Deep Learning-Based Dual-Modal Bird Species Identification Using Audio and Images

Pratheeksha N S; Shruthi M G

doi:10.36948/ijfmr.2025.v07i06.60493

Deep Learning-Based Dual-Modal Bird Species Identification Using Audio and Images

Author(s)	Ms. Pratheeksha N S, Prof. Shruthi M G
Country	India
Abstract	Identifying bird species plays a vital role in tracking biodiversity, supporting ecological studies, and advancing conservation efforts. Conventional methods depend on field observations and specialist expertise, making them time-consuming, expensive, and susceptible to mistakes. This work introduces a deep learning system that combines audio and image analysis to classify bird species with high accuracy and reliability. The audio component analyzes bird call recordings by extracting Mel-Frequency Cepstral Coefficients (MFCCs) using Librosa, then applies a Convolutional Neural Network (CNN) to classify species from spectrogram patterns. For images, the system employs VGG19 transfer learning with preprocessing steps including resizing, normalization, and data augmentation to enhance model performance. These two modules are brought together in a Flask web application where users can upload either audio clips or photos and receive instant predictions with confidence scores. Testing on public datasets shows that combining audio and visual data yields better results than using either modality alone, especially when dealing with background noise in recordings or poor image quality. The platform provides an accessible and scalable tool for researchers, conservation professionals, and amateur naturalists, making sophisticated AI-powered species recognition practical for real-world fieldwork.
Keywords	Bird Species Identification, Deep Learning, Dual-Modal Classification, Audio Recognition, Image Rec-ognition, MFCC, CNN, VGG19, Flask Web Application, Biodiversity Monitoring.
Field	Computer
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-11-13
DOI	https://doi.org/10.36948/ijfmr.2025.v07i06.60493

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Deep Learning-Based Dual-Modal Bird Species Identification Using Audio and Images

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