David Kliesch: Uncertainty discussion of spatial sound field changes in auditoria

17. August 2018 | von
2019-12-20 um 11:00 – 12:00
Institut für Technische Akustik
Kopernikusstraße 5
52074 Aachen

Rooms are often characterised by room acoustic metrics which help to categorize them by required standards. Through a previous work sound elds have been sampled in set grids and datasets have been generated. It has been shown that as an example the change in the clarity index C80 between measurement positions next to each other may already exceed the just noticeable threshold of 1dB. As a common experience it is unlikely to perceive a di erence while for example moving along a row of seats in a concert hall. The reproducibility of measurements is problematic since small di erences in measurement positions may already lead to an uncertainty. GUM, the guide to expression of uncertainties in measurement, o ers a framework which is used to estimate a model function accounting for a number of in uence quantities.
According to the formulation stage of the GUM framework one approach to estimate a model function is to nd a mean value which associates any given input distance between two microphones with an expected variation in a discussed metric. Using the dataset any sampling position can be compared with each other to evaluate metrics as a function of frequency and bandwidth. The sampling locations are uncertain themselves and have to be accounted for in a re ned model function. The distance between the microphones serve as input quantity while the average change of the examined metric is estimated as output quantity. In reference to GUM’s calculation stage the model function can be used to describe the propagation of measurement uncertainties. Since the measurement function may be not strictly linear the resulting output distribution is distorted. Using Monte-Carlo simulations these newly distorted  distribution functions can be estimated. The conclusion is that boundaries for the measurement accuracy in dependence of a tolerable uncertainty can be established.

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