Method and apparatus for automatically discriminating information bearing audio segments and background noise audio segments

• US 20060241937A1
• Filed: 04/21/2005
• Published: 10/26/2006
• Est. Priority Date: 04/21/2005
• Status: Abandoned Application

• Current Assignee: Motorola, Inc. (Motorola Solutions, Inc.)
• Original Assignee: Motorola, Inc. (Motorola Solutions, Inc.)
• Inventors: Ma, Changxue C.
• Application Number:
  US11/111,385
• Publication Number:
  US 20060241937A1
• Time in Patent Office: Days
• US Class Current: 704/206
• CPC Class Codes: G10L 2025/783 based on threshold decision
• CPC Class Codes: G10L 25/78 Detection of presence or absence of voice signals switching of direction of transmission by voice frequency in two-way loud-speaking telephone systems H04M9/10

Abstract

A system (100) for automatically discriminating information bearing audio segments and mere background noise segments processes digitized audio to extract two discriminants between information bearing audio and mere background audio that have a relatively low correlation. One discriminant is based on the rate (relative to the sample rate) at which a specified Boolean test involving sample values is met. Another possible discriminant is based on the variance of time-frequency magnitudes in a number of time windows and frequency bands. The two discriminants are suitably used as the independent variables of probability density functions that model information bearing audio and background noise audio.

Claims

1. A method of discriminating information bearing audio segments and background noise audio segments comprising:

for each k^th sample in a series of samples, testing if a Boolean test;

((S<sub>K−

1</sub>>

−

h1 AND S_K<

h2) OR (S<sub>K−

1</sub><

h3 AND S_K>

−

h4))

where, S_K is a k^th audio sample,

S<sub>K−

1</sub> is a (k−

1)^th sample that precedes the k^th audio sample,

h1 is a first, positive valued predetermined threshold,

h2 is a second positive valued predetermined threshold,

h3 is a third positive valued predetermined threshold, and

h4 is a fourth positive valued predetermined threshold,

is met, and if so, incrementing a count;

after a predetermined number of samples, inputting the count into a decision function; and

evaluating the decision function to determine if the audio segment is more likely to be background noise or information bearing audio.

2. The method according to claim 1 wherein:

h1, h2, h3, h4 are equal to a common value h.

3. The method according to claim 2 where h is established by determining an average absolute magnitude audio sample level and evaluating a piecewise defined function that is equal to the average absolute magnitude audio sample level up to a predetermined limit HO, and beyond HO is equal to HO.

4. The method according to claim 1 further comprising:

processing the audio segment to compute, in addition to the count, at least one other discriminant between information bearing audio and background noise; and

inputting the at least one other discriminant into the decision function

5. The method according to claim 1 further comprising:

processing the series of samples to obtain a plurality of measurements of the magnitude corresponding to a plurality of frequency bands;

computing a variance of the plurality of measurements of magnitude; and

inputting the variance of the plurality of measurements of magnitude to the decision function.

6. The method according to claim 1 further comprising:

processing the series of samples to obtain a plurality of measurements of magnitude for a plurality of time intervals;

computing a variance of the measurements of magnitude; and

inputting the variance of the measurements of magnitude to the decision function.

7. The method according to claim 1 further comprising:

performing joint time frequency analysis on the series of samples to compute a plurality of time-frequency magnitudes that includes magnitudes corresponding to different times and magnitudes corresponding to different frequencies;

computing a variance of the time-frequency magnitudes; and

inputting the variance of the time-frequency magnitudes to the decision function.

8. An apparatus for discriminating information bearing audio segments and background noise audio segments, the apparatus comprising:

a Boolean tester for applying a Boolean test;

((S<sub>K−

1</sub>>

−

h1 AND S_K<

h2) OR (S<sub>K−

1</sub><

h3 AND S_K>

−

h4))

where, S_K is a k^th audio sample,

S<sub>K−

1</sub> is a (k−

1)^th sample that precedes the k^th sample, and

h1 is a first positive valued predetermined threshold,

h2 is a second positive valued predetermined threshold,

h3 is a third positive valued predetermined threshold,

h4 is a fourth positive valued predetermined threshold, to each k^th sample in a series of samples; and

a summer for summing, over a predetermined number of samples, a number of times that the Boolean tester produces a positive result and outputting a sum;

a decision function evaluator for receiving the sum as input and evaluating a decision function.

9. The apparatus according to claim 8 wherein h1, h2, h3, h4 are equal to a common value h.

10. The apparatus according to claim 8 further comprising:

a joint time frequency analyzer for evaluating a plurality of time-frequency magnitudes; and

a joint time frequency variance calculator for receiving a plurality of time-frequency magnitudes and outputting a variance of the plurality of time-frequency magnitudes; and

wherein, the decision function evaluator is adapted to received the variance of the plurality of time-frequency magnitudes as input and evaluate the decision function based, in part, on the variance.

