Noise reduction system and method

• US 20080130914A1
• Filed: 04/24/2007
• Published: 06/05/2008
• Est. Priority Date: 04/25/2006
• Status: Abandoned Application

• Current Assignee: Incel Vision Incorporated
• Original Assignee: Incel Vision Incorporated
• Inventors: Cho, Jung Kwon
• Application Number:
  US11/790,206
• Publication Number:
  US 20080130914A1
• Time in Patent Office: Days
• US Class Current: 381/94.1
• CPC Class Codes: G10L 2021/02166 Microphone arrays; Beamforming
• CPC Class Codes: G10L 21/0208 Noise filtering

Abstract

A noise reduction system and a noise reduction method are provided. The noise reduction method estimates directions of arrival of signals by directly using a signal subspace of the signals. Noise of the signals is suppressed at directions other than the directions of arrival. In one embodiment, the signals include audio signals. The signals may be multiple wide-band signals and/or coherent signals in multipath environment with a low signal-to-noise ratio.

Claims

1. A noise reduction system, comprising:

an input unit including a linear detector array for detecting analog signals at a plurality of time snaps, thereby constructing analog signals in time domain;

a first converter coupled with the input unit, the first converter receiving the analog signals in time domain and transforming the analog signals in time domain into digital signals in time domain; and

a signal processor coupled with the first converter for receiving the digital signals in time domain, the signal processor further comprising;

a transformation unit for converting the digital signals in time domain into digital signals in frequency domain; and

noise suppressing unit configured to suppress noise in the digital signals in frequency domain by multiplying a weighting vector to the digital signals in frequency domain to obtain noise reduced digital signals in frequency domain.

2. The system of claim 1, wherein:

the signal processor further comprises an inverse transformation unit for receiving the noise reduced digital signals in frequency domain and converting the noise reduced digital signals in frequency domain into noise reduced digital signals in time domain.

3. The system of claim 2, further comprising:

a second converter coupled with the signal processor, the second converter receiving the noise reduced digital signals in time domain and transforming the noise reduced digital signals in time domain into noise reduced analog signals in time domain.

4. The system of claim 3, wherein:

the second converter comprises an digital-to-analog converter.

5. The system of claim 3, further comprising:

an output unit for outputting the noise reduced analog signals in time domain.

6. The system of claim 5, wherein:

the output unit comprises a speaker.

7. The system of claim 1, wherein:

the linear detector array includes a plurality of detectors, the detectors being linearly arranged and equally spaced among one another.

8. The system of claim 7, wherein:

the detectors comprise a plurality of microphones, and the analog signals comprise audio signals.

9. A communication apparatus, comprising:

the system as recited in claim 8.

10. The system of claim 7, wherein:

the detectors comprise antennas, and the analog signals comprise electromagnetic radiation signals.

11. The system of claim 1 wherein:

the first converter comprises an analog-to-digital converter having a sampling rate of about 16 KHz.

12. The system of claim 1, wherein:

the transformation unit performs discrete Fourier transform, and the inverse transformation unit performs inverse discrete Fourier transform.

13. The system of claim 1, wherein:

the noise suppressing unit further comprises a weighting vector preparation unit, the weighting vector preparation unit computes the weighting vector based on directions of arrival (DOA) estimated by using a spatial spectrum of the analog signals.

14. The system of claim 13, wherein:

the weighting vector preparation unit computes the spatial spectrum by directly using a signal subspace, the signal subspace being decomposed from a spectral density matrix.

15. The system of claim 14, wherein:

the weighting vector preparation unit computes the spectral density matrix based on a covariance matrix constructed from a plurality of snapshot vectors of the digital signals in time domain.

16. The system of claim 1, wherein:

the noise suppressing unit further comprising a plurality of multipliers for multiplying a weighting vector to the digital signals in frequency domain to obtain noise reduced digital signals in frequency domain.

17. A signal processor, comprising:

a transformation unit configured to receive digital signals in time domain, the digital signals corresponding to a plurality of analog signals detected by a linear detector array, and to convert the digital signals in time domain into digital signals in frequency domain; and

a noise suppression unit configured to receive the digital signals in frequency domain and suppress noise in the digital signals in frequency domain to obtain noise reduced digital signals in frequency domain, the noise reduced digital signals being obtained by multiplying a weighting vector with the digital signals in frequency domain.

18. The signal processor of claim 17, further comprising:

an inverse transformation unit for converting the noise reduced digital signals in frequency domain into noise reduced digital signals in time domain, and outputting the noise reduced digital signals in time domain for further processing.

19. The signal processor of claim 17, wherein:

the noise suppression unit is further configured to;

construct a plurality of snapshot vectors based on the digital signals in time domain;

construct a spectral density matrix based on a covariance matrix defined according to the snapshot vectors;

decompose the spectral density matrix into a signal subspace and a noise subspace;

estimate directions of arrival by using a spatial spectrum obtained by directly using the signal subspace; and

compute the weighting vector based on the directions of arrival.

20. The signal processor of claim 19, wherein:

the noise suppression unit maximizes gain of the digital signals in frequency domain at the directions of arrival (DOA) by using the weighting vector.

21. The signal processor of claim 19, wherein:

the noise suppression unit minimizes gain of the digital signals in frequency domain at directions other than the directions of arrival (DOA) by using the weighting vector.

22. A communication apparatus, comprising:

the signal processor of claim 19.

23. A method for reducing noise in audio signals detected by a linear microphone array, comprising:

preparing a plurality of snapshot vectors of the audio signals;

constructing a covariance matrix from the snapshot vectors, and constructing a spectral density matrix from the covariance matrix;

eigendecomposing the spectral density matrix to obtain a plurality of eigenvectors and a plurality of eigenvalues, thereby obtaining a signal subspace and a noise subspace;

estimating directions of arrival of the audio signals by a spatial spectrum derived from directly using the signal subspace;

preparing a weighting vector based on the directions of arrival;

obtaining noise reduced audio signals using the weighting vector; and

outputting the noise reduced audio signals.

24. The method of claim 23, wherein the audio signals include multiple wide band signals.

25. The method of claim 23, wherein the audio signals include coherent signals in a multipath environment.

26. The method of claim 23, further comprising:

transforming the audio signals into audio signals in frequency domain.

27. The method of claim 26, wherein obtaining the noise reduced audio signals further comprises:

multiplying the weighting vector with the audio signals in frequency domain to obtain noise reduced audio signals in frequency domain.

28. The method of claim 27, further comprising:

transforming the noise reduced audio signals in frequency domain to obtain the noise reduced audio signals in time domain.

29. The method of claim 23, further comprising:

obtaining a Euclidean distance between the signal subspace and a directional vector.

30. The method of claim 29, wherein:

the spatial spectrum P_DUSS(θ

) is given as

$P_{\mathrm{DUSS}}<br><br>\left(\mathrm{\theta <br><br>}\right)=\frac{1}{1-d^2<br><br>\left(\mathrm{\theta <br><br>}\right)},$

where θ

is an angle corresponding to the directions of arrival (DOA), and d(θ

) is the Euclidean distance between the signal subspace and the directional vector.

31. The method of claim 23, wherein:

the eigenvalues includes non-zero eigenvalues and zero eigenvalues.

32. The method of claim 31, wherein:

the signal subspace comprises the eigenvectors that correspond to non-zero eigenvalues, and the noise subspace comprises the eigenvectors that correspond to zero eigenvalues.

33. The method of claim 23, wherein:

the weighting vector is prepared using a minimum variance method.