A Scaleable and Adaptive Audio Service to Support Large Scale Collaborative Work and Entertainment

We describe an audio-service for collaborative network applications, designed to support many simultaneous audio sources and to operate across the Internet. Our service introduces and exploits a new technique called distributed partial mixing to dynamically adapt to varying numbers of speakers and network congestion. A collection of networked partial audio mixers is arranged as a distributed graph. Each partial mixer adaptively mixes subsets of its input audio streams into one or more mixed streams, which it forwards along with any unmixed streams. This reduces network traffic but at the cost of also reducing audio quality. Wide range of network experiments demonstrate how distributed partial mixing dynamically manages the trade-off between responsiveness, stability and TCP-fairness, and thus performing effective congestion control while maximising the audio quality for the end user. Distributed partial mixing has been integrated within a new generation large scale collaborative virtual platform (MASSIVE-3) and is since in use for Inhabited TV applications.