What is the way to measure the SNR of the beamforming method?

OB requires a good separation between signal- and noise-related eigenvalues, but the SNR in this benchmark case seems to be too low for that.

What is the amplitude of the wave?

For the benchmark, A was chosen such that10 lgCmn = 71.16− ( 10− 0.34127 ff0 − 0.87242( ff0)2 + 0.16300 ( ff0)3 − 0.0082341 ( ff0)4) (9)with f0 = 1000 Hz.

What is the amplitude of the source?

(3)Since the sources are incoherent, the following expressions are obtained for the cross-powers between microphones located in ~xm = (xm, y, zm) and ~xn = (xn, y, zn), both with y = −1 m:Cmn = 12 pp∗ =|σ|22(4π)2 exp ( − ik β2 (−M(~xm − ~xn)) )

What is the geometry of the benchmark?

The geometry of this benchmark can be imagined as a large wind tunnel with 2Ö2 m2 cross-section, capable of testing full-size wind turbine blades, see Fig. 5.A line source of 2 m length was simulated through the origin of a Cartesian coordinate system (x, y, z), between z = −1 m and z = +1 m.

What frequency range is the SNR of the CSM diagonal?

The signal-to-noise ratio (SNR) varied between -25.7 dB at the lowest frequency (50 Hz) to -15.7 dB at the highest frequency (10 kHz).

What was the source strength of the CSM?

2. For the estimation of the source strength, a spatial integration over a square with a side length of 0.1 m centered at the nominal source position was suggested.

What is the difficulty with measurements of trailing edge noise in a closed wind tunnel?

This is typically the difficulty with measurements of trailing edge noise (e.g., from wind turbine blade sections) in a closed wind tunnel test section with microphones mounted flush in a wall, where sound measurements are hampered by boundary layer turbulence.

how many points are there in the array?

A(4π)2β2 √ ∆y2 + ( xm β)2 arctan z0 − zm√ ∆y2 + ( xm β )2 + arctan z0 + zm√ ∆y2 + ( xm β )2 (7)At the center of the array, where xm = zm = 0 m the authors have (since ∆y = z0 = 1 m ):Cmn = A(4π)2β2∆yπ 2 =A32πβ2∆y (8)Hence, if A = 1 Pa2m, the sound pressure level L at the array center is 74.17 dB.

How can the TUD solution be used to solve the minimization problem?

The minimization problem can be solved explicitly, yielding an expression which is almost equal to SPI without the integration threshold.

Open AccessProceedings Article10.2514/6.2017-3719

A Microphone Array Method Benchmarking Exercise using Synthesized Input Data

Ennes Sarradj, +10 more

- 05 Jun 2017

Abstract: Citation (APA) Sarradj, E., Herold, G., Sijtsma, P., Merino Martinez, R., Malgoezar, A., Snellen, M., Geyer, T. F., Bahr, C. J., Porteous, R., Moreau, D. J., & Doolan, C. J. (2017). A Microphone Array Method Benchmarking Exercise using Synthesized Input Data. In 23rd AIAA/CEAS Aeroacoustics Conference: 5-9 June 2017, Denver, Colorado [AIAA 2017-3719] American Institute of Aeronautics and Astronautics Inc. (AIAA). https://doi.org/10.2514/6.2017-3719

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Most frequently asked questions

1. What contributions have the authors mentioned in the paper "Delft university of technology a microphone array method benchmarking exercise using synthesized input data" ?

Sarradj et al. this paper presented a Microphone Array Method Benchmarking Exercise using Synthesized Input Data.

2. What is the challenge for this benchmark problem?

The challenge for this benchmark problem provided by PSA3 is to extract the acoustic signal from a line source out of array measurements that are severely affected by incoherent noise.

3. How long are the source signals simulated?

The source signals are simulated in the time domain as white noise (with a normal distribution of amplitude probability) with a duration of 10 s.

4. How many frames of samples are used to compute the CSM?

From the microphone signals, the CSM is computed using 999 frames of 1024 samples each with a Hanning window applied and 50% overlap.

Figure 2. Results for subcase a of the four point source case.

Table 1. Locations of the sources with respect to the array center (given in m).

Figure 5. Geometry of the line source benchmark.

Figure 6. Results for the line source case.

Table 3. Overview of contributions for case of four sources (all dimensions in meters).

Citations

•Journal Article•10.1007/S13272-019-00383-4

A review of acoustic imaging methods using phased microphone arrays

Roberto Merino-Martínez, +24 more

- 16 Mar 2019

- CEAS Aeronautical Journal

TL;DR: This paper provides a review of the most well-known and state-of-the-art acoustic imaging methods and recommendations on when to use them, as well as a broad overview for general aeroacoustic experts.

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307

•Journal Article•10.1017/JFM.2018.377

Noise reduction mechanisms of sawtooth and combed-sawtooth trailing-edge serrations

Francesco Avallone, +3 more

- 10 Aug 2018

- Journal of Fluid Mechanics

TL;DR: In this article, a combed-saw-tooth serration was used to mitigate turbulent boundary-layer trailing-edge noise in wind-turbine blades, and it was found that the presence of combs significantly reduced the intensity of scattered noise but not the frequency range of noise reduction.

