Biodiv Sci ›› 2023, Vol. 31 ›› Issue (1): 22370.  DOI: 10.17520/biods.2022370

• Original Papers: Animal Diversity • Previous Articles     Next Articles

Syllable clustering analysis-based passive acoustic monitoring technology and its application in bird monitoring

Keyi Wu1, Wenda Ruan1, Difeng Zhou1, Qingchen Chen1,*(), Chengyun Zhang1, Xinyuan Pan2, Shang Yu3, Yang Liu4, Rongbo Xiao5   

  1. 1. School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006
    2. College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou 510642
    3. Guangzhou Naturesense Ecological Technology Co., Guangzhou 510630
    4. School of Ecology, Sun Yat-sen University, Guangzhou 510006
    5. School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006
  • Received:2022-06-30 Accepted:2022-11-24 Online:2023-01-20 Published:2022-12-02
  • Contact: *Qingchen Chen, E-mail:


Aims: Passive acoustic monitoring has proven to be an effective method for monitoring bird biodiversity, as it allows for the analysis of important information such as bird songs and calls. The complexity and variations of bird songs and calls make it difficult to quickly and accurately identify bird species using voiceprint analysis. Solving this problem is essential for the successful implementation of a voiceprint-based bird diversity monitoring scheme.

Methods: This paper proposes a syllable clustering analysis-based approach for bird song/call monitoring framework. The first step is to extract syllables from voiceprint data using audio features such as pitch and frequency flatness. These syllables are then trained using a combination of unsupervised representation learning and a Dirichlet process hybrid model. The final steps are clustering the syllables and inferring their categories.

Results: (1) The analysis results show that, the proposed framework can achieve nearly 90% clustering accuracy when handling the published recordings of Lonchura striata song repository; (2) On the basis, the paper conducts unsupervised syllable clustering analysis on ten species of birds monitored in Baiyun Mountain Forest Park, Guangzhou, between April and May 2022. It verifies that the proposed framework can not only support bird species identification, but also meet the rapid species identification application requirements. This can be extended further to obtain the statistics and changes in time, frequency and quantity of various bird songs/calls.

Conclusion: The analysis results of this paper show us that, the syllable clustering-based bird song/call monitoring framework can significantly reduce the requirements for manually annotated training data. This also overcomes the shortcomings of the traditional framework in dealing with overlapping bird songs. Therefore, it provides a comprehensive solution for applications such as rapid species recognition, syllable sequence analysis, and population abundance analysis in bird diversity monitoring.

Key words: passive acoustic monitoring, birds syllable, unsupervised clustering, species identification, syllabic sequence analysis, abundance analysis