用生物信息学方法解析海豚叫声序列，发现攻击情境下的同步脉冲模式。
Interpreting Dolphin Vocal Sequences via Multiple Sequence Alignment
- 将海豚叫声视为高维频谱向量，用连续高斯核替代离散打分进行多序列比对
- 识别出攻击情境中难以通过传统光谱图发现的同步爆发脉冲时间模式
- 适用于研究动物语言复杂性与群体社会结构，适合声学与认知科学交叉研究者
理解海豚通信对于揭示野生群体的语言复杂性和社会结构至关重要。我们借鉴生物信息学中的ClustalW算法，将连续声学数据处理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打分，构建多序列比对（MSA）可视化结果。该框架揭示了共享的结构模式，特别是在攻击情境中出现的同步爆发脉冲等时间规律，这些模式在传统光谱图分析中难以察觉。
原文摘要 · Abstract (English)
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.
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