# Automatic Detection and Unsupervised Clustering-Based Classification of Cetacean Vocal Signals +
Footnote †: This paper is an extended version of our paper published in IEEE International Conference on Electrical, Automation and Computer Engineering (ICEAC), Changchun, China, 26-28 December 2023. [PERSON]  
1 School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, China; [EMAIL_ADDRESS] (Y.L. ); [EMAIL_ADDRESS] (H.Y. ); [EMAIL_ADDRESS] (F.J.) 1  
[PERSON]  
2 School of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, China; [EMAIL_ADDRESS] 2  
[PERSON]  
1 School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, China; [EMAIL_ADDRESS] (Y.L. ); [EMAIL_ADDRESS] (H.Y. ); [EMAIL_ADDRESS] (F.J.) 1  
[PERSON]  
1 School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, China; [EMAIL_ADDRESS] (Y.L. ); [EMAIL_ADDRESS] (H.Y. ); [EMAIL_ADDRESS] (F.J.) 1  
[PERSON]  
1 School of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, China; [EMAIL_ADDRESS] 1  
[PERSON]  
3 Key Laboratory of Marine Environmental Survey Technology and Application, South China Sea Marine Survey and Technology Center of State Oceanic Administration (SMST), Ministry of Natural Resources, Guangzhou 510300, China; [EMAIL_ADDRESS] 3  
Footnote 1: [[https://doi.org/10.3390/app1507385](https://doi.org/10.3390/app1507385)]([https://doi.org/10.3390/app1507385](https://doi.org/10.3390/app1507385))  
Footnote 2  
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: [https://www.cs.utut.edu/~caetilianlyn/](https://www.cs.utut.edu/~caetilianlyn/)  
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is through passive acoustic monitoring (PAM) [2]. PAM is more effective than active monitoring in some way. For example, it provides a non-invasive, cost-effective, and environmentally friendly way to study cetaceans over large areas and extended periods. It allows researchers to gather detailed, species-specific data without disturbing the animals. However, PAM also has some limitations and disadvantages. The implementation of PAM systems depends very much on their application and operational circumstances. PAM activity results are not easily adapted directly to standard analysis tools that have proven successful in visually based active monitoring [3]. Since many cetacean species mainly rely on vocal signals such as echolocation click, whistle, and burst pulse for foraging, positioning, and communication, their vocal signals have become the most common and important research object for studying cetaceans [4; 5]. However, passive acoustic monitoring of cetaceans faces some challenges [2; 3]. Firstly, the hydrophones in PAM collect data over many months and produce a large volume of data. Many of the underwater acoustic data contain noise from the ocean environment, which is hard to distinguish from the signals we are interested in [6]. Furthermore, some cetacean vocal signals are broadband signals, which usually only last a few seconds or minutes [7]. Consequently, the transient nature of the signals and the high variability in cetacean sounds at the individual level make it difficult to recognize the different species of cetaceans through the collected waveform. To tackle this difficulty, we shall investigate more effective ways to detect and classify vocal signals of cetaceans automatically. Early research on cetacean vocal detection focused on threshold-based methods [8; 9; 10; 11]. These methods compute the standard deviation or root mean square of signals' parameters such as the peak frequency, frequency band, or width of the principal spectral peak and set a proper threshold. Detection is triggered when the signal values are above the threshold. [PERSON] et al.