Method for packet loss and/or frame erasure concealment in a voice communication system

1. A method for decoding an encoded speech signal, comprising:

if a segment of the encoded speech signal is good, decoding the segment to derive an excitation signal, long-term predictive parameters and short-term predictive parameters;

if the segment is bad, scaling a random sequence of samples to derive the excitation signal and deriving the long-term predictive parameters and short-term predictive parameters based on parameters associated with a previously decoded segment, wherein scaling the random sequence comprises:

calculating a scaling factor; and

applying the scaling factor to scale the random sequence relative to a level of previous long-term excitation;

wherein calculating the scaling factor comprises increasing the value of the scaling factor towards an upper limit with decreasing periodicity and decreasing the value of the scaling factor towards a lower limit with increasing periodicity;

filtering the excitation signal in a long-term synthesis filter under the control of the long-term predictive parameters, thereby generating a first output signal; and

filtering the first output signal in a short-term synthesis filter under the control of the short-term predictive parameters, thereby generating a second output signal.

2. The method of claim 1 , wherein the level of previous long-term excitation is measured in terms of signal energy.

3. The method of claim 1 , wherein the level of previous long-term excitation is measured in terms of average signal amplitude.

4. The method of claim 1 , wherein scaling the random sequence comprises scaling the random sequence such that the level of the random sequence approaches a level of previous long-term excitation for decreasing periodicity, and the level of the random sequence decreases as compared to the level of previous long-term excitation for increasing periodicity.

5. The method of claim 1 , wherein scaling the random sequence comprises scaling the random sequence as a function of periodicity.

6. The method of claim 5 , wherein scaling the random sequence as a function of periodicity comprises scaling the random sequence in accordance with a monotonic decreasing function.

7. The method of claim 1 , wherein scaling the random sequence comprises multiplying a first factor that corresponds to a level of previous long-term excitation by a second factor that operates to reduce the level of previous long-term excitation with increasing periodicity.

8. The method of claim 1 , wherein scaling the random sequence comprises:

using a measure of periodicity to control the scaling of the random sequence.

9. The method of claim 8 , wherein using a measure of periodicity comprises using a measure of an instantaneous periodicity of a previously-decoded segment of the encoded speech signal.

10. The method of claim 8 , wherein using a measure of periodicity comprises using a smoothed periodicity measure.

11. The method of claim 10 , wherein using a smoothed periodicity measure comprises low pass filtering an instantaneous periodicity measure of a previously-decoded segment of the encoded speech signal.

12. The method of claim 11 , wherein using a smoothed periodicity measure comprises calculating:

c s ( k )= α·c s ( k −1)+(1−α)· c ( k ),

wherein c s (k) is the smoothed periodicity measure, c s (k−1) is the smoothed periodicity measure of a previously-decoded segment of the encoded speech signal, c(k) is an instantaneous periodicity measure, and α is a predetermined factor that controls smoothing.

13. The method of claim 1 , wherein deriving the long-term predictive parameters and short-term predictive parameters based on parameters associated with a previously-decoded segment comprises using long-term predictive parameters and short-term predictive parameters associated with the previously-decoded segment.

14. The method of claim 1 , further comprising:

determining if a number of consecutively-received bad segments exceeds a predetermined threshold;

if the number of consecutively-received bad segments exceeds the predetermined threshold, gradually reducing the second output signal.

15. The method of claim 1 , further comprising:

monitoring a number of consecutively-received bad segments; and

gradually reducing a scaling factor used for scaling the random sequence in relation to the number of consecutively-received bad segments.

16. The method of claim 1 , wherein the long-term predictive parameters include a long-term filter coefficient, the method further comprising:

monitoring a number of consecutively-received bad segments; and

gradually reducing the long-term filter coefficient in relation to the number of consecutively-received bad segments.

17. The method of claim 1 , wherein the long-term predictive parameters include a long-term filter coefficient, the method further comprising:

determining if a number of consecutively-received bad segments exceeds a predetermined threshold;

if the number of consecutively-received bad segments exceeds the predetermined threshold, gradually reducing a scaling factor used for scaling the random sequence in relation to the number of consecutively-received bad segments and gradually reducing the long-term filter coefficient in relation to the number of consecutively-received bad segments.

