Method of noise reduction using correction vectors based on dynamic aspects of speech and noise normalization

• US 20030191638A1
• Filed: 04/05/2002
• Published: 10/09/2003
• Est. Priority Date: 04/05/2002
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Deng, Li, Droppo, James G., Acero, Alejandro
• Granted Patent: US 7,117,148 B2
• Time in Patent Office: Days
• US Class Current: 704/228
• CPC Class Codes: G10L 21/0208 Noise filtering

Abstract

A method and apparatus are provided for reducing noise in a signal. Under one aspect of the invention, a correction vector is selected based on a noisy feature vector that represents a noisy signal. The selected correction vector incorporates dynamic aspects of pattern signals. The selected correction vector is then added to the noisy feature vector to produce a cleaned feature vector. In other aspects of the invention, a noise value is produced from an estimate of the noise in a noisy signal. The noise value is subtracted from a value representing a portion of the noisy signal to produce a noise-normalized value. The noise-normalized value is used to select a correction value that is added to the noise-normalized value to produce a cleaned noise-normalized value. The noise value is then added to the cleaned noise-normalized value to produce a cleaned value representing a portion of a cleaned signal.

Claims

1. A method for reducing noise in a noisy input signal, the method comprising:

converting a frame of the noisy input signal into an input feature vector;

selecting a mixture component of a trained model based at least in part on the input feature vector;

identifying a correction vector that incorporates dynamic aspects of a pattern signal based on the selected mixture component; and

adding the correction vector to the input feature vector to form a clean feature vector.

2. The method of claim 1 wherein identifying a correction vector comprises identifying a correction vector having at least one delta coefficient.

3. The method of claim 2 wherein identifying a correction vector further comprises identifying a correction vector having at least one acceleration coefficient.

4. The method of claim 3 wherein the input feature vector and the clean feature vector each have at least one delta coefficient and at least one acceleration coefficient.

5. The method of claim 1 wherein converting a frame of the noisy input signal further comprises converting a set of n frames of the noisy input signal into n input feature vectors, selecting a mixture component further comprises selecting a mixture component based at least in part on the n input feature vectors, and adding the correction vector to the input feature vector comprises adding the correction vector to one of the feature vectors in the set of n feature vectors.

6. The method of claim 1 wherein identifying a correction vector comprises selecting a correction vector based on the selected mixture component and filtering the correction vector relative to time.

7. The method of claim 6 wherein filtering the correction vector comprises filtering a sequence of correction vectors.

8. The method of claim 7 wherein filtering the sequence of correction vectors comprises applying the sequence of correction vectors to a time-invariant filter.

9. A computer-readable medium having computer-executable instructions for reducing noise in a noisy signal through steps comprising:

forming a correction vector based on dynamic aspects of a signal; and

adding the correction vector to a feature vector representing a portion of the noisy signal to produce a clean feature vector representing a portion of a clean signal.

10. The computer-readable medium of claim 9 wherein forming a correction vector comprises forming a correction vector having static coefficients and dynamic coefficients.

11. The computer-readable medium of claim 10 wherein forming a correction vector having dynamic coefficients comprises forming a correction vector having delta coefficients.

12. The computer-readable medium of claim 10 wherein forming a correction vector having dynamic coefficients further comprises forming a correction vector having acceleration coefficients.

13. The computer-readable medium of claim 9 wherein forming a correction vector comprises:

converting n frames of the noisy signal into n respective feature vectors; and

using the n feature vectors to select a correction vector.

14. The computer-readable medium of claim 13 wherein using the n feature vectors to select a correction vector comprises:

comparing the set of n feature vectors to distributions of training sets of n feature vectors to find a distribution that best matches the set of n feature vectors; and

selecting a correction vector that is associated with the distribution that best matches the set of n feature vectors.

15. The computer-readable medium of claim 9 wherein forming the correction vector comprises:

selecting a sequence of correction vectors;

applying the sequence of correction vectors to a filter to produce a sequence of filtered correction vectors; and

selecting one of the filtered correction vectors.

16. The computer-readable medium of claim 15 wherein selecting a sequence of correction vectors comprises selecting a sequence of correction vectors having only static coefficients.

17. The computer-readable medium of claim 15 wherein the filter has a transfer function that is based on dynamic aspects of a signal.

18. The computer-readable medium of claim 17 wherein the filter is a time-invariant filter.

19. A method for reducing noise in a noisy signal, the method comprising:

estimating noise in a portion of the noisy signal;

subtracting a feature vector representation of the noise estimate from a feature vector representation of the portion of the noisy signal to produce a noise-normalized value;

using the noise-normalized value to identify a correction vector;

adding the correction vector to the noise-normalized value to produce a noise-normalized clean value; and

adding the feature vector representation of the noise estimate to the noise-normalized clean value to produce a feature vector representation of a portion of a clean signal.

20. The method of claim 19 wherein the feature vector representation is a cepstral domain representation.

21. The method of claim 19 wherein using the noise-normalized value to identify a correction vector comprises:

applying the noise-normalized value to a set of distributions of noise-normalized training values to identify a distribution that best matches the noise-normalized value; and

selecting a correction vector associated with the distribution that best matches the noise-normalized value.

22. A computer-readable medium having computer-executable instructions for reducing noise in a noisy signal through steps comprising:

subtracting a noise value from a noisy signal value derived from the noisy signal to produce a noise-normalized value;

selecting a correction value based on the noise-normalized value;

adding the correction value to the noise-normalized value to produce a cleaned noise-normalized value; and

adding the noise value to the cleaned noise-normalized value to produce a cleaned value.

23. The computer-readable medium of claim 22 further comprising generating the noise value based on an estimate of the noise in the noisy signal.

24. The computer-readable medium of claim 23 wherein the noisy signal value is a feature vector representing a portion of the noisy signal and the noise value is a feature vector representing an estimate of the noise in the portion of the noisy signal.

25. The computer-readable medium of claim 24 wherein the feature vectors are cepstral feature vectors.

26. The computer-readable medium of claim 22 wherein selecting a correction value comprises:

comparing the noise-normalized value to distribution values that describe distributions of training noise-normalized values;

based on the comparison, selecting one of the distributions of training noise-normalized values; and

selecting a correction value associated with the selected distribution.