Development of Kansei estimation models for the sense of presence in audio-visual content

The term `presence´ is widely used to express the performances of audio-visual content and systems. Until now, many advanced audio-visual reproduction systems have been proposed to enable the perception of a higher degree of presence. Methodologies for evaluating the sense of presence derived from these systems also play an important role in the further evolution of audio-visual content and systems. This paper describes the construction of Kansei models which quantitatively evaluate the degree of presence in audio-visual content. Subjective evaluation experiments were conducted to investigate the relationship between the features of audio-visual content and the perceived sense of presence using 40 different types of audio-visual content. On the basis of evaluation scores and extracted audio-visual features, models were separately constructed for audio, visual and audio-visual content using neural networks. The performance tests of the constructed models demonstrated sufficient accuracy in estimating the sense of presence and statistically significant correlations between the model output and the subjective evaluation scores.