Parameter optimization of forward sound propagation models using Bayesian inference for sound field control purposes

Research output: Research - peer-reviewArticle in proceedings – Annual report year: 2018

Documents

View graph of relations

Sound field control in outdoor concerts requires accurate estimates of the transfer functions between sources and receivers. Feed forward approaches are based on direct measurements of the transfer functions in a dense grid of points. This makes them intractable for large scale situations, showing the need of propagation models in order to characterize the sound field in such large areas. Uncertainty in the parameters introduced in the propagation models, such as meteorological, acoustical and geometrical ones, lead to inaccurate estimates of the transfer functions and therefore to a poor performance of the sound field control strategy. In this paper we present first results of the method introduced by Heuchel et al. [1] to increase the accuracy of the predictions. The parameters of the propagation model are optimized through auxiliary measurements and Bayesian inference.
Original languageEnglish
Title of host publicationEuronoise 2018 Proceedings
Number of pages8
Publication date2018
Pages2301-2308
StatePublished - 2018
EventEuronoise 2018 - Convention Center Creta Maris, Hersonissos, Crete, Greece
Duration: 27 May 201831 May 2018

Conference

ConferenceEuronoise 2018
LocationConvention Center Creta Maris
CountryGreece
CityHersonissos, Crete
Period27/05/201831/05/2018
Download as:
Download as PDF
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
PDF
Download as HTML
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
HTML
Download as Word
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
Word

Download statistics

No data available

ID: 151020506