Department of Building Services Engineering, The Hong Kong Polytechnic University, Hong Kong, China.
J Acoust Soc Am. 2013 Sep;134(3):2049-65. doi: 10.1121/1.4817880.
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.
本研究在香港的两个具有不同容纳人数和设计的多用途表演厅进行了详细的双耳声音测量。研究考察了在有限数量的测量点的情况下使用神经网络对声音特性进行预测的有效性。采用均方根偏差、统计参数分布匹配以及模拟和测量之间均值差异消失的 t 检验结果作为神经网络性能的评估标准。将观众相对于声源的位置用作神经网络的输入。结果表明，无论表演厅的设置和设计如何，使用每个特定厅区域中的九个均匀分布的测量点的神经网络训练方案都是最佳选择。还发现，神经网络对厅内空间感的预测不需要大量的训练数据，但与混响相关的参数预测的准确性随着训练数据量的增加而提高。
