DocumentCode
60709
Title
Design of IIR Filters With Bayesian Model Selection and Parameter Estimation
Author
Botts, Jonathan ; Escolano, J. ; Ning Xiang
Author_Institution
Grad. Program in Archit. Acoust., Rensselaer Polytech. Inst., Troy, NY, USA
Volume
21
Issue
3
fYear
2013
fDate
Mar-13
Firstpage
669
Lastpage
674
Abstract
Bayesian model selection and parameter estimation are used to address the problem of choosing the most concise filter order for a given application while simultaneously determining the associated filter coefficients. This approach is validated against simulated data and used to generate pole-zero representations of head-related transfer functions.
Keywords
IIR filters; parameter estimation; Bayesian model selection; IIR filters design; concise Illter order; head-related transfer functions; parameter estimation; pole-zero representations; Autoregressive processes; Bayesian methods; Data models; Frequency domain analysis; Mathematical model; Parameter estimation; Transfer functions; Bayesian methods; IIR filters; Monte Carlo methods; head-related transfer function; model comparison; parameter estimation;
fLanguage
English
Journal_Title
Audio, Speech, and Language Processing, IEEE Transactions on
Publisher
ieee
ISSN
1558-7916
Type
jour
DOI
10.1109/TASL.2012.2226159
Filename
6338273
Link To Document