Bird Classification based on Image or Audio using Deep Learning

Registration ID: IJNRD_193115

Published ID: IJNRD2304714

DOI: Click Here to Get

Mr. Shreejith K B , Maanikya , Poojary Dheeraj Kumar , Rachana Nayak , Rakshit Lingappa Poojari

Bird Classification, Image Based, Audio Based, Biodiversity, CNN

Birds are a vital group of animals that ecologists monitor using autonomous recording units as a crucial indicator of the health of an environment. Bird-watching is a popular hobby which offers relaxation in everyday life. Innumerable people visit bird sanctuaries to observe different species. Nowadays some bird species are found rarely and if found, classification of bird species prediction of the same is difficult. Numerous bird species have become extinct because of anthropogenic activities and climate change. Habitat destruction is a significant threat to biodiversity worldwide. Thus, monitoring the distribution of species and identifying the elements that make up the biodiversity of a region are essential for designing conservation stratagems. Bird classification has been an important task in the field of ornithology and wildlife conservation. With deep learning advancements, image and audio-based bird classification methods have gained significant attention. We employ convolutional neural networks (CNNs) to learn discriminative features from bird images and audio. We use a large dataset of bird images to train the CNN model. This model is capable of automatically extracting high-level features from images, audio and classifying birds into different species with high accuracy based on deep learning techniques using either images or audio data.

Published Paper   E-Certificate

Mr. Shreejith K B, Maanikya, Poojary Dheeraj Kumar, Rachana Nayak, & Rakshit Lingappa Poojari (April-2023). Bird Classification based on Image or Audio using Deep Learning. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(4), h95-h100. https://ijnrd.org/papers/IJNRD2304714.pdf

Volume 8 Issue 4, April-2023

Pages : h95-h100

Paper Reg. ID: IJNRD_193115

Published Paper Id: IJNRD2304714

Downloads: 000121983

Research Area: Computer Science & Technology 

Country: Mangalore, Karnataka, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2304714.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2304714

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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