Evidence map›Paper›PMID 36009611›Full record

ArticleAnimals : an open access journal from MDPI2022

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

Sebastian Schneider, Kurt Hammerschmidt, Paul Wilhelm Dierkes

Abstract read
In one paragraph

Article in Animals : an open access journal from MDPI, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

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3citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

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3 · Its place in the literature

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3 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Sebastian SchneiderBioscience Education and Zoo Biology, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany.
Kurt HammerschmidtCognitive Ethology Laboratory, German Primate Center, 37077 Göttingen, Germany.
Paul Wilhelm DierkesBioscience Education and Zoo Biology, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany.ORCID 0000-0002-6046-6406

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

bioacousticsclustering methodsfeature extractionfrequency-modulated vocalizationsmultidimensionalvocalization classificationvocal repertoire

Identifiers

PMID36009611
PMCPMC9404437

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.