Προσαρμοστικά Περιβάλλοντα Επαυξημένης Ηχητικής Πραγματικότητας με χρήση Δυναμικά Μεταβαλλόμενης Ηχητικής Μείξης
Date Issued
March 7, 2021
Type
Διδακτορική Διατριβή
Abstract
The field of Augmented Reality Audio (ARA) has introduced the ARA mixer as a method to mix the real with the virtual environment. The goal of this dissertation is to upgrade the function of ARA mix legacy model through the integration of adaptation to control the auditory fluctuations of unpredictable real environment in typical ARA applications. We propose a new perceptual ARA mixer model which adapts the blending of auditory environments in terms of binaural loudness. Consequently, a new technique of real-time binaural loudness prediction through feature extraction was integrated to ARA mixer. The effectiveness of proposed model of ARA mixer was demonstrated and evaluated through qualitative and quantitative methods; thus, it was applied and evaluated in a typical ARA game. Results show that among ARA mix quality tests, subjects prefer adaptive ARA mix. Analysis of players’ performance under ARA game task shows that better success rates can be realized in adaptive mode of ARA mixer instead of legacy ARA mix model. We show that this approach to ARA mixer is significantly more efficient than legacy ARA mixer model and that it provides an improved interactive experience through the tasks of a typical ARA application like the ARA game.
Subjects