Grimsley Jasmine M S, Gadziola Marie A, Wenstrup Jeffrey J
Department of Anatomy and Neurobiology, Northeast Ohio Medical University Rootstown, OH, USA.
Front Behav Neurosci. 2013 Jan 9;6:89. doi: 10.3389/fnbeh.2012.00089. eCollection 2012.
Mouse pups vocalize at high rates when they are cold or isolated from the nest. The proportions of each syllable type produced carry information about disease state and are being used as behavioral markers for the internal state of animals. Manual classifications of these vocalizations identified 10 syllable types based on their spectro-temporal features. However, manual classification of mouse syllables is time consuming and vulnerable to experimenter bias. This study uses an automated cluster analysis to identify acoustically distinct syllable types produced by CBA/CaJ mouse pups, and then compares the results to prior manual classification methods. The cluster analysis identified two syllable types, based on their frequency bands, that have continuous frequency-time structure, and two syllable types featuring abrupt frequency transitions. Although cluster analysis computed fewer syllable types than manual classification, the clusters represented well the probability distributions of the acoustic features within syllables. These probability distributions indicate that some of the manually classified syllable types are not statistically distinct. The characteristics of the four classified clusters were used to generate a Microsoft Excel-based mouse syllable classifier that rapidly categorizes syllables, with over a 90% match, into the syllable types determined by cluster analysis.
当小鼠幼崽感到寒冷或与巢穴隔离时，它们会高频发声。所发出的每种音节类型的比例携带有关疾病状态的信息，并正被用作动物内部状态的行为标记。基于这些发声的频谱-时间特征，人工分类确定了10种音节类型。然而，对小鼠音节进行人工分类既耗时又容易受到实验者偏差的影响。本研究使用自动聚类分析来识别CBA/CaJ小鼠幼崽发出的声学上不同的音节类型，然后将结果与先前的人工分类方法进行比较。聚类分析基于其频带识别出两种具有连续频率-时间结构的音节类型，以及两种具有突然频率转变的音节类型。虽然聚类分析计算出的音节类型比人工分类少，但这些聚类很好地代表了音节内声学特征的概率分布。这些概率分布表明，一些人工分类的音节类型在统计学上并无差异。利用这四种分类聚类的特征，生成了一个基于Microsoft Excel的小鼠音节分类器，该分类器能快速将音节以超过90%的匹配率归类为由聚类分析确定的音节类型。
