International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

EchoTweet: A Bird Call Classification System

Abstract

This project aims to develop a bird sound classifier using machine learning techniques for automatic species identification from audio recordings. By exploring diverse feature extraction methods and comparing different machine learning models, the classifier seeks to accurately categorize bird vocalizations. The development of a user-friendly interface and integration into wildlife monitoring systems further enhances its utility for ecological research and conservation efforts.

Country : India

1 Harshita Sonkar2 Laxmi Pawar3 Akanksha Puri4 Rahul Gupta5 Prof. Swati Vyas

  1. Student, Smt. Indira Gandhi College of Engineering, Ghansoli, Navi Mumbai, Maharashtra, India
  2. Student, Smt. Indira Gandhi College of Engineering, Ghansoli, Navi Mumbai, Maharashtra, India
  3. Student, Smt. Indira Gandhi College of Engineering, Ghansoli, Navi Mumbai, Maharashtra, India
  4. Student, Smt. Indira Gandhi College of Engineering, Ghansoli, Navi Mumbai, Maharashtra, India
  5. Professor, Dept. of AIML, Smt. Indira Gandhi College of Engineering, Ghansoli, Navi Mumbai, Maharashtra, India

IRJIET, Volume 8, Issue 4, April 2024 pp. 152-156

doi.org/10.47001/IRJIET/2024.804020

Full Paper
Download

References

  1. Mu, W., Yin, B., Huang, X. et al. Environmental sound classification using temporal-frequency attention based convolutional neural network. Sci Rep 11, 21552 (2021).
  2. Bountourakis, V., Vrysis, L., Papanikolaou, G. Machine learning algorithms for environmental sound recognition: Towards soundscape semantics. ACM Int Conf Proc Ser, pp. 1–7 (2015).
  3. DaSilva, B., Happi, A. W., Braeken, A. & Touhafi, A. Evaluation of classical Machine Learning techniques towards urban sound recognition on embedded systems. Appl. Sci. 2, 1–27 (2019).
  4. Akbal, E. An automated environmental sound classification method based on statistical and textural feature. Appl. Acous. 167107413 (2020).
  5. Akbal, E. An automated environmental sound classification method based on statistical and textural feature. Appl. Acous. 167107413 (2020).
  6. Wang, J.C., Wang, J.F., He, K.W., Hsu, C.S. Environmental sound classification using hybrid SVM/KNN classifier and MPEG-7 audio low-level descriptor. International Joint Conference on Neural Networks. IEEE, pp. 1731–1735 (2006).
  7. Zhang, Z., Xu, S., Qiao, T., Zhang, S., Cao, S. Attention based convolutional recurrent neural network for environmental sound classification, arXiv: 1907.02230 (2019).
  8. Lukas Muller and Mario Marti. Bird Sound Classification using a Bidirectional LSTM. InCLEF, 2018.
  9. Dan Stowell, Michael D. Wood, Hanna Pamuła, Yannis Stylianou, and Herve Glotin. Automatic acoustic detection of birds through deep learning: The first bird audio detection challenge. Methods in Ecology and Evolution, 10(3):368–380, 2019.
  10. Alexis Joly, Herve Goeau, Herve Glotin, Concetto Spampinato, Pierre Bonnet, Willem-Pier Vellinga, Julien Champ, Robert Planque, Simone Palazzo, and Henning Muller. Lifeclef 2016.
  11. Stefan Kahl, Thomas Wilhelm-Stein, Hussein Hussein, Holger Klinck, Danny Kowerko, Marc Ritter, and Maximilian Eibl. Large-Scale Bird Sound Classification using Convolutional Neural Networks. 09 2017.

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies