IP Library Granted Patent US 6,881,889
Granted Patent B2
US 6,881,889 · App. 10/861,286 · Granted Apr 19, 2005

Generating a music snippet

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Quick Facts
Patent No.
US 6,881,889
App. No.
10/861,286
Granted
Apr 19, 2005
Kind
B2
Abstract

Systems and methods for extracting a music snippet from a music stream are described. In one aspect, one or more music sentences are extracted from the music stream. The one or more sentences are extracted as a function of peaks and valleys of acoustic energy across sequential music stream portions. The music snippet is selected based on the one or more music sentences.

Claims (70)

1. A method for extracting a music snippet from a music stream, the method comprising:

extracting one or more music sentences from the music stream as a function of peaks and valleys of acoustic energy across sequential music stream portions; and

selecting the music snippet as a function of the one or more music sentences.

2. A method as recited in claim 1 , wherein extracting the one or more sentences is a function of a target sentence length.

3. A method as recited in claim 1 , wherein the music snippet comprises more than a single sentence.

4. A method as recited in claim 1 , wherein the music snippet is a sentence of the one or more sentences that comprises a most-salient frame.

5. A method as recited in claim 1 , wherein extracting the one or more sentences further comprises:

calculating a respective sentence boundary possibility for each frame of multiple frames derived from the music stream; and

for each of the one or more sentences, determining a last frame for the sentence as a function of a corresponding sentence boundary possibility.

6. A method as recited in claim 1 , wherein extracting the one or more sentences is a function of a target sentence length selected from eight (8) to sixteen (16) bars in length.

7. A method as recited in claim 1 , and wherein the method further comprises adjusting music snippet length as a function of boundary confidence of previous and subsequent music sentences.

8. A method as recited in claim 1 , wherein the method further comprises:

dividing the music stream into multiple frames of fixed length;

identifying a most-salient frame of the multiple frames; and

wherein the music snippet is a sentence of the one or more sentences that comprises the most-salient frame.

9. A method as recited in claim 8 , wherein the fixed length is a configurable amount of time.

10. A method as recited in claim 8 , wherein each frame overlaps another frame with respect to time by a set amount.

11. A method as recited in claim 8 , wherein identifying the most-salient frame further comprises calculating a respective saliency value for each frame, and wherein the most-salient frame is a frame of the multiple frames having a largest value of the respective saliency values.

12. A method as recited in claim 11 , wherein calculating the respective saliency value for a frame of the multiple frames is based on acoustic energy of the frame, a frequency of occurrence of the frame across the music stream, and a positional weight of the frame.

13. A computer-readable medium for extracting a music snippet from a music stream, the computer-readable medium comprising computer-program instructions executable by a processor for:

extracting one or more music sentences from the music stream as a function of peaks and valleys of acoustic energy across sequential music stream portions; and

selecting the music snippet as a function of the one or more music sentences.

14. A computer-readable medium as recited in claim 13 , wherein the music snippet comprises more than a single sentence.

15. A computer-readable medium as recited in claim 13 , wherein the computer-program instructions for extracting further comprise instructions for identifying at least a subset of the one or more sentences as a function of a target sentence length.

16. A computer-readable medium as recited in claim 13 , wherein the computer-program instructions for extracting the one or more sentences further comprise instructions for:

calculating a respective sentence boundary possibility for each frame of multiple frames derived from the music stream; and

for each of the one or more sentences, determining a last frame for the sentence as a function of a corresponding sentence boundary possibility.

17. A computer-readable medium as recited in claim 13 , wherein the computer-program instructions for extracting the one or more sentences further comprise instructions for identifying the one or more sentences as a function of a target sentence length selected from eight (8) to sixteen (16) bars in length.

18. A computer-readable medium as recited in claim 13 , wherein the computer-program instructions further comprise instructions for adjusting music snippet length as a function of boundary confidence of previous and subsequent music sentences.

19. A computer-readable medium as recited in claim 13 , wherein the computer-program instructions further comprise instructions for:

dividing the music stream into multiple frames of fixed length;

identifying a most-salient frame of the multiple frames; and

wherein the music snippet is a sentence of the one or more sentences that comprises the most-salient frame.

20. A computer-readable medium as recited in claim 19 , wherein the fixed length is a configurable amount of time.

21. A computer-readable medium as recited in claim 19 , wherein each frame overlaps another frame with respect to time by a set amount.

