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Active noise cancellation method based on improved variable step size LMS self-adaption

An active noise and self-adaptive technology, applied in pattern recognition, impedance network, instrument, etc. in the signal, can solve the problem of large influence of step size

Pending Publication Date: 2020-10-23
HARBIN INST OF TECH
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0013] The step size of the GSVS-LMS algorithm changes slowly when the error tends to 0, and the convergence characteristics are optimized, but the step size is also greatly affected by the color noise when it is applied to the adaptive noise cancellation system

Method used

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  • Active noise cancellation method based on improved variable step size LMS self-adaption
  • Active noise cancellation method based on improved variable step size LMS self-adaption
  • Active noise cancellation method based on improved variable step size LMS self-adaption

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specific Embodiment 1

[0060] Such as figure 2 As shown, the present invention provides a kind of active noise cancellation method based on the self-adaptation of the improved variable step size LMS, the method is based on the adaptive noise cancellation system, and the system includes a signal source, a noise source, an adaptive filter, An adaptive noise canceller, a high-sensitivity pickup, a low-sensitivity pickup and a controller, the method comprising the steps of:

[0061] Step 1: Set the order M of the filter, and adjust the constants α and β of the step size of the given filter;

[0062] Step 2: Input the environmental noise vector;

[0063] Step 3: Initialize the adaptive noise cancellation system, starting from time 1, let μ(1)=μ(2)=β;

[0064] Step 4: pass the input environmental noise vector through an adaptive filter to obtain a noise estimate;

[0065] Step 5: Subtracting the noise estimate obtained in Step 4 and the main signal to obtain an error signal, and outputting the error...

specific Embodiment 2

[0070] The invention provides a variable step size LMS self-adaptive active noise cancellation method using the third-order autocorrelation of the error signal e(k) to adjust the step size. The algorithm step size adjustment of the invention is not affected by the Gaussian color noise of the system. Faster convergence speed can be obtained while maintaining the steady-state error, and the noise filtering is relatively clean in practical applications.

[0071] The principle of adaptive active noise cancellation system is as follows: figure 1 As shown, the main signal is the noise-contaminated signal picked up by the high-sensitivity pickup channel:

[0072] d(k)=s(k)+u 0 (k) (1)

[0073] where s(k) is the far-field sound signal, u 0 (k) is the ambient noise collected by the high-sensitivity pickup channel.

[0074] The reference signal is collected by a low-sensitivity pickup and is uncorrelated with the far-field sound signal s(k) but with u 0 (k) The associated ambient n...

specific Embodiment 3

[0126] Next, a specific implementation manner of the present invention in a practical application scenario will be introduced. The application scenario is that the high-sensitivity pickup collects the sound of vehicles traveling in the distance mixed with nearby whistle interference, and the low-sensitivity pickup only collects nearby whistle interference. Through the active algorithm based on the improved variable step size LMS adaptive algorithm The noise cancellation system suppresses the interference of the whistle, so as to obtain the sound of vehicles moving in the distance with a clean sound spectrum. In order to observe the noise canceling effect of the present invention more intuitively, the nearby whistle sound collected by the low-sensitivity pickup in the implementation process is simulated by superimposing four frequencies of 1000Hz, 1500Hz, 2000Hz and 2500Hz respectively, and the high-sensitivity pickup The collected sound is simulated by a mixture of tank marchi...

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Abstract

The invention relates to an active noise cancellation method based on improved variable step size LMS self-adaption. The invention is a self-adaption based noise cancellation system. The system comprises a signal source, a noise source, a self-adaptive filter, a self-adaptive noise canceller, a high-sensitivity pickup, a low-sensitivity pickup and a controller. According to the active noise cancellation method based on variable step size LMS self-adaption for adjusting the step length through three-order self-correlation of the error signal e (k), the step size adjustment of the algorithm is not affected by Gaussian color noise of the system, a higher convergence rate can be obtained while steady-state errors are kept, and noise filtering is thorough in practical application. Compared witha classic LMS algorithm and the GSVS-LMS algorithm, the classic LMS algorithm have the disadvantages that the output distortion is relatively serious; although the outputs of the GSVS-LMS algorithm and the algorithm of the invention have noise residues, the signal waveform is not distorted, and the algorithm of the invention can restore the original signal faster than the GSVS-LMS algorithm.

Description

technical field [0001] The invention relates to the technical field of self-adaptive noise cancellation, and relates to an adaptive active noise cancellation method based on an improved variable step size LMS. Background technique [0002] In recent years, with the development of adaptive signal processing theory, adaptive filtering algorithm has been widely used in the field of noise elimination due to its strong self-tracking and self-learning ability. In particular, the least mean square error (LMS) algorithm based on the least mean square error criterion proposed by Windrow and Hoff in the 1960s has attracted much attention in practical applications due to its advantages of simple implementation, low computational complexity, and small amount of calculation. . The LMS algorithm uses the idea of ​​the steepest descent method, replaces the exact value of the gradient with the estimated value of the gradient, iterates along the negative direction of the gradient estimate, ...

Claims

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Application Information

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IPC IPC(8): G06K9/00H03H17/02
CPCH03H17/0219G06F2218/04
Inventor 侯成宇付善银陈迪张立宪
Owner HARBIN INST OF TECH
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