Skip to Main content Skip to Navigation
Conference papers

Physically-based auralization of railway rolling noise

Abstract : Railway noise contributes significantly to noise pollution both outside and within cities. In recent years, prediction models have been developed to study exposure levels and evaluate abatement solutions. Going one step further, auralization may provide an effective mean for evaluating perceptually the impact of railway noise on the soundscape near existing or future infrastructures. This paper extends railway noise emission models to propose an auralization approach based on physical parameters. As a first step, the approach focuses on rolling noise radiated by the track and wheels, which represents the main noise source over a wide range of speed. The excitation of the wheel/rail system by surface roughness is modeled in the time domain based on the system mobilities. Next, rail emission is modeled as a set of discrete coherent monopoles, while the wheel contribution uses resonant filters based on its structural response. Finally, the contribution of track sleepers is included following the standard TWINS model. Validations of the approach compare auralized pass-by levels with measured data. Preliminary results from listening tests evaluating the realism of auralized pass-by noise samples are also presented.
Complete list of metadatas

Cited literature [10 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-02405722
Contributor : Nadine Martin <>
Submitted on : Wednesday, December 11, 2019 - 6:34:02 PM
Last modification on : Thursday, July 9, 2020 - 5:02:04 PM
Long-term archiving on: : Thursday, March 12, 2020 - 11:07:24 PM

File

000819.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-02405722, version 1

Collections

Citation

Julien Maillard, Abbès Kacem, Nadine Martin, Baldrik Faure. Physically-based auralization of railway rolling noise. Proeedings of the 23rd International Congress on Acoustics, Sep 2019, Aachen, Germany. ⟨hal-02405722⟩

Share

Metrics

Record views

54

Files downloads

55