Jilin Provincial Key Laboratory of Animal Resource Conservation and Utilization, No. 2555, Street Jingyue, Northeast Normal University, Changchun 130117, China.
Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA 90095, USA.
J Exp Biol. 2019 Dec 18;222(Pt 24):jeb214072. doi: 10.1242/jeb.214072.
For analysis of vocal syntax, accurate classification of call sequence structures in different behavioural contexts is essential. However, an effective, intelligent program for classifying call sequences from numerous recorded sound files is still lacking. Here, we employed three machine learning algorithms (logistic regression, support vector machine and decision trees) to classify call sequences of social vocalizations of greater horseshoe bats () in aggressive and distress contexts. The three machine learning algorithms obtained highly accurate classification rates (logistic regression 98%, support vector machine 97% and decision trees 96%). The algorithms also extracted three of the most important features for the classification: the transition between two adjacent syllables, the probability of occurrences of syllables in each position of a sequence, and the characteristics of a sequence. The results of statistical analysis also supported the classification of the algorithms. The study provides the first efficient method for data mining of call sequences and the possibility of linguistic parameters in animal communication. It suggests the presence of song-like syntax in the social vocalizations emitted within a non-breeding context in a bat species.
为了分析语音语法，将不同行为背景下的叫声序列结构进行准确分类至关重要。然而，仍然缺乏一种有效的、智能化的程序来对大量录制声音文件中的叫声序列进行分类。在这里，我们使用了三种机器学习算法（逻辑回归、支持向量机和决策树）来对大马蹄蝠（）在攻击和痛苦情境下的社交叫声序列进行分类。这三种机器学习算法获得了非常高的分类准确率（逻辑回归 98%，支持向量机 97%，决策树 96%）。这些算法还提取了三个对分类最重要的特征：两个相邻音节之间的过渡，序列中每个位置出现音节的概率，以及序列的特征。统计分析的结果也支持算法的分类。这项研究为叫声序列的数据挖掘和动物交流中的语言参数提供了第一个有效的方法。它表明在非繁殖背景下，蝙蝠物种发出的社交叫声中存在类似歌曲的语法。
