Secondary Path Modeling for Active Noise Control

• US 20080144853A1
• Filed: 12/06/2007
• Published: 06/19/2008
• Est. Priority Date: 12/06/2006
• Status: Active Grant

• Current Assignee: Brigham Young University (The Church of Jesus Christ of Latter-Day Saints)
• Original Assignee: Brigham Young University (The Church of Jesus Christ of Latter-Day Saints)
• Inventors: Faber, Benjamin M., Blotter, Jonathan, Sommerfeldt, Scott D.
• Granted Patent: US 8,270,625 B2
• Time in Patent Office: Days
• US Class Current: 381/71.11
• CPC Class Codes: G10K 11/17817 between the output signals and the error signals, i.e. secondary path
• CPC Class Codes: G10K 11/17854 the filter being an adaptive filter
• CPC Class Codes: G10K 11/17855 for improving speed or power requirements
• CPC Class Codes: G10K 11/17879 using both a reference signal and an error signal
• CPC Class Codes: G10K 2210/30232 Transfer functions, e.g. impulse response

Abstract

Methods for modeling the secondary path of an ANC system to improve convergence and tracking during noise control operation, and their associated uses are provided. In one aspect, for example, a method for modeling a secondary path for an active noise control system is provided. Such a method may include receiving a reference signal, filtering the reference signal with an initial secondary path model to obtain a filtered reference signal, calculating an autocorrelation matrix from the filtered reference signal, and calculating a plurality of eigenvalues from the autocorrelation matrix. The method may further include calculating a maximum difference between the plurality of eigenvalues and iterating a test model to determine an optimized secondary path model having a plurality of optimized eigenvalues that have a minimized difference that is less than the maximum difference of the plurality of eigenvalues, such that the optimized secondary path model may be utilized in the active noise control system.

Claims

1. A method for modeling a secondary path for an active noise control system, comprising:

receiving a reference signal;

filtering the reference signal with an initial secondary path model to obtain a filtered reference signal;

calculating an autocorrelation matrix from the filtered reference signal;

calculating a plurality of eigenvalues from the autocorrelation matrix;

calculating a maximum difference between the plurality of eigenvalues;

iterating a test model to determine an optimized secondary path model having a plurality of optimized eigenvalues that have a minimized difference that is less than the maximum difference of the plurality of eigenvalues, wherein the optimized secondary path model may be utilized in the active noise control system.

2. The method of claim 1, wherein iterating the test model further includes:

generating a plurality of adjusted secondary path models;

filtering the reference signal with each of the plurality of adjusted secondary path models to obtain a plurality of adjusted filtered reference signals;

calculating a plurality of adjusted autocorrelation matrixes from the plurality of adjusted filtered reference signals;

calculating a plurality of adjusted eigenvalues from each of the adjusted autocorrelation matrixes;

calculating an adjusted maximum difference for each plurality of adjusted eigenvalues; and

selecting the optimized secondary path model from the plurality of adjusted secondary path models, wherein the optimized secondary path model is capable of generating the plurality of optimized eigenvalues.

3. The method of claim 2, wherein the minimized difference is the smallest difference from all of the pluralities of adjusted eigenvalues.

4. The method of claim 1, wherein calculating the maximum difference further includes calculating the span of the plurality of eigenvalues.

5. The method of claim 1, wherein calculating the maximum difference further includes calculating the root mean square of the plurality of eigenvalues.

6. The method of claim 1, wherein calculating the maximum difference further includes calculating the crest factor of the plurality of eigenvalues.

7. The method of claim 1, wherein the secondary path is modeled offline.

8. The method of claim 1, wherein the secondary path is modeled online.

9. The method of claim 2, wherein selecting the optimized secondary path model further includes selecting the optimized secondary path model using a genetic search algorithm.

15. A method of actively minimizing noise in a system, comprising:

receiving a reference signal from a working environment;

filtering the reference signal with the optimized secondary path model of claim 1 to produce a filtered reference signal;

filtering the reference signal with an adaptive control filter to generate a control output signal;

introducing the control output signal into the working environment to minimize noise associated with the reference signal; and

adjusting the adaptive control filter with the filtered reference signal.

16. The method of claim 15, wherein the adaptive control filter is adjusted with the filtered reference signal prior to activation of active noise control.

17. The method of claim 15, wherein the adaptive control filter is adjusted with the filtered reference signal after activation of active noise control.

10. A method for modeling a secondary path for an active noise control system, comprising:

obtaining an initial secondary path model; and

calculating an updated secondary path model that maintains phase of the initial secondary path model, but equalizes the magnitude of the initial secondary path model.

11. The method of claim 10, wherein calculating an updated secondary path model further includes:

obtaining an initial time domain impulse response of the initial secondary path model;

calculating a Fast Fourier Transform (FFT) of the initial time domain impulse response;

dividing the FFT response at each frequency by the magnitude of the response at that frequency and multiplying by the FFT'"'"'s mean value; and

calculating an inverse FFT to obtain an optimized time domain impulse response for use as the updated secondary path model.

12. The method of claim 10, wherein calculating an updated secondary path model further includes:

obtaining an initial time domain impulse response of the initial secondary path model;

calculating a Fast Fourier Transform (FFT) of the initial time domain impulse response;

dividing the FFT response at each frequency by the magnitude of the response at that frequency and multiplying by the inverse of the amplitude of the reference signal at that frequency; and

calculating an inverse FFT to obtain an optimized time domain impulse response for use as the updated secondary path model.

13. The method of claim 10, wherein the secondary path is modeled offline.

14. The method of claim 10, wherein the secondary path is modeled online.

18. A method of actively minimizing noise in a system, comprising:

receiving a reference signal from a working environment;

filtering the reference signal with the optimized secondary path model of claim 10 to produce a filtered reference signal;

filtering the reference signal with an adaptive control filter to generate a control output signal;

introducing the control output signal into the working environment to minimize noise associated with the reference signal; and

adjusting the adaptive control filter with the filtered reference signal.

19. The method of claim 18, wherein the adaptive control filter is adjusted with the filtered reference signal prior to activation of active noise control.

20. The method of claim 18, wherein the adaptive control filter is adjusted with the filtered reference signal after activation of active noise control.