An Advance Gradient Optimization Method to Optimize Sound Distraction Levels of a Passenger Vehicle’s Interior Stationary Noise Samples with Active Noise Equalization

Abstract:

In order to quickly optimize a passenger vehicle’s interior stationary noise samples’ sound distraction levels with active noise equalization, the traditional enumeration method used to search for optimal gain coefficient vector of active noise equalization system and to optimize sound quality was analyzed; subjective evaluation was used to evaluate the distraction levels of the passenger vehicle’s interior stationary noise samples within 20500 Hz; Back Propagation (BP) neural network using the barks’ total sound pressure linear amplitudes within 20500 Hz as inputs was used to fit the noise samples’ sound distraction levels; the trained BP neural network’s weights were used to deduce the network’s inputs’ sensitivities and contributions to the sound distraction levels; an equation was deduced to predict the total sound pressure linear amplitude of a bark after active noise equalization with a given gain coefficient, the original sound pressure amplitude spectrum and the reference signal of the active equalization system; based on this equation, the sound distraction levels’ sensitivities and contributions, an advance gradient optimization method was designed to search for optimal gain coefficient vector and to optimize sound distraction levels of the noise samples. The time consumption of the optimization process is low. Active noise equalization using the gain coefficient vectors acquired by the advance gradient optimization method was executed and the equalized noise samples’ sound distraction levels were evaluated with subjective evaluation. The result shows good accuracy and the sound distraction levels are improved significantly.