A Bimodal Learning Approach to Assist Multi-sensory Effects Synchronization

TL;DR

This paper introduces a bimodal neural network that uses audio and video signals to improve the synchronization of sensory effects in mulsemedia applications, enhancing timing accuracy and reducing manual effort.

Contribution

The paper presents a novel bimodal neural network architecture that leverages both audio and video data to assist in synchronizing sensory effects in mulsemedia applications, outperforming unimodal methods.

Findings

Bimodal approach yields better synchronization accuracy than unimodal methods.

The model trained on Google's AudioSet demonstrates effective scene component prediction.

Experimental results confirm the superiority of combined audio-video signals for timing synchronization.

Abstract

In mulsemedia applications, traditional media content (text, image, audio, video, etc.) can be related to media objects that target other human senses (e.g., smell, haptics, taste). Such applications aim at bridging the virtual and real worlds through sensors and actuators. Actuators are responsible for the execution of sensory effects (e.g., wind, heat, light), which produce sensory stimulations on the users. In these applications sensory stimulation must happen in a timely manner regarding the other traditional media content being presented. For example, at the moment in which an explosion is presented in the audiovisual content, it may be adequate to activate actuators that produce heat and light. It is common to use some declarative multimedia authoring language to relate the timestamp in which each media object is to be presented to the execution of some sensory effect. One problem in this setting is that the synchronization of media objects and sensory effects is done manually by the author(s) of the application, a process which is time-consuming and error prone. In this paper, we present a bimodal neural network architecture to assist the synchronization task in mulsemedia applications. Our approach is based on the idea that audio and video signals can be used simultaneously to identify the timestamps in which some sensory effect should be executed. Our learning architecture combines audio and video signals for the prediction of scene components. For evaluation purposes, we construct a dataset based on Google's AudioSet. We provide experiments to validate our bimodal architecture. Our results show that the bimodal approach produces better results when compared to several variants of unimodal architectures.