Browsing by Author "Lozano, H."
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Item Household sound identification system for people with hearing disabilities(2008) Lozano, H.; Hernáez, I.; Navas, E.; González, F. J.; Idígoras, I.; Medical Technologies; Centros PRE-FUSION TECNALIA - (FORMER)This article concerns the classification of household sounds for the development of an application to help people with hearing disabilities to resolve everyday situations which may present them with serious problems: telephone calls, the sound of the door bell, an alarm clock, the completion of a domestic appliance programme, etc. It presents the study carried out in order to obtain the best acoustical parameters by adopting training and evaluation techniques using GMM models with a varying number of Gaussian components. Parameters such as MFCCs, ZCR, Roll-Off Point and Spectral Centroid are tested using the classifier.Item "Non-speech" sounds classification for people with hearing disabilities(2007) Lozano, H.; Hernaez, I.; Navas, E.; González, F. J.; Idigoras, I.; Eizmendi, Gorka; Azkoitia, Jose Miguel; Craddock, Gerald; Medical Technologies; Centros PRE-FUSION TECNALIA - (FORMER)People with hearing disabilities experience the problems that stem from not being able to detect or identify sounds on a daily basis. Studying the techniques and algorithms which enable this task to be performed automatically may lead to significant technological progress which will offer huge benefits to deaf people. With the objective of developing an application which is capable of detecting and classifying the different sounds that may emerge in the home, a study is being carried out which shows the most important parameters for processing impulsive sounds such as door bells, alarm clocks, a baby crying which obtain high accuracy ratios and give the classifier high reliability. To date, an initial prototype has been developed which implements a GMM (Gaussian Mixture Model) classifier which is based on the Gaussian probability distribution for sound event prediction. In order to check the classifier's accuracy, typical speech recognition parameters have been used, such as MFCC (Mel frequency cepstral coefficient), as well as parameters used to recognise musical instruments and background sounds: Spectral Centroid, Roll-Off Point and ZCR (16 parameters in total). By varying a series of factors (number of parameters, the sounds used to train the classifier...) the GMM's behaviour has been analysed obtaining results with over 90% accuracy in frames and up to 100% accuracy using the sound average, identifying doors, telephones and alarm clocks.Item Robotic Ubiquitous Cognitive Ecology for Smart Homes(2015-12-01) Amato, G.; Bacciu, D.; Broxvall, M.; Chessa, S.; Coleman, S.; Di Rocco, M.; Dragone, M.; Gallicchio, C.; Gennaro, C.; Lozano, H.; McGinnity, T. M.; Micheli, A.; Ray, A. K.; Renteria, A.; Saffiotti, A.; Swords, D.; Vairo, C.; Vance, P.; Medical TechnologiesRobotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent-based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a proof of concept smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feedback received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work.