The Effects of Tones in Noise on Human Annoyance and Performance

Authors

Joonhee Lee, University of Nebraska - LincolnFollow

Date of this Version

Spring 4-22-2016

Document Type

Article

Citation

Joonhee Lee. "The Effects of Tones in Noise on Human Annoyance and Performance." (2016).

Comments

A DISSERTATION Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Doctor of Philosophy, Major: Architectural Engineering, Under the Supervision of Professor Lily M. Wang. Lincoln, Nebraska: May, 2016

Copyright © 2016 Joonhee Lee

Abstract

Building mechanical equipment often generates prominent tones because most systems include rotating parts like fans and pumps. These tonal noises can cause unpleasant user experiences in spaces and, in turn, lead to increased complaints by building occupants. Currently, architectural engineers can apply the noise criteria guidelines in standards or publications to achieve acceptable noise conditions for assorted types of spaces. However, these criteria do not apply well if the noise contains perceptible tones. The annoyance thresholds experienced by the general population with regards to the degree of tones in noise is a significant piece of knowledge that has not been well-established. Thus, this dissertation addresses the relationship between human perception and noises with tones in the built environment.

Four phases of subjective testing were conducted in an indoor acoustic testing chamber at the University of Nebraska to achieve the research objective. The results indicate that even the least prominent tones in noises can significantly decrease the cognitive performance of participants on a mentally demanding task. Factorial repeated-measures analysis of variance of test results have proven that tonality has a crucial influence on working memory capacity of subjects, whereas loudness levels alone did not. A multidimensional annoyance model, incorporating psycho-acoustical attributes of noise in addition to loudness and tonality, has been proposed as a more accurate annoyance model.

Advisor: Lily M. Wang