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

• US 20050259558A1
• Filed: 07/26/2005
• Published: 11/24/2005
• 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 7,181,390 B2
• Time in Patent Office: Days
• US Class Current: 369/275.100
• 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, the correction vector having at least one delta coefficient; and

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

3. The method of claim 1 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.

2. (canceled)

5. (canceled)

6. (canceled)

9. (canceled)

10. (canceled)

11. (canceled)

12. (canceled)

13. (canceled)

14. (canceled)

15. (canceled)

16. (canceled)

17. (canceled)

18. (canceled)

19. (canceled)

20. (canceled)

21. (canceled)

22. (canceled)

23. (canceled)

24. (canceled)

25. (canceled)

26. (canceled)

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

converting a set of n frames of the noisy input signal into n input feature vectors;

selecting a mixture component based at least in part on the n input feature vectors;

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

adding the correction vector to one of the feature vectors in the set of n feature vectors.

28. 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 by selecting a correction vector based on the selected mixture component and filtering the correction vector relative to time; and

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

7. The method of claim 28 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.