# A Machine Learning Model for Predicting Noise Limits of Motor Vehicles in UNECE R51 Regulations
## 1 Introduction
[7] used an unsteady computation fluid dynamics (CFD) simulation to predict vehicle exterior wind noise and obtain quantitative information about acoustic power of exterior wind noise in vehicle, and indicated the location of the dominant source of exterior wind noise. [PERSON] et al. [8] presented a numerical process on the dynamic coupling of exterior parts of a vehicle to determine flow and pressure around the vehicle and optimised the vehicle shape for reducing exterior wind noise. [PERSON] et al. [9] aimed to provide a comparison of the total environmental impact from exterior noise exposure and energy demand and correlated effects of exterior noise-level and energy demand along different travel routes. [PERSON] [10] proposed a psycho-acoustic test to establish a relationship between the critical distances and the real reaction times needed to respond to the vehicle exterior noise of various approaching vehicles, and achieved a sound compromise between the conflicting objectives of pedestrian safety and vehicle exterior noise-level reduction. [PERSON] et al. [11] used exterior transfer path approach to evaluate the sensitivity of variables, such as speaker location, noise source routing, exterior noise transfer functions, and sound source design to achieve the required levels and directivity, while minimising overall exterior noise intrusion. Artificial neural networks were also utilised by researchers to investigate road traffic noises. [PERSON] et al. [12] identified a generalised loss of mean absolute error between predicted and expected feature vectors in Deep Neural Networks (DNN), which is upper bounded by the sum of an approximation error, and estimation error, and an optimisation error. They performed a set of images denoising, and speech enhancement experiments as a showcase, and the upper bounds for DNN based vector-to-vector regression were found valid. [PERSON] et al. [13] used an emotional artificial neural network to model road traffic noise. They designed a sensitive analysis experiment and ranked the vitality of input parameters in the road traffic noise level in two places, followed by the number of cars, medium vehicles, heavy vehicles, average speed, and percentage of heavy vehicles, respectively. [PERSON], et al. [14] proposed two multitask artificial neural network (ANN) models to model acoustic behaviours of an induction motor as a power system of the vehicle. They demonstrated that multitask ANN models are effective for extracting common characteristics from multiple tasks and modelling the noise and acoustic quality of an induction motor. [PERSON] and [PERSON] [15] developed an ANN prediction model for automobile interior noise based on an extension data mining (EDM) algorithm considering basic vehicle parameters, such as the total number of engine valves, engine torque, vehicle, and vehicle speed as inputs of the model. [PERSON] et al. [16] used an ANN to predict the sound and vibration of a diesel engine. This proposed ANN model can be used to predict vibration and sound pressure level (SPL) in an acceptable accuracy, which used a Levenberg-Marquardt training algorithm providing more accurate predictions than the regression approach. The number of hidden layers in an ANN model is usually chosen by using the trial-and-error method for stability and accuracy.