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通过使用动物发声比较不同聚类方法和音频转换技术来介绍CASE(声音事件聚类与分析)软件。

Introducing the Software CASE (Cluster and Analyze Sound Events) by Comparing Different Clustering Methods and Audio Transformation Techniques Using Animal Vocalizations.

作者信息

Schneider Sebastian, Hammerschmidt Kurt, Dierkes Paul Wilhelm

机构信息

Bioscience Education and Zoo Biology, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany.

Cognitive Ethology Laboratory, German Primate Center, 37077 Göttingen, Germany.

出版信息

Animals (Basel). 2022 Aug 10;12(16):2020. doi: 10.3390/ani12162020.

Abstract

Unsupervised clustering algorithms are widely used in ecology and conservation to classify animal sounds, but also offer several advantages in basic bioacoustics research. Consequently, it is important to overcome the existing challenges. A common practice is extracting the acoustic features of vocalizations one-dimensionally, only extracting an average value for a given feature for the entire vocalization. With frequency-modulated vocalizations, whose acoustic features can change over time, this can lead to insufficient characterization. Whether the necessary parameters have been set correctly and the obtained clustering result reliably classifies the vocalizations subsequently often remains unclear. The presented software, CASE, is intended to overcome these challenges. Established and new unsupervised clustering methods (community detection, affinity propagation, HDBSCAN, and fuzzy clustering) are tested in combination with various classifiers (k-nearest neighbor, dynamic time-warping, and cross-correlation) using differently transformed animal vocalizations. These methods are compared with predefined clusters to determine their strengths and weaknesses. In addition, a multidimensional data transformation procedure is presented that better represents the course of multiple acoustic features. The results suggest that, especially with frequency-modulated vocalizations, clustering is more applicable with multidimensional feature extraction compared with one-dimensional feature extraction. The characterization and clustering of vocalizations in multidimensional space offer great potential for future bioacoustic studies. The software CASE includes the developed method of multidimensional feature extraction, as well as all used clustering methods. It allows quickly applying several clustering algorithms to one data set to compare their results and to verify their reliability based on their consistency. Moreover, the software CASE determines the optimal values of most of the necessary parameters automatically. To take advantage of these benefits, the software CASE is provided for free download.

摘要

无监督聚类算法在生态学和保护领域被广泛用于对动物声音进行分类,但在基础生物声学研究中也具有诸多优势。因此,克服现有挑战很重要。一种常见做法是一维提取发声的声学特征,即仅为整个发声提取给定特征的平均值。对于声学特征会随时间变化的调频发声,这可能导致特征描述不足。所设置的必要参数是否正确以及所获得的聚类结果随后能否可靠地对发声进行分类,往往仍不明确。所展示的软件CASE旨在克服这些挑战。已有的和新的无监督聚类方法(社区检测、亲和传播、HDBSCAN和模糊聚类)与各种分类器(k近邻、动态时间规整和互相关)相结合,使用经过不同变换的动物发声进行测试。将这些方法与预定义的聚类进行比较,以确定它们的优缺点。此外,还提出了一种多维数据变换程序,能更好地表示多个声学特征的变化过程。结果表明,特别是对于调频发声,与一维特征提取相比,多维特征提取的聚类更适用。在多维空间中对发声进行特征描述和聚类为未来的生物声学研究提供了巨大潜力。软件CASE包含所开发的多维特征提取方法以及所有使用的聚类方法。它允许快速将多种聚类算法应用于一个数据集,以比较结果并根据其一致性验证其可靠性。此外,软件CASE会自动确定大多数必要参数的最优值。为利用这些优势,软件CASE可免费下载。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df7b/9404437/bdbe7d0e73a2/animals-12-02020-g001.jpg

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