# Automatic Detection and Unsupervised Clustering-Based Classification of Cetacean Vocal Signals +
## 3 Discussion  
An automatic processing method is proposed to extract the vocal features of five cetacean species from two open source databases and reconstruct the vocal features to build the eight datasets. Then, the clustering algorithm is used to achieve the unsupervised classification and verify the effectiveness of the datasets for classification. The results will be discussed in two dimensions: detection and clustering results. In the detection results, each step of endpoint detection is put together to compare the graphical changes and show the different forms of the acoustic signals in Figure 2. In the beginning, the collected waveform of the common dolphin after initialization still has \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class Number** & **Dataset** & **K-Means** & **PCA** & **GMM** & **SSC** \\ \hline m = 2 & RDRW & 94.44 \(\pm\) 0.41 & 91.20 \(\pm\) 0.59 & 92.86 \(\pm\) 0.11 & 73.84 \(\pm\) 0.66 \\  & RWNW & 99.69 \(\pm\) 0.23 & 99.72 \(\pm\) 0.15 & 99.80 \(\pm\) 0.01 & 98.18 \(\pm\) 0.1 \\  & RDNW & 99.86 \(\pm\) 0.1 & 99.63 \(\pm\) 0.45 & 100.00 \(\pm\) 0 & 97.76 \(\pm\) 0.65 \\ \hline m = 3 & RDPWBW & 75.70 \(\pm\) 0.32 & 74.01 \(\pm\) 0.51 & 74.47 \(\pm\) 0.05 & 58.48 \(\pm\) 0.53 \\  & CDRWNW & 92.46 \(\pm\) 0.24 & 92.78 \(\pm\) 0.25 & 92.83 \(\pm\) 0.49 & 87.32 \(\pm\) 0.88 \\  & RDRWNW & 96.10 \(\pm\) 0.12 & 95.78 \(\pm\) 1.56 & 94.51 \(\pm\) 0.01 & 86.49 \(\pm\) 0.25 \\ \hline m = 4 & RWRDPWBW & 75.52 \(\pm\) 0.42 & 72.51 \(\pm\) 0.83 & 69.07 \(\pm\) 1.40 & 40.80 \(\pm\) 0.29 \\ \hline m = 5 & NWRWRDPWBW & 80.28 \(\pm\) 0.27 & 78 \(\pm\) 1.19 & 76.23 \(\pm\) 0.58 & 54.19 \(\pm\) 0.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Clustering results of the datasets evaluated by ACC metric (%). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class Number** & **Dataset** & **K-Means** & **PCA** & **GMM** & **SSC** \\ \hline m = 2 & RDRW & 89.49 \(\pm\) 0.48 & 84.08 \(\pm\) 0.95 & 77.92 \(\pm\) 1.12 & 64.00 \(\pm\) 0.82 \\  & RWNW & 99.38 \(\pm\) 0.14 & 99.45 \(\pm\) 0.29 & 99.47 \(\pm\) 0.00 & 96.42 \(\pm\) 0.68 \\  & RDNW & 99.72 \(\pm\) 0.16 & 99.26 \(\pm\) 0.90 & 100.00 \(\pm\) 0.00 & 95.59 \(\pm\) 0.52 \\ \hline m = 3 & RDPWBW & 66.18 \(\pm\) 0.56 & 62.58 \(\pm\) 0.82 & 63.34 \(\pm\) 0.09 & 44.235 \(\pm\) 0.41 \\  & CDRWNW & 86.78 \(\pm\) 0.35 & 87.27 \(\pm\) 0.38 & 87.31 \(\pm\) 0.74 & 77.76 \(\pm\) 0.58 \\  & RDRWNW & 92.63 \(\pm\) 0.24 & 92.06 \(\pm\) 2.68 & 89.86 \(\pm\) 0.02 & 77.22 \(\pm\) 0.23 \\ \hline m = 4 & RWRDPWBW & 62.35 \(\pm\) 0.61 & 57.39 \(\pm\) 0.99 & 52.95 \(\pm\) 1.94 & 31.25 \(\pm\) 0.16 \\ \hline m = 5 & NWRWRDPWBW & 69.32 \(\pm\) 0.47 & 65.58 \(\pm\) 1.35 & 62.80 \(\pm\) 0.61 & 44.55 \(\pm\) 0.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Clustering results of the datasets evaluated by F-score metric (%). significant noise, and some vocal signals are covered by noise. As shown in Figure 2c, the EMD method helps to eliminate the background noise and reveals most of the vocal signals. TKEO reflects the energy variation of the audio signal after EMD and traces the characteristics of a section signal. It strengthens the vocal parts and attenuates the unstable noise parts. The collected waveform is simplified into a curve representing the energy variation. The short-time average ZCR curve is shown in the same way. Most vocal signals in this figure are detected and recognized. The comparison of the two detection methods in Figure 3 shows that our method finds more information on the vocal signals than the TKEO detection method. This is because our detection method locates the vocal signals according to the endpoints, while the TKEO detection method locates the vocal signals at the peaks and the single threshold. Through the enlarged image of 0.1 s signal in Figure 3, it's observed that the endpoint detection method selects the vocal signals from the front end of high peaks to the back end of the lower peaks.