Bird Sound Detection with Binarized Neural Networks


  • Muhammad Munim Zabidi Universiti Teknologi Malaysia
  • Wong Kah Liang
  • Usman Ullah Sheikh
  • Shahidatul Sadiah
  • Afiq Hamzah



By analysing the behavioural patterns of bird species in a specific region, researchers can predict future changes in the ecosystem. Many birds can be identified by their sounds, and autonomous recording units (ARUs) can capture real-time bird vocalisations. The recordings are analysed to see if there are any bird sounds. The sound of a bird can be used for further analysis, such as determining its species. Bird sound detection using Deep Neural Networks (DNNs) has been shown to outperform traditional methods. DNNs, however, necessitate a lot of storage and processing power. The use of Binarized Neural Networks (BNNs) is one of the most recent approaches to overcoming this limitation. In this paper, a bird sound detection architecture based on the XNOR-Net variant of BNN is used. Performance analysis of XNOR-Net in terms of the number of hidden layers used was performed, and the configuration with the highest accuracy was built. The system was tested using Xeno-Canto and UrbanSound8K datasets to represent bird and non-bird sounds, respectively. We achieved 96.06 per cent training accuracy and 94.08 per cent validation accuracy. We believe that BNNs are an effective method for detecting bird sounds.




How to Cite

Zabidi, M. M., Wong, K. L., Sheikh, U. U., Abdul Manan, S. S. ., & Hamzah, M. A. N. . (2022). Bird Sound Detection with Binarized Neural Networks. ELEKTRIKA- Journal of Electrical Engineering, 21(1), 48–53.