@conference {60,
	title = {Automatic cry detection in early childhood education settings},
	booktitle = {7th IEEE International Conference on Development and Learning, 2008. ICDL 2008},
	year = {2008},
	month = {08/2008},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Monterey, CA},
	abstract = {<p><span style="color: rgb(68, 68, 68); font-family: {\textquoteright}Lucida Grande{\textquoteright}, Verdana, sans-serif; font-size: 14px; background-color: rgba(0, 0, 0, 0.0470588);">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].</span></p>
},
	keywords = {Acoustic noise, auditory moods, automatic cry detection, behavioural sciences computing, Deafness, early childhood education settings, education, Educational robots, Emotion recognition, human coders, Humans, learning (artificial intelligence), Machine Learning, Mood, preschool classrooms, Prototypes, Robustness, Working environment noise},
	isbn = {978-1-4244-2661-4},
	author = {Ruvolo, P. and Movellan, J.}
}
