  Sonification is the science of communication of data and events to users
through sounds. Auditory icons, earcons, and speech are the common auditory
display schemes utilized in sonification, or more specifically in the use of
audio to convey information. Once the captured data are perceived, their
meanings, and more importantly, intentions can be interpreted more easily and
thus can be employed as a complement to visualization techniques. Through
auditory perception it is possible to convey information related to temporal,
spatial, or some other context-oriented information. An important research
question is whether the emotions perceived from these auditory icons or earcons
are predictable in order to build an automated sonification platform. This
paper conducts an experiment through which several mainstream and conventional
machine learning algorithms are developed to study the prediction of emotions
perceived from sounds. To do so, the key features of sounds are captured and
then are modeled using machine learning algorithms using feature reduction
techniques. We observe that it is possible to predict perceived emotions with
high accuracy. In particular, the regression based on Random Forest
demonstrated its superiority compared to other machine learning algorithms.
