Quantitative measurement of pass-by noise radiated by vehicles running at high speeds

Yang, Daoguo; Wang, Ziteng; Li, Bing; Luo, Y.; Lian, Xiaojuan

doi:10.1016/j.jsv.2010.10.001

Cited by 46 publications

(23 citation statements)

References 11 publications

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“…Therefore, the resolution is not as high as the localization results using DSB reported in the literature, e.g. [32,90] for cars or [33] for trains, which illustrated the localization results in frequency bands. In the following section, the localization is also be shown in frequency bands, i.e.…”

Section: Rms Mapmentioning

confidence: 56%

“…This is the so-called Short-time Fourier transform (STFT), which overcomes the limitation of FT on the loss of time information after the time-frequency transformation [61]. By the inner product between the signal x(t) and the window function g(t), the STFT is defined as 32) where is the hop size of the window function. STFT delivers also the time information with the trade-off between time and frequency resolutions.…”

Section: Short-time Fourier Transformmentioning

confidence: 99%

“…The detected sources can be regarded as equivalent sound sources. Using RMS values saves the computational cost and produces fewer sound sources compared with localization in frequency bands, wherein the source position could be frequency dependent [32]. Although objective evaluation criteria are able to take human hearing perception into account, listening tests are still required to evaluate the modeled moving sources and auralizations.…”

Section: Rms Mapmentioning

confidence: 99%

“…Researchers have also been studying the localization for moving sources in the past decades [29,30,31,32,33] by eliminating the Doppler effect [34,35,36]. In addition, Sijtsma et al [37] introduced the time-domain transfer function (TDTF) incorporated with Doppler effect to enable the localization of moving sources with arbitrary motions, for example, acceleration and circular motions [37].…”

Section: Introductionmentioning

confidence: 99%

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Modeling of Moving Sound Sources Based on Array Measurements

Meng¹

2018

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When auralizing moving sound sources in Virtual Reality (VR) environments, the two main input parameters are the location and radiated signal of the source. An array measurement-based model is developed to characterize moving sound sources regarding the two parameters in this thesis. This model utilizes beamforming, i.e. delay and sum beamforming (DSB) and compressive beamforming (CB) to obtain the locations and signals of moving sound sources. A spiral and a pseudorandom microphone array are designed for DSB and CB, respectively, to yield good localization ability and meet the requirement of CB. The de-Dopplerization technique is incorporated in the time-domain DSB to address moving source problems. Time-domain transfer functions (TDTFs) are calculated in terms of the spatial locations within the steering window of the moving source. TDTFs then form the sensing matrix of CB, thus allowing CB to solve moving source problem. DSB and CB are further extended to localize moving sound sources, and the reconstructed signals from the beamforming outputs are investigated to obtain the source signals. Moreover, localization and signal reconstruction are evaluated through varying parameters in the beamforming procedures, i.e. steering position, steering window length and source speed for a moving periodic signal using DSB, and regularization parameter, signal to noise ratio (SNR), steering window length, source speed, array to source motion trajectory and mismatch for a moving engine signal using CB. The parameter studies show guidelines of parameter selection based on the given situations in this thesis for modeling moving source using beamforming. Both algorithms are able to reconstruct the moving signals in the given scenarios. Although CB outperforms DSB in terms of signal reconstruction under particular conditions, the localization abilities of the two algorithms are quite similar. The practicability of the model has been applied on pass-by measurements of a moving loudspeaker using the designed arrays, and the results can match the conclusions drawn above from simulations. Finally, a framework on how to apply the model for moving source auralization is proposed.I

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Section: Rms Mapmentioning

confidence: 56%

Section: Short-time Fourier Transformmentioning

confidence: 99%

Section: Rms Mapmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling of Moving Sound Sources Based on Array Measurements

Meng¹

2018

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“…[7] If these are the cases, separation of the sound sources might be useful by independent component analysis. [8] The PFs of Fig.…”

Section: Curve-fitting By a Sigmoidmentioning

confidence: 99%

Analysis of Formula 1 Sound by Doppler Effect

Lee¹

2013

THE JOURNAL OF THE ACOUSTICAL SOCIETY OF KOREA

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The sound generated from the formula 1 (F1) machine is characteristic in that the frequency felt by the listener changes drastically by the Doppler effect as the machine bypasses him. In textbooks, longitudinal Doppler effect is usually described. In this paper, we consider a more general case where the listener is away from the rectilinear path of the machine's motion. As the machine bypasses the listener, in this case, the frequency drops not abruptly but gradually, the temporal width for the frequency drop depending on the machine's speed. The frequency shift felt by the listener is analyzed as functions of time and/or machine location in our study. The machine's speed is estimated from the frequency analysis of an actual F1 sound.

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Acoustic Holography

and

Choi

2013

Sound Visualization and Manipulation

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Quantitative measurement of pass-by noise radiated by vehicles running at high speeds

Cited by 46 publications

References 11 publications

Modeling of Moving Sound Sources Based on Array Measurements

Modeling of Moving Sound Sources Based on Array Measurements

Analysis of Formula 1 Sound by Doppler Effect

Acoustic Holography

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