# Automatic Detection and Unsupervised Clustering-Based Classification of Cetacean Vocal Signals +
## 2 Experiments and Results
### Detection
utilized a straightforward yet efficient lookup-table approach based on multi-jump resonance systems, mapping the frequency drift to an amplitude jump [54; 55]. In this paper, signals are sourced from open-access databases, but the available information regarding monitoring sensors and the environment is limited. Therefore, the direct application of existing schemes, if feasible, is not straightforward. For this reason, we will temporarily overlook the frequency drift and address it in future work. We also compare our detection method with the Teager-Kaiser energy operator detection method in [9] in Figure 3b. A cut of the collected waveform of the common dolphin, also used in Figure 2, is chosen and captured in the 0.1 s fragment for comparison. In Figure 3, two detection methods are performed on the same waveform period, and the signals between the solid lines and the dashed lines are the vocal signals detected. The detection of common dolphin vocal signals based on the dual parameters-dual thresholds endpoint detection method is shown in Figure 3a. Figure 3b shows the detection of common dolphin vocal signals based on the TKEO detection method [9] for comparison. Figure 3 is the amplification version of the waveform before 0.1 s, which is used to confirm the scope of the vocal signals and compare the effectiveness of the two detection methods. All the segments are saved for reconstruction, and the background noises are removed from the sections of audio signals. The segments of vocal signals are the output of the TKEO. Since only the vocal signal is needed for the feature vector, the most obvious peak characteristic of the vocal signals are picked out and combined to observe the peak value variation. Finally, the mel frequency cepstral coefficients (MFCCs) of the reconstruction signals are computed using the MFCC function. In Figure 4, the waveform of the original acoustic signals is shown on the left side of the figure, and the MFCC feature of the reconstruction signals on the right side of the figure. The classification datasets comprise these five cetacean species' acoustic and environmental data. The details of these datasets are listed in Table 2, and to be simplified, the abbreviation of datasets are named according to the acronym of cetacean species. The statistics information of different cetacean species' acoustic data is summarized in Table 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Dataset** & **Class** & **Sample** & **Species** \\ \hline
1 & RDRW & 2 & 3598 & Rissos’ Dolphin, Right Whale \\ \hline
2 & RWNW & 2 & 4514 & Right Whale, NoWhale * \\ \hline
3 & RDNW & 2 & 3598 & Rissos’ Dolphin, NoWhale \\ \hline
4 & RDPWBW & 3 & 792 & Rissos’ Dolphin, Pilot Whale, Beaked Whale \\ \hline
5 & CDRWNW & 3 & 552 & Common Dolphin, Right Whale, NoWhale \\ \hline
6 & RDRWNW & 3 & 5397 & Rissos’ Dolphin, Right Whale, NoWhale \\ \hline
7 & RWRDPWBW & 4 & 1056 & Rissos’ Dolphin, Right Whale, Pilot Whale, Beaked Whale \\ \hline
8 & NWRWRDPWBW & 5 & 1320 & NoWhale, Right Whale, Rissos’ Dolphin, Pilot Whale, Beaked Whale \\ \hline \hline \end{tabular}  
* This is the environmental sound without whale calls.  
\end{table} 
Table 2: The information of the datasets for two to five classifications. Figure 3: Comparison of different methods for detecting common dolphin vocal signals: (a) Detection of common dolphin vocal signals based on dual parameters-dual thresholds endpoint detection method. (b) Detection of common dolphin vocal signals based on the Teager-Kaiser energy operator method. (The black solid lines represent the start points, and the red dashed lines represent the endpoints). Reproduced with permission from [PERSON] et al., published by IEEE International Conference on Electrical, Automation and Computer Engineering (ICEACE), 2023 [33].