A demonstrator tool of web-based virtual reality for participatory evaluation of urban sound environment

Jiang, Like; Masullo, Massimiliano; Maffei, Luigi; Meng, Fanyu; Vorländer, Michael

doi:10.1016/j.landurbplan.2017.09.007

Cited by 32 publications

(34 citation statements)

References 13 publications

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“…Similar results were reported when auralization was implemented in form of a web-based virtual reality (VR) tool, which synthesized signals based on forward modelling, resulting in artificial perception [19]. More problematically, theoretical models or empirical equations are not always achievable.…”

Section: Introductionsupporting

confidence: 58%

“…For one focus point, the output of DSB is denoted as 13 CHAPTER 2. Fundamentals 19) where ( ) is the beamformer's output signal, is the number of microphones, ( ) is the signal received by the th microphone, is its amplitude weight, = (^−^0)/ is the compensation of the time delay for the th microphone in terms of the array origin,^is the distance between the focus point of the array and the th microphone, and^0 is the distance between the focus point and the array origin. Uniform weight is used here, = 1/ .…”

Section: Delay and Sum Beamforming Dsb For A Stationary Sourcementioning

confidence: 99%

“…Higher order frequency components are unable to be detected from the spectrum. In this sense, following the idea of Pieren et al [16], the higher order tones can be synthesized based on the detected tones (Another reference is [19], which applies the proposed method of Pieren et al and synthesized car engine noise).…”

Section: Parameterizationmentioning

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: Introductionsupporting

confidence: 58%

Section: Delay and Sum Beamforming Dsb For A Stationary Sourcementioning

confidence: 99%

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Modeling of Moving Sound Sources Based on Array Measurements

Meng¹

2018

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“…Today, researchers are beginning to represent the future landscape in a controlled laboratory by using immersive virtual reality (VR) technologies for participants. In related field work, VR has verified that it can effectively reproduce visual and aural information for participatory evaluation [7][8][9][10].…”

Section: Introductionmentioning

confidence: 91%

“…It was also interesting to find that the attitude toward noise of wind turbines has no significant correlation with subjective annoyance [7][8][9][10]. This suggested a phenomenon, generally, that people have a bad impression of the noise of wind turbines in mind from first sight, even if they do not have any real experience with wind turbines in the landscape.…”

Section: Introductionmentioning

confidence: 99%

Validity of VR Technology on the Smartphone for the Study of Wind Park Soundscapes

Behm

Bill

et al. 2018

IJGI

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The virtual reality of the landscape environment supplies a high level of realism of the real environment, and may improve the public awareness and acceptance of wind park projects. The soundscape around wind parks could have a strong influence on the acceptance and annoyance of wind parks. To explore this VR technology on realism and subjective responses toward different soundscapes of ambient wind parks, three different types of virtual reality on the smartphone tests were performed: aural only, visual only, and aural-visual combined. In total, 21 aural and visual combinations were presented to 40 participants. The aural and visual information used were of near wind park settings and rural spaces. Perceived annoyance levels and realism of the wind park environment were measured. Results indicated that most simulations were rated with relatively strong realism. Perceived realism was strongly correlated with light, color, and vegetation of the simulation. Most wind park landscapes were enthusiastically accepted by the participants. The addition of aural information was found to have a strong impact on whether the participant was annoyed. Furthermore, evaluation of the soundscape on a multidimensional scale revealed the key components influencing the individual's annoyance by wind parks were the factors of "calmness/relaxation" and "naturality/pleasantness". "Diversity" of the soundscape might correlate with perceived realism. Finally, the dynamic aural-visual stimuli using virtual reality technology could improve the environmental assessment of the wind park landscapes, and thus, provide a more comprehensible scientific decision than conventional tools. In addition, this study could improve the participatory planning process for more acceptable wind park landscapes.

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