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1. (WO2019005232) MIXTURE MODEL BASED SOFT-CLIPPING DETECTION
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What is claimed is:

1. A method, comprising:

receiving input audio samples;

generating soft-clipping information indicating whether the input audio samples include soft-clipping distortion, wherein generating the soft-clipping information includes:

fitting a mixture model to the input audio samples, wherein fitting the mixture model to the input audio samples includes generating a fitted mixture model having fitted parameters, the fitted parameters of the fitted mixture model identifying at least a portion of the soft-clipping information; and

outputting the soft-clipping information.

2. The method of claim 1, wherein:

receiving the input audio samples comprises receiving an input data signal that includes the input audio samples;

generating the soft-clipping information includes:

evaluating a soft-clipping distortion metric based on the fitted parameters of the fitted mixture model, wherein evaluating the soft-clipping distortion metric includes identifying a soft-clipping distortion value of the soft-clipping information; and

outputting the soft-clipping information comprises outputting the soft-clipping distortion value.

3. The method of claim 1 or 2, wherein evaluating the soft-clipping distortion metric includes:

evaluating the soft-clipping distortion metric using a trained machine learning model.

4. The method of any of claims 1 to 3, wherein the mixture model is a restricted mixture model.

5. The method of claim 4, wherein the restricted mixture model includes:

a Laplacian distribution having a zero mean;

a first Gaussian distribution; and

a second Gaussian distribution, wherein the first Gaussian distribution and the second Gaussian distribution are symmetrical.

6. The method of any of claims 1 to 5, wherein fitting the mixture model to the input audio samples includes:

fitting the mixture model to the input audio samples using expectation maximization.

7. The method of any of claims 1 to 3, wherein generating the fitted mixture model comprises generating the fitted mixture model such that the fitted parameters of the fitted mixture model include:

a weight of a Gaussian distribution of the fitted mixture model;

a mean of the Gaussian distribution of the fitted mixture model;

a standard deviation of the Gaussian distribution of the fitted mixture model; and a standard deviation of a Laplacian distribution of the fitted mixture model.

8. The method of claim 7, wherein generating the soft-clipping information includes:

evaluating a soft-clipping distortion metric based on the fitted parameters of the fitted mixture model.

9. The method of claim 8, wherein evaluating the soft-clipping distortion metric includes:

generating an indication that the input audio samples include soft-clipping distortion in response to a determination that the mean of the Gaussian distribution of the fitted mixture model exceeds a first multiple of the standard deviation of the Gaussian distribution of the fitted mixture model and that the mean of the Gaussian distribution of the fitted mixture model exceeds a second multiple of the standard deviation of the Laplacian distribution of the fitted mixture model.

10. The method of claim 9, wherein the first multiple is three and the second multiple is two.

11. The method of any of claims 8 to 10, wherein evaluating the soft-clipping distortion metric includes:

identifying a soft-clipping distortion value indicating a severity of the soft-clipping distortion.

12. The method of claim 11, wherein identifying the soft-clipping distortion value includes:

identifying a multiple of the weight of the Gaussian distribution of the fitted mixture model as the soft-clipping distortion value.

13. The method of claim 12, wherein the third multiple is two.

14. The method of claim 11, wherein identifying the soft-clipping distortion value includes:

identifying, as the soft-clipping distortion value, a result of evaluating the fitted parameters of the fitted mixture model using a machine learning model.

15. The method of any of claims 1 to 14, wherein fitting the mixture model to the input audio samples includes:

identifying a defined cardinality of input audio quantiles from the input audio samples; and

fitting the mixture model to the input audio quantiles.

16. The method of any of claims 1 to 15, wherein fitting the mixture model to the input audio samples includes:

identifying hard-clipped audio samples from the input audio samples;

identifying background audio samples from the input audio samples;

identifying target audio samples including the input audio samples other than the hard-clipped audio samples and the background audio samples; and

fitting the mixture model to the target audio samples.

17. The method of any of claims 1 to 16, wherein generating the soft-clipping information includes:

identifying a sequence of temporal portions of the input audio samples; and fitting the mixture model to the input audio samples by:

for a temporal portion from the sequence of temporal portions:

fitting the mixture model to the temporal portion; and

generating the soft-clipping information indicating whether the temporal portion includes soft-clipping distortion.

18. The method of claim 2 or any claim dependent from claim 2, wherein the input data signal is an audio signal or an audio signal component of a video signal.

19. The method of claim 1, wherein:

receiving the input audio samples comprises receiving an input data signal, wherein the input data signal includes the input audio samples;

generating the soft-clipping information includes:

identifying the mixture model that includes a Laplacian distribution having a zero mean, a first Gaussian distribution, and a second Gaussian distribution, wherein the first Gaussian distribution and the second Gaussian distribution are symmetrical; and

fitting the mixture model includes:

identifying a sequence of temporal portions of the input audio samples;

for each temporal portion from the sequence of temporal portions:

generating a respective fitted mixture model by fitting the mixture model to the respective input audio samples from the temporal portion using expectation maximization;

generating respective soft-clipping information indicating whether the temporal portion includes soft-clipping distortion by evaluating a soft-clipping distortion metric based on fitted parameters of the respective fitted mixture model, wherein, in response to a determination that the temporal portion includes soft-clipping distortion, generating the respective soft-clipping information includes identifying a respective soft-clipping distortion value indicating a severity of the soft-clipping distortion for the respective input audio samples from the temporal portion; and

including the respective soft-clipping information for the temporal portion in the soft-clipping information; and

wherein the method further comprises:

generating an average soft-clipping distortion value for the input data signal; including the average soft-clipping distortion value for the input data signal in the soft-clipping information for the input data signal;

identifying a maximum soft-clipping distortion value for the input data signal; and

including the maximum soft-clipping distortion value for the input data sig in the soft-clipping information.

20. An apparatus, comprising:

a memory; and

a processing device operatively coupled with the memory to perform operations comprising the method of any of claims 1 to 19.