发表机构
Wild Dolphin Project; Georgia Institute of Technology（野生海豚项目; 佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文采用ClustalW算法与高斯核相似性度量生成多序列比对，以揭示海豚发声中的共享结构模式和时间基序，助力理解其交流与社会结构。
AI 中文摘要
理解海豚的交流对于揭示野生群体的语言复杂性和社会结构至关重要。我们将ClustalW生物信息学算法应用于连续声学数据分析，将发声视为高维频谱特征向量。通过用连续高斯核相似性度量替代离散评分，我们的框架生成了多序列比对（MSA）可视化，揭示了共享的结构模式。这些比对突出了时间基序，例如在攻击性情境中难以通过标准声谱图检查检测到的同步突发脉冲。
英文摘要
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.
