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Reliability improvement of a sound quality index for a vehicle HVAC system using a regression and neural network model

Authors
  • Yoon, Ji-Hyun
  • Yang, In-Hyung
  • Jeong, Jae-Eun
  • Park, Sang-Gil
  • Oh, Jae-Eung
Type
Published Article
Journal
Applied Acoustics
Publisher
Elsevier
Publication Date
Jan 01, 2012
Accepted Date
Feb 26, 2012
Volume
73
Issue
11
Pages
1099–1103
Identifiers
DOI: 10.1016/j.apacoust.2012.02.018
Source
Elsevier
Keywords
License
Unknown

Abstract

The reduction of vehicle interior noise has long been the main interest of noise and vibration harshness (NVH) engineers. A driver’s perception of vehicle noise is largely affected by psychoacoustic noise characteristics and SPL. Among the various types of vehicle interior noise, the sound of the heating, ventilation, and air conditioning (HVAC) systems is a source of distraction for drivers. HVAC noise is not as loud as the overall noise level; however, it affects a driver’s subjective perception and may lead to feelings of nervousness or annoyance. Therefore, vehicle engineers work not only to reduce noise, but also to improve sound quality. In this paper, HVAC noise samples were taken from many types of vehicles. Objective and subjective sound quality (SQ) evaluations were obtained, simple and multiple regression models were generated, and these were used with the Semantic Differential Method (SDM) to determine what characteristics trigger a “pleasant” response from listeners. The regression analysis produced diagnostic statistics and regression estimates. In addition, neural network (NN) models were created using three objective numerical inputs (loudness, sharpness, and roughness) of the SQ metrics and one subjective output (“pleasant”). The NN model was used primarily because human perceptions are very complex and often hard to estimate. The estimation models were compared via correlations between SQ output indices and hearing test results. Results demonstrated that the NN model is most highly correlated with SQ indices, which led to determination of suggested methods for SQ metrics prediction.

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