Effects of test method and participant musical training on preference ratings of stimuli with different reverberation times

Selecting an appropriate listening test design for concert hall research depends on several factors, including listening test method and participant critical-listening experience. Although expert listeners afford more reliable data, their perceptions may not be broadly representative. The present paper contains two studies that examined the validity and reliability of the data obtained from two listening test methods, a successive and a comparative method, and two types of participants, musicians and non-musicians. Participants rated their overall preference of auralizations generated from eight concert hall conditions with a range of reverberation times (0.0-7.2 s). Study 1, with 34 participants, assessed the two methods. The comparative method yielded similar results and reliability as the successive method. Additionally, the comparative method was rated as less difficult and more preferable. For study 2, an additional 37 participants rated the stimuli using the comparative method only. An analysis of variance of the responses from both studies revealed that musicians are better than non-musicians at discerning their preferences across stimuli. This result was confirmed with a k-means clustering analysis on the entire dataset that revealed five preference groups. Four groups exhibited clear preferences to the stimuli, while the fifth group, predominantly comprising non-musicians, demonstrated no clear preference.

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

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.

Subjective ranking of concert halls substantiated through orthogonal objective parameters

This paper studies the global subjective assessment, obtained from mean values of the results of surveys addressed to members of the audience of live concerts in Spanish auditoriums, through the mean values of the three orthogonal objective parameters (Tmid, IACCE3, and LEV), expressed in just noticeable differences (JNDs), regarding the best-valued hall. Results show that a linear combination of the relative variations of orthogonal parameters can largely explain the overall perceived quality of the sample. However, the mean values of certain orthogonal parameters are not representative, which shows that an alternative approach to the problem is necessary. Various possibilities are proposed.

Neural network predictions of acoustical parameters in multi-purpose performance halls

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.