Research & Development

Abstract

Object-based audio can be used to customize, personalize, and optimize audio reproduction depending on the specific listening scenario. To investigate and exploit the benefits of object-based audio, a framework for intelligent metadata adaptation was developed. The framework uses detailed semantic metadata that describes the audio objects, the loudspeakers, and the room. It features an extensible software tool for real-time metadata adaptation that can incorporate knowledge derived from perceptual tests and/or feedback from perceptual meters to drive adaptation and facilitate optimal rendering. One use case for the system is demonstrated through a rule-set (derived from perceptual tests with experienced mix engineers) for automatic adaptation of object levels and positions when rendering 3D content to two- and five-channel systems.

This paper was originally published at the Audio Engineering Society conference on spatial reproduction (AES Tokyo Metadapter), July 2018. It is available to AES members or to purchase from http://www.aes.org/e-lib/browse.cfm?elib=19637.

Related work is discussed in the open access journal paper “A system architecture for semantically informed rendering of object-based audio” (https://doi.org/10.17743/jaes.2019.0025).

This work was supported by the EPSRC Programme Grant S3A: Future Spatial Audio for an Immersive Listener Experience at Home (EP/L000539/1).

The paper was written in collaboration with the following authors: James Woodcock, Richard J. Hughes, Yan Tang, William J. Davies, Bruno M. Fazenda, and Trevor J. Cox (University of Salford); Andreas Franck, Dylan Menzies, Marcos F. Simón Gálvez, and Filippo M. Fazi (University of Southampton); and Philip Coleman, Hansung Kim, Qingju Liu, Tim Brookes, Russell Mason, Philip J.B. Jackson, and Adrian Hilton (University of Surrey).

This publication is part of the Immersive and Interactive Content section

Topics