Efficiency and uncertainties of room-acoustic decay parameter estimation

Sound energy decay analysis plays a fundamental role in a broad range of room-acoustic applications. This paper addresses the challenges of analyzing multiple-slope energy decays often encountered in experimentally measured data. Previous efforts by Xiang et al. [J. Acoust. Soc. Am., 129, 741–752 (2011)] have established a parametric model derived from Schroeder integration, that breaks the Schroeder decay functions down to single or multiple exponential decays. Several advanced methods based on this parametric model, such as nonlinear regressions, Bayesian methods, and artificial neural networks have been developed to cope with the challenges in decay parameter estimations. Using these methods, a wide range of data resolutions can meet the need for room-acoustic decay analysis. Yet for high efficiency, acousticians can use lower resolutions, still adequately representing energy decay processes. This paper discusses conditions of representing Schroeder integration by desirable, sufficiently less data points for higher efficiency of the decay parameter estimation. At the same time, increased efficiency brings uncertainties. Within the Bayesian framework, the numerical uncertainties are investigated against those of experimental measurements. Driven by experimental data in performing arts venues, this paper quantifies uncertainties for leveraging between adequate accuracies and the analysis efficiency and discusses the probabilistic versus deterministic estimations.