Perception of Wind Noise in Vehicles

Daniel Carr, Purdue University

Abstract

Predictors of people’s responses to noise inside cars are used by car companies to identify and address potential noise problems from tests. Because significant advances have been made in the reduction of engine, powertrain, and tire/road noise, it is now important to pursue reductions in wind or aerodynamic noise. While models of loudness are commonly used to predict people’s responses to stationary wind noise, some wind noises are less acceptable than is predicted by the loudness metric. Additional sound characteristics may account for this. The research described in this dissertation was conducted in two main stages. The focus of the first stage was on improving acceptability predictions for stationary noise, by using additional sound quality metrics along with predictions of loudness. Three listening studies were designed and conducted, including one study with aspiration noise. Test sounds were a combination of recordings made on cars in a wind tunnel and modified recordings. Methods to modify individual sound characteristics were developed to de-correlate metrics across the set of test sounds, and to examine trends of acceptability with particular sound characteristics. Models of acceptability for stationary wind noise are significantly improved when a metric that predicts the sharpness of a sound is included in the model with the loudness metric. The focus of the second stage of the research was on improving acceptability predictions for non-stationary noise, particularly noise with the kind of variations that are expected from wind gusts. A simulation method was developed to generate sounds with controlled gusting features by modifying stationary noise recordings. Two listening studies were conducted containing simulated gusting sounds, and a gusting unacceptability metric was designed to predict subjects’ responses based on the strength, modulation rate, and duration of the gusts. The inclusion of this gusting metric significantly improved the goodness of fit of linear and logistic models of non-stationary noise acceptability containing Loudness and Sharpness.

Degree

Ph.D.

Advisors

Davies, Purdue University.

Subject Area

Ethnic studies

COinS