# Automatic Detection and Unsupervised Clustering-Based Classification of Cetacean Vocal Signals +
### Unsupervised Classification  
We use the vocal features picked out from the automatic detection process for unsupervised clustering-based classification and implement the categorization of multiple cetacean species. Conventional clustering algorithms such as K-means, PCA (principal component analysis), GMM, and SSC (sparse subspace clustering) are conducted for comparison. We demonstrate that different clustering methods show distinct clustering ability for classifying vocal signals. Four typical clustering algorithms are evaluated as unsupervised classification of five cetacean species. Clustering is a method to classify the data into different clusters by analyzing the intrinsic relationship between the data. The data points in the same cluster have similar characteristics and stick closer than those in different clusters. The classic types of clustering algorithms include the distance-based method such as K-means [35], dimension reduction clustering such as PCA [36], subspace clustering such as SSC [37], probabilistic models such as GMM [38], etc. To test the availability of the datasets, the clustering algorithms are performed on the datasets. In the meantime, we are interested in measuring the classification performance of different clustering algorithms on the datasets. Consequently, the four types of clustering algorithms, including K-means, PCA, SSC, and GMM, are applied for comparison. K-means has been used to cluster time series data, achieving efficient clustering results due to its high speed, simplicity, and ease of implementation [39]. The K-means algorithm assigns the samples into \(K\) clusters by calculating and comparing the Euclidean distance between data points iteratively. The \(K\) in the algorithm is the class number of cetacean species within a dataset. PCA is a dimension reduction method that maps high-dimensional time-series data into low-dimensional features by orthogonal decomposition and then clusters the new orthogonal features, also known as the principal components. In other words, PCA eliminates the clutter signals in the data and preserves the most significant information; thus, the principal feature can be effectively extracted from time-series data [40]. GMM has shown improved classification for underwater bioacoustic data such as fish chorusing events [29; 41; 42], odontocetes' vocal signals [18; 19; 43]. GMM is a probabilistic model that estimates the probabilities of data points in each cluster and categorizes them into the same cluster according to their probability. The probabilistic of a class represents the distribution of these class subsets within the whole dataset. Therefore, GMM can be applied not only in supervised classification but also in unsupervised classification. SSC aims to reveal the essential characteristics of data and use the sparsity of data in a specific space to represent data. It has been successfully and widely applied in many fields such as image processing [44; 45; 46; 47], pattern recognition [48; 49; 50], and subspace clustering of high dimensional data [37; 51; 52]. SSC is based on the assumption that similar data will \begin{table}
\begin{tabular}{c c c c c} \hline
**Class Name** & **Species** & **Sample Number** & **Mean Values** & **Standard Deviation** \\ \hline RD & Rissos’ Dolphin & 1799 & \(6.45\times 10^{-6}\) & \(2.75\times 10^{-10}\) \\ \hline RW & Right Whale & 2257 & \(7.98\times 10^{-5}\) & \(2\times 10^{-8}\) \\ \hline PW & Pilot Whale & 264 & \(2.31\times 10^{-6}\) & \(1.11\times 10^{-11}\) \\ \hline BW & Beaked Whale & 722 & \(1.97\times 10^{-5}\) & \(2.04\times 10^{-9}\) \\ \hline CD & Common Dolphin & 184 & \(2.02\times 10^{-5}\) & \(6.98\times 10^{-9}\) \\ NW & Environmental Sound & 8985 & \(1.2\times 10^{-2}\) & \(9.89\times 10^{-5}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Feature information.