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.

Abstract
In this paper we describe an algorithm for heart sound classification (classes Normal, Murmur and Extrasystole) based on the discretization of sound signals using the SAX (Symbolic Aggregate Approximation) representation. The general strategy is to automatically discover relevant top frequent motifs and relate them with the occurrence of systolic (S1) and diastolic (S2) sounds in the audio signals. The algorithm was tuned using motifs generated from a collection of audio signals obtained from a clinical trial in a hospital. Validation was performed on a separate set of unlabeled audio signals. Results indicate ability to improve the precision of the classification of the classes Normal and Murmur.

Abstract
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. © 2013 ACM.

Abstract
In this paper we describe an approach to classifying heart sounds (classes Normal, Murmur and Extra-systole) that is based on the discretization of sound signals using the SAX (Symbolic Aggregate Approximation) representation. The ability of automatically classifying heart sounds or at least support human decision in this task is socially relevant to spread the reach of medical care using simple mobile devices or digital stethoscopes. In our approach, sounds are first pre-processed using signal processing techniques (decimate, low-pass filter, normalize, Shannon envelope). Then the pre-processed symbols are transformed into sequences of discrete SAX symbols. These sequences are subject to a process of motif discovery. Frequent sequences of symbols (motifs) are adopted as features. Each sound is then characterized by the frequent motifs that occur in it and their respective frequency. This is similar to the term frequency (TF) model used in text mining. In this paper we compare the TF model with the application of the TFIDF (Term frequency - Inverse Document Frequency) and the use of bi-grams (frequent size two sequences of motifs). Results show the ability of the motifs based TF approach to separate classes and the relative value of the TFIDF and the bi-grams variants. The separation of the Extra-systole class is overly difficult and much better results are obtained for separating the Murmur class. Empirical validation is conducted using real data collected in noisy environments. We have also assessed the cost-reduction potential of the proposed methods by considering a fixed cost model and using a cost sensitive meta algorithm.

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.

Abstract
The interest in the use of Raman spectroscopy in cancer diagnosis is due to its capability to unveil the evolution of the biochemical composition of tissue and cells throughout disease progression. Differences between Raman spectra of normal and malignant tissues and cells, both in vivo and in vitro, are being used as a method for the early detection of cancer. In this survey paper we review recently published works related to the analysis of data resulting from Raman spectroscopy. We structure our analysis according with the three steps of the process: data preprocessing, data reduction and data classification. © 2014 L & H Scientific Publishing, LLC.