Adaptive reduction of noise signals and background signals in a speech-processing system

• US 20070043559A1
• Filed: 08/21/2006
• Published: 02/22/2007
• Est. Priority Date: 08/19/2005
• Status: Active Grant

• Current Assignee: MaxLinear Communications, LLC (f/k/a Entropic Communications, LLC) (Maxlinear Incorporated)
• Original Assignee: Trident Microsystems Limited (Avnet Incorporated)
• Inventors: Fischer, Joern
• Granted Patent: US 7,822,602 B2
• Time in Patent Office: Days
• US Class Current: 704/219
• CPC Class Codes: G10L 21/02 Speech enhancement, e.g. noise reduction or echo cancellation reducing echo effects in line transmission systems H04B3/20; echo suppression in hands-free telephones H04M9/08
• CPC Class Codes: G10L 21/0208 Noise filtering

Abstract

An audio input signal is filtered using an adaptive filter to generate a prediction output signal with reduced noise, wherein the filter is implemented using a plurality of coefficients to generate a plurality of prediction errors and to generate an error from the plurality of prediction errors, wherein the absolute values of the coefficients are continuously reduced by a plurality of reduction parameters.

Claims

1. A method for reducing noise signals and background signals in a speech-processing system, comprising:

adaptively filtering an audio input signal s(t) to generate a prediction output signal sv(t) using a plurality of coefficients (c_i(t);

c1-c4) to generate a plurality of prediction errors (sv1-sv4) and generating an error (e) from the plurality of prediction errors (sv1-sv4), where the prediction output signal sv(t) is the sum of the plurality of prediction errors;

where the absolute values of the coefficients (c_i(t);

c1-c4) are reduced by a plurality of reduction parameters (k).

2. The method of claim 1, where the reduction of the coefficients (c_i(t)) is generated by multiplying the coefficients by a factor less than one, for example, by a factor between about 0.8 and 1.0.

3. The method of claim 1, where the coefficients (c_i(t)) are computed according to the equation

c_i(t+1)=c_i(t)+(μ

·

e·

s(t−

i))−

kc_i(t)

where

k, with 0>

k<

<

1, in particular k<

=0.0001 is a reduction parameter;

μ

<

<

1, in particular μ

<

=0.01 is a learning rate;

s(t) is an audio input signal at a time t;

e is an error resulting from the difference of all the individual prediction errors (sv1-sv4) from the audio input signal s(t);

sv(t) is the prediction output signal resulting from the sum of all the individual prediction errors, where N is the number of coefficients c_i(t); and

c_i(t) is an individual coefficient having an index i at time t.

4. The method of claim 3, where the coefficients (c_i(t)) are computed according to the equation

c_i(t+1)=c_i(t)+(μ

·

e·

s(t−

i))−

kc_i(t)

where

e=S(t)−

sv(t) and
sv(t)=Σ

_{i=1 . . . N}c_i(t−

1)·

s(t−

i).

5. The method of claim 1, where the prediction output signal (sv(t)) as a prediction of the audio input signal with reduced noise is used as an input signal for a second filter (F2) to generate a second prediction (sv*(t)).

6. The method of claim 5, where the second filter (F2) comprises a prediction filter having a second filter with a set of second coefficients, wherein a learning rate (μ

*) to adapt the coefficients is selected that is several powers of ten less than a learning rate (μ

) of the first filter (F1).

7. The method of claim 6, comprising subtracting the second prediction from the prediction output signal (sv(t)).

8. The method of claim 7, where a learning rule is asymmetrically designed to determine the subsequent coefficients (c_i*(t);

c*1-c*4) such that the absolute values of the subsequent coefficients (c_i*(t);

c*1-c*4) fall more significantly in absolute value than they rise and can rapidly fall to zero, but rise only with a small gradient.

9. The method of claim 1, where instead of the audio input signal (S(t)) to determine individual prediction errors (sv1-sv4), only its sign is used.

10. The method of claim 1, where the coefficients (c_i(t);

c1-c4) are limited to prevent drifting of the coefficients, in particular, from −

4 . . . 4, when the audio input signal is normalized from −

1 . . . 1.

11. The method of claim 1, where a maximum of a speech signal component of the audio input signal (s(t)) is detected, and the output signal (o(t)) is renormalized to this maximum.

12. The method of claim 1, where the output signal (sv(t);

sv*(t)) of the first and/or second filter relative to its input signal (s(t);

sv(t)) is used as a measure for the presence of speech in the input signal.

13. The method of claim 1, where the step of adaptively filtering comprises least mean squares processing.

14. The method of claim 13, where the step of adaptively filtering comprises FIR filtering.

15. The method of claim 1, comprising multiplying a sigmoid function by the prediction output signal (sv(t)) to prevent an overmodulation of the signal in case of a bad prediction.

16. The method of claim 1, comprising mixing the audio input signal (s(t)) with the prediction output signal (sv(t)).

17. The method of claim 1, further comprising programming an application-specific integrated circuit.

18. A device for the reduction of noise signals and background signals in a speech-processing system, comprising:

an adaptive filter that filters an audio input signal (s(t)) and provides a prediction output signal sv(t) with reduced noise;

memory that stores a plurality of coefficients for the adaptive filter;

wherein the adaptive filter generates a plurality of prediction errors and an error (e) from the plurality of prediction errors, where

a coefficient supply circuit reduces the absolute values of the coefficients using at least one reduction parameter (k).

19. The device of claim 18, where the coefficient supply circuit multiplies the coefficients (c_i(t)) by the reduction parameter (k) in the form of a factor smaller than one, for example, by a factor between 0.8 and 1.0.

20. The device of claim 18, comprising a second filter stage with a second filter connected following a first filter stage to receive the prediction output signal as a predictive measure of the audio input signal with reduced noise as an input signal for the second filter to generate a second prediction (sv*(t)).

21. The device of claim 20, further comprising an adder that provides a difference signal indicative of the difference between error predictions (sv*1-sv*4) of the second filter (F2) from the prediction output signal (sv(t)) of the first filter (F1) in order to generate a prediction (sv*(t)).

22. The device of claim 20, where the second filter (F2) comprises an LMS adaptation filter to implement error prediction.

23. The device of claim 18, where the first filter (F1) comprises a FIR filter.

24. The device of claim 18, which is formed by a field-programmable component or an application specific integrated circuit.

25. The device of claim 21, further comprising a subtraction circuit to subtract the values of the prediction (sv*(t)) from the values of the audio input signal (s(t)) to generate a noise-reduced audio output signal (o*(t)).

26. The device of claim 18, further comprising:

a multiplier (15;

15*) to weight the optionally time-delayed audio input signal (s(t)), or to weight the prediction output signal (sv(t)) by a weighting factor (η

;

η

*) smaller than one, in particular, for example 0.1; and

an adder (7;

14*) to add the weighted signal to the prediction output signal (sv(t)) or to the prediction (sv*(t)) to generate a noise-reduced audio output signal (o(t);

o*(t)).