
Auralisation of freight train-rolling-noise
It is known that the annoyance caused by the freight train pass-by noise does not depend solely on acoustic variables such as maximum or average sound pressure levels (SPL). Much of the annoyance effect is also determined by the subjective perception of the persons concerned. In order
to be able to assess this subjective annoyance in laboratory studies, corresponding noises of train pass-by events must be generated, which can then be presented to a group of subjects. In order to carry out these psychoacoustic investigations of freight train noise effects, the generated
noise events must be both realistic and representative. In the work presented here an auralisation of the dominant rolling noise is performed. The aim is to use the results to compare the annoyance of trains with different wagon compositions, equipped with gray-cast brakes and composite brakes.
In order to do so, spatial wavelength spectra of roughness of the rail and wheels are used to create artificial surface roughness. In addition, wheel flats and joins of the rails are generated. An effective roughness is determined by the speed-dependent transformation into the time domain.
This roughness excites vibrations in the rail, wheels and superstructures of the wagons. The impulse responses of the structures are modeled modally by damped harmonic oscillators, defined by amplitudes, natural frequencies and damping ratios. These parameters were determined for different
types of wheels and wagons by microphone-array pass-by measurements. Time-domain-beamforming was used to determine trajectories of individual wheels. In a second step, the emissions of individual wheels were calculated by beamforming on the moving source and the desired parameters of the harmonic
oscillators are determined for individual wheels. Pre-studies have shown that in comparison to recorded sounds the synthesized sounds provide very similar auditory impressions.
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Document Type: Research Article
Affiliations: German Aerospace Center, Germany
Publication date: 07 December 2017
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