Geometrical parameter combinations that correlate with early interaural cross-correlation coefficients in a performance hall

Abstract

The previous binaural data of the authors measured inside two multi-purpose performance halls are re-analyzed using regression in this study. It is done in an attempt to establish a framework that can improve the prediction of early interaural cross-correlation coefficients (IACCs), but with as little measurement effort and parameters as possible. The results show that regression models consist of linear combinations of polynomials of geometrical parameters, when used together with the measurement schemes suggested previously by the authors, are sufficient for predicting the IACCs to within engineering tolerance. The predictions are better than those obtained previously by the neural network approach of the authors. The relative importance of the geometrical parameters in the prediction of IACCs is also investigated.

On the performance of existing acoustic energy models when applied to multi-purpose performance halls

“…10 that the newly estimated tRs using Eq. (11) give predictions very close to those for the minimum G80 deviations. The hall average tRs and T20s given in Table 7 are used in the foregoing analysis, unless otherwise specified.…”

“…For the present two halls, the lower reverberance could lead to stronger effects of geometrical parameters other than the source-receiver distance on the variations of acoustical indices. Examples of these parameters are the source azimuthal angle and elevation angle [11] and the architectural shape [26]. Further investigations are needed to determine a reliable tR estimation method for multi-purpose performance halls.…”

“…There is recently an effort to relate the interaural cross-correlation coefficients with the geometrical dimensions of a performance hall using empirical/regression formulae [11].…”