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A reconstruction method of vehicle interior noise signal

An in-vehicle noise and signal technology, applied in the field of in-vehicle noise signal reconstruction, can solve problems such as errors, and achieve the effects of improving reconstruction accuracy, reducing non-stationarity, and reducing modeling difficulty

Active Publication Date: 2019-02-15
SHANGHAI UNIV OF ENG SCI
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Problems solved by technology

[0005] However, the noise signal in the car belongs to mechanical vibration and acoustic signal, which has strong nonlinearity and non-stationarity. Although BP neural network has good nonlinear fitting ability, it can be well applied to multi-source data fusion technology, but the processing Signals with non-stationary characteristics will inevitably cause errors

Method used

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  • A reconstruction method of vehicle interior noise signal
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  • A reconstruction method of vehicle interior noise signal

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Embodiment

[0082] The normal noise source signal data obtained under high-speed working conditions is used below, such as image 3 As shown in the figure, the contribution analysis diagram of each key point determines the key noise source signal that affects the noise signal in the car. The data is preprocessed, the noise source signal sample library is obtained, and the number of neurons in the input layer of the BP network is determined to be n=4, and the output layer l=1.

[0083] Such as figure 1 As shown, it is a schematic flow chart of signal reconstruction in an embodiment of the present invention,

[0084] In the first step, the SDA method decomposes and reconstructs the data signal, and the data signal is decomposed by EMD to obtain a limited number of relatively stable IMF components and a trend item r n ;

[0085]

[0086] Use the EMD method proposed in this paper to solve the extreme point number and energy proportion of each IMF component, see Figure and Table 1, deter...

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Abstract

The invention relates to a method for reconstructing a vehicle interior noise signal, comprising the following steps: 1) decomposing and analyzing a signal; decomposing and analyzing the source signalto obtain three stable signal component categories, namely, a high-frequency component, an intermediate-frequency component and a low-frequency component; 2) component fitness calculation: respectively train that BP neural network model and taking the performing weight and the threshold value of the BP neural network model as component fitness value to obtain the optimal component fitness value;3) performing Signal reconstruction model: According to the categories of input signal components, the BP network is trained by assigning the fitness value of the optimal component to the noise reconstruction as the initial weight and threshold. After convergence, the corresponding reconstruction algorithm model of each signal component is obtained, and the noise signal of the occupant's ear sideis reconstructed by reconstruction superposition according to the reconstruction algorithm model. Compared with the prior art, the invention has the advantages of reducing the non-stationarity of thesignal and the difficulty of modeling, improving the reconstruction accuracy and the like.

Description

technical field [0001] The invention relates to the field of signal processing and information fusion, in particular to a method for reconstructing noise signals in a vehicle. Background technique [0002] In order to realize the active control (ANC) of the occupant's ear side noise in the vehicle, it is necessary to provide the primary reference signal for the control system first. For the pickup of the primary reference signal, the traditional method is to install a microphone near the occupant's ear to obtain the primary reference signal. This method inevitably introduces secondary pollution from the secondary sound source, which is not conducive to the rapid convergence of the system. Therefore, it is meaningful to study the reconstruction method of the occupant's ear side noise and obtain the reference signal of ANC. [0003] At present, the main methods of sound field reconstruction include near-field acoustic holography (NAH) and multi-sensor data fusion (MSDF). The...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/045G06F2218/00G06F18/25
Inventor 王孝兰杨东坡王岩松郭辉刘宁宁
Owner SHANGHAI UNIV OF ENG SCI
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