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Article

Animal Sound Classification Using Dissimilarity Spaces

1
Department of Information Engineering, University of Padova, Via Gradenigo 6, 35131 Padova, Italy
2
Department of Information Technology and Cybersecurity, Missouri State University, 901 S, National Street, Springfield, MO 65804, USA
3
Department of Computer Science and Engineering, University of Bologna, Via dell’Università 50, 47521 Cesena, Italy
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(23), 8578; https://doi.org/10.3390/app10238578
Received: 30 October 2020 / Revised: 20 November 2020 / Accepted: 26 November 2020 / Published: 30 November 2020
The classifier system proposed in this work combines the dissimilarity spaces produced by a set of Siamese neural networks (SNNs) designed using four 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. The proposed approach uses clustering methods to determine a set of centroids (in both a supervised and unsupervised fashion) from the spectrograms in the dataset. Such centroids are exploited to generate the dissimilarity space through the Siamese networks. In addition to feeding the SNNs with spectrograms, experiments process the spectrograms using the heterogeneous auto-similarities of characteristics. Once the similarity spaces are computed, each pattern is “projected” into the space to obtain a vector space representation; this descriptor is then coupled to 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 standalone approach is also evaluated on the challenging Dataset for Environmental Sound Classification (ESC50) dataset. View Full-Text
Keywords: audio sound classification; clustering; prototype selection; Siamese network; dissimilarity space audio sound classification; clustering; prototype selection; Siamese network; dissimilarity space
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MDPI and ACS Style

Nanni, L.; Brahnam, S.; Lumini, A.; Maguolo, G. Animal Sound Classification Using Dissimilarity Spaces. Appl. Sci. 2020, 10, 8578. https://doi.org/10.3390/app10238578

AMA Style

Nanni L, Brahnam S, Lumini A, Maguolo G. Animal Sound Classification Using Dissimilarity Spaces. Applied Sciences. 2020; 10(23):8578. https://doi.org/10.3390/app10238578

Chicago/Turabian Style

Nanni, Loris, Sheryl Brahnam, Alessandra Lumini, and Gianluca Maguolo. 2020. "Animal Sound Classification Using Dissimilarity Spaces" Applied Sciences 10, no. 23: 8578. https://doi.org/10.3390/app10238578

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