@article {EGELER:2024:0736-2935:7247,
title = "Railway psychoacoustic annoyance - an indicator for planning infrastructure projects",
journal = "INTER-NOISE and NOISE-CON Congress and Conference Proceedings",
parent_itemid = "infobike://ince/incecp",
publishercode ="ince",
year = "2024",
volume = "270",
number = "4",
publication date ="2024-10-04T00:00:00",
pages = "7247-7258",
itemtype = "ARTICLE",
issn = "0736-2935",
url = "https://ince.publisher.ingentaconnect.com/content/ince/incecp/2024/00000270/00000004/art00029",
doi = "doi:10.3397/IN_2024_3936",
author = "EGELER, Jonas and HUTH, Christine and SCHLESINGER, Anton and ENDE, Christoph and KOCH, Thomas and BARTNITZEK, Jens and HOEHLE, Laura and SCHLUETER, Benjamin",
abstract = "The joint project "Real-time Calculation, Auralization and Visualization of Sound Propagation and Noise Protection Measures for Infrastructure Projects" (EAV-Infra), funded by the German Federal Ministry for Economic Affairs, investigates the potentials of digital infrastructure
planning based on Building Information Modeling (BIM) in combination with state-of-the art Auralization and Visualization technology as well as Psychoacoustic Modelling. Currently, log-scaled sound pressure levels, which are only informative to experts, are used to communicate the topic of
noise exposure to the public. Auralization, in combination with a hearing-oriented noise index, will constitute a more intuitive and understandable evaluation basis for residents. One focal point of EAV-Infra is the development of a linearly-scaled metric for railway noise, which correlates
better with human perception than the LAeq. Binaural recordings of a wide spectrum of different railway pass-by scenarios were presented in listening experiments to obtain semantic-differential information about the sounds as well as global and continuous annoyance ratings for individual passings.
Currently, a railway psychoacoustic annoyance metric is being developed by comparing two fitting methods: a classical model based on common psychoacoustic quantities, as well as an AI model with a Mel-log-spectrogram-CRNN architecture. The approach and first results will be discussed in this
contribution.",
}