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 Coe cients (MFCCs) as input for the task of classifying anuran sounds. CCS Concepts •Computing methodologies ! Neural networks;
The aim of this work is to describe an exploratory study on the use of a SAX-based Multiresolution Motif Discovery method for Heart Sound Classification. The idea of our work is to discover relevant frequent motifs in the audio signals and use the discovered motifs and their frequency as characterizing attributes. We also describe different configurations of motif discovery for defining attributes and compare the use of a decision tree based algorithm with random forests on this kind of data. Experiments were performed with a dataset obtained from a clinic trial in hospitals using the digital stethoscope DigiScope. This exploratory study suggests that motifs contain valuable information that can be further exploited for Heart Sound Classification.
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