# Automatic Detection and Unsupervised Clustering-Based Classification of Cetacean Vocal Signals +
## 3 Discussion
However, the TKEO detection method may not contain all the whole vocal signals due to the limitation of the threshold-based technique. A visual comparison between the collected waveform and the reconstruction signals is shown in Figure 4. In the beginning, the collected waveform is full of noise, and it is difficult to distinguish between the background noise and the vocal signals. The proposed detection method cuts off the background noise and reserves the stronger and clearer parts of the vocal signals to observe the energy changes of the vocal signals. For example, the effectiveness of endpoint detection is shown in Figures 2 and 3. The vocal signal is simplified into a single MFCC curve. In this way, the vocal signals are better revealed concisely and clearly. Moreover, the extraction rates from the statistics Table 3 do not exceed 10% or just around 10%. It indicates that the vocal signals within a section of the acoustic signals are rare. If the whole audio file is taken as the classification feature, it will be difficult to train for classification. Thus, it is a good choice to select the most significant parts of the vocal signals to build the feature vectors and concentrate all the vocal signals to analyze them conveniently. The acoustic data of some cetacean species in the database are rare and insufficient for classification training, and the deep learning method may not be suitable here. The unsupervised clustering methods do not need too much data for pretraining and classifying data according to their internal characteristics. In this paper, the unsupervised clustering-based classification method performs well on feature vectors. The clustering results in Tables 4 and 5 demonstrate the classification accuracy of different clustering methods on our datasets. With the increasing number of species classes, it becomes harder to classify the different vocal signals of cetaceans, and the clustering accuracy decreases. Classical unsupervised clustering algorithms obtain an average accuracy rate of 84.83%. However, the ACC results of the five-class dataset can be up to 80%. From the comparison of clustering methods, K-means and GMM perform better than the PCA and SSC in classifying our datasets. The clustering results evaluated by the F-score metric are lower than the ACC results, but the tendency of the results is alike. Nevertheless, the proposed method has a higher classification accuracy than the GMM and SVM methods in [18; 19], with 67-75% accuracy in classifying three species. The DEC method in [29] achieves the highest accuracy of 77.5% to classify two distinguishing signals (the vocal and coral reef bioacoustic signals) in the manually labeled dataset. Our method classifies five classes of species with high accuracy. Generally, K-means and GMM are more capable of classifying the vocal signals than the other two methods. However, K-means is more scalable than GMM because K-means performs well not only on small-class datasets but also in larger-class datasets. However, the accuracy of GMM is decreasing with the increasing number of classes.