Bayesian admittance estimation and model selection for finite-differential method

Authors
Chen, Ziqi
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Other Contributors
Braasch, Jonas
Perry, Chris (Christopher S.)
Xiang, Ning
Issue Date
2021-08
Keywords
Architecture
Degree
MS
Terms of Use
Attribution-NonCommercial-NoDerivs 3.0 United States
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute (RPI), Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
Surface acoustic impedance or admittance at the boundary is essential for estimating and evaluating a room's acoustic performance. In this article, a Bayesian-based model selection method and parameter estimation is presented to predict the admittance beyond the frequency limit of measurement. A frequency-dependent admittance model is chosen to estimate the boundary condition. The Bayesian-network sampling approach presented in this article shows the capability to predict the surface admittance boudary condition dependent on the frequency. This Bayesian method demonstrates a clear estimation of the pole number and parameters of the chosen frequency-dependent admittance model. The analysis results indicate the potential of applying the Bayesian-based method on boundary condition estimation.
Description
August 2021
School of Architecture
Department
School of Architecture
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
Access
CC BY-NC-ND. Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 license. No commercial use or derivatives are permitted without the explicit approval of the author.