VoICE: A semi-automated pipeline for standardizing vocal analysis across models
VoICE: A semi-automated pipeline for standardizing vocal analysis across models
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DOI：
10.1038/srep10237
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发表时间：
2015-05-28
影响因子：
4.6
通讯作者：
White, Stephanie A.
中科院分区：
文献类型：
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作者：
Burkett, Zachary D.;Day, Nancy F.;White, Stephanie A.
动物模型中的声音交流研究为语言和交流障碍的神经遗传学基础提供了关键的见解。目前的声音分析方法缺乏标准化，在跨实验室和跨物种的比较中产生了歧义。在这里，我们提出了VoICE（声乐库存聚类引擎），一种通过在录音会话中对所有发声之间的频谱相似性进行评分来创建高维数据集来对声乐元素进行分组的方法。然后对该数据集进行分层聚类，生成树状图，该树状图通过自动算法修剪成有意义的发声“类型”。当应用于鸟鸣时，声乐学习的一个关键模型，Voice捕捉到了震耳欲聋之后声学特性的已知恶化，包括改变的序列。在哺乳动物神经发育模型中，我们发现了缺乏自闭症易感基因Cntnap2的小鼠的声音库减少。VoICE将对科学界有用，因为它可以标准化跨物种和实验室的发声分析。
The study of vocal communication in animal models provides key insight to the neurogenetic basis for speech and communication disorders. Current methods for vocal analysis suffer from a lack of standardization, creating ambiguity in cross-laboratory and cross-species comparisons. Here, we present VoICE (Vocal Inventory Clustering Engine), an approach to grouping vocal elements by creating a high dimensionality dataset through scoring spectral similarity between all vocalizations within a recording session. This dataset is then subjected to hierarchical clustering, generating a dendrogram that is pruned into meaningful vocalization "types" by an automated algorithm. When applied to birdsong, a key model for vocal learning, VoICE captures the known deterioration in acoustic properties that follows deafening, including altered sequencing. In a mammalian neurodevelopmental model, we uncover a reduced vocal repertoire of mice lacking the autism susceptibility gene, Cntnap2. VoICE will be useful to the scientific community as it can standardize vocalization analyses across species and laboratories.