Voice activity detector

• US 20050182620A1
• Filed: 09/28/2004
• Published: 08/18/2005
• Est. Priority Date: 09/30/2003
• Status: Active Grant

• Current Assignee: STMicroelectronics Asia Pacific Pte Limited (STMicroelectronics NV)
• Original Assignee: STMicroelectronics Asia Pacific Pte Limited (STMicroelectronics NV)
• Inventors: George, Sapna, Kabi, Prakash Padhi
• Granted Patent: US 7,653,537 B2
• Time in Patent Office: Days
• US Class Current: 704/216
• 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 and method is provided for determining whether a data frame of a coded speech signal corresponds to voice or to noise. In one embodiment, a voice activity detector determines a cross-correlation of data. If the cross-correlation is lower than a predetermined cross-correlation value, then the data frame corresponds to noise. If not, then the voice activity detector determines a periodicity of the cross-correlation and a variance of the periodicity. If the variance is less than a predetermined variance value, then the data frame corresponds to voice. In another embodiment, a method determines energy of the data frame and an average energy of the coded speech signal. If the data frame is one of a predetermined number of initial data frames, then a comparison between the average energy to the energy of the data frame is used to determine whether the data frame is noise or voice.

Claims

1. A method for determining whether a data frame of a coded speech signal corresponds to voice or to noise, including the steps of:

determining a cross-correlation Y(r) of data of said data frame;

determining a periodicity of the cross-correlation;

determining a variance a2 of the periodicity;

determining said data frame corresponds to said noise when the cross-correlation is lower than a predetermined cross-correlation value; and

determining said data frame corresponds to voice if the variance is less than a predetermined variance value.

2. The method claimed in claim 1, wherein the cross-correlation, Y(τ

), is calculated in accordance with the following;

$Y<br><br>\left(\mathrm{\tau <br><br>}\right)=\underset{n=0}{\overset{N/2-1}{\sum <br><br>}}<br><br>x_1<br><br>\left(n\right)<br><br>x_2<br><br>\left(n+\mathrm{\tau <br><br>}\right).$

where,

τ

is a lag between sequences x₁(n) and x₂(n);

x₁(n) is a first half of said data frame;

x₂(n) is a second half of said data frame; and

N is the size of the frame;

3. The method claimed in claim 1, wherein the predetermined cross-correlation value corresponds to that of white or pink noise.

4. The method claimed in claim 1, wherein the predetermined correlation value is 0.4.

5. The method claimed in claim 2, wherein the periodicity is determined by measuring:

a distance Diff_pp between positive peaks;

a distance Diff_nn between negative peaks;

a distance Diff_pn between consecutive positive and negative peaks; and

a distance Diff_np between consecutive negative and positive peaks,

where the peaks are identified by using;

Y(τ

−

1)<

Y(τ

)>

Y(τ

+1) for maxima and
Y(τ

−

1)>

Y(τ

)<

Y(τ

+1) for minima.

6. The method claimed in claim 5, wherein the variance, σ

², is calculated as follows;

${\mathrm{\sigma <br><br>}}^2=\frac{\sum <br><br>{\left(x-\mathrm{\mu <br><br>}\right)}^2}{L}.$

where

x is a sequence comprised of the periodicity whose variance is being measured;

μ

is the mean of the sequence x; and

L is the number of samples in the sequence.

7. The method claimed in claim 6, wherein the variance is normalized by μ

² substantially as follows;

$\mathrm{\varepsilon <br><br>}=\frac{{\mathrm{\sigma <br><br>}}^2}{{\mathrm{\mu <br><br>}}^2}=\frac{\sum <br><br>{\left(x-\mathrm{\mu <br><br>}\right)}^2}{L·<br><br>{\mathrm{\mu <br><br>}}^2}=\frac{1}{L}<br><br>\sum <br><br>{\left\{\left(\frac{x}{\mathrm{\mu <br><br>}}\right)-1\right\}}^2.$

8. The method claimed in claim 7, wherein the predetermined variance value is 0.2.

9. A method for determining whether a data frame of a coded speech signal corresponds to voice or to noise, including the steps of:

determining an energy of said data frame;

determining an average speech energy of the coded speech signal;

if the data frame is one of a predetermined number of initial data frames of the coded speech signal, further including the steps of

determining a cross-correlation of data of said data frame,

determining a periodicity of the cross-correlation, and

determining a variance of the periodicity; and

else, comparing the energy of the data frame with the average speech energy, where the data frame corresponds to said voice if the average speech energy is less than or equal to the energy of the data frame.

