Empirical mode decomposition for analyzing acoustical signals

• US 20030033094A1
• Filed: 02/13/2002
• Published: 02/13/2003
• Est. Priority Date: 02/14/2001
• Status: Active Grant

• Current Assignee: United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration
• Original Assignee: United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration
• Inventors: Huang, Norden E.
• Granted Patent: US 6,862,558 B2
• Time in Patent Office: Days
• US Class Current: 702/39
• CPC Class Codes: G06F 17/14 Fourier, Walsh or analogous domain transformations , e.g. Laplace, Hilbert, Karhunen-Loeve, transforms for correlation function computation G06F17/156; spectrum analysers G01R23/16
• CPC Class Codes: G06F 17/141 Discrete Fourier transforms
• CPC Class Codes: G10L 15/20 Speech recognition techniques specially adapted for robustness in adverse environments, e.g. in noise, of stress induced speech G10L21/02 takes precedence
• CPC Class Codes: G10L 19/0212 using orthogonal transformation
• CPC Class Codes: G10L 21/0208 Noise filtering

Abstract

The present invention discloses a computer implemented signal analysis method through the Hilbert-Huang Transformation (HHT) for analyzing acoustical signals, which are assumed to be nonlinear and nonstationary. The Empirical Decomposition Method (EMD) and the Hilbert Spectral Analysis (HSA) are used to obtain the HHT. Essentially, the acoustical signal will be decomposed into the Intrinsic Mode Function Components (IMFs). Once the invention decomposes the acoustic signal into its constituting components, all operations such as analyzing, identifying, and removing unwanted signals can be performed on these components. Upon transforming the IMFs into Hilbert spectrum, the acoustical signal may be compared with other acoustical signals.

Claims

1. A computer implemented method of analyzing an acoustical signal, comprising:

inputting the acoustical signal;

extracting a set of intrinsic mode functions from the acoustical signal; and

storing said set of intrinsic mode functions of the acoustical signal.

2. The computer implemented method according to claim 1, further comprising:

identifying a specific acoustical signal.

3. The computer implemented method according to claim 2,

wherein said specific acoustical signal is identified in said set of intrinsic mode functions.

4. The computer implemented according to claim 2,

wherein said specific acoustical signal is noise.

5. The computer implemented method according to claim 2, further comprising:

removing said specific acoustical signal from said set of intrinsic mode functions; and

reconstructing the acoustical signal.

6. The computer implemented method according to claim 5,

wherein reconstructing the acoustical signal includes summing up said set of intrinsic mode function.

7. A computer implemented method of analyzing an acoustical signal, comprising:

inputting the acoustical signal;

extracting a set of intrinsic mode functions from the acoustical signal;

storing said set of intrinsic mode functions of the acoustical signal; and

transforming said set of intrinsic mode functions with a Hilbert transform to generate a Hilbert spectrum.

8. The computer implemented method 1 according to claim 7, further comprising:

identifying a specific acoustical signal in the Hilbert spectrum.

9. The computer implemented method according to claim 8,

wherein said specific acoustical signal is noise.

10. The computer implemented method according to claim 8, further comprising:

storing the Hilbert spectrum.

11. The computer implemented method according to claim 8, further comprising:

removing said specific acoustical signal from said set of intrinsic mode functions; and

reconstructing the acoustical signal.

12. The computer implemented method according to claim 11,

wherein reconstructing the acoustical signal includes summing up said set of intrinsic mode function.

13. A computer implemented method of analyzing an acoustical signal, comprising:

inputting a first acoustical signal;

extracting a first set of intrinsic mode functions from the first acoustical signal;

transforming said first set of intrinsic mode functions with a Hilbert transform to generate a first Hilbert spectrum; and

storing said first Hilbert spectrum.

14. The computer implemented method according to claim 13,

wherein the first acoustical signal is generated from a first human voice source.

15. A computer implemented method according to claim 13, comprising:

inputting a second acoustical signal;

extracting a second set of intrinsic mode functions from the second acoustical signal;

transforming said second set of intrinsic mode functions with a Hilbert transform to generate a seconfd Hilbert spectrum;

storing said second Hilbert spectrum of the second acoustical signal; and

comparing said first and second Hilbert spectra.

16. The computer implemented method according to claim 15,

wherein the second acoustical signal is generated from a second human voice source.

17. The computer implemented method according to claim 15,

wherein the step of comparing said first and second Hilbert spectra includes obtaining a correlation coefficient between said Hilbert spectra.

18. The computer implemented method according to claim 13, further comprising:

providing a second Hilbert spectrum; and

comparing said first and second Hilbert spectra.

19. The computer implemented method according to claim 18,

wherein the step of providing the Hilbert spectrum of the specific acoustical signal includes retrieving said second Hilbert spectrum from a database.

20. The computer implemented method to claim 18,

wherein the step of comparing said first and second Hilbert spectra includes obtaining a correlation coefficient between said Hilbert spectra.