Audio identification based on data structure

1. An apparatus comprising:

memory; and

one or more processors to execute instructions to:

execute a constant Q transform on query time slices of query audio;

binarize the constant Q transformed query time slices;

execute a two-dimensional Fourier transform on query time windows within the binarized constant Q transformed query time slices to generate two-dimensional Fourier transforms of the query time windows;

sequentially order the two-dimensional Fourier transforms in a query data structure; and

identify the query audio as a cover rendition of reference audio based on a comparison between the query data structure and a reference data structure associated with the reference audio.

2. The apparatus of claim 1 , wherein the constant Q transformed query time slices include a first constant Q transformed query time slice, and the one or more processors are to:

calculate a median value of a range of constant Q transformed query time slices that includes the first constant Q transformed query time slice; and

binarize the first constant Q transformed query time slice based on the median value of the range.

3. The apparatus of claim 1 , wherein the one or more processors are to:

execute a constant Q transform on reference time slices of the reference audio;

binarize the constant Q transformed reference time slices;

execute a two-dimensional Fourier transform on reference time windows within the binarized constant Q transformed reference time slices to generate two-dimensional Fourier transforms of the reference time windows; and

sequentially order the two-dimensional Fourier transforms of the reference time windows in the reference data structure.

4. The apparatus of claim 1 , wherein the one or more processors are to:

store an association in a database between the reference audio and the reference data structure, the association indicates that the reference data structure is an identifier of the reference audio;

receive a request to identify the query audio, the request received from a device; and

control the device by causing the device to present a notification that the query audio is the cover rendition, the notification including the identifier.

5. The apparatus of claim 1 , wherein the one or more processors are to:

generate a similarity matrix that indicates degrees to which reference portions of the reference data structure are similar to query portions of the query data structure;

compute a distance measure between the query data structure and the reference data structure based on the similarity matrix; and

store an association in a database between the reference audio and the query audio based on a computed distance measure, the association identifying the query audio as the cover rendition.

6. The apparatus of claim 5 , wherein the one or more processors are to:

convolve the similarity matrix with a checkerboard kernel to generate a first convolved similarity matrix, the first convolved similarity matrix including positive elements and negative elements; and

replace the negative elements with zeros to generate a second convolved similarity matrix; and wherein:

the computing of the distance measure between the query data structure and the reference data structure is based on the second convolved similarity matrix.

7. The apparatus of claim 1 , wherein the one or more processors are to:

generate a similarity matrix based on reference portions of the reference data structure with respect to query portions of the query data structure;

convolve the similarity matrix with a checkerboard kernel to generate a first convolved similarity matrix including negative elements;

replace respective ones of the negative elements with zeros to generate a second convolved similarity matrix;

identify diagonals with the negative elements replaced with zeros in the second convolved similarity matrix;

compute a first set of lengths and sums of the diagonals;

compute multiplicative products of the first set;

rank the diagonals based on the multiplicative products;

identify a dominant subset of the diagonals based on the ranking;

sum multiplicative products of a second set of lengths and sums of the dominant subset; and

compute a distance measure between the query data structure and the reference data structure based on the summed multiplicative products.

8. The apparatus of claim 1 , wherein the one or more processors are to group the binarized constant Q transformed query time slices of the query audio into the query time windows prior to the executing of the two-dimensional Fourier transform on the query time windows, the query time windows including overlapping query time windows of uniform duration.

9. The apparatus of claim 1 , wherein the one or more processors are to apply a blur algorithm to the two-dimensional Fourier transforms of the query time windows prior to the sequential ordering of the two-dimensional Fourier transforms in the query data structure.

10. A non-transitory machine-readable medium comprising instructions that, when executed, cause one or more processors to at least:

execute a constant Q transform on query time slices of query audio;

binarize the constant Q transformed query time slices;

execute a two-dimensional Fourier transform on query time windows within the binarized constant Q transformed query time slices to generate two-dimensional Fourier transforms of the query time windows;

sequentially order the two-dimensional Fourier transforms in a query data structure; and

identify the query audio as a cover rendition of reference audio based on a comparison between the query data structure and a reference data structure associated with the reference audio.

11. The non-transitory machine-readable medium of claim 10 , wherein the constant Q transformed query time slices include a first constant Q transformed query time slice, and the instructions, when executed, cause the one or more processors to:

calculate a median value of a range of constant Q transformed query time slices that includes the first constant Q transformed query time slice; and

binarize the first constant Q transformed query time slice based on the median value of the range.

12. The non-transitory machine-readable medium of claim 10 , wherein the instructions, when executed, cause the one or more processors to:

execute a constant Q transform on reference time slices of the reference audio;

binarize the constant Q transformed reference time slices;

execute a two-dimensional Fourier transform on reference time windows within the binarized constant Q transformed reference time slices to generate two-dimensional Fourier transforms of the reference time windows; and

sequentially order the two-dimensional Fourier transforms of the reference time windows in the reference data structure.

13. The non-transitory machine-readable medium of claim 10 , wherein the instructions, when executed, cause the one or more processors to:

store an association in a database between the reference audio and the reference data structure, the association indicates that the reference data structure is an identifier of the reference audio;

receive a request to identify the query audio, the request received from a device; and

control the device by causing the device to present a notification that the query audio is the cover rendition, the notification including the identifier.

