Inversion for acoustic impedance of a wall by using artificial neural network

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

A new approach for measuring acoustic impedance is developed by using artificial neural network (ANN) algorithm. Instead of using impedance tube, a rectangular room or a box is simulated with known boundary conditions at some boundaries and an unknown acoustic impedance at one side of the wall. A training data basis for the ANN algorithm is evaluated by similar source method which was developed earlier by Too and Su [Too G-PJ, Su T-K. Estimation of scattering sound field via nearfield measurement by source methods. Appl Acoust. 1999;58:261-81 (SCI) (EI)] for the estimation of interior and exterior sound field. The training data basis is constructed by evaluating of acoustic pressure at a field point with various acoustic impedance conditions at one side of the wall. Then, the inversion for unknown acoustic impedance of a wall is performed by measuring several field data and substituting these data into ANN algorithm. The simulation result indicates that the prediction of acoustic impedance is very accurate with error percentage under 1%. In addition, one field point measurement in the present approach for acoustic impedance provides more straightforward and easier evaluation than that in the two point measurement of impedance tube.

Original languageEnglish
Pages (from-to)377-389
Number of pages13
JournalApplied Acoustics
Volume68
Issue number4
DOIs
Publication statusPublished - 2007 Apr 1

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Acoustic impedance
acoustic impedance
inversions
Neural networks
Acoustic fields
sound fields
education
impedance
tubes
rooms
boxes
Acoustics
Boundary conditions
Scattering
boundary conditions
acoustics
evaluation
predictions
scattering
simulation

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering
  • Acoustics and Ultrasonics

Cite this

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abstract = "A new approach for measuring acoustic impedance is developed by using artificial neural network (ANN) algorithm. Instead of using impedance tube, a rectangular room or a box is simulated with known boundary conditions at some boundaries and an unknown acoustic impedance at one side of the wall. A training data basis for the ANN algorithm is evaluated by similar source method which was developed earlier by Too and Su [Too G-PJ, Su T-K. Estimation of scattering sound field via nearfield measurement by source methods. Appl Acoust. 1999;58:261-81 (SCI) (EI)] for the estimation of interior and exterior sound field. The training data basis is constructed by evaluating of acoustic pressure at a field point with various acoustic impedance conditions at one side of the wall. Then, the inversion for unknown acoustic impedance of a wall is performed by measuring several field data and substituting these data into ANN algorithm. The simulation result indicates that the prediction of acoustic impedance is very accurate with error percentage under 1{\%}. In addition, one field point measurement in the present approach for acoustic impedance provides more straightforward and easier evaluation than that in the two point measurement of impedance tube.",
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Inversion for acoustic impedance of a wall by using artificial neural network. / Too, Gee-Pinn James; Chen, S. R.; Hwang, Sheng-Jye.

In: Applied Acoustics, Vol. 68, No. 4, 01.04.2007, p. 377-389.

Research output: Contribution to journalArticle

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