Browsing by Author "Schlecht, Sebastian J., Prof., Department of Information and Communications Engineering, Aalto University, Finland"

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Data-driven room-acoustic modelling
(2024) Götz, Georg
School of Electrical Engineering | Doctoral dissertation (article-based)
The study of room acoustics has traditionally been of interest in architectural planning and design. With the spread of virtual- and augmented-reality technology, room-acoustic modelling has also become increasingly relevant for audio engines. The dynamic and fast-paced nature of such applications requires rendering systems to operate in real-time. However, accurate state-of-theart room-acoustic-simulation technology is often computationally expensive, limiting its use for audio engines. Data-driven methods offer the potential to bypass expensive simulations, while ensuring convincing perceptual experiences. This dissertation works towards data-driven audio engines by exploring the interaction between room-acoustic modelling and data-driven methods. It comprises five peer-reviewed publications that investigate automatic data acquisition, robust room-acoustic analysis in complex environments, and data-driven room-acoustics rendering. As sound propagates through a room, it interacts with various surfaces, leading to a gradual energy decay over time. The properties of this energy decay significantly influence the acoustic impression evoked by a room, making it a widely studied topic in room-acoustic research. The first part of this thesis provides an overview of sound-energy decay, its analysis, and challenges associated with complex geometries featuring multiple rooms and non-uniform absorption-material distributions. To this end, it introduces a neural network for multi-exponential sound-energy-decay analysis. Moreover, spatial and directional variations of sound-energy decay are investigated, and a compact representation to model them is proposed. The second part of this thesis is centred around data-driven methods and explores how they can be applied to room-acoustics research. After elaborating on the properties of room-acoustic data, techniques for its large-scale acquisition are investigated. Two of the contained publications describe autonomous robot systems for conducting room-acoustic measurements. While the first one describes the general idea and the design constraints of a practical system, the second one extends the measurement strategy to complex geometries featuring multiple connected rooms. An overview of commonly used machine-learning concepts is provided, focusing on the ones relevant for the included publications. Finally, several applications of data-driven methods in roomacoustics research are described, including a summary of a late-reverberation rendering system proposed in one of the appended publications.