Active Noise Equalization of Vehicle Low Frequency Interior Distraction Level and its Optimization

On the study of reducing the disturbance on driver’s attention induced by low frequency vehicle interior stationary noise, a subjective evaluation is firstly carried out by means of rank rating method which introduces Distraction Level (DL) as evaluation index. A visual-finger response test is developed to help evaluating members better recognize the Distraction Level during the evaluation. A non-linear back propagation artificial neural network (BPANN) is then modeled for the prediction of subjective Distraction Level, in which linear sound pressure RMS amplitudes of five Critical Band Rates (CBRs) from 20 to 500Hz are selected as inputs of the model. These inputs comprise an input vector of BPANN. Furthermore, active noise equalization (ANE) on DL is realized based on Filtered-x Least Mean Square (FxLMS) algorithm that controls the gain coefficients of inputs of trained BPANN. Meanwhile, reference signal of this ANE system is extracted using proposed coherence-based method which chooses the frequency of reference signal according to the coherence level between interior noise and vehicle body vibration. Finally, after analyzing the relation among transfer function of input layer, hidden layer and output layer of modeled BPANN, the sensitivity of DL to the inputs is derived to bring forward a novel optimization methodology that is able to search the optimal gain coefficient vector using Sensitivity-based Gradient Optimization Method (SGOM) and thereby optimize the DL during ANE process. The result shows that DLs are improved significantly by applying this ANE system.