  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 .
