Vocalization Features to Recognize Small Dolphin Species for Limited Datasets
Identifying small dolphin species based on their vocalizations remains a challenging task due to their similar vocal signatures and frequency modulation patterns, particularly when the available data sets are relatively limited. To address this issue, a new feature set has been introduced that focuses on capturing both the predominant frequency range of the vocalizations and other higher level details in the spectral contour, which are valuable for distinguishing between small dolphin species. These features are computed from two distinct representations of the vocalizations: the short time Fourier transform and Mel frequency cepstral coefficients. By utilizing these features with two popular classifiers (K-Nearest Neighbors and Support Vector Machines), a model accuracy of $95.47\%$ has been achieved, representing an improvement over previous studies.