Analysis of a sound field in a room using dictionary learning

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The sound field in a room is often modeled as a superposition of elementary waves, such as plane or spherical waves. These wave expansions provide a powerful means to interpolate or extrapolate the sound field within (and outside) the measurement domain. However, projecting the sound field of a large domain in a room on a planar or spherical wave base yields a high number of very elemental components. We examine the use of dictionary learning to find a set of alternative basis functions that are suitable to represent the sound field enclosed in a room. The resulting dictionary is able to capture the dominant features of the sound field, and represent it using only a sparse set of functions, the dictionary atoms. In this study, high resolution measurements of the sound pressure in a room are simulated and used as a training set to learn a dictionary. We analyze the spatial properties of the learned dictionary, and compare it to simple elementary basis functions such as plane and spherical waves.
Original languageEnglish
Title of host publicationProceedings of the 23rd International Congress on Acoustics
PublisherDeutsche Gesellschaft für Akustik e.V.
Publication date2019
ISBN (Print)978-3-939296-15-7
Publication statusPublished - 2019
Event23rd International Congress on Acoustics - Eurogress, Aachen , Germany
Duration: 9 Sep 201913 Sep 2019


Conference23rd International Congress on Acoustics
Internet address

Bibliographical note

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  • Sounds field reconstruction
  • Room acoustics
  • Dictionary learning

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