Object-based audio promises format-agnostic reproduction and extensive personalization of spatial audio content. However, in practical listening scenarios, such as in consumer audio, ideal reproduction is typically not possible. To maximize the quality of listening experience, a different approach is required, for example modifications of metadata to adjust for the reproduction layout or personalization choices. This paper proposes a novel system architecture for semantically informed rendering (SIR), that combines object audio rendering with high-level processing of object metadata. In many cases, this processing uses novel, advanced metadata describing the objects to optimally adjust the audio scene to the reproduction system or listener preferences. The proposed system is evaluated with several adaptation strategies, including semantically motivated downmix to layouts with few loudspeakers, manipulation of perceptual attributes, perceptual reverberation compensation, and orchestration of mobile devices for immersive reproduction. These examples demonstrate how SIR can significantly improve the media experience and provide advanced personalization controls, for example by maintaining smooth object trajectories on systems with few loudspeakers, or providing personalized envelopment levels. An example implementation of the proposed system architecture is described and provided as an open, extensible software framework that combines object-based audio rendering and high-level processing of advanced object metadata.
Franck, Andreas; Francombe, Jon; Woodcock, James; Hughes, Richard; Coleman, Philip; Menzies, Dylan; Cox, Trevor J.; Jackson, Philip J.B.; Fazi, Filippo Maria
Affiliations: Institute of Sound and Vibration Research, University of Southampton, Southampton, Hampshire, UK; BBC Research and Development, Dock House, MediaCityUK, Salford, UK; Acoustics Research Centre, University of Salford, Salford, UK; Institute of Sound Recording, University of Surrey, Guildford, Surrey, UK; Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, Surrey, UK(See document for exact affiliation information.)
JAES Volume 67 Issue 7/8 pp. 498-509; July 2019
Publication Date: August 14, 2019
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