D. Belcher, M. Christner, and E. Shabalina, “Noise Prediction Software for Open-Air Events Part 2: Experiences and Validation,” in Proc. AES Convention 142, May 2017, Paper 9791. [Online]. Available: https://aes.org/publications/elibrary-page/?id=18665
Belcher D, Christner M, Shabalina E. Noise Prediction Software for Open-Air Events Part 2: Experiences and Validation. In: AES Convention 142. Audio Engineering Society; 2017. Paper 9791. Available from: https://aes.org/publications/elibrary-page/?id=18665
@inproceedings{Belcher2017_18665,
author = {Belcher, Daniel and Christner, Matthias and Shabalina, Elena},
title = {{Noise Prediction Software for Open-Air Events Part 2: Experiences and Validation}},
booktitle = {AES Convention 142},
note = {Paper 9791},
year = {2017},
month = may,
publisher = {Audio Engineering Society},
url = {https://aes.org/publications/elibrary-page/?id=18665}
}
TY - CPAPER
TI - Noise Prediction Software for Open-Air Events Part 2: Experiences and Validation
AU - Belcher, Daniel
AU - Christner, Matthias
AU - Shabalina, Elena
T2 - AES Convention 142
M1 - Paper 9791
PY - 2017
DA - 2017/05/06
UR - https://aes.org/publications/elibrary-page/?id=18665
PB - Audio Engineering Society
LA - en
AB - The prediction and minimization of noise in the neighborhood during the planning phase of open-air events is becoming more important. The common available software for calculating environmental noise did not consider complex summation of sound because typical noise sources in traffic or industry are not coherent. State of the art sound systems with arrays of loudspeakers and subwoofers effectively use coherence in order to achieve their high directivity. The propagation models were not only extended for complex summation, but also for import of complex data from a system design tool (see Part 1 for details). This paper presents experiences with the simulation software NoizCalc in the field since its launch, its validation by means of a comparison with accompanying measurements and a derivation of uncertainty, in order to set the informative value of a prediction into context.
ER -