11. An apparatus for discriminating information bearing audio segments and background noise audio segments, the apparatus comprising:

a processor;

a memory for storing programming instructions, said memory coupled to said processor, wherein said processor is programmed by said programming instructions to;

test whether a Boolean test;

((S<sub>K−

1</sub>>

−

h1 AND S_K<

h2) OR (S<sub>K−

1</sub><

h3 AND S_K>

−

h4))

where, S_K is a k^th audio sample,

S<sub>K−

1</sub> is a (k−

1)^th sample that precedes the k^th sample, and

h1 is a first positive valued predetermined threshold,

h2 is a second positive valued predetermined threshold,

h3 is a third positive valued predetermined threshold,

h4 is a fourth positive valued predetermined threshold,

is met for each k^th sample in a series of samples, and if so, increment a count;

after a predetermined number of samples, input the count into a decision function; and

evaluate the decision function to determine if the audio segment is more likely to be background noise or information bearing audio.

12. The apparatus according to claim 11 wherein h1 h1, h2, h3, h4 are equal to a common value h.

13. The apparatus according to claim 11 wherein the processor is also programmed to:

establish h by;

determining an average absolute magnitude audio sample level; and

evaluate a piecewise defined function that is equal to the average absolute magnitude audio sample level up to a predetermined limit HO, and beyond HO is equal to HO.

14. The apparatus according to claim 11 wherein the processor is also programmed to:

process the audio segment to compute, in addition to the count, at least one other discriminant between information bearing audio and background noise; and

input the at least one other discriminant into the decision function.

15. The apparatus according to claim 11 further wherein the processor is further programmed to:

process the series of samples to obtain a plurality of measurements of the magnitude corresponding to a plurality of frequency bands;

compute a variance of the plurality of measurements of magnitude; and

input the variance of the plurality of measurements of magnitude to the decision function.

16. The apparatus according to claim 11 wherein the processor is also programmed by said programming instructions to:

process the series of samples to obtain a plurality of measurements of magnitude for a plurality of time intervals;

compute a variance of the plurality of measurements of magnitude; and

input the variance of the measurements of magnitude to the decision function.

17. The apparatus according to claim 11 wherein the processor is also programmed by said programming instructions to:

perform joint time frequency analysis on the series of samples to compute a plurality of time-frequency magnitudes that includes magnitudes corresponding to different times and magnitudes corresponding to different frequencies;

compute a variance of the time-frequency magnitudes; and

input the variance of the time-frequency magnitudes to the decision function.

18. A computer readable medium storing programming instructions for discriminating information bearing audio segments and background noise audio segments, including programming instructions for:

for each k^th sample in a series of samples, testing if a Boolean test;

((S<sub>K−

1</sub>>

−

h1 AND S_K<

h2) OR (S<sub>K−

1</sub><

h3 AND S_K>

−

h4))

where, S_K is a k^th audio sample,

S<sub>K−

1</sub> is a (k−

1)^th sample that precedes the k^th audio sample,

h1 is a first positive valued predetermined threshold,

h2 is a second positive valued predetermined threshold,

h3 is a third positive valued predetermined threshold, and

h4 is a fourth positive valued predetermined threshold,

is met, and if so, incrementing a count;

after a predetermined number of samples, inputting the count into a decision function; and

evaluating the decision function to determine if the audio segment is more likely to be background noise or information bearing audio.

19. The computer readable medium according to claim 18 wherein:

h1,h2,h3, h4 are equal to a common value h.

20. The computer readable medium according to claim 19 where h is established by determining an average absolute magnitude audio sample level and evaluating a piecewise defined function that is equal to the average absolute magnitude audio sample level up to a predetermined limit HO, and beyond HO is equal to HO

21. The computer readable medium according to claim 18 further comprising programming instructions for:

processing the audio segment to compute, in addition to the count, at least one other discriminant between information bearing audio and background noise; and

inputting the at least one other discriminant into the decision function

22. The computer readable medium according to claim 18 further comprising programming instructions for:

processing the series of samples to obtain a plurality of measurements of the magnitude corresponding to a plurality of frequency bands;

computing a variance of the plurality of measurements of magnitude; and

inputting the variance of the plurality of measurements of magnitude to the decision function.

23. The computer readably medium according to claim 18 further comprising programming instructions for:

processing the series of samples to obtain a plurality of measurements of magnitude for a plurality of time intervals;

computing a variance of the measurements of magnitude; and

inputting the variance of the measurements of magnitude to the decision function.

24. The computer readable medium according to claim 18 further comprising programming instructions for:

performing joint time frequency analysis on the series of samples to compute a plurality of time-frequency magnitudes that includes magnitudes corresponding to different times and magnitudes corresponding to different frequencies;

computing a variance of the time-frequency magnitudes; and

inputting the variance of the time-frequency magnitudes to the decision function.