TY - JOUR
T1 - A perceptual dissimilarities based nonlinear sound quality model for range hood noise
AU - Li, Han
AU - Chen, Kean
AU - Wang, Xue
AU - Gao, Yan
AU - Yu, Weiwei
N1 - Publisher Copyright:
© 2018 Acoustical Society of America.
PY - 2018/10/1
Y1 - 2018/10/1
N2 - The application of sound quality in household appliances has gradually increased in recent years. In addition to modeling algorithms, appropriate acoustic metrics that characterize product sounds also play an important role in developing models. In this study, an artificial neural network based sound quality model for range hood noise was established with the combination of prior metric selection by multidimensional scaling (MDS) analysis of perceptual dissimilarities. First, sounds in different environments, speeds, and positions were recorded, and their annoyance was evaluated by grouped anchor semantic differential subjective jury testing. Then, the timbre space underlying dissimilarity judgments were analyzed by CLASCAL, an accurate MDS algorithm. Each dimension of the space was well explained by some metrics through stepwise regression. Finally, a sound quality model was established based on a back propagation neural network (BPNN). Results show that the combination of BPNN and CLASCAL can address the interpretation of the sound quality model and the ability to model nonlinearity for high accuracy. In addition, the application of noise control on range hoods showed that passive and active noise control (ANC) measures improve sound quality, especially ANC systems.
AB - The application of sound quality in household appliances has gradually increased in recent years. In addition to modeling algorithms, appropriate acoustic metrics that characterize product sounds also play an important role in developing models. In this study, an artificial neural network based sound quality model for range hood noise was established with the combination of prior metric selection by multidimensional scaling (MDS) analysis of perceptual dissimilarities. First, sounds in different environments, speeds, and positions were recorded, and their annoyance was evaluated by grouped anchor semantic differential subjective jury testing. Then, the timbre space underlying dissimilarity judgments were analyzed by CLASCAL, an accurate MDS algorithm. Each dimension of the space was well explained by some metrics through stepwise regression. Finally, a sound quality model was established based on a back propagation neural network (BPNN). Results show that the combination of BPNN and CLASCAL can address the interpretation of the sound quality model and the ability to model nonlinearity for high accuracy. In addition, the application of noise control on range hoods showed that passive and active noise control (ANC) measures improve sound quality, especially ANC systems.
UR - http://www.scopus.com/inward/record.url?scp=85055434099&partnerID=8YFLogxK
U2 - 10.1121/1.5064280
DO - 10.1121/1.5064280
M3 - 文章
C2 - 30404500
AN - SCOPUS:85055434099
SN - 0001-4966
VL - 144
SP - 2300
EP - 2311
JO - Journal of the Acoustical Society of America
JF - Journal of the Acoustical Society of America
IS - 4
ER -