کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
754741 895884 2012 5 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Reliability improvement of a sound quality index for a vehicle HVAC system using a regression and neural network model
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مهندسی مکانیک
پیش نمایش صفحه اول مقاله
Reliability improvement of a sound quality index for a vehicle HVAC system using a regression and neural network model
چکیده انگلیسی

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Acoustics - Volume 73, Issue 11, November 2012, Pages 1099–1103
نویسندگان
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