<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ruvolo, P.</style></author><author><style face="normal" font="default" size="100%">Movellan, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automatic cry detection in early childhood education settings</style></title><secondary-title><style face="normal" font="default" size="100%">7th IEEE International Conference on Development and Learning, 2008. ICDL 2008</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Acoustic noise</style></keyword><keyword><style  face="normal" font="default" size="100%">auditory moods</style></keyword><keyword><style  face="normal" font="default" size="100%">automatic cry detection</style></keyword><keyword><style  face="normal" font="default" size="100%">behavioural sciences computing</style></keyword><keyword><style  face="normal" font="default" size="100%">Deafness</style></keyword><keyword><style  face="normal" font="default" size="100%">early childhood education settings</style></keyword><keyword><style  face="normal" font="default" size="100%">education</style></keyword><keyword><style  face="normal" font="default" size="100%">Educational robots</style></keyword><keyword><style  face="normal" font="default" size="100%">Emotion recognition</style></keyword><keyword><style  face="normal" font="default" size="100%">human coders</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">learning (artificial intelligence)</style></keyword><keyword><style  face="normal" font="default" size="100%">Machine Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Mood</style></keyword><keyword><style  face="normal" font="default" size="100%">preschool classrooms</style></keyword><keyword><style  face="normal" font="default" size="100%">Prototypes</style></keyword><keyword><style  face="normal" font="default" size="100%">Robustness</style></keyword><keyword><style  face="normal" font="default" size="100%">Working environment noise</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2008</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Monterey, CA</style></pub-location><isbn><style face="normal" font="default" size="100%">978-1-4244-2661-4</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span style=&quot;color: rgb(68, 68, 68); font-family: 'Lucida Grande', Verdana, sans-serif; font-size: 14px; background-color: rgba(0, 0, 0, 0.0470588);&quot;&gt;We present results on applying a novel machine learning approach for learning auditory moods in natural environments [1] to the problem of detecting crying episodes in preschool classrooms. The resulting system achieved levels of performance approaching that of human coders and also significantly outperformed previous approaches to this problem [2].&lt;/span&gt;&lt;/p&gt;
</style></abstract><accession-num><style face="normal" font="default" size="100%">10367600</style></accession-num></record></records></xml>