Temporally-aware algorithms for the classification of anuran sounds

Several authors have shown that the sounds of anurans can be used as an indicator of climate change. Hence, the recording, storage and further processing of a huge number of anuran sounds, distributed over time and space, are required in order to obtain this indicator. Furthermore, it is desirable to have algorithms and tools for the automatic classification of the different classes of sounds. In this paper, six classification methods are proposed, all based on the data-mining domain, which strive to take advantage of the temporal character of the sounds. The definition and comparison of these classification methods is undertaken using several approaches. The main conclusions of this paper are that: (i) the sliding window method attained the best results in the experiments presented, and even outperformed the hidden Markov models usually employed in similar applications; (ii) noteworthy overall classification performance has been obtained, which is an especially striking result considering that the sounds analysed were affected by a highly noisy background; (iii) the instance selection for the determination of the sounds in the training dataset offers better results than cross-validation techniques; and (iv) the temporally-aware classifiers have revealed that they can obtain better performance than their nontemporally-aware counterparts.Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía, Spain): excellence eSAPIENS number TIC 570

Informe sobre diseño e intervención enmarcado en el proceso del informe social proteccional aplicado a usuario en Centro de Diagnóstico Ambulatorio DAM Ayún Ñuble

Tesis (Magíster en Intervención Socio-Jurídica en Familia)Hoy en día se logra identificar sin mayores dificultades que el Estado de Chile es la entidad de velar por los derechos de los niños, niñas y adolescentes. Siendo los Juzgados de Familia en cohesión con el Servicio Nacional de Menores y organismos colaboradores los encargados de cumplir con dicha labor. En dicho escenario, bajo amparo de la Ley de los juzgados de familia, emergen las medidas de protección. Las que buscan conocer la situación del niño, niña o adolescente y de esta forma, aplicar la sentencia más atingente para restituir sus derechos vulnerados. Contexto en que los programas de diagnóstico ambulatorio (DAM) realizan sus procesos periciales en aras a conocer la situación específica de un niño, niña o adolescente, su familia y entorno social con el fin de sugerir al Juez el tratamiento más adecuado para interferir y mejorar su problema. La presente Tesina tiene por objetivo conocer la línea de acción del programa de diagnóstico ambulatorio DAM Ayún Ñuble. Su estructura, objetivos y sobre todo su forma de desplegar estrategias de intervención desde el marco del informe social proteccional practicado a una usuaria del centro, lo que permitirá conocer las técnicas, actividades y metodologías implementadas durante el proceso pericial, desde la teoría ecológica de sistema

Animal Sound Classification using Sequential Classifiers

Several authors have shown that the sounds of anurans can be used as an indicator of climate change. But the recording, storage and further processing of a huge number of anuran’s sounds, distributed in time and space, are required to obtain this indicator. It is therefore highly desirable to have algorithms and tools for the automatic classification of the different classes of sounds. In this paper five different classification methods are proposed, all of them based on the data mining domain, which try to take advantage of the sound sequential behaviour. Its definition and comparison is undertaken using several approaches. The sequential classifiers have revealed that they can obtain a better performance than their non-sequential counterpart. The sliding window with an underlying decision tree has reached the best results in our tests, even overwhelming the Hidden Markov Models usually employed in similar applications. A quite remarkable overall classification performance has been obtained, a result even more relevant considering the low quality of the analysed sounds.Junta de Andalucía TIC-570

Exploring Symmetry of Binary Classification Performance Metrics

Selecting the proper performance metric constitutes a key issue for most classification problems in the field of machine learning. Although the specialized literature has addressed several topics regarding these metrics, their symmetries have yet to be systematically studied. This research focuses on ten metrics based on a binary confusion matrix and their symmetric behaviour is formally defined under all types of transformations. Through simulated experiments, which cover the full range of datasets and classification results, the symmetric behaviour of these metrics is explored by exposing them to hundreds of simple or combined symmetric transformations. Cross-symmetries among the metrics and statistical symmetries are also explored. The results obtained show that, in all cases, three and only three types of symmetries arise: labelling inversion (between positive and negative classes); scoring inversion (concerning good and bad classifiers); and the combination of these two inversions. Additionally, certain metrics have been shown to be independent of the imbalance in the dataset and two cross-symmetries have been identified. The results regarding their symmetries reveal a deeper insight into the behaviour of various performance metrics and offer an indicator to properly interpret their values and a guide for their selection for certain specific applications.University of Seville (Spain) by Telefónica Chair “Intelligence in Networks

Improving classification algorithms by considering score series in wireless acoustic sensor networks

The reduction in size, power consumption and price of many sensor devices has enabled the deployment of many sensor networks that can be used to monitor and control several aspects of various habitats. More specifically, the analysis of sounds has attracted a huge interest in urban and wildlife environments where the classification of the different signals has become a major issue. Various algorithms have been described for this purpose, a number of which frame the sound and classify these frames,while others take advantage of the sequential information embedded in a sound signal. In the paper, a new algorithm is proposed that, while maintaining the frame-classification advantages, adds a new phase that considers and classifies the score series derived after frame labelling. These score series are represented using cepstral coefficients and classified using standard machine-learning classifiers. The proposed algorithm has been applied to a dataset of anuran calls and its results compared to the performance obtained in previous experiments on sensor networks. The main outcome of our research is that the consideration of score series strongly outperforms other algorithms and attains outstanding performance despite the noisy background commonly encountered in this kind of application

