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This study adapts the ClustalW bioinformatics algorithm to analyze dolphin vocalizations by treating them as high-dimensional spectral feature vectors, enabling a more nuanced understanding of their communication. By employing a continuous Gaussian kernel similarity measure instead of discrete scoring, the researchers generate Multiple Sequence Alignment (MSA) visualizations that uncover shared structural patterns in dolphin vocal sequences. Key findings include the identification of temporal motifs, such as synchronized burst pulses during aggressive interactions, which are often missed in traditional spectrogram analyses.
Synchronized burst pulses in dolphin communication reveal complex social dynamics that standard analysis methods overlook.
Dolphin communication understanding is essential for uncovering the linguistic complexity and social structures of wild pods. We adapt the ClustalW bioinformatics algorithm to analyze continuous acoustic data, treating vocalizations as high-dimensional spectral feature vectors. By replacing discrete scoring with a continuous Gaussian kernel similarity measure, our framework generates Multiple Sequence Alignment (MSA) visualizations that reveal shared structural patterns. These alignments highlight temporal motifs such as synchronized burst pulses in aggressive contexts that are difficult to detect through standard spectrogram inspection.
