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A Study on the Sound Quality Evaluation Model of the Air Cleaner

[+] Author Affiliations
Jeong-Guon Ih, Su-Won Jang, Cheol-Ho Jeong

Korea Advanced Institute of Science and Technology, Daejeon, Korea

Youn-Young Jeung, Kye-Sup Jun

Woongjin Coway Company, Ltd., Seoul, Korea

Paper No. IMECE2007-41115, pp. 523-526; 4 pages
doi:10.1115/IMECE2007-41115
From:
  • ASME 2007 International Mechanical Engineering Congress and Exposition
  • Volume 3: Design and Manufacturing
  • Seattle, Washington, USA, November 11–15, 2007
  • Conference Sponsors: ASME
  • ISBN: 0-7918-4297-5 | eISBN: 0-7918-3812-9
  • Copyright © 2007 by ASME

abstract

In operating the air cleaner for a long time, people in a quiet enclosed space expect calm sound at low operational levels for a routine cleaning of air; in contrast, a powerful, yet not-annoying, sound is expected at high operational levels for an immediate cleaning of pollutants. In this context, it is important to evaluate and design the air cleaner noise to satisfy such contradictory expectation from the customers. In this study, a model for evaluating the air cleaner sound quality was developed based on the objective and subjective analyses. Sound signals from various air cleaners were recorded and they were edited by increasing or decreasing the loudness at three wide specific-loudness bands: 20–400 Hz (0–3.8 Bark), 400–1250 Hz (3.8–10 Bark), 1.25k–12.5k Hz bands (10–22.8 Bark). Subjective tests using the edited sounds were conducted by the semantic differential method (SDM) and the method of successive intervals (MSI). SDM test for 7 adjective pairs was conducted to find the relation between subjective feeling and frequency bands. Two major feelings, performance and annoyance, were factored out from the principal component analysis. We found that the performance feeling was related to both low and high frequency bands; whereas the annoyance feeling was related to high frequency bands. MSI test using the 7 scales was conducted to derive the sound quality index to express the severity of each perceptive descriptor. Annoyance and performance indices of air cleaners were modeled from the subjective responses of the juries and the measured sound quality metrics: loudness, sharpness, roughness, and fluctuation strength. Multiple regression method was employed to generate sound quality evaluation models. Using the developed indices, sound quality of the measured data were evaluated and compared with the subjective data. The difference between predicted and tested scores was less than 0.5 point.

Copyright © 2007 by ASME
Topics: Sound quality

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