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156

•Journal Article•10.1016/J.JSV.2018.12.010

Experimental characterization of the turbulent boundary layer over a porous trailing edge for noise abatement

Alejandro Rubio Carpio, +5 more

- 17 Mar 2019

- Journal of Sound and Vibration

TL;DR: In this paper, the authors investigated the hydrodynamic and acoustic properties of a NACA 0018 chord with solid and porous trailing edge inserts and found that the porous inserts with higher permeability provided up to 11 dB noise attenuation with respect to the solid case for frequencies below a cross-over Strouhal number St = 0.26.

...read moreread less

123

Journal Article•10.1016/J.JSV.2019.115064

Beamforming of phased microphone array for rotating sound source localization

Wei Ma, +3 more

- 17 Feb 2020

- Journal of Sound and Vibration

TL;DR: A new approach to calculate the cross-spectral matrix based on numerically solving the transcendental equation is proposed as an alternative with higher computational efficiency to the steering vector in the former algorithm.

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•Proceedings Article•10.2514/6.2015-2206

A Comparison of Microphone Phased Array MethodsApplied to the Study of Airframe Noise in WindTunnel Testing

Christopher J. Bahr, +11 more

- 22 Jun 2015

TL;DR: Results show that with appropriate handling of background noise, all advanced methods can identify dominant acoustic sources for a broad range of frequencies, and there is no obvious best method, though multiple methods may be used to bound expected behavior.

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...

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References

•Journal Article•10.1016/J.JSV.2005.12.046

A deconvolution approach for the mapping of acoustic sources (DAMAS) determined from phased microphone arrays

Thomas F. Brooks, +1 more

- 01 Jul 2006

- Journal of Sound and Vibration

TL;DR: DecDeconvolution Approach for the Mapping of Acoustic Sources (DAMAS) as mentioned in this paper removes beamforming characteristics from output presentations, and a unique linear system of equations accounts for reciprocal influence at different locations over the array survey region.

...read moreread less

833

•Proceedings Article•10.2514/6.2004-2954

A Deconvolution Approach for the Mapping of Acoustic Sources (DAMAS) Determined from Phased Microphone Arrays

Thomas F. Brooks, +1 more

- 04 May 2004

TL;DR: The Deconvolution Approach for the Mapping of Acoustic Sources (DAMAS) method removes beamforming characteristics from output presentations and appears to dramatically increase the value of arrays to the field of experimental acoustics.

...read moreread less

372

Journal Article•10.1016/J.JSV.2009.11.009

A fast signal subspace approach for the determination of absolute levels from phased microphone array measurements

Ennes Sarradj

- 26 Apr 2010

- Journal of Sound and Vibration

TL;DR: In this article, a fast beamforming method is proposed that can be used in conjunction with a phased microphone array in applications with focus on the correct quantitative estimation of acoustic source spectra.

...read moreread less

162

CLEAN based on spatial source coherence

Aiaa Paper

- 01 Jan 2007

Journal Article•10.1016/J.APACOUST.2016.09.015

A Python framework for microphone array data processing

Ennes Sarradj, +1 more

- 15 Jan 2017

- Applied Acoustics

TL;DR: Object-oriented design based on Python allows for easy-to-use scripting and graphical user interfaces, the practical combination with other data handling and scientific computing libraries, and the possibility to extend the software by implementing new processing methods with minimal effort.

...read moreread less

A Microphone Array Method Benchmarking Exercise using Synthesized Input Data

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AI Agents for this Paper

Most frequently asked questions

1. What contributions have the authors mentioned in the paper "Delft university of technology a microphone array method benchmarking exercise using synthesized input data" ?

2. What is the challenge for this benchmark problem?

3. How long are the source signals simulated?

4. How many frames of samples are used to compute the CSM?

5. What is the way to measure the SNR of the beamforming method?

6. What is the amplitude of the wave?

7. What is the amplitude of the source?

8. What is the geometry of the benchmark?

9. What frequency range is the SNR of the CSM diagonal?

10. What was the source strength of the CSM?

11. What is the difficulty with measurements of trailing edge noise in a closed wind tunnel?

12. how many points are there in the array?

13. How can the TUD solution be used to solve the minimization problem?

Figures

Citations

A review of acoustic imaging methods using phased microphone arrays

Noise reduction mechanisms of sawtooth and combed-sawtooth trailing-edge serrations

Experimental characterization of the turbulent boundary layer over a porous trailing edge for noise abatement

Beamforming of phased microphone array for rotating sound source localization

A Comparison of Microphone Phased Array MethodsApplied to the Study of Airframe Noise in WindTunnel Testing

References

A deconvolution approach for the mapping of acoustic sources (DAMAS) determined from phased microphone arrays

A Deconvolution Approach for the Mapping of Acoustic Sources (DAMAS) Determined from Phased Microphone Arrays

A fast signal subspace approach for the determination of absolute levels from phased microphone array measurements

CLEAN based on spatial source coherence

A Python framework for microphone array data processing

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