An auralizator for wind turbine noise consists of an emission synthesizer,apropagation filter,avegetation noise synthesizer and as uitable reproduction system. This article describes the propagation filtering, considering the specificg eometry of ah ighly elevated source and distant receivers, and the vegetation noise synthesizer.I ti s shown that the propagation filtering can be implemented efficiently as aseries of finite impulse response filters (FIR)w ith arelative small number of taps. Measurements have revealed that awind turbine has to be modeled as largely extended source in order to correctly simulate the ground effect. Based on numerical simulations, a model to incorporate energy neutral short-time levelfluctuation effects wasderived. Due to the source extension, the fluctuations are significantly smaller than expected for aconcentrated point source. The vegetation noise synthesizer uses an emission model that predicts awind speed dependent volume-specific sound power spectrum for different tree species. The model is steered by atime varying wind speed function that is generated by arandom process with apredefined power density spectrum. PACS no. 43.28.Js, 43.60.Dh 13 ACTA ACUSTICA UNITED WITH ACUSTICA Heutschi et al.:A uralization of wind turbine noise Vol. 100 (2014)

An auralizator for wind turbine noise consists of an emission synthesizer,apropagation filter,avegetation noise synthesizer and asuitable reproduction system. This article describes the synthesis of wind turbine sounds, considering spectral content and frequencyd ependent amplitude modulation (AM).T he generated sound pressure signal is composed of discrete tonal components and amplitude modulated broadband noise, whereas the latter is processed in 1/3 octave bands. Foreach band, levelfluctuations are synthesized as the superposition of arandom process and aperiodic function with the blade passing frequency. The model uses around 120 low-precision input parameters which are obtained by signal analysis of audio recordings. The separation of the stochastic and the periodic part of the levelfluctuations is performed by using the autocorrelation function. The stochastic levelfluctuations are further investigated by making use of the cross-correlation function. In this article the signal analysis algorithms and the synthesis model are exemplarily applied to recordings from twomodern 2MWturbines. The original and the synthesized sounds are provided. The quite complexw ind turbine sound can be decomposed into only af ew components that can be described by fewp arameters. As demonstrated by al istening test, the reconstruction of the wind turbine audio signal can be easily confused with the original sound.

A measurement method for recording adaptation luminances at road tunnels under actual traffic conditions is described and the application of the results discussed. For conducting the survey, a photometer with a recording instrument was installed in a vehicle. The luminance of the road track, the tunnel walls can be recorded, and also, with a glare lens, the veiling luminance. The measurements were made from the driver's position taking into account effects caused by the vehicle (windscreen, etc.) and the speed. Recordings of a tunnel can be conducted in a relatively short time, so that the flow of traffic is not impaired. With the assessed luminance data the adaptation luminance can be calculated and different tunnels can be compared. To characterise a tunnel under physio-optical aspects, a criterion is proposed.