Kansei Estimation Models for the Sense of Presence in Audio-Visual Content with Different Audio Reproduction Methods

Abstract

The performances of both audio-visual content and systems are often evaluated by the sense of presence, which can be divided into two aspects: content presence and system presence. In our previous study, we constructed neural-network-based Kansei estimation models to evaluate content presence. Herein we aim to incorporate system presence into the Kansei models. We initially examined five audio reproduction methods, which simulate different systems, and conducted subjective evaluation experiments using 12 audio-visual content items. The experiments indicate the audio reproduction method influences both the audio-only and audio-visual conditions, but the effect is larger in the audio-only condition. Thus, we introduced four features related to the spatial impression of sound as new inputs into the previous Kansei estimation models. The expanded models successfully estimated both the content presence and system presence regardless of the condition. Hence, these models can quantitatively estimate the sense of presence in both audio-visual content and systems.