1 College of Engineering Trivandrum, India.
2 Government Engineering College Idukki, India.
3 APJ Abdul Kalam Technological University, India.
Article DOI: 10.30574/wjaets.2025.14.2.0043
Received on 22 December 2024; revised on 04 February 2025; accepted on 07 February 2025
Automatic bird vocalization analysis is advancing research in ecology and conservation. In recent years, numerous studies have employed deep learning models to categorize bird calls. This study examined the efficacy of Haar Wavelet Residual Convolutional Neural Network (WRCNN) for multi-label bird species classification. Initially, Haar wavelet transforms were applied to the mel spectrograms of bird call recordings. These transformed spectrograms were subsequently input into the WRCNN for multi-scale spectral analysis. The model obtained a macro-average F1-score of 0.60, showcasing its potential in multi-label tasks and exhibiting notable improvements over baseline methods. Experiments were conducted utilizing the Xeno-Canto bird sound database.
Multi-Label; Sequential; Haar Wavelet; Convolutional Neural Network; Residual Network
Preview Article PDF
Noumida A and Rajeev Rajan. Multi-label bird species classification using Haar wavelet- based residual convolutional neural network. World Journal of Advanced Engineering Technology and Sciences, 2025, 14(02), 018–025. Article DOI: https://doi.org/10.30574/wjaets.2025.14.2.0043.
Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0