# Automatic Detection and Unsupervised Clustering-Based Classification of Cetacean Vocal Signals +
[9] constructed a Teager-Kaiser energy operator (TKEO) to detect sperm whale clicks, where the peak picking algorithm and forward-backward search algorithm are applied to detect the time instant of the highest peak as a vocal sound. These two methods only determine the existence of vocal signals. After detection, they still require manual processing to locate the vocal signals. The energy ratio mapping algorithm (ERMA) [11] was developed to improve the performance of energy-based detection of odontocete vocal signals. It evaluates the frequency bands of data to detect vocal signals through a noise-adaptive threshold in the TKEO function. However, its extension to cetacean vocal signals is not straightforward due to the short time duration of vocal signals. Generally, the threshold-based detection technique has shown its effectiveness in cetacean vocal detection. However, it is difficult to decide on a proper threshold, which usually requires some adaptive or self-learning technique due to the time-varying environment. Automatic classification of the cetaceans' vocal signals is also an important issue. With the development of artificial intelligence technology, machine learning methods have been applied to many different fields with no exception to the classification of cetacean vocal signals [12; 13]. There are two main kinds of machine learning methods used in this task: supervised and unsupervised learning. In supervised classification, a convolutional neural network (CNN) [14] is a popular tool for automatic cetacean classification [15; 16; 17]. [PERSON] et al. [17] combined rCNN (region-based convolutional neural network) and CNN networks to classify spectrograms created by fast Fourier transform (FFT) of fin whale and blue whale vocal signals and achieved a precision higher than 90%. In [15; 16], the authors trained a CNN network to extract vocal signal features and predict the species of odontocetes for classification. [PERSON] et al. [18; 19] employed TKEO and cepstral analysis to construct feature vectors of odontocetes' vocal signals and then train a Gaussian mixture model (GMM) and support vector machines (SVMs) model for classification of odontocetes, with 67-75% accuracy. Other supervised classification methods, e.g., K-nearest neighbors(KNN) method [20], feed-forward neural networks [21], and artificial neural network [22], have also been used in classifying the vocal signals of cetaceans. The supervised methods must label the vocal signals manually to generate the training data set. Moreover, they usually have large training datasets, which conflicts with the real-world situation of data deficiency. For instance, the CNN method [16] generates 4128 to 167,645 vocal samples for classification. Thereby, unsupervised methods are used to reduce labeling burden and classify cetacean calls [5; 23; 24]. Clustering is the main and significant method in unsupervised frameworks. It groups data into clusters based on their internal characteristics information [25]. [PERSON] et al. [26] proposed a hybrid classifier with unsupervised clustering to classify beaked whale calls automatically. This method flags the energy-band spectral characteristics of whale calls. It utilizes a hierarchical cluster tree or dendrogram to cluster data into species-specific groups with a recall rate of 82.8% for [PERSON]'s beaked whale and 77.9% for [PERSON]'. Reference [27] presents and evaluates several methods for automated species-level classification of vocal signals from three beaked whale species. Feature sets of three vocal types are selected for classification based on unsupervised step-wise discriminant analysis. The manually validated datasets of species-level BW vocal signals and unidentified dolphin vocal signals are used to evaluate six clustering algorithms, including k-means\(++\) algorithm, Ward hierarchical clustering, and four graph-based clustering methods.