Aroma Release of Olfactory Displays Based on Audio-Visual Content

Abstract

Variant approaches used to release scents in most recent olfactory displays rely on time for decision making. The applicability of such an approach is questionable in scenarios like video games or virtual reality applications, where the specific content is dynamic in nature and thus not known in advance. All of these are required to enhance the experience and involvement of the user while watching or participating virtually in 4D cinemas or fun parks, associated with short films. Recently, associating the release of scents to the visual content of the scenario has been studied. This research enhances one such work by considering the auditory content along with the visual content. Minecraft, a computer game, was used to collect the necessary dataset with 1200 audio segments. The Inception v3 model was used to classified the sound and image dataset. Further ground truth classification on this dataset resulted in four classes: grass, fire, thunder, and zombie. Higher accuracies of 91% and 94% were achieved using the transfer learning approach for the sound and image models, respectively.