes different bird species by their unique voice patterns. This methodology saves a lot of time & effort; which reduces the need for manual identification. The method offers efficient and scalable solutions for biodiversity monitoring, as bird species identification is automated and can be done without spotting the bird. Test results show the effectiveness of the proposed model, projecting its potential for real-world applications in the field of ecological monitoring, research, conservation efforts and citizenscience applications.
Institution: Dr. B.C. Roy Engineering College
Language: English
Topic: Music and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
SDG: Life on land
Open Access status: hybrid
OpenAlex ID: W7214009136