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. 2017 Nov 28;7:16455. doi: 10.1038/s41598-017-16651-4

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© The Author(s) 2017

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Contribution of acoustic features to material identification. Comparisons of the contributory weights on each acoustic feature under constant (horizontal axis) and varying (vertical axis) room conditions. Each panel represent one feature as indicated above the panel. Each circle represents one participant. The error bars indicate the 95% bootstrap confidence intervals. The contribution of the spectral bandwidth to material identification was larger under the varying room condition (p < 0.05), and that of spectral roughness was larger under the constant room condition (p < 0.01). The contributions of the attack time and decay rate did not differ statistically between the conditions (p > 0.1). **p < 0.01, *p < 0.05.