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:
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.
Athanasios Kehagias (2021). Community Detection Toolbox (https://www.mathworks.com/matlabcentral/fileexchange/45867-community-detection-toolbox), MATLAB Central File Exchange. Retrieved March 1, 2021.
Jordan Sorokin (2021). Jorsorokin/HDBSCAN (https://github.com/Jorsorokin/HDBSCAN), GitHub. Retrieved March 1, 2021.
Mo Chen (2021). Normalized Mutual Information (https://www.mathworks.com/matlabcentral/fileexchange/29047-normalized-mutual-information), MATLAB Central File Exchange. Retrieved March 1, 2021.
Laurens van der Maaten & Geoffrey Hinton
Sebastian Schneider (2021). CASE (Cluster & Analyse Sound Events) (https://www.mathworks.com/matlabcentral/fileexchange/88039-case-cluster-analyse-sound-events), MATLAB Central File Exchange. Retrieved .
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