A framework for intelligent metadata adaptation in object-based audio

摘要

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.