Data-driven room-acoustic modelling

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Volume Title
School of Electrical Engineering | Doctoral thesis (article-based) | Defence date: 2024-07-04
Date
2024
Major/Subject
Mcode
Degree programme
Language
en
Pages
65 + app. 67
Series
Aalto University publication series DOCTORAL THESES, 133/2024
Abstract
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.
Description
Supervising professor
Pulkki, Ville, Prof., Aalto University, Department of Information and Communications Engineering, Finland
Thesis advisor
Schlecht, Sebastian J., Prof., Department of Information and Communications Engineering, Aalto University, Finland
Keywords
room acoustics, sound-energy decay, inhomogeneous and anisotropic late reverberation, autonomous measurement robots, automatic data acquisition, machine learning, late-reverberation rendering
Other note
Parts
  • [Publication 1]: Georg Gotz, Abraham Martinez Ornelas, Sebastian J. Schlecht, and Ville Pulkki. Autonomous robot twin system for room acoustic measurements. Journal of the Audio Engineering Society, Vol. 69, no. 4, pp. 261–272, April 2021.
    DOI: 10.17743/jaes.2021.0002 View at publisher
  • [Publication 2]: Georg Gotz, Ishwarya Ananthabhotla, Sebastia V. Amengual Gari, and Paul Calamia. Autonomous room acoustic measurements using rapidly-exploring random trees and Gaussian processes. In Proceedings of the 10th Convention of the European Acoustics Association (Forum Acusticum), Turin, Italy, pp. 1655–1662, September 2023.
    DOI: 10.61782/fa.2023.0796 View at publisher
  • [Publication 3]: Georg Gotz, Ricardo Falcon Perez, Sebastian J. Schlecht, and Ville Pulkki. Neural network for multi-exponential sound energy decay analysis. Journal of the Acoustical Society of America, Vol. 152, no. 2, pp. 942–953, August 2022.
    DOI: 10.1121/10.0013416 View at publisher
  • [Publication 4]: Georg Gotz, Sebastian J. Schlecht, and Ville Pulkki. Common-slope modeling of late reverberation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 31, pp. 3945–3957, September 2023.
    DOI: 10.1109/TASLP.2023.3317572 View at publisher
  • [Publication 5]: Georg Gotz, Teodors Kerimovs, Sebastian J. Schlecht, and Ville Pulkki. Dynamic late reverberation rendering using the commonslope model. In Proceedings of the AES 6th International Conference on Audio for Games, Tokyo, Japan, April 2024.
Citation