By means of this tool it is possible to extract up to 44 features of an animal sound in different ways. In addition, the tool includes various clustering algorithms (community detection; affinity propagation; HDBSCAN and fuzzy clustering) as well as algorithms to detect similarities between signals (k-nearest-neighbor; jaccard; dynamic-time-warping) in order to effectively classify animal sounds. The tool uses a graphical user interface to allow the user to work with the software as easily and intuitively as possible.
The following algorithms were adopted:
Affinity Propagation:
Kaijun Wang (2021). Adaptive Affinity Propagation clustering (https://www.mathworks.com/matlabcentral/fileexchange/18244-adaptive-affinity-propagation-clustering), MATLAB Central File Exchange. Retrieved March 1, 2021.
Community Detection:
Athanasios Kehagias (2021). Community Detection Toolbox (https://www.mathworks.com/matlabcentral/fileexchange/45867-community-detection-toolbox), MATLAB Central File Exchange. Retrieved March 1, 2021.
HDBSCAN:
Jordan Sorokin (2021). Jorsorokin/HDBSCAN (https://github.com/Jorsorokin/HDBSCAN), GitHub. Retrieved March 1, 2021.
NMI:
Mo Chen (2021). Normalized Mutual Information (https://www.mathworks.com/matlabcentral/fileexchange/29047-normalized-mutual-information), MATLAB Central File Exchange. Retrieved March 1, 2021.
t-SNE:
Laurens van der Maaten & Geoffrey Hinton
https://lvdmaaten.github.io/tsne/
引用格式
Schneider, Sebastian, et al. “Introducing the Software CASE (Cluster and Analyze Sound Events) by Comparing Different Clustering Methods and Audio Transformation Techniques Using Animal Vocalizations.” Animals, vol. 12, no. 16, MDPI AG, Aug. 2022, p. 2020, doi:10.3390/ani12162020.
MATLAB 版本兼容性
创建方式
R2020a
与 R2020a 及更高版本兼容
平台兼容性
Windows macOS Linux标签
致谢
参考作品: Community Detection Toolbox, Normalized Mutual Information, Adaptive Affinity Propagation clustering
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