ROLLING AUDIO RECOGNITION

• US 20110173208A1
• Filed: 01/13/2010
• Published: 07/14/2011
• Est. Priority Date: 01/13/2010
• Status: Active Grant

• Current Assignee: Rovi Technologies Corporation (Adeia Inc.)
• Original Assignee: Rovi Technologies Corporation (Adeia Inc.)
• Inventors: VOGEL, BRIAN KENNETH
• Granted Patent: US 8,886,531 B2
• Time in Patent Office: Days
• US Class Current: 707/746
• CPC Class Codes: G06F 16/683 using metadata automatically derived from the content
• CPC Class Codes: G06F 16/783 using metadata automatically derived from the content
• CPC Class Codes: G10L 25/48 specially adapted for particular use

Abstract

An audio fingerprint is generated by transforming an audio sample of a recording to a time-frequency domain and storing each time-frequency pair in a matrix array, detecting a plurality of local maxima for a predetermined number of time slices, selecting a predetermined number of largest-magnitude maxima from the plurality of local maxima detected by said detecting, and generating one or more hash values corresponding to the predetermined number of largest-magnitude maxima.

Claims

1. A method for generating an audio fingerprint, comprising the steps of:

transforming an audio sample of a recording to a time-frequency domain and storing each time-frequency pair in a matrix array;

detecting a plurality of local maxima for a predetermined number of time slices;

selecting a predetermined number of largest-magnitude maxima from the plurality of local maxima detected by said detecting; and

generating one or more hash values corresponding to the predetermined number of largest-magnitude maxima.

2. The method of claim 1, further comprising the steps of:

grouping the plurality of local maxima detected by said detecting in accordance with a time-slice interval; and

grouping a plurality of time-slices intervals, thereby defining a sub-spectrogram.

3. The method of claim 2, further comprising the step of:

determining a predetermined number of highest magnitude local maxima within each sub-spectrogram.

4. The method of claim 3, wherein determining of the predetermined number of highest magnitude local maxima includes:

sorting the plurality of local maxima within each sub-spectrogram in either descending or ascending magnitude; and

selecting a predetermined number of the largest-magnitude maxima.

5. The method of claim 1, further comprising the step of:

storing each maxima in a data structure according to a time index, a frequency index and a magnitude of each maxima.

6. The method of claim 5, further comprising the step of:

generating a hash value based on at least a portion of each maxima stored in the data structure.

7. The method of claim 1, further comprising the step of:

setting all elements of the matrix array that are below a predetermined noise floor to zero; and

setting all elements of the matrix array that are outside a predetermined frequency range to zero.

8. The method of claim 1, further comprising the step of:

performing noise filtering of the predetermined number of highest magnitude local maxima.

9. An apparatus for generating an audio fingerprint, comprising:

at least one processor configured to;

transform an audio sample of a recording to a time-frequency domain and storing each time-frequency pair in a matrix array;

detect a plurality of local maxima for a predetermined number of time slices;

select a predetermined number of largest-magnitude maxima from the plurality of local maxima detected by said detection; and

generate one or more hash values corresponding to the predetermined number of largest-magnitude maxima.

10. The apparatus of claim 9, further comprising:

the at least one processor further configured to;

group the plurality of local maxima detected by said detecting in accordance with a time-slice interval; and

group a plurality of time-slices intervals, thereby defining a sub-spectrogram.

11. The apparatus of claim 10, wherein the at least one processor is further configured to determine a predetermined number of highest magnitude local maxima within each sub-spectrogram.

12. The apparatus of claim 11, wherein the predetermined number of highest magnitude local maxima are determined by:

sorting the plurality of local maxima within each sub-spectrogram in either descending or ascending magnitude; and

selecting a predetermined number of the largest-magnitude maxima.

13. The apparatus of claim 9, further comprising:

a storage device configured to store each maxima in a data structure according to a time index, a frequency index and a magnitude of each maxima.

14. The apparatus of claim 13, wherein the at least one processor is further configured to generate a hash value based on at least a portion of each maxima stored in the data structure.

15. The apparatus of claim 9, wherein the at least one processor is further configured to set all elements of the matrix array that are below a predetermined noise floor to zero and set all elements of the matrix array that are outside a predetermined frequency range to zero.

16. The apparatus of claim 9, further comprising:

a noise filter operable to perform noise filtering of the predetermined number of highest magnitude local maxima.

17. A computer-readable medium having stored thereon sequences of instructions, the sequences of instructions including instructions which when executed by a computer system causes the computer system to perform:

transforming an audio sample of a recording to a time-frequency domain and storing each time-frequency pair in a matrix array;

detecting a plurality of local maxima for a predetermined number of time slices;

selecting a predetermined number of largest-magnitude maxima from the plurality of local maxima detected by said detecting; and

generating one or more hash values corresponding to the predetermined number of largest-magnitude maxima.

18. The computer-readable medium of claim 17, further having stored thereon a sequence of instructions which when executed by the computer system causes the computer system to perform:

grouping the plurality of local maxima detected by said detecting in accordance with a time-slice interval; and

grouping a plurality of time-slices intervals, thereby defining a sub-spectrogram.

19. The computer-readable medium of claim 18, further having stored thereon a sequence of instructions which when executed by the computer system causes the computer system to perform:

determining a predetermined number of highest magnitude local maxima within each sub-spectrogram.

20. The computer-readable medium of claim 19, wherein determining of the predetermined number of highest magnitude local maxima includes:

sorting the plurality of local maxima within each sub-spectrogram in either descending or ascending magnitude; and

selecting a predetermined number of the largest-magnitude maxima.

21. The computer-readable medium of claim 17, further having stored thereon a sequence of instructions which when executed by the computer system causes the computer system to perform:

storing each maxima in a data structure according to a time index, a frequency index and a magnitude of each maxima.

22. The computer-readable medium of claim 21, further having stored thereon a sequence of instructions which when executed by the computer system causes the computer system to perform:

generating a hash value based on at least a portion of each maxima stored in the data structure.

23. The computer-readable medium of claim 17, further having stored thereon a sequence of instructions which when executed by the computer system causes the computer system to perform:

setting all elements of the matrix array that are below a predetermined noise floor to zero; and

setting all elements of the matrix array that are outside a predetermined frequency range to zero.

24. The computer-readable medium of claim 17, further having stored thereon a sequence of instructions which when executed by the computer system causes the computer system to perform:

performing noise filtering of the predetermined number of highest magnitude local maxima.