## 5 Conclusions  
In this study, we compared WT with two other widely used methods for the detection of _Cetacean_ acoustic signals. The results of the experiments showed that WT has certain advantages in the detection of _Cetacean_ click signals. It seems that WT can detect more clicks for further research on aspects such as the regularity of click signals over a long timescale, which reflects the biological clock of _Odontoceti_ or their territories. It is still difficult for us to verify the accuracy of signal detection. Therefore, we conducted a misdetection test to indirectly verify the accuracy. WT had the fewest misdetections, indicating that its anti-interference ability is better than that of the manual method and STFT in the detection of clicks. As for whistle and burst pulse, STFT performed better. Theoretically, the accuracy of time-frequency analysis depends on how similar the signal and the base wavelet are. Therefore, if we were to choose a suitable wavelet for WT, it might perform better. However, since STFT is sufficient for whistle and burst-pulse detection, it is not necessary to modify WT. In conclusion, in _Cetacean_ acoustic signal detection, traditional STFT is better for whistles and burst pulses, and WT is better for clicks. We believe that _Odontoceti_ can distinguish the click signals emitted by themselves, avoiding interference from other individuals in the group. More precise click-detection methods will aid in the study of how these animals identify individuals. The existing studies have shown that the duration and interval of click signals are related to the distance and purpose of _Odontoceti_'s detection targets. The high time resolution of WT is very useful in this research. By combining hydrophone data and video monitoring data, we can expect that we will be able to determine the relationship between _Odontoceti_ acoustic signals and some of their behaviors in the future. This study also offers suggestions on how to build more accurate datasets for different acoustic signals. It will be useful for the training of more accurate artificial neural networks for edge-computing signal-detecting devices. [PERSON] and [PERSON] contributed equally to this paper. Conceptualization and methodology, R.H. and Y.D. ; software, R.H. and Y.Y. ; validation, R.H., Y.D. and Y.Y. ; formal analysis, Y.D. ; investigation, R.H., Y.D. and S.L. ; resources, Y.D., S.L. and W.F. ; data curation, R.H.; writing--original draft preparation, R.H.; writing--review and editing, Y.D., S.L. and Y.W. ; visualization, R.H.; supervision, W.F. and S.Z. ; project administration and funding acquisition, Y.D. and W.F. All authors have read and agreed to the published version of the manuscript. This research was funded by the Laoshan Laboratory, grant No. LSKJ202201801, and the Shanghai Science and Technology Committee, grant number 20 dz1206400. Not applicable. The following software used in the experiment can be downloaded at: [[https://docs.scipy.org/doc/scipy-1.7.1/reference/](https://docs.scipy.org/doc/scipy-1.7.1/reference/)]([https://docs.scipy.org/doc/scipy-1.7.1/reference/](https://docs.scipy.org/doc/scipy-1.7.1/reference/)) (accessed on 5 August 2022), Software: SciPy 1.7.1 [32]. The following software used in the experiment can be downloaded at: [[https://pywavelets.readthedocs.io/en/v1.1.1/](https://pywavelets.readthedocs.io/en/v1.1.1/)]([https://pywavelets.readthedocs.io/en/v1.1.1/](https://pywavelets.readthedocs.io/en/v1.1.1/)) (accessed on 5 August 2022), Software: PyWavelets v1.1.1 [40]. This research was also supported by the South China Sea Fisheries Research Institute, Chinese Academy of Fisheries Science, including the hydrophone dataset. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.