18. A method for decoding an encoded speech signal, comprising:

if a segment of the encoded speech signal is good, decoding the segment to derive an excitation signal and predictive parameters for controlling a synthesis filter;

if the segment is bad, scaling a random sequence of samples to derive the excitation signal, and deriving the predictive parameters based on parameters associated with a previously decoded segment, wherein scaling the random sequence comprises:

calculating a scaling factor; and

applying the scaling factor to scale the random sequence relative to a level of previous long-term excitation;

wherein calculating the scaling factor comprises increasing the value of the scaling factor towards an upper limit with decreasing periodicity and decreasing the value of the scaling factor towards a lower limit with increasing periodicity; and

filtering the excitation signal in a synthesis filter under the control of the predictive parameters.

19. A method for decoding an encoded speech signal, comprising:

if a segment of the encoded speech signal is good, decoding the segment to derive an excitation signal;

if the segment is bad, scaling a random sequence of samples to derive the excitation signal, wherein scaling the random sequence comprises:

calculating a scaling factor; and

applying the scaling factor to scale the random sequence relative to a level of previous long-term excitation;

wherein calculating the scaling factor comprises increasing the value of the scaling factor towards an upper limit with decreasing periodicity and decreasing the value of the scaling factor towards a lower limit with increasing periodicity; and

filtering the excitation signal in a synthesis filter under the control of predictive parameters.

20. A speech decoder, comprising:

a controller configured to derive an excitation signal, long-term predictive parameters and short-term predictive parameters;

a long-term synthesis filter that filters the excitation signal under the control of the long-term predictive parameters to generate a first output signal;

a short-term synthesis filter that filters the first output signal under the control of the short-term predictive parameters to generate a second output signal;

wherein the controller is configured

(a) to derive the excitation signal, long-term predictive parameters and short-term predictive parameters from decoded information pertaining to a segment of an encoded speech signal if the segment is good, and

(b) to derive the long-term predictive parameters and short-term predictive parameters based on parameters associated with a previously decoded segment and to derive the excitation signal by scaling a random sequence of samples if the segment is bad, wherein scaling the random sequence comprises:

calculating a scaling factor; and

applying the scaling factor to scale the random sequence relative to a level of previous long-term excitation;

wherein calculating the scaling factor comprises increasing the value of the scaling factor towards an upper limit with decreasing periodicity and decreasing the value of the scaling factor towards a lower limit with increasing periodicity.

21. The speech decoder of claim 20 , wherein the level of previous long-term excitation is measured in terms of signal energy.

22. The speech decoder of claim 20 , wherein the level of previous long-term excitation is measured in terms of average signal amplitude.

23. The speech decoder of claim 20 , wherein the controller is configured to scale the random sequence such that the level of the random sequence approaches a level of a previous long-term excitation for decreasing periodicity, and the level of the random sequence decreases as compared to that of the level of previous long-term excitation for increasing periodicity.

24. The speech decoder of claim 20 , wherein the controller is configured to scale the random sequence as a function of periodicity.

25. The speech decoder of claim 24 , wherein the controller is configured to scale the random sequence in accordance with a monotonic decreasing function.

26. The speech decoder of claim 20 , wherein the controller is configured to scale the random sequence by multiplying a first factor that corresponds to a level of previous long-term excitation by a second factor that operates to reduce the level of previous long-term excitation with increasing periodicity.

27. The speech decoder of claim 20 , wherein the controller is configured to use a measure of periodicity to control the scaling of the random sequence.

28. The speech decoder of claim 27 , wherein the controller is configured to use a measure of an instantaneous periodicity of a previously-decoded segment of the encoded speech signal to control the scaling of the random sequence.

29. The speech decoder of claim 27 , wherein the controller is configured to use a smoothed periodicity measure to control the scaling of the random sequence.

30. The speech decoder of claim 29 , wherein the controller is further configured to low pass filter an instantaneous periodicity measure of a previously-decoded segment of the encoded speech signal to derive the smoothed periodicity measure.

31. The speech decoder of claim 29 , wherein the controller is further configured to calculate the smoothed periodicity measure in accordance with:

c s ( k )=α· c s ( k− 1)+(1−α)· c ( k ),

wherein c s (k) is the smoothed periodicity measure, c s (k−1) is the smoothed periodicity measure of a previously-decoded segment of the encoded speech signal, c(k) is an instantaneous periodicity measure, and α is a predetermined factor that controls smoothing.

32. The speech decoder of claim 20 , wherein the controller is configured to use the long-term predictive parameters and short-term predictive parameters associated with a previously decoded segment if the segment is bad.

33. The speech decoder of claim 20 , wherein the controller is further configured to gradually reduce the second output signal based on whether a number of consecutively-received bad segments exceeds a predetermined threshold.

34. The speech decoder of claim 20 , wherein the controller is further configured to monitor a number of consecutively-received bad segments and to gradually reduce a scaling factor used for scaling the random sequence in relation to the number of consecutively-received bad segments.

