Apparatus and method for generating an audio fingerprint and using a two-stage query

1. A method comprising:

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

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;

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;

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

comparing the one or more hash values to corresponding hash values of known recordings in a first stage query to identify a set of possible matches; and

comparing the set of possible matches to a full-recording fingerprint of the sample in a second stage query, the full-recording fingerprint being a recording fingerprint of a substantial length or the entire length of a known recording.

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:

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

8. An apparatus comprising:

at least one processor configured to:

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

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;

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;

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

compare the one or more hash values to corresponding hash values of known recordings in a first stage query to identify a set of possible matches; and

compare the set of possible matches to a full-recording fingerprint of the sample in a second stage query, the full-recording fingerprint being a recording fingerprint of a substantial length or the entire length of a known recording.

9. The apparatus of claim 8 , 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.

10. The apparatus of claim 9 , wherein the at least one processor is further configured to determine a predetermined number of highest magnitude local maxima within each sub-spectrogram.

11. The apparatus of claim 10 , 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 if the largest-magnitude maxima.

12. The apparatus of claim 8 , 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.

13. The apparatus of claim 12 , 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.

14. The apparatus of claim 8 , further comprising:

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

15. A non-transitory 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 a sample of a recording to a time-frequency domain and storing each time-frequency pair in a matrix array;

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;

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;

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

comparing the one or more hash values to corresponding hash values of known recordings in a first stage query to identify a set of possible matches; and

comparing the set of possible matches to a full-recording fingerprint of the sample in a second stage query, the full-recording fingerprint being a recording fingerprint of a substantial length or the entire length of a known recording.

16. The computer-readable medium of claim 15 , 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.

17. The computer-readable medium of claim 16 , further having stored thereon a sequence of instructions which when executed by the computer system causes the computer system to perform:

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

18. The computer-readable medium of claim 17 , 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.

19. The computer-readable medium of claim 15 , further having stored thereon as 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 it time index, a frequency index and a magnitude of each maxima.

20. The computer-readable medium of claim 19 , 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.

21. The computer-readable medium of claim 15 , 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.