Index of content:
Volume 134, Issue 3, September 2013
- ARCHITECTURAL ACOUSTICS 
Optimizing stepwise rotation of dodecahedron sound source to improve the accuracy of room acoustic measures134(2013); http://dx.doi.org/10.1121/1.4817879View Description Hide Description
Dodecahedron sound sources are widely used for acoustical measurement purposes as they produce a good approximation of omnidirectional radiation. Evidence shows that such an assumption is acceptable only in the low-frequency range (namely below 1 kHz), while at higher frequencies sound radiation is far from being uniform. In order to improve the accuracy of acoustical measurements obtained from dodecahedron sources, international standard ISO 3382 suggests an averaging of results after a source rotation. This paper investigates the effects of such rotations, both in terms of variations in acoustical parameters and spatial distribution of sound reflections. Taking advantage of a spherical microphone array, the different reflection patterns were mapped as a function of source rotation, showing that some reflections may be considerably attenuated for different aiming directions. This paper investigates the concept of averaging results while changing rotation angles and the minimum number of rotations required to improve the accuracy of the average value. Results show that averages of three measurements carried out at 30° angular steps are closer to actual values and show much less fluctuation. In addition, an averaging of the directional intensity components of the selected responses stabilizes the spatial distribution of the reflections.
134(2013); http://dx.doi.org/10.1121/1.4817880View Description Hide Description
A detailed binaural sound measurement was carried out in two multi-purpose performance halls of different seating capacities and designs in Hong Kong in the present study. The effectiveness of using neural network in the predictions of the acoustical properties using a limited number of measurement points was examined. The root-mean-square deviation from measurements, statistical parameter distribution matching, and the results of a t-test for vanishing mean difference between simulations and measurements were adopted as the evaluation criteria for the neural network performance. The audience locations relative to the sound source were used as the inputs to the neural network. Results show that the neural network training scheme using nine uniformly located measurement points in each specific hall area is the best choice regardless of the hall setting and design. It is also found that the neural network prediction of hall spaciousness does not require a large amount of training data, but the accuracy of the reverberance related parameter predictions increases with increasing volume of training data.