Abstract
In this work, we combine a Siamese neural network and different clustering techniques to generate a dissimilarity space that is then used to train an SVM for automated animal audio classification. The animal audio datasets used are (i) birds and (ii) cat sounds, which are freely available. We exploit different clustering methods to reduce the spectrograms in the dataset to a number of centroids that are used to generate the dissimilarity space through the Siamese network. Once computed, we use the dissimilarity space to generate a vector space representation of each pattern, which is then fed into an support vector machine (SVM) to classify a spectrogram by its dissimilarity vector. Our study shows that the proposed approach based on dissimilarity space performs well on both classification problems without ad-hoc optimization of the clustering methods. Moreover, results show that the fusion of CNN-based approaches applied to the animal audio classification problem works better than the stand-alone CNNs.
Department(s)
Information Technology and Cybersecurity
Document Type
Article
DOI
https://doi.org/10.3390/APP10124176
Rights Information
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Keywords
Animal audio, Audio classification, Dissimilarity space, Ensemble of classifiers, Pattern recognition, Siamese network
Publication Date
6-1-2020
Recommended Citation
Nanni, Loris, Andrea Rigo, Alessandra Lumini, and Sheryl Brahnam. "Spectrogram classification using dissimilarity space." Applied Sciences 10, no. 12 (2020): 4176.
Journal Title
Applied Sciences (Switzerland)
