Audio separation system and method

1. A method comprising:

determining a first spectrogram of an audio signal;

defining a similarity matrix of the audio signal based on the first spectrogram and a transposed version of the first spectrogram;

identifying two or more similar frames in the similarity matrix that are more similar to a designated frame than to one or more other frames in the similarity matrix;

creating a repeating spectrogram model based on the two or more similar frames that are identified in the similarity matrix;

deriving a mask based on the repeating spectrogram model and the first spectrogram of the audio signal, the mask representative of similarities between the repeating spectrogram model and the first spectrogram of the audio signal; and

extracting a repeating structure from the audio signal by applying the mask to the audio signal.

2. The method of claim 1 , wherein the first spectrogram is a magnitude spectrogram that represents magnitudes of a Short Time Fourier Transform (STFT) of the audio signal.

3. The method of claim 1 , wherein the first spectrogram is a magnitude spectrogram and defining the similarity matrix is performed by matrix multiplying the magnitude spectrogram by a transposed version of the magnitude spectrogram.

4. The method of claim 1 , wherein identifying the two or more similar frames includes: determining which frames in the similarity matrix are more similar to the designated frame in the similarity matrix than the one or more other frames and that are temporally separated by at least a designated time delay; and identifying the frames that are more similar to the designated frame and temporally separated by at least the designated time delay as the two or more similar frames.

5. The method of claim 1 , wherein creating the repeating spectrogram model includes calculating a median of the two or more similar frames for each of one or more frequency channels of the first spectrogram.

6. The method of claim 1 , wherein deriving the mask includes: creating a refined repeating spectrogram model that represents a comparison of the repeating spectrogram model and the first spectrogram at each of a plurality of time-frequency bins of the repeating spectrogram model and the first spectrogram; and normalizing the refined repeating spectrogram model by the first spectrogram at each of the plurality of time-frequency bins.

7. The method of claim 6 , wherein the refined repeating spectrogram model represents a minimum between the repeating spectrogram model and the first spectrogram at each of the time-frequency bins.

8. The method of claim 1 , wherein extracting the repeating structure includes symmetrizing the mask, applying the mask to a Short Time Fourier Transform (STFT) of the audio signal, and inverting the STFT after applying the mask to the STFT.

9. A system comprising:

a processor and a memory, the memory storing instructions which, when executed by the processor, cause the processor to implement:

an identification module configured to determine a first spectrogram of an audio signal, define a similarity matrix of the audio signal based on the first spectrogram and a transposed version of the first spectrogram, and identify two or more similar frames in the similarity matrix that are more similar to a designated frame than to one or more other frames in the similarity matrix, the identification module also configured to create a repeating spectrogram model based on the two or more similar frames that are identified in the similarity matrix; and

a masking module configured to derive a mask based on the repeating spectrogram model and the first spectrogram of the audio signal, the mask representative of similarities between the repeating spectrogram model and the first spectrogram of the audio signal, the masking module further configured to extract a repeating structure from the audio signal by applying the mask to the audio signal.

10. The system of claim 9 , wherein the identification module is configured to determine the first spectrogram as a magnitude spectrogram that represents magnitudes of a Short Time Fourier Transform (STFT) of the audio signal.

11. The system of claim 9 , wherein the first spectrogram is a magnitude spectrogram and the identification module is configured to define the similarity matrix by matrix multiplying the magnitude spectrogram by a transposed version of the magnitude spectrogram.

12. The system of claim 9 , wherein the identification module is configured to identify the two or more similar frames by determining which frames in the similarity matrix are more similar to the designated frame in the similarity matrix than the one or more other frames and that are temporally separated by at least a designated time delay and identifying the frames that are more similar to the designated frame and temporally separated by at least the designated time delay as the two or more similar frames.

13. The system of claim 9 , wherein the identification module is configured to create the repeating spectrogram model by calculating a median of the two or more similar frames for each of one or more frequency channels of the first spectrogram.

14. The system of claim 9 , wherein the masking module is configured to create a refined repeating spectrogram model that represents a comparison of the repeating spectrogram model and the first spectrogram at each of a plurality of time-frequency bins of the repeating spectrogram model and the first spectrogram and normalize the refined repeating spectrogram model by the first spectrogram at each of the plurality of time-frequency bins.

15. The system of claim 14 , wherein the refined repeating spectrogram model represents a minimum between the repeating spectrogram model and the first spectrogram at each of the time-frequency bins.

16. The system of claim 9 , wherein the masking module is configured to extract the repeating structure by symmetrizing the mask, applying the mask to a Short Time Fourier Transform (STFT) of the audio signal, and inverting the STFT after applying the mask to the STFT.

17. A non-transitory computer readable storage medium comprising one or more sets of instructions configured to direct a processor of a system to:

determine a first spectrogram of an audio signal;

define a similarity matrix of the audio signal based on the first spectrogram and a transposed version of the first spectrogram;

identify two or more similar frames in the similarity matrix that are more similar to a designated frame than to one or more other frames in the similarity matrix;

create a repeating spectrogram model based on the two or more similar frames that are identified in the similarity matrix;

derive a mask based on the repeating spectrogram model and the first spectrogram of the audio signal, the mask representative of similarities between the repeating spectrogram model and the first spectrogram of the audio signal; and

extract a repeating structure from the audio signal by applying the mask to the audio signal.

18. The computer readable storage medium of claim 17 , wherein the one or more sets of instructions are configured to direct the processor to determine the first spectrogram as a magnitude spectrogram that represents magnitudes of a Short Time Fourier Transform (STFT) of the audio signal.

19. The computer readable storage medium of claim 17 , wherein the first spectrogram is a magnitude spectrogram and the one or more sets of instructions are configured to direct the processor to define the similarity matrix by matrix multiplying the magnitude spectrogram by a transposed version of the magnitude spectrogram.

20. The computer readable storage medium of claim 17 , wherein the one or more sets of instructions are configured to direct the processor to identify the two or more similar frames by: determining which frames in the similarity matrix are more similar to the designated frame in the similarity matrix than the one or more other frames and that are temporally separated by at least a designated time delay; and identifying the frames that are more similar to the designated frame and temporally separated by at least the designated time delay as the two or more similar frames.

21. The computer readable storage medium of claim 17 , wherein the one or more sets of instructions are configured to direct the processor to create the repeating spectrogram model by calculating a median of the two or more similar frames for each of one or more frequency channels of the first spectrogram.

22. The computer readable storage medium of claim 17 , wherein the one or more sets of instructions are configured to direct the processor to derive the mask by:

creating a refined repeating spectrogram model that represents a comparison of the repeating spectrogram model and the first spectrogram at each of a plurality of time-frequency bins of the repeating spectrogram model and the first spectrogram; and normalizing the refined repeating spectrogram model by the first spectrogram at each of the plurality of time-frequency bins.

23. The computer readable storage medium of claim 22 , wherein the refined repeating spectrogram model represents a minimum between the repeating spectrogram model and the first spectrogram at each of the time-frequency bins.

24. The computer readable storage medium of claim 17 , wherein the one or more sets of instructions are configured to direct the processor to extract the repeating structure by symmetrizing the mask, applying the mask to a Short Time Fourier Transform (STFT) of the audio signal, and inverting the STFT after applying the mask to the STET.