Details
Name
Elsa Ferreira GomesCluster
Computer ScienceRole
Affiliated ResearcherSince
01st November 2016
Nationality
PortugalCentre
Artificial Intelligence and Decision SupportContacts
+351220402963
elsa.f.gomes@inesctec.pt
2019
Authors
Nogueira, DM; Ferreira, CA; Gomes, EF; Jorge, AM;
Publication
Journal of Medical Systems
Abstract
2018
Authors
Tavares, PC; Gomes, EF; Henriques, PR;
Publication
Proceedings of the 10th International Conference on Computer Supported Education
Abstract
2017
Authors
Tavares, PC; Henriques, PR; Gomes, EF;
Publication
CSEDU 2017 - Proceedings of the 9th International Conference on Computer Supported Education, Volume 1, Porto, Portugal, April 21-23, 2017.
Abstract
2016
Authors
Colonna, J; Peet, T; Ferreira, CA; Jorge, AM; Gomes, EF; Gama, J;
Publication
Proceedings of the Ninth International C* Conference on Computer Science & Software Engineering, C3S2E '16, Porto, Portugal, July 20-22, 2016
Abstract
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.
2016
Authors
Gomes, EF; Batista, F; Jorge, AM;
Publication
Proceedings of the Ninth International C* Conference on Computer Science & Software Engineering, C3S2E '16, Porto, Portugal, July 20-22, 2016
Abstract
The aim of this work is to develop an application for Android able to classifying urban sounds in a real life context. It also enables the collection and classification of new sounds. To train our classifier we use the UrbanSound8K data set available online. We have used a hybrid approach to obtain features, by combining SAX-based multiresolution motif discovery with Mel-Frequency Cepstral Coefficients (MFCC). We also describe different configurations of motif discovery for defining attributes and compare the use of Random Forest and SVM algorithms on this kind of data. Copyright 2016 ACM.
Supervised Thesis
2019
Author
BRUNO MIGUEL FERREIRA TEIXEIRA
Institution
IPP-ISEP
2018
Author
TIAGO MARQUES OLIVEIRA
Institution
IPP-ISEP
2018
Author
DIOGO MIGUEL RODRIGUES E SILVA
Institution
IPP-ISEP
2018
Author
GABRIEL MOREIRA DA ROCHA
Institution
IPP-ISEP
2018
Author
DIOGO MANUEL PEREIRA VIEIRA
Institution
IPP-ISEP
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