A research team led by Professor Zhang Junguo from the School of Technology at Beijing Forestry University has published a significant review in the top-tier journal Artificial Intelligence Review. The paper systematically examines the applications, challenges, and future directions of machine learning in assessing bird diversity through soundscape analysis, providing key guidance for this evolving field. 

The broad application of passive acoustic monitoring provides a critical data foundation for studying soundscape ecology, necessitating automated analysis methods to accurately extract ecological information from vast soundscape data. This review comprehensively and cohesively examines two predominant approaches in soundscape analysis: soundscape component recognition and acoustic indices methods. Focusing on machine learning (ML)-based analysis methods for bird diversity assessment over the past five years, this review surveys representative research within each category, outlining their respective strengths and limitations. This not only addresses the growing interest in this field but also identifies research gaps and poses key questions for future studies. The insights from this review are anticipated to significantly enhance the understanding of ML applications in soundscape analysis, guiding subsequent investigative efforts in this rapidly evolving discipline, and thereby better supporting long-term biodiversity monitoring and conservation initiatives. 

Professor Xie Jiangjian and doctoral candidate Xie Shanshan from the School of Technology are the co-first authors of the paper. Professor Zhang Junguo and Professor Qian Kun from Beijing Institute of Technology serve as the corresponding authors. The research also received guidance from Professor Liu Yang of Sun Yat-sen University and Professor Björn W. Schuller of Imperial College London. 

This work was jointly supported by the Beijing Natural Science Foundation (Grant No. 5252014), the National Natural Science Foundation of China (Grant No. 62303063), the Key Open Research Funding of the Laboratory of Intelligent Processing Technology for Digital Music (Zhejiang Conservatory of Music), Ministry of Culture and Tourism (Grant No. 2024DMKLB001), and the National Key Research and Development Program of China (Grant No. 2023YFF1304301). 

Paper link: https://link.springer.com/article/10.1007/s10462-025-11414-4

Written by Xie Jiangjian, Xie Shanshan
Translated and edited by Song He
Reviewed by Yu Yangyang