Low-dimensional learned feature spaces quantify individual and group differences in vocal repertoires.
Low-dimensional learned feature spaces quantify individual and group differences in vocal repertoires.
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DOI：
10.7554/elife.67855
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发表时间：
2021-05-14
期刊：
影响因子：
7.7
通讯作者：
Pearson J
中科院分区：
文献类型：
--
作者：
Goffinet J;Brudner S;Mooney R;Pearson J
行为数据规模和复杂性的增加给数据分析带来了越来越大的挑战。一种常见的策略涉及用少量精心挑选的特定于领域的功能替换整个行为，但这种方法存在几个关键的限制。例如，精心挑选的特征可能会错过重要的变异性维度，并且它们之间的相关性使统计测试变得复杂。相比之下，我们在这里应用变分自动编码器（VAE）（一种无监督学习方法）直接从数据中学习特征并量化两个模型物种的发声行为：实验室小鼠和斑胸草雀。 VAE 集中在一种简洁的表示上，在各种常见的分析任务上优于精心挑选的特征，能够在斑胸草雀的数十毫秒的时间尺度上测量每时每刻的声音变化，提供强有力的证据证明小鼠超声波发声并不像通常认为的那样聚集，并以比以前的方法更高的保真度捕获导师和学生鸟鸣的相似性。总之，我们展示了现代无监督学习方法在量化复杂和高维声音行为方面的实用性。
Increases in the scale and complexity of behavioral data pose an increasing challenge for data analysis. A common strategy involves replacing entire behaviors with small numbers of handpicked, domain-specific features, but this approach suffers from several crucial limitations. For example, handpicked features may miss important dimensions of variability, and correlations among them complicate statistical testing. Here, by contrast, we apply the variational autoencoder (VAE), an unsupervised learning method, to learn features directly from data and quantify the vocal behavior of two model species: the laboratory mouse and the zebra finch. The VAE converges on a parsimonious representation that outperforms handpicked features on a variety of common analysis tasks, enables the measurement of moment-by-moment vocal variability on the timescale of tens of milliseconds in the zebra finch, provides strong evidence that mouse ultrasonic vocalizations do not cluster as is commonly believed, and captures the similarity of tutor and pupil birdsong with qualitatively higher fidelity than previous approaches. In all, we demonstrate the utility of modern unsupervised learning approaches to the quantification of complex and high-dimensional vocal behavior.