Scalable Room Acoustic Modelling (SCReAM)

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We spend the majority of our lives indoors. Within enclosed spaces, sound is reflected numerous times, leading to reverberation. We are accustomed to perceiving reverberation-we unconsciously use it to navigate the space, and, when absent, we notice. Similarly, our electronic devices, such as laptops, TVs or smart home devices, are exposed to reverberation and need to take into account its presence. Being able to predict, synthesise, and control reverberation is therefore important. This is done using room acoustic models. Existing room acoustic models suffer from two main limitations. First, they were originally developed from very different starting points and for very different purposes, which has led to a highly fragmented research field where advancements in one area do not translate to advancements in other areas, slowing down research. Second, each model has a specific accuracy and a specific computational complexity, with some very accurate models taking several days to run (physical models), while others run in real-time but with low accuracy and only aim to create a pleasing reverberant sound (perceptual models). Thus, there is no single model that allows to scale continuously from one extreme to the other. This project will overcome both limitations by defining a novel, unifying room acoustic model that combines appealing properties of all main types of models and that can scale on demand from a lightweight perceptual model to a full-scale physical model. Such a SCalable Room Acoustic Model (SCReAM) will bring benefits in many applications, ranging from consumer electronics and communications, to computer games, immersive media, and architectural acoustics. The model will be able to adapt in real time, enabling end-users to get the best possible auditory experience allowed by the available computing resources. Audio software developers will not need to update their development chains once more powerful machines become available, thus reducing costs. Electronic equipment, such as hands-free devices, smart loudspeakers, and sound reinforcement systems, will be able to build a more flexible internal representation of room acoustics, allowing them to reduce unwanted echoes, to remove acoustic feedback, and/or to improve the tonal balance of reproduced sound.The main hypothesis of the project is that a connection exists between physical models and perceptual models based on so-called delay networks, and that this connection can be leveraged to develop the sought-after unifying and scalable model.The research will be conducted at the University of Surrey with industrial support by Sonos (audio consumer electronics), Electronic Arts (computer games), Audio Software Development Limited (computer games audio consultancy), and Adrian James Acoustics (acoustics consultancy).