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@article{180238,
        author = {Varshitha N R and Sowjanya K M and Vaishnavi Khuba and Shivani and Dr.Kavyasri M N},
        title = {Sound based bird species recognition},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {685-689},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180238},
        abstract = {Ecological monitoring and biodiversity 
assessment 
increasingly 
utilize 
acoustic 
bird 
identification as a non-disruptive methodology. This 
research develops a deep learning framework for 
automated recognition of avian species through their 
vocalizations. The approach employs convolutional 
neural 
networks 
applied 
to 
spectrogram 
representations derived from publicly accessible 
datasets 
including Xeno-Canto and BirdCLEF 
competitions. 
Preprocessing 
incorporates 
noise 
reduction techniques and data augmentation strategies 
to enhance model robustness. Evaluation across 
multiple species demonstrates effective performance 
under varying acoustic conditions and background 
interference. The system's potential for deployment in 
mobile applications and remote monitoring platforms 
offers significant value for ornithological research and 
conservation efforts. Future research directions include 
incorporating spatio-temporal contextual information 
to refine species classification accuracy.},
        keywords = {Acoustic ecology, Avian vocalization  recognition, Machine learning, Neural networks,  Spectral analysis, Conservation technology, Field  applications.},
        month = {June},
        }