Bird sound detection with binarized neural networks

• Main Authors: Ahmad Zabidi, Muhammad Mun’im, Wong, Kah Liang, Sheikh, Usman Ullah, Sadiah, Shahidatul, Nurudin, Muhammad Afiq
• Format: Article
• Language: English
• Published: Penerbit UTM Press 2022
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/104830/1/ShahidatulSadiah2022_BirdSoundDetectionwithBinarizedNeuralNetworks.pdf; http://eprints.utm.my/104830/; http://dx.doi.org/10.11113/elektrika.v21n1.349
• Institution: Universiti Teknologi Malaysia

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.