Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed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)

How to cite: Nanni, L.; Brahnam, S.; Lumini, A.; Maguolo, G. Animal Sound Classification Using Dissimilarity Spaces. Preprints 2020, 2020100526 (doi: 10.20944/preprints202010.0526.v1). Nanni, L.; Brahnam, S.; Lumini, A.; Maguolo, G. Animal Sound Classification Using Dissimilarity Spaces. Preprints 2020, 2020100526 (doi: 10.20944/preprints202010.0526.v1).

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.

Subject Areas

audio classification; dissimilarity space; siamese network; ensemble of classifiers; pattern recognition; animal audio

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