Apparatus and method for voice activity detection in a communication system

• US 6,453,291 B1
• Filed: 04/16/1999
• Issued: 09/17/2002
• Est. Priority Date: 02/04/1999
• Status: Active Grant

• Current Assignee: Google Technology Holdings LLC (Alphabet Inc.)
• Original Assignee: Motorola, Inc. (Motorola Solutions, Inc.)
• Inventors: Ashley, James Patrick
• Primary Examiner(s): Knepper, David D.
• Application Number:
  US09/293,448
• Time in Patent Office: 1,250 Days
• Field of Search: 704/208, 704/210, 704/214, 704/215, 704/226, 704/233, 704/248, 704/253, 704/202, 704/200, 381/94.1, 381/94.2, 348/473, 375/240.01
• US Class Current: 704/233
• 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

In order for the Voice Activity Detector (VAD) decision to overcome the problem of being over-sensitive to fluctuating, non-stationary background noise conditions, a bias factor is used to increase the threshold on which the VAD decision is based. This bias factor is derived from an estimate of the variability of the background noise estimate. The variability estimate is further based on negative values of the instantaneous SNR.

Claims

1. A method for voice activity detection (VAD) within a communication system, the method comprising the steps of:

estimating a signal characteristic of an input signal;

estimating a noise characteristic of the input signal;

estimating a signal-to-noise ratio (SNR) of the input signal based on the estimated signal and noise characteristics;

estimating the variability of the noise characteristic;

deriving a VAD threshold based on the estimated SNR; and

biasing the VAD threshold based on the variability of the noise characteristic.

2. The method of claim 1 wherein the step of estimating the variability of the estimated SNR comprises the step of updating the variability estimate only when the SNR is less than a threshold.

3. The method of claim 1 wherein the step of estimating the variability of the noise characteristic further comprises the step of calculating an SNR variability factor ψ

(m), wherein $\mathrm{\psi <br><br>}<br><br>\left(m\right)=\{\begin{array}{cc}0.99<br><br>\mathrm{\psi <br><br>}<br><br>\left(m-1\right)+0.01<br><br>{\mathrm{SNR}}^2,& \mathrm{SNR}<<br><br>0\\ \mathrm{\psi <br><br>}<br><br>\left(m-1\right)& \text{otherwise.}\end{array}$

4. The method of claim 2 wherein the step of estimating the variability of the noise characteristic further comprises the step of setting ψ

(m) to zero when a frame count is less than or equal to four (m≦

4).

5. The method of claim 3 wherein the step of estimating the variability of the noise characteristic further comprises the steps of determining when a forced update flag is set and setting ψ

(m) to zero based on the determination.

6. The method of claim 1 wherein the step of biasing the VAD threshold comprises the step of calculating a voice metric bias factor μ

(m), essentially calculated as μ

(m)=max{g_s(ψ

(m)−

ψ

_th), 0}, and adding this factor to the voice metric threshold v_th.

7. The method of claim 1 wherein the step of estimating the signal characteristic of the input signal comprises the step of estimating the signal characteristic of a speech signal.

8. The method of claim 1 further comprising the step of determining a data rate for the signal based on the voice activity detection.

9. An apparatus comprising a Voice Activity Detection (VAD) system for detecting voice in a signal wherein the VAD system detects voice by estimating a signal-to-noise ratio (SNR) of an input signal, estimating a variation (μ

) in the estimated SNR, deriving a VAD threshold based on the estimated SNR, and biasing the VAD threshold based on a variation of the estimated SNR.

10. The apparatus of claim 9 wherein the variation is estimated only when the SNR is less than a threshold.

11. The apparatus of claim 9 wherein μ

is based on a variability factor ψ

(m), wherein $\mathrm{\psi <br><br>}<br><br>\left(m\right)=\{\begin{array}{cc}0.99<br><br>\mathrm{\psi <br><br>}<br><br>\left(m-1\right)+0.01<br><br>{\mathrm{SNR}}^2,& \mathrm{SNR}<<br><br>0\\ \mathrm{\psi <br><br>}<br><br>\left(m-1\right)& \text{otherwise.}\end{array}$

12. The apparatus of claim 11 wherein ψ

(m) is set to zero when a frame count is less than or equal to four (m≦

4).

13. The apparatus of claim 12 wherein ψ

(m) is set to zero based on a forced flag update.

14. The apparatus of claim 9 wherein the variation (μ

) is essentially calculated as ψ

(m)=max{g_s(ψ

(m)−

ψ

_th), 0}.

15. The apparatus of claim 9 where the input signal is generally a speech signal.

16. A method for estimating the variability of the background noise within a communication system, the method comprising the steps of:

estimating a signal characteristic of an input signal;

estimating a noise characteristic of the input signal;

estimating a signal-to-noise ratio (SNR) of the input signal based on the estimated signal and noise characteristics; and

updating the estimate of the variability of the background noise when the current estimate of the SNR is less than a threshold.

17. The method of claim 16 wherein the step of updating the estimate of the variability of the background noise further comprises the step of calculating an SNR variability factor ψ

(m), wherein $\mathrm{\psi <br><br>}<br><br>\left(m\right)=\{\begin{array}{cc}0.99<br><br>\mathrm{\psi <br><br>}<br><br>\left(m-1\right)+0.01<br><br>{\mathrm{SNR}}^2,& \mathrm{SNR}<<br><br>0\\ \mathrm{\psi <br><br>}<br><br>\left(m-1\right)& \text{otherwise.}\end{array}$