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AAR aims to seamlessly merge virtual sound events into the listener’s real environment. To this end, various audio rendering models can be used to spatialise virtual sound events in real time and apply reverberation effects that match the acoustic properties of the real environment. The quality of experience of the listeners will strongly depend on the coherence between the acoustical cues conveyed by the real sound sources and the perception of the spatial processing applied to the virtual events. On the basis of an experiment simulating an AAR use case, the presentation compares the respective merits and constraints of two rendering approaches and analyses the objective and perceptual factors that contribute to the overall quality of the experience, particularly from the point of view of ‘plausibility’.
As part of the project HAIKUS (ANR-19-CE23-0023), funded by the French national research agency, IRCAM, LORIA and IJLRA organized a one-day workshop focusing on methodological advances for Audio Augmented Reality and its applications.
Audio Augmented Reality (AAR) seeks to integrate computer-generated and/or pre-recorded auditory content into the listener's real-world environment. Hearing plays a vital role in understanding and interacting with our spatial environment. It significantly enhances the auditory experience and increases user engagement in Augmented Reality (AR) applications, particularly in artistic creation, cultural mediation, entertainment and communication industries.
Audio-signal processors are a key component of the AAR workflow, as they are required for real-time control of 3D sound spatialisation and artificial reverberation applied to virtual sound events. These tools have now reached a level of maturity, capable of supporting large multichannel loudspeaker systems as well as binaural rendering on headphones. However, the accuracy of the spatial processing applied to virtual sound objects is essential to ensure their seamless integration into the listener's real environment, thereby guaranteeing a high-quality user experience. To achieve this level of integration, methods are needed to identify the acoustic properties of the environment and adjust the spatialization engine's parameters accordingly. Ideally, such methods should enable automatic inference of the acoustic channel's characteristics, based solely on live recordings of the natural, and often dynamic, sounds present in the real environment (e.g. voices, noise, ambient sounds, moving sources). These topics are gaining increasing attention, especially in light of recent advances on data-driven approaches within the field of acoustics. In parallel, perceptual studies are conducted to define the level of requirements needed to guarantee a coherent sound experience.
Organising committee: Antoine Deleforge (INRIA), François Ollivier (MPIA-IJLRA), Olivier Warusfel (IRCAM)
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