Article
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Preserved in Portico This version is not peer-reviewed
Animal Sound Classification Using Dissimilarity Spaces
Version 1
: Received: 23 October 2020 / Approved: 26 October 2020 / Online: 26 October 2020 (13:57:01 CET)
A peer-reviewed article of this Preprint also exists.
Nanni, L.; Brahnam, S.; Lumini, A.; Maguolo, G. Animal Sound Classification Using Dissimilarity Spaces. Appl. Sci. 2020, 10, 8578. Nanni, L.; Brahnam, S.; Lumini, A.; Maguolo, G. Animal Sound Classification Using Dissimilarity Spaces. Appl. Sci. 2020, 10, 8578.
Abstract
The classifier system proposed in this work combines the dissimilarity spaces produced by a set of Siamese neural networks (SNNs) designed using 4 different backbones, with different clustering techniques for training SVMs for automated animal audio classification. The system is evaluated on two animal audio datasets: one for cat and another for bird vocalizations. Different clustering methods reduce the spectrograms in the dataset to a set of centroids that generate (in both a supervised and unsupervised fashion) the dissimilarity space through the Siamese networks. In addition to feeding the SNNs with spectrograms, additional experiments process the spectrograms using the Heterogeneous Auto-Similarities of Characteristics. Once the similarity spaces are computed, a vector space representation of each pattern is generated that is then trained on a Support Vector Machine (SVM) to classify a spectrogram by its dissimilarity vector. Results demonstrate that the proposed approach performs competitively (without ad-hoc optimization of the clustering methods) on both animal vocalization datasets. To further demonstrate the power of the proposed system, the best stand-alone approach is also evaluated on the challenging Dataset for Environmental Sound Classification (ESC50) dataset. The MATLAB code used in this study is available at https://github.com/LorisNanni.
Keywords
audio classification; dissimilarity space; siamese network; ensemble of classifiers; pattern recognition; animal audio
Subject
Computer Science and Mathematics, Algebra and Number Theory
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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