22. A computer-readable medium as recited in claim 19 , wherein the instructions for identifying the most-salient frame further comprise instructions for calculating a respective saliency value for each frame, and wherein the most-salient frame is a frame of the multiple frames having a largest value of the respective saliency values.

23. A computer-readable medium as recited in claim 22 , wherein the instructions for calculating the respective saliency value for a frame of the multiple frames further comprise instructions for determining the respective saliency value as a function of acoustic energy of the frame, a frequency of occurrence of the frame across the music stream, and a positional weight of the frame.

24. A computing device for extracting a music snippet from a music stream, the computing device comprising;

a processor; and

a memory coupled to the processor, the memory comprising computer-program instructions executable by the processor for:

extracting one or more music sentences from the music stream as a function of peaks and valleys of acoustic energy across sequential music stream portions; and

selecting the music snippet as a function of the one or more music sentences.

25. A computing device as recited in claim 24 , wherein the music snippet comprises more than a single sentence.

26. A computing device as recited in claim 24 , wherein the computer-program instructions for extracting further comprise instructions for identifying at least a subset of the one or more sentences as a function of a target sentence length.

27. A computing device as recited in claim 24 , wherein the computer-program instructions for extracting the one or more sentences further comprise instructions for:

calculating a respective sentence boundary possibility for each frame of multiple frames derived from the music stream; and

for each of the one or more sentences, determining a last frame for the sentence as a function of a corresponding sentence boundary possibility.

28. A computing device as recited in claim 24 , wherein the computer-program instructions for extracting the one or more sentences further comprise instructions for identifying the one or more sentences as a function of a target sentence length selected from eight (8) to sixteen (16) bars in length.

29. A computing device as recited in claim 24 , wherein the computer-program instructions further comprise instructions for adjusting music snippet length as a function of boundary confidence of previous and subsequent music sentences.

30. A computing device as recited in claim 24 , wherein the computer-program instructions further comprise instructions for:

dividing the music stream into multiple frames of fixed length;

identifying a most-salient frame of the multiple frames; and

wherein the music snippet is a sentence of the one or more sentences that comprises the most-salient frame.

31. A computing device as recited in claim 30 , wherein the fixed length is a configurable amount of time.

32. A computing device as recited in claim 30 , wherein each frame overlaps another frame with respect to time by a set amount.

33. A computing device as recited in claim 30 , wherein the instructions for identifying the most-salient frame further comprise instructions for calculating a respective saliency value for each frame, and wherein the most-salient frame is a frame of the multiple frames having a largest value of the respective saliency values.

34. A computing device as recited in claim 33 , wherein the instructions for calculating the respective saliency value for a frame of the multiple frames further comprise instructions for determining the respective saliency value as a function of acoustic energy of the frame, a frequency of occurrence of the frame across the music stream, and a positional weight of the frame.

35. A computing device for extracting a music snippet from a music stream, the computing device comprising:

extracting means to extract one or more music sentences from the music stream as a function of peaks and valleys of acoustic energy across sequential music stream portions; and

selecting means to select the music snippet as a function of the one or more music sentences.

36. A computing device as recited in claim 24 , wherein the extracting means further comprises identifying means to identify at least a subset of the one or more sentences as a function of a target sentence length.

37. A computing device as recited in claim 24 , wherein the extracting means further comprises:

calculating means to calculate a respective sentence boundary possibility for each frame of the multiple frames; and

for each of the one or more sentences, determining means to determine a last frame for the sentence as a function of a corresponding sentence boundary possibility.

38. A computing device as recited in claim 24 , wherein the extracting means further comprises identifying means to identify the one or more sentences as a function of a target sentence length selected from eight (8) to sixteen (16) bars in length.

39. A computing device as recited in claim 24 , wherein the computing device further comprises adjusting means to adjust music snippet length as a function of boundary confidence of previous and subsequent music sentences.

40. A computing device as recited in claim 24 , and further comprising:

dividing means to divide the music stream into multiple frames of fixed length;

identifying means to identify a most-salient frame of the multiple frames; and

wherein the music snippet is a sentence of the one or more sentences that comprises the most-salient frame.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 9, 2014
From: MICROSOFT CORPORATION
To: MICROSOFT TECHNOLOGY LICENSING, LLC
Reel/Frame 034541/0477 →