"El juego dramático como metodología de enseñanza para el trabajo colaborativo en un taller de fútbol"

Tesis (Profesor de Educación Física, Licenciado en Educación)La presente tesis evidencia el resultado positivo o negativo que tiene una metodología innovadora de enseñanza como lo es la pedagogía teatral, y su efecto en el trabajo colaborativo, aplicado en un grupo de niños de la comuna de La Florida. Así mismo hace énfasis en encontrar una metodología innovadora de enseñanza la cual permitiría crear actividades lúdicas y didácticas para los niños. Para ello se trabajará con la metodología del juego dramático, con la que se realizarán diversos juegos los cuales estarán destinados a desarrollar el trabajo colaborativo. Las clases se realizaron una vez por semana los días miércoles en el taller extradeportivo de fútbol filial Colo-Colo 73`. Estas actividades están enfocadas en el fútbol pero con una línea común que es la del juego dramático. El juego dramático es una metodología de enseñanza innovadora la cual es muy poco valorada y conocida en nuestro país. Esta se enfoca en la expresividad, comunicación entre pares y el trabajo en equipo. Nuestro principal objetivo es “evaluar el impacto del juego dramático en el trabajo colaborativo en estudiantes que participan de un taller deportivo en instituciones extra escolares y de qué manera perciben dicha intervención”. A través de este objetivo nos planteamos las diferentes actividades que realizaremos en la intervención del proyecto “Jugando hago amigos”, del cual se realizaron una serie de evaluaciones dirigidas a los padres y niños del taller

The impact of class imbalance in classification performance metrics based on the binary confusion matrix

A major issue in the classification of class imbalanced datasets involves the determination of the most suitable performance metrics to be used. In previous work using several examples, it has been shown that imbalance can exert a major impact on the value and meaning of accuracy and on certain other well-known performance metrics. In this paper, our approach goes beyond simply studying case studies and develops a systematic analysis of this impact by simulating the results obtained using binary classifiers. A set of functions and numerical indicators are attained which enables the comparison of the behaviour of several performance metrics based on the binary confusion matrix when they are faced with imbalanced datasets. Throughout the paper, a new way to measure the imbalance is defined which surpasses the Imbalance Ratio used in previous studies. From the simulation results, several clusters of performance metrics have been identified that involve the use of Geometric Mean or Bookmaker Informedness as the best null-biased metrics if their focus on classification successes (dismissing the errors) presents no limitation for the specific application where they are used. However, if classification errors must also be considered, then the Matthews Correlation Coefficient arises as the best choice. Finally, a set of null-biased multi-perspective Class Balance Metrics is proposed which extends the concept of Class Balance Accuracy to other performance metrics

Optimal Representation of Anuran Call Spectrum in Environmental Monitoring Systems Using Wireless Sensor Networks

The analysis and classification of the sounds produced by certain animal species, notably anurans, have revealed these amphibians to be a potentially strong indicator of temperature fluctuations and therefore of the existence of climate change. Environmental monitoring systems using Wireless Sensor Networks are therefore of interest to obtain indicators of global warming. For the automatic classification of the sounds recorded on such systems, the proper representation of the sound spectrum is essential since it contains the information required for cataloguing anuran calls. The present paper focuses on this process of feature extraction by exploring three alternatives: the standardized MPEG-7, the Filter Bank Energy (FBE), and the Mel Frequency Cepstral Coefficients (MFCC). Moreover, various values for every option in the extraction of spectrum features have been considered. Throughout the paper, it is shown that representing the frame spectrum with pure FBE offers slightly worse results than using the MPEG-7 features. This performance can easily be increased, however, by rescaling the FBE in a double dimension: vertically, by taking the logarithm of the energies; and, horizontally, by applying mel scaling in the filter banks. On the other hand, representing the spectrum in the cepstral domain, as in MFCC, has shown additional marginal improvements in classification performance.University of Seville: Telefónica Chair "Intelligence Networks

A Fuzzy Logic intelligent agent for Information Extraction: Introducing a new Fuzzy Logic-based term weighting scheme

In this paper, we propose a novel method for Information Extraction (IE) in a set of knowledge in order to answer to user consultations using natural language. The system is based on a Fuzzy Logic engine, which takes advantage of its flexibility for managing sets of accumulated knowledge. These sets may be built in hierarchic levels by a tree structure. The aim of this system is to design and implement an intelligent agent to manage any set of knowledge where information is abundant, vague or imprecise. The method was applied to the case of a major university web portal, University of Seville web portal, which contains a huge amount of information. Besides, we also propose a novel method for term weighting (TW). This method also is based on Fuzzy Logic, and replaces the classical TF–IDF method, usually used for TW, for its flexibility