35. The speech decoder of claim 20 , wherein the controller is further configured to monitor a number of consecutively-received bad segments and to gradually reduce a long-term filter coefficient in relation to the number of consecutively-received bad segments.

36. The speech decoder of claim 20 , wherein the controller is further configured to determine if a number of consecutively-received bad segments exceeds a predetermined threshold, and, if the number of consecutively-received bad segments exceeds the predetermined threshold, to gradually reduce a scaling factor used for scaling the random sequence in relation to the number of consecutively-received bad segments and to gradually reduce a long-term filter coefficient in relation to the number of consecutively-received bad segments.

37. A speech decoder, comprising:

a controller configured to derive an excitation signal and predictive parameters; and

a synthesis filter that filters the excitation signal under the control of the predictive parameters;

wherein the controller is configured

(a) to derive the excitation signal, long-term predictive parameters and short-term predictive parameters from decoded information pertaining to a segment of an encoded speech signal if the segment is good, and

(b) to derive the long-term predictive parameters and short-term predictive parameters based on parameters associated with a previously decoded segment and to derive the excitation signal by scaling a random sequence of samples if the segment is bad, wherein scaling the random sequence comprises:

calculating a scaling factor; and

applying the scaling factor to scale the random sequence relative to a level of previous long-term excitation;

wherein calculating the scaling factor comprises increasing the value of the scaling factor towards an upper limit with decreasing periodicity and decreasing the value of the scaling factor towards a lower limit with increasing periodicity.

38. A speech decoder, comprising:

a controller that derives an excitation signal; and

a synthesis filter that filters the excitation signal under the control of predictive parameters;

wherein the controller is configured to derive the excitation signal from decoded information pertaining to a segment of an encoded speech signal if the segment is good and to derive the excitation signal by scaling a random sequence of samples if the segment is bad, wherein scaling the random sequence comprises:

calculating a scaling factor; and

applying the scaling factor to scale the random sequence relative to a level of previous long-term excitation;

wherein calculating the scaling factor comprises increasing the value of the scaling factor towards an upper limit with decreasing periodicity and decreasing the value of the scaling factor towards a lower limit with increasing periodicity.

39. A method for processing a speech signal, comprising:

if a segment of the speech signal is good, using decoded information associated with the segment to derive an excitation signal, long-term predictive parameters and short-term predictive parameters

if the segment is bad, scaling a random sequence of samples to derive the excitation signal and deriving the long-term predictive parameters and short-term predictive parameters based on parameters associated with a previously-processed segment of the speech signal, wherein scaling the random sequence comprises:

calculating a scaling factor; and

applying the scaling factor to scale the random sequence relative to a level of previous long-term excitation;

wherein calculating the scaling factor comprises increasing the value of the scaling factor towards an upper limit with decreasing periodicity and decreasing the value of the scaling factor towards a lower limit with increasing periodicity;

filtering the excitation signal in a long-term synthesis filter under the control of the long-term predictive parameters, thereby generating a first output signal; and

filtering the first output signal in a short-term synthesis filter under the control of the short-term predictive parameters, thereby generating a second output signal.

40. A method for processing a speech signal, comprising:

if a segment of the speech signal is good, using decoded information associated with the segment to derive an excitation signal and predictive parameters for controlling a synthesis filter;

if the segment is bad, scaling a random sequence of samples to derive the excitation signal, and deriving the predictive parameters based on parameters associated with a previously-processed segment, wherein scaling the random sequence comprises:

calculating a scaling factor; and

applying the scaling factor to scale the random sequence relative to a level of previous long-term excitation;

wherein calculating the scaling factor comprises increasing the value of the scaling factor towards an upper limit with decreasing periodicity and decreasing the value of the scaling factor towards a lower limit with increasing periodicity; and

filtering the excitation signal in a synthesis filter under the control of the predictive parameters.

41. A method for processing a speech signal, comprising:

if a segment of the speech signal is good, using decoded information associated with the segment to derive an excitation signal;

if the segment is bad, scaling a random sequence of samples to derive the excitation signal, wherein scaling the random sequence comprises:

calculating a scaling factor; and

applying the scaling factor to scale the random sequence relative to a level of previous long-term excitation;

wherein calculating the scaling factor comprises increasing the value of the scaling factor towards an upper limit with decreasing periodicity and decreasing the value of the scaling factor towards a lower limit with increasing periodicity; and

filtering the excitation signal in a synthesis filter under the control of predictive parameters.