Perceptually Optimised Virtual Acoustics

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents the development of a method of perceptually optimising the acoustics and reverb of a virtual space. A spatial filtering technique was developed to group artificially rendered reflections by what spatial attribute they contribute to e.g. apparent source width, distance, loudness, colouration etc. The current system alters the level of different reflection groups depending on the desired type of optimisation. It is hoped that in the future this system could be coupled with machine learning techniques, such that it is able to determine the initial perceptual qualities of the artificial reverb, then optimise the acoustics depending on the user’s needs. Such a system could ultimately be used to universally identify what spatial qualities are good and bad, then generically optimise the acoustics automatically.
Original languageEnglish
Title of host publicationProceeding of the 4th Workshop on Intelligent Music Production
Number of pages4
Publication statusPublished - 10 Sep 2018

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acoustics
machine learning
spatial filtering
loudness
color
optimization

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Johnson, D., & Lee, H. (2018). Perceptually Optimised Virtual Acoustics. In Proceeding of the 4th Workshop on Intelligent Music Production
Johnson, Dale ; Lee, Hyunkook. / Perceptually Optimised Virtual Acoustics. Proceeding of the 4th Workshop on Intelligent Music Production. 2018.
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Johnson, D & Lee, H 2018, Perceptually Optimised Virtual Acoustics. in Proceeding of the 4th Workshop on Intelligent Music Production.

Perceptually Optimised Virtual Acoustics. / Johnson, Dale; Lee, Hyunkook.

Proceeding of the 4th Workshop on Intelligent Music Production. 2018.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - This paper presents the development of a method of perceptually optimising the acoustics and reverb of a virtual space. A spatial filtering technique was developed to group artificially rendered reflections by what spatial attribute they contribute to e.g. apparent source width, distance, loudness, colouration etc. The current system alters the level of different reflection groups depending on the desired type of optimisation. It is hoped that in the future this system could be coupled with machine learning techniques, such that it is able to determine the initial perceptual qualities of the artificial reverb, then optimise the acoustics depending on the user’s needs. Such a system could ultimately be used to universally identify what spatial qualities are good and bad, then generically optimise the acoustics automatically.

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BT - Proceeding of the 4th Workshop on Intelligent Music Production

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Johnson D, Lee H. Perceptually Optimised Virtual Acoustics. In Proceeding of the 4th Workshop on Intelligent Music Production. 2018