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Spectrogram Classification Using Dissimilarity Space

DEI, Via Gradenigo 6, 35131 Padova, Italy
DISI, University of Bologna, Via dell’Università 50, 47521 Cesena, Italy
Department of Information Technology and Cybersecurity, Missouri State University, 901 S. National Street, Springfield, MO 65804, USA
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(12), 4176;
Received: 19 May 2020 / Revised: 9 June 2020 / Accepted: 9 June 2020 / Published: 17 June 2020
(This article belongs to the Special Issue Texture and Colour in Image Analysis)
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. View Full-Text
Keywords: audio classification; dissimilarity space; siamese network; ensemble of classifiers; pattern recognition; animal audio audio classification; dissimilarity space; siamese network; ensemble of classifiers; pattern recognition; animal audio
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MDPI and ACS Style

Nanni, L.; Rigo, A.; Lumini, A.; Brahnam, S. Spectrogram Classification Using Dissimilarity Space. Appl. Sci. 2020, 10, 4176.

AMA Style

Nanni L, Rigo A, Lumini A, Brahnam S. Spectrogram Classification Using Dissimilarity Space. Applied Sciences. 2020; 10(12):4176.

Chicago/Turabian Style

Nanni, Loris, Andrea Rigo, Alessandra Lumini, and Sheryl Brahnam. 2020. "Spectrogram Classification Using Dissimilarity Space" Applied Sciences 10, no. 12: 4176.

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