10. The method claimed in claim 9, wherein determining the energy of the data frame comprises determining:

$E^l=\underset{n=\left(l-1\right),N+1}{\overset{l·<br><br>N}{\sum <br><br>}}<br><br>{x<br><br>\left(n\right)}^2$

where the energy in an l^th analysis frame of size N is E^l.

11. The method claimed in claim 10, wherein the average speech energy determined over k data frames is as follows:

$E_s^a=\frac{1}{k}<br><br>\underset{l=1}{\overset{k}{\sum <br><br>}}<br><br>E^l.$

12. A voice activity detector for determining whether a data frame of a coded speech signal corresponds to voice or to noise, including:

means for determining a cross-correlation Y(τ

) of data of said data frame;

means for determining a periodicity of the cross-correlation;

means for determining a variance σ

² of the periodicity;

means for determining said data frame corresponds to said noise when the cross-correlation is lower than a predetermined cross-correlation value; and

means for determining said data frame corresponds to voice if the variance is less than a predetermined variance value.

13. The voice activity detector claimed in claim 12, wherein the cross-correlation, Y(τ

), is calculated in accordance with the following;

$Y<br><br>\left(\mathrm{\tau <br><br>}\right)=\underset{n=0}{\overset{N/2-1}{\sum <br><br>}}<br><br>x_1<br><br>\left(n\right)<br><br>x_2<br><br>\left(n+\mathrm{\tau <br><br>}\right)$

where,

τ

is a lag between sequences x₁(n) and x2(n);

x₁(n) is a first half of said data frame;

x₂(n) is a second half of said data frame; and

N is the size of the frame.

14. The voice activity detector claimed in claim 12, wherein the predetermined cross-correlation value corresponds to that of white or pink noise.

15. The voice activity detector claimed in claim 12, wherein the predetermined correlation value is 0.4.

16. The voice activity detector claimed in claim 13, wherein the periodicity is determined by measuring:

a distance Diff_pp between positive peaks;

a distance Diff_nn between negative peaks;

a distance Diff_pn between consecutive positive and negative peaks; and

a distance Diff_np between consecutive negative and positive peaks,

wherein the peaks are identified by using;

Y(τ

−

1)<

Y(τ

)>

Y(τ

+1) for maxima and
Y(τ

−

1)>

Y(τ

)<

Y(τ

+1) for minima.

17. The voice activity detector claimed in claim 16, wherein the variance, σ

², is calculated as follows;

${\mathrm{\sigma <br><br>}}^2=\frac{\sum <br><br>{\left(x-\mathrm{\mu <br><br>}\right)}^2}{L}$

where

x is a sequence comprised of the periodicity whose variance is being measured;

μ

is the mean of the sequence x; and

L is the number of samples in the sequence.

18. The voice activity detector claimed in claim 17, wherein the variance is normalized by μ

² substantially as follows;

$\mathrm{\varepsilon <br><br>}=\frac{{\mathrm{\sigma <br><br>}}^2}{{\mathrm{\mu <br><br>}}^2}=\frac{\sum <br><br>{\left(x-\mathrm{\mu <br><br>}\right)}^2}{L·<br><br>{\mathrm{\mu <br><br>}}^2}=\frac{1}{L}<br><br>\sum <br><br>{\left\{\left(\frac{x}{\mathrm{\mu <br><br>}}\right)-1\right\}}^2.$

19. The voice activity detector claimed in claim 18, wherein the predetermined variance value is 0.2.