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

During the operation of the air cleaner, people in an enclosed space, generally a quiet enclosed room with a longtime exposure, expect a calm and quiet sound at low operational levels for a routine cleaning of air; in contrast, a powerful and well-cleaning, 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 seemingly contradictory expectations and demands 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 by 30% 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). Two kinds of 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 components analysis. It was found that the performance feeling was related to both low and high frequency bands; whereas the annoyance feeling was related to high frequency bands. Additionally, MSI test using the 7 scales was conducted to derive the sound quality index to express the severity of each perceptive descriptor. The annoyance index and performance index of air cleaners were modeled from the subjective responses of the juries and the measured sound quality metrics such as loudness, sharpness, roughness, and fluctuation strength. The multiple regression method with stepwise regression was employed to generate sound quality evaluation models that can predict the annoyance or performance feelings to the air cleaner sound. Using the developed sound quality indices, the sound quality of the measured data were evaluated and compared with the subjective data. The difference between predicted and test scores was usually less than 0.5 point. This validated the derived sound quality indices. By using the derived sound quality indices, it was able to modify the sound spectrum to suggest a desirable sound in the view point of sound quality.