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

Cheung, L. Y.; Tang, S. K.

Abstract

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. © 2013 Acoustical Society of America.

Citation

Cheung, L. Y., & Tang, S. K. (2013). Neural network predictions of acoustical parameters in multi-purpose performance halls. The Journal of the Acoustical Society of America, 134(3), 2049-2065. https://doi.org/10.1121/1.4817880

• Journal Article Type: Article
• Acceptance Date: Jul 22, 2013
• Online Publication Date: Aug 26, 2013
• Publication Date: Sep 1, 2013
• Deposit Date: Jul 11, 2022
• Journal: Journal of the Acoustical Society of America
• Print ISSN: 0001-4966
• Publisher: Acoustical Society of America
• Peer Reviewed: Peer Reviewed
• Volume: 134
• Issue: 3
• Pages: 2049-2065
• DOI: https://doi.org/10.1121/1.4817880
• Public URL: https://hull-repository.worktribe.com/output/4015928