Automatic Classification of Anuran Sounds Using Convolutional Neural Networks

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Colonna, J (Author) Other; Peet, T (Author) Other; Ferreira, CA (Author) Other; Jorge, AM (Author) FCUP; Gomes, EF (Author) Other; João Gama (Author) FEP

Conference proceedings International

Title: ACM International Conference Proceeding Series

Pages: 73-78

9th International C* Conference on Computer Science and Software Engineering, C3S2E 2016

20 July 2016 through 22 July 2016

Indexing

Scopus - 33 Citations

Other information

Authenticus ID: P-00K-S9H

DOI: 10.1145/2948992.2949016

Abstract (EN): Anurans (frogs or toads) are closely related to the ecosystem and they are commonly used by biologists as early indicators of ecological stress. Automatic classification of anurans, by processing their calls, helps biologists analyze the activity of anurans on larger scale. Wireless Sensor Networks (WSNs) can be used for gathering data automatically over a large area. WSNs usually set restrictions on computing and transmission power for extending the network's lifetime. Deep Learning algorithms have gathered a lot of popularity in recent years, especially in the field of image recognition. Being an eager learner, a trained Deep Learning model does not need a lot of computing power and could be used in hardware with limited resources. This paper investigates the possibility of using Convolutional Neural Networks with Mel-Frequency Cepstral Coefficients (MFCCs) as input for the task of classifying anuran sounds. © 2016 ACM.

Language: English

Type (Professor's evaluation): Scientific