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ASGIR: Audio Spectrogram Transformer Guided Classification And Information Retrieval For Birds

Authors :
Chaudhuri, Yashwardhan
Mundra, Paridhi
Batra, Arnesh
Phukan, Orchid Chetia
Buduru, Arun Balaji
Publication Year :
2024

Abstract

Recognition and interpretation of bird vocalizations are pivotal in ornithological research and ecological conservation efforts due to their significance in understanding avian behaviour, performing habitat assessment and judging ecological health. This paper presents an audio spectrogram-guided classification framework called ASGIR for improved bird sound recognition and information retrieval. Our work is accompanied by a simple-to-use, two-step information retrieval system that uses geographical location and bird sounds to localize and retrieve relevant bird information by scraping Wikipedia page information of recognized birds. ASGIR offers a substantial performance on a random subset of 51 classes of Xeno-Canto dataset Bird sounds from European countries with a median of 100\% performance on F1, Precision and Sensitivity metrics. Our code is available as follows: https://github.com/MainSample1234/AS-GIR .<br />Comment: Accepted to INTERSPEECH'24

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2407.18927
Document Type :
Working Paper