Audio signal classification by shape parameter estimation for a plurality of audio signal samples

1. A method comprising:

estimating at least one shaping parameter value of a generalized Gaussian random variable for a plurality of samples of the audio signal;

generating at least one audio signal classification value by mapping the at least one shaping parameter value to one of at least two probability values associated with each of at least two interval estimates;

comparing the at least one audio signal classification value to at least one previous audio signal classification value; and

generating the at least one audio signal classification decision dependent at least in part on the result of the comparison.

2. The method as claimed in claim 1 , wherein the at least one audio signal classification decision is updated to be the value of the at least one audio signal classification value if the result of the comparison indicates that the at least one audio signal classification value is the same as each of the at least one previous audio signal classification value and the at least one audio signal classification decision is not the same as an immediate proceeding audio signal classification decision.

3. The method as claimed in claim 1 , wherein the at least one previous audio signal classification value is stored in a first in first out memory.

4. The method as claimed in claim 1 , wherein each of the at least two probability values is associated with one of at least two distributions of pre-determined shaping parameter values, and wherein each of the at least two distributions of predetermined shaping parameter values is each associated with a different audio signal type.

5. The method as claimed in claim 1 , wherein generating the at least one audio signal classification value further comprises:

mapping the estimated shaping parameter value to a closest interval estimate; and

assigning the audio signal classification value a value representative of an audio signal type, wherein the value representative of the audio signal type is determined according to the greatest of the at least two probability values associated with the closest interval estimate.

6. The method for as claimed in claim 1 , wherein mapping the shaping parameter value comprises:

determining the closest interval estimate to the at least one shaping parameter value, wherein each interval estimate further comprises a classification value;

generating the at least one audio signal classification value dependent on the closest interval estimate classification value.

7. The method as claimed in claim 1 , wherein determining the closest interval estimate comprises:

selecting the interval estimate with a greatest probability value for the shaping parameter value.

8. The method as claimed in claim 1 , wherein estimating the shaping parameter value comprises:

calculating the ratio of a second moment of a normalized audio signal to the first moment of a normalized audio signal.

9. The method as claimed in claim 8 , wherein the normalized audio signal is formed by subtracting a mean value from the audio signal to form a resultant value and dividing the resultant value by a standard deviation value, wherein the calculation of the standard deviation at least comprises:

calculating a variance value for at least part of the audio signal;

updating a long term tracking variance with the variance value for the at least part of the audio signal; and wherein the calculation of the mean comprises;

calculation a mean value for at least part of the audio signal; and

updating a long term tracking mean with the mean value for the at least part of the audio signal.

10. The method as claimed in claim 1 , wherein the estimated shaping parameter value of the shaping parameter of a generalized Gaussian random variable is estimated using a method of estimation derived from a Mallat method of estimation.

11. The method as claimed in claim 1 , wherein the estimated shaping parameter value of the shaping parameter of a generalized Gaussian random variable is estimated using a Mallat method of estimation.

12. The method as claimed in claim 1 , wherein the estimated shaping parameter value of the shaping parameter of a generalized Gaussian random variable is estimated using a kurtosis value.

13. An apparatus comprising at least one processor and at least one memory including computer program code the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus at least to;

estimate at least one shaping parameter value of a generalized Gaussian random variable for a plurality of samples of the audio signal;

generate at least one audio signal classification value by mapping the at least one shaping parameter value to one of at least two probability values associated with each of at least two interval estimates; and

compare the at least one audio signal classification value to at least one previous audio signal classification value; and

generate the at least one audio signal classification decision dependent at least in part on the result of the comparison.

14. The apparatus as claimed in claim 13 , wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the apparatus to:

update the at least one audio signal classification decision to be the value of the at least one audio signal classification value if the result of the comparison indicates that the at least one audio signal classification value is the same as each of the at least one previous audio signal classification value and the at least one audio signal classification decision is not the same as an immediate proceeding audio signal classification decision.

15. The apparatus as claimed in claim 13 , wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the apparatus to:

store the at least one previous audio signal classification value is stored in a first in first out memory.

16. The apparatus as claimed in claim 13 , wherein each of the at least two probability values is associated with one of at least two distributions of pre-determined shaping parameter values, and wherein each of the at least two distributions of predetermined shaping parameter values is each associated with a different audio signal type.

17. The apparatus as claimed in claim 13 , wherein the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to generate the at least one audio signal classification value is further configured to cause the apparatus to:

map the estimated shaping parameter value to a closest interval estimate; and

assign the audio signal classification value a value representative of an audio signal type, wherein the value representative of the audio signal type is determined according to the greatest of the at least two probability values associated with the closest interval estimate.

18. The apparatus as claimed in claim 13 , wherein the at least one memory and the computer program code configured to map the shaping parameter value, with the at least one processor, is further configured to cause the apparatus to:

determine the closest interval estimate to the at least one shaping parameter value, wherein each interval estimate further comprises a classification value;

generate the at least one audio signal classification value dependent on the closest interval estimate classification value.

19. The apparatus as claimed in claim 13 , wherein the at least one memory and the computer program code configured to determine the closest interval estimate, with the at least one processor, is further configured to cause the apparatus to:

select the interval estimate with a greatest probability value for the shaping parameter value.

20. The apparatus as claimed in claim 13 , wherein the at least one memory and the computer program code configured to estimate the shaping parameter, with the at least one processor, is further configured to cause the apparatus to:

calculate the ratio of a second moment of a normalized audio signal to the first moment of a normalized audio signal.

21. The apparatus as claimed in claim 20 , wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the apparatus to:

form the normalized audio signal by subtracting a mean value from the audio signal to form a resultant value and dividing the resultant value by a standard deviation value, wherein the apparatus is configured to calculate of the standard deviation by calculating a variance value for at least part of the audio signal and updating a long term tracking variance with the variance value for the at least part of the audio signal, and wherein the apparatus is configured to calculate the mean by calculating a mean value for at least part of the audio signal and updating a long term tracking mean with the mean value for the at least part of the audio signal.

22. The apparatus as claimed in claim 13 , further configured to estimate the estimated shaping parameter of the shaping parameter of a generalized Gaussian random variable using a method of estimation derived from a Mallat method of estimation.

23. The apparatus as claimed in claim 13 , further configured to estimate the estimated shaping parameter of the shaping parameter of a generalized Gaussian random variable using a Mallat method of estimation.

24. The apparatus as claimed in claim 13 , further configured to estimate the estimated shaping parameter of the shaping parameter of a generalized Gaussian random variable using a kurtosis value.