Conference Paper2016Cited by 7

Identification of bird species from their singing

Sujoy Debnath, Partha Protim Roy, Amin Ahsan Ali, M Ashraful Amin

2016 5th International Conference on Informatics, Electronics and Vision (ICIEV)

IEEE, pp. 182–186

  • Life on Land — Relevance: 77%
DOI: 10.1109/iciev.2016.7759992URL

Abstract

Biodiversity is one of the mysterious phenomenon that we are understanding more and more as time passes by. Aiding the process of understanding nature is an initiative by BIOTOPE society; an open platform to bring together researchers to retrieve information by identifying bird species from calls or singing of different bird species. The main idea behind this work is to identify bird species from their calls or songs. The work presented in this article explores the automated method to identify bird species based on songs and calls of different bird species. This will help in archiving and identifying new species or subspecies. Moreover, it will help behavioral and ecological studies of birds and aid to the surveillance of bird population. In this work we implement a system that will be able to automatically identify bird species from acoustic recordings. Proposed system's ROC performance analysis shows that using Random Decision Tree we can yield 0.96 area under the curve.

Keywords

  • bird identification
  • bird calls
  • random decision trees
  • acoustic recordings
  • Biodiversity
  • bird song
  • automated species classification

CCDS Authors

Amin Ahsan Ali, PhD
Amin Ahsan Ali, PhD

Amin Ahsan Ali, PhD

  • Director, AI/ML Wing

Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

ACL, CHI, UbiComp, ECCV, IJCAI, ECAI, WACV, IPSN, PAKDD, LREC, IJCNN, ICPR, ICIP, BEA, BLP, ICMLA, Scientific Reports, Scientific Data, Pattern Recognition, Information Sciences, Ad Hoc Networks, Drug and Alcohol Dependence, PLOS ONE

Research Interests

Artificial Intelligence, Machine Learning

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

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Cited By

Showing the 6 most-cited of 7 citing works recorded by OpenAlex.

  1. 1.B. Chandu, Akash Munikoti, Karthik S. Murthy, Ganesh Murthy V., Chaitra Nagaraj. (2020). Automated Bird Species Identification using Audio Signal Processing and Neural Networks. , 1–5[10.1109/aisp48273.2020.9073584]
  2. 2.Jyoti Madake, Shripad Bhatlawande, Parth Kudal, Swati Shilaskar. (2023). A high accuracy approach for bird species identification using VGG16. AIP conference proceedings, 2810, 030004[10.1063/5.0129167]
  3. 3.Yismaw Alemayehu, Monika Sadananda. (2026). Bioacoustics Monitoring of Bird Vocal Behavior for Species Identification, Survey, and Habitat Impact With Implications for Conservation. International Journal of Zoology, 2026(1)[10.1155/ijz/8038831]
  4. 4.Ashish Kumar Pandey. (2025). Bird Species Prediction Based on Voice Using CRNN. International Journal for Research in Applied Science and Engineering Technology, 13(5), 6681–6688[10.22214/ijraset.2025.71743]
  5. 5.(2023). Bird Species Identification using Audio Processing and AlexNet Neural Network. International Journal of Food and Nutritional Sciences, 11(12)[10.48047/ijfans/v11/i12/173]
  6. 6.(2023). Automated Audio Signal Processing for Bird Species Identification. International Research Journal of Modernization in Engineering Technology and Science[10.56726/irjmets43306]
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