14. The non-transitory machine-readable medium of claim 10 , wherein the instructions, when executed, cause the one or more processors to:

generate a similarity matrix that indicates degrees to which reference portions of the reference data structure are similar to query portions of the query data structure;

compute a distance measure between the query data structure and the reference data structure based on the similarity matrix; and

store an association in a database between the reference audio and the query audio based on a computed distance measure, the association identifying the query audio as the cover rendition.

15. The non-transitory machine-readable medium of claim 14 , wherein the instructions, when executed, cause the one or more processors to:

convolve the similarity matrix with a checkerboard kernel to generate a first convolved similarity matrix, the first convolved similarity matrix including positive elements and negative elements; and

replace the negative elements with zeros to generate a second convolved similarity matrix; and wherein:

the computing of the distance measure between the query data structure and the reference data structure is based on the second convolved similarity matrix.

16. The non-transitory machine-readable medium of claim 10 , wherein the instructions, when executed, cause the one or more processors to:

generate a similarity matrix based on reference portions of the reference data structure with respect to query portions of the query data structure;

convolve the similarity matrix with a checkerboard kernel to generate a first convolved similarity matrix including negative elements;

replace respective ones of the negative elements with zeros to generate a second convolved similarity matrix;

identify diagonals with the negative elements replaced with zeros in the second convolved similarity matrix;

compute a first set of lengths and sums of the diagonals;

compute multiplicative products of the first set;

rank the diagonals based on the multiplicative products;

identify a dominant subset of the diagonals based on the ranking;

sum multiplicative products of a second set of lengths and sums of the dominant subset; and

compute a distance measure between the query data structure and the reference data structure based on the summed multiplicative products.

17. The non-transitory machine-readable medium of claim 10 , wherein the instructions, when executed, cause the one or more processors to group the binarized constant Q transformed query time slices of the query audio into the query time windows prior to the executing of the two-dimensional Fourier transform on the query time windows, the query time windows including overlapping query time windows of uniform duration.

18. The non-transitory machine-readable medium of claim 10 , wherein the instructions, when executed, cause the one or more processors to apply a blur algorithm to the two-dimensional Fourier transforms of the query time windows prior to the sequential ordering of the two-dimensional Fourier transforms in the query data structure.

19. A method comprising:

executing, using one or more processors, a constant Q transform on query time slices of query audio;

binarizing, using the one or more processors, the constant Q transformed query time slices;

executing, using the one or more processors, a two-dimensional Fourier transform on query time windows within the binarized constant Q transformed query time slices to generate two-dimensional Fourier transforms of the query time windows;

sequentially ordering, using the one or more processors, the two-dimensional Fourier transforms in a query data structure; and

identifying, using the one or more processors, the query audio as a cover rendition of reference audio based on a comparison between the query data structure and a reference data structure associated with the reference audio.

20. The method of claim 19 , wherein the constant Q transformed query time slices include a first constant Q transformed query time slice, and binarizing the constant Q transformed query time slices includes:

calculating a median value of a range of constant Q transformed query time slices that includes the first constant Q transformed query time slice; and

binarizing the first constant Q transformed query time slice based on the median value of the range.

21. The method of claim 19 , further including:

executing a constant Q transform on reference time slices of the reference audio;

binarizing the constant Q transformed reference time slices;

executing a two-dimensional Fourier transform on reference time windows within the binarized constant Q transformed reference time slices to generate two-dimensional Fourier transforms of the reference time windows; and

sequentially ordering the two-dimensional Fourier transforms of the reference time windows in the reference data structure.

22. The method of claim 19 , further including:

storing an association in a database between the reference audio and the reference data structure, the association indicates that the reference data structure is an identifier of the reference audio;

receiving a request to identify the query audio, the request received from a device; and

controlling the device by causing the device to present a notification that the query audio is the cover rendition, the notification including the identifier.

23. The method of claim 19 , further including:

generating a similarity matrix that indicates degrees to which reference portions of the reference data structure are similar to query portions of the query data structure;

computing a distance measure between the query data structure and the reference data structure based on the similarity matrix; and

storing an association in a database between the reference audio and the query audio based on a computed distance measure, the association identifying the query audio as the cover rendition.

24. The method of claim 23 , further including:

convolving the similarity matrix with a checkerboard kernel to generate a first convolved similarity matrix, the first convolved similarity matrix including positive elements and negative elements; and

replacing the negative elements with zeros to generate a second convolved similarity matrix; and wherein:

the computing of the distance measure between the query data structure and the reference data structure is based on the second convolved similarity matrix.

25. The method of claim 19 , further including:

generating a similarity matrix based on reference portions of the reference data structure with respect to query portions of the query data structure;

convolving the similarity matrix with a checkerboard kernel to generate a first convolved similarity matrix including negative elements;

replacing respective ones of the negative elements with zeros to generate a second convolved similarity matrix;

identifying diagonals with the negative elements replaced with zeros in the second convolved similarity matrix;

calculating a first set of lengths and sums of the diagonals;

calculating multiplicative products of the first set;

ranking the diagonals based on the multiplicative products;

identifying a dominant subset of the diagonals based on the ranking;

summing multiplicative products of a second set of lengths and sums of the dominant subset; and

calculating a distance measure between the query data structure and the reference data structure based on the summed multiplicative products.

26. The method of claim 19 , further including grouping the binarized constant Q transformed query time slices of the query audio into the query time windows prior to the executing of the two-dimensional Fourier transform on the query time windows, the query time windows including overlapping query time windows of uniform duration.

27. The method of claim 19 , further including applying a blur algorithm to the two-dimensional Fourier transforms of the query time windows prior to the sequential ordering of the two-dimensional Fourier transforms in the query data structure.