Abstract
This study presents an unsupervised method to infer discreteness, syntax and temporal structures of fruit-bats vocalizations, as a case study of graded vocal systems, and evaluates the complexity of communication patterns in relation with behavioral context. The method improved the baseline for unsupervised labeling of vocal units (i.e. syllables) through manifold learning, by investigating how dimen- sionality reduction on mel-spectrograms affects labeling, and comparing it with unsupervised labels based on acoustic similarity. We then encoded vocalizations as syllabic sequences to analyze the type of syntax, and extracted the Maximal Repetitions (MRs) to evaluate syntactical structures. We found evidence for: i) associative syntax, rather than combinatorial (context classification is unaffected by permutation of sequences, F 1 > 0.9); ii) context-dependent use of syllables (Wilcoxon rank-sum tests, p-value < 0.05); iii) heavy-tail distribution of MRs (truncated power-law, exponent {\alpha} < 2), indicative of mechanism encoding com- binatorial complexity. Analysis of MRs and syllabic transition networks revealed that mother-pupil interactions were characterized by repetitions, while commu- nication in conflict-contexts exhibited higher complexity (longer MRs and more interconnected vocal sequences) than non-agonistic contexts. We propose that communicative complexity is higher in scenarios of disagreement, reflecting lower compressibility of information.
Abstract (translated)
这项研究提出了一种无监督方法，用于推断果蝠叫声中的离散性、语法结构和时间结构，并以此作为分级声音系统的案例研究。该方法评估了在行为背景下的交流模式的复杂性。 具体而言，本方法通过流形学习改进了基于无监督标签的声音单元（即音节）的基本标注水平，探讨了梅尔频谱图降维对标注的影响，并将其与基于声学相似性的无监督标签进行了比较。然后我们将声音编码为音节序列以分析语法类型，并提取最大重复次数（MRs）来评估语法结构。 研究发现有以下几点证据： i) 关联性语法规则，而不是组合性规则（序列的排列对上下文分类没有影响，F1 > 0.9） ii) 音节使用的背景依赖性（威尔科克森秩和检验，p值 < 0.05） iii) 最大重复次数的幂律分布（截断幂律，指数{\alpha} < 2），表明存在编码组合复杂性的机制 通过对最大重复次数和音节转换网络进行分析发现，母蝙蝠与幼崽之间的互动特征是重复性更强，而冲突背景下的交流则表现出更高的复杂性（更长的MRs以及更加相互连接的声音序列）。研究提出，在分歧情景下，沟通的复杂度更高，反映了信息可压缩性的降低。
URL
https://arxiv.org/abs/2512.01033
