Impedance boundary conditions for pseudo-spectral time-domain methods in room acoustics

Spa, Carlos; Garriga, Adan; Escolano, José

doi:10.1016/j.apacoust.2009.11.015

Cited by 22 publications

(12 citation statements)

References 33 publications

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“…By identifying the material surfaces, we are able to ascertain the absorption coefficients of surfaces simultaneously. To identify the material surfaces, we used a classification number (1)(2)(3)(4)(5) Results of analyses show that the optimum network [4,6,1] with MSE ≤ 0.0018 and correlation coefficient r ≥ 0.9 was obtained for both training and validation subsets. To confirm their performance, the testing subset (39 surface images) showed MSE ≤ 0.07 with correlation coefficient r ≥ 0.9.…”

Section: Resultsmentioning

confidence: 99%

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Practical Subsystems to Identify Room Dimensions and Material Surfaces Using Photograph Images for Room Acoustic Simulations

Yahya¹,

Otsuru²,

Tomiku³

et al. 2012

AIJ J. Technol. Des.

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A practical technique for simulating room acoustics parameters is proposed. The technique comprises Subsystems 1 and 2, each of which uses photographic images. Subsystem 1 uses a Gray Level Cooccurrence Matrix and a Feed Forward Neural Network to identify material surfaces. Subsystem 2 uses a Dimension Vision Predictor with the author's "ruler method" to identify the dimensions. Examinations conducted in practical rooms revealed good correlation coefficients of r ≥ 0.90 for Subsystem 1 and r ≥ 0.99 for Subsystem 2. Finally, simulations of reverberation times were conducted using Finite Element Analysis (FEA) with identified parameters. Sufficient agreement was confirmed.

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Section: Resultsmentioning

confidence: 99%

“…The FEA numerical technique, well known in acoustical fields, has been used in many fields to obtain the sound field of a room with high accuracy 6,7) . The FEA method used for this study was time domain analysis.…”

Section: Finite Element Analysis (Fea)mentioning

confidence: 99%

Practical Subsystems to Identify Room Dimensions and Material Surfaces Using Photograph Images for Room Acoustic Simulations

Yahya¹,

Otsuru²,

Tomiku³

et al. 2012

AIJ J. Technol. Des.

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“…Detailed explanations of the experiment can be found in Refs. [28], [29]. In this case, we tested the DIF boundary conditions with two digital filters.…”

Section: A Validation Testsmentioning

confidence: 99%

A GPU Implementation of an Explicit Compact FDTD Algorithm with a Digital Impedance Filter for Room Acoustics Applications

Spa

Rey²,

Hernández

2015

IEEE/ACM Trans. Audio Speech Lang. Process.

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In recent years, computational engineering has undergone great changes due to the development of the Graphics Processing Unit (GPU) technology. For example, in room acoustics, the wave-based methods, that formerly were considered too expensive for 3-D impulse response simulations, are now chosen to exploit the parallel nature of GPU devices considerably reducing the execution time of the simulations. There exist contributions related to this topic that have explored the performance of different GPU algorithms, however, the computational analysis of a general explicit model that incorporates algorithms with different neighboring orders and a general frequency dependent impedance boundary model has not been properly developed. In this paper, we present a GPU implementation of a complete room acoustic model based on a family of explicit Finite-Difference Time-Domain (FDTD) algorithms. We first develop a strategy for implementing a Frequency Independent (FI) impedance model which is free from thread divergences and then, we extend the model adding a Digital Impedance Filter (DIF) boundary subroutine able to compute the acoustic pressure of different nodes such as corners or edges without an additional performance penalty. Both implementations are validated and deeply analyzed by performing different 3-D numerical experiments. Finally, we define a performance metric which is able to objectively measure the computing throughput of a FDTD implementation using a simple number. The robustness of this metric allows us to compare algorithms even if these have been run in different GPU cards or have been formulated with other explicit models.

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“…We mention that a nonlinear damping is imposed on the boundary. For a more physical explanation of the system (1.1)-(1.6), we refer the reader to the earlier literature [1][2][3][4] and recent literature [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Well-posedness for a variable-coefficient wave equation with nonlinear damped acoustic boundary conditions

2012

Nonlinear Analysis: Theory, Methods & Applications

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Impedance boundary conditions for pseudo-spectral time-domain methods in room acoustics

Cited by 22 publications

References 33 publications

Practical Subsystems to Identify Room Dimensions and Material Surfaces Using Photograph Images for Room Acoustic Simulations

Practical Subsystems to Identify Room Dimensions and Material Surfaces Using Photograph Images for Room Acoustic Simulations

A GPU Implementation of an Explicit Compact FDTD Algorithm with a Digital Impedance Filter for Room Acoustics Applications

Well-posedness for a variable-coefficient wave equation with nonlinear damped acoustic boundary conditions

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