Abstract
Recently, the issue of sound quality inside vehicles has attracted interest from both researchers and industry alike due to health concerns and also to increase the appeal of vehicles to consumers. This work extends the analysis of interior acoustic noise inside a vehicle under several conditions by comparing measured power levels and two different models for acoustic noise, namely the Gaussian and the alpha-stable distributions. Noise samples were collected in a scenario with real traffic patterns using a measurement setup composed of a Raspberry Pi Board and a microphone strategically positioned. The analysis of the acquired data shows that the observed noise levels are higher when traffic conditions are good. Additionally, the interior noise presented considerable impulsiveness, which tends to be more severe when traffic is slower. Finally, our results suggest that noise sources related to the vehicle itself and its movement are the most relevant ones in the composition of the interior acoustic noise.
Highlights
Acoustic noise has received much attention in the automotive industry due to the increasing demand for in-vehicle voice assistant systems [1]
Noise evaluation is an essential issue in this field, enabling the design of in-vehicle multimedia systems with better noise control and fewer disturbances that degrade acoustic communications performance in a vehicle interior
Such disturbances may have different sources, and identifying them in order to focus on the most dominant sources will result in more efficient noise controls and optimized systems for audio applications
Summary
Acoustic noise has received much attention in the automotive industry due to the increasing demand for in-vehicle voice assistant systems [1]. Noise evaluation is an essential issue in this field, enabling the design of in-vehicle multimedia systems with better noise control and fewer disturbances that degrade acoustic communications performance in a vehicle interior. Such disturbances may have different sources, and identifying them in order to focus on the most dominant sources will result in more efficient noise controls and optimized systems for audio applications. This work presents an experimental evaluation of the characteristics of the acoustic noise inside a vehicle under the perspective of an in-vehicle voice reception system. We capture sources related to the traffic that might impact a car’s interior environment, providing insights concerning the acoustic noise and its source in the vehicle interior
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