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

L. Y. Cheung, Shiu Keung Tang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)2049-2065
Number of pages17
JournalJournal of the Acoustical Society of America
Volume134
Issue number3
DOIs
Publication statusPublished - 1 Sep 2013

ASJC Scopus subject areas

  • Acoustics and Ultrasonics
  • Arts and Humanities (miscellaneous)

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