Feature Selection Using Genetic Algorithms for the Generation of a Recognition and Classification of Children Activities Model Using Environmental Sound In the area of recognition and classification of children activities, numerous works have been proposed that make use of different data sources. In most of them, sensors embedded in childrenxe2x80x99s garments are used. In this work, the use of environmental sound data is proposed to generate a recognition and classification of children activities model through automatic learning techniques, optimized for application on mobile devices. Initially, the use of a genetic algorithm for a feature selection is presented, reducing the original size of the dataset used, an important aspect when working with the limited resources of a mobile device. For the evaluation of this process, five different classification methods are applied, knearest neighbor (kNN), nearest centroid (NC), artificial neural networks (ANNs), random forest (RF), and recursive partitioning trees (Rpart). Finally, a comparison of the models obtained, based on the accuracy, is performed, in order to identify the classification method that presents the best performance in the development of a model that allows the identification of children activity based on audio signals. According to the results, the best performance is presented by the fivefeature model developed through RF, obtaining an accuracy of 0.92, which allows to conclude that it is possible to automatically classify children activity based on a reduced set of features with significant accuracy.

Weakly Supervised Sound Activity Detection and Event Classification in Acoustic Sensor Networks In this paper we consider human daily activity recognition using an acoustic sensor network (ASN) which consists of nodes distributed in a home environment. Assuming that the ASN is permanently recording, the vast majority of recordings is silence. Therefore, we propose to employ a computationally efficient twostage sound recognition system, consisting of an initial sound activity detection (SAD) and a subsequent sound event classification (SEC), which is only activated once sound activity has been detected. We show how a lowlatency activity detector with high temporal resolution can be trained from weak labels with low temporal resolution. We further demonstrate the advantage of using spatial features for the subsequent event classification task.