Method and apparatus for detecting a voice activity in an input audio signal

Abstract

The disclosure provides a method and an apparatus for detecting a voice activity in an input audio signal composed of frames. A noise attribute of the input signal is determined based on a received frame of the input audio signal. A voice activity detection (VAD) parameter is derived based on the noise attribute of the input audio signal using an adaptive function. The derived VAD parameter is compared with a threshold value to provide a voice activity detection decision. The input audio signal is processed according to the voice activity detection decision.

Claims

1. A method for encoding an input audio signal for use by an audio signal encoder, wherein the audio signal encoder comprises a receiver and an audio signal processor, and wherein the input audio signal is composed of frames, the method comprising: receiving one or more frames of the input audio signal; determining a noise attribute of the input audio signal based on the received frames of the input audio signal; dividing the received frames of the input audio signal into one or more frequency sub-bands; obtaining a signal to noise ratio of each of the one or more frequency sub-bands; calculating a sub-band specific parameter of each frequency sub-band based on the signal to noise ratio of the frequency sub-band using an adaptive function, wherein at least one parameter of the adaptive function is selected based on the noise attribute of the input audio signal; deriving a modified segmental signal to noise ratio (mssnr) by summing up the calculated sub-band specific parameters of the frequency sub-bands; comparing the mssnr with a threshold value to provide a voice activity detection decision (VADD); and encoding the input audio signal based on the VADD; wherein deriving the mssnr by summing up the calculated sub-band specific parameters of the frequency sub-bands comprises: summing up the calculated sub-band specific parameters (sbsp) of the frequency sub-bands as follows: $\mathrm{mssnr}=\underset{i=1}{\overset{N}{.Math.}}\mathrm{sbsp}\left(i\right)$ wherein N is the number of frequency sub-bands into which the frames of the input audio signal is divided, and sbsp(i) is the sub-band specific parameter of the i.sup.th frequency sub-band calculated based on the signal to noise ratio of the i.sup.th frequency sub-band using the adaptive function; and wherein the sub-band specific parameter of the i.sup.th frequency sub-band sbsp(i) is calculated as follows:
sbsp(i)=(f(snr(i))+α(i)).sup.β wherein snr(i) is the signal to noise ratio of the i.sup.th frequency sub-band, (f(snr(i))+α(i))β is the adaptive function, and α(i), β are configurable variables of the adaptive function.

2. The method according to claim 1, wherein the noise attribute of the input audio signal is one of: a long term signal to noise ratio, a background noise variation, or a long term signal to noise ratio and a background noise variation.

3. The method according to claim 1, wherein the adaptive function is a non-linear function.

4. The method according to claim 1, wherein obtaining a signal to noise ratio of a frequency sub-band comprises: obtaining a signal energy of the frequency sub-band, estimating a background noise energy of the frequency sub-band, and calculating the signal to noise ratio of the frequency sub-band based on the signal energy and the background noise energy of the frequency sub-band.

5. The method according to claim 4, wherein the signal energy of the frequency sub-band is a smoothed signal energy, the smoothed signal energy is a weighted average of a signal energy of the frequency sub-band in a currently received frame and the signal energy of the frequency sub-band in at least one previously received frame.

6. The method according to claim 1, wherein comparing the modified segmental signal to noise ratio (mssnr) with a threshold value (thr) to provide a voice activity detection decision (VADD) comprises: comparing the mssnr with the threshold value (thr) that is the following: $\mathrm{thr}=\{\begin{array}{cc}{k}_1& \mathrm{lsnr}>e_2\\ k_2& e_1<\mathrm{lsnr}\le e_2\\ k_3& \mathrm{lsnr}\le e_1\end{array}$ wherein k.sub.1 >k.sub.2 >k.sub.3 and e.sub.1 <e.sub.2 are integer or floating numbers, and lsnr is a long term signal to noise ratio of the input audio signal; and generating the voice activity detection decision (VADD) according to: $VADD=\{\begin{array}{cc}1& \mathrm{mssnr}>\mathrm{thr}\\ 0& \mathrm{mssnr}\le \mathrm{thr}\end{array}$ wherein VADD =1 indicates that the voice activity is present in the received frames of the input audio signal, and VADD=0 indicates that the voice activity is absent in the received frames of the input audio signal.

7. The method according to claim 1, wherein the variable α(i) of the adaptive function depends on a long term signal to noise ratio (lsnr) of the input audio signal as follows:
α(i)=g(i,lsnr) wherein g(i, lsnr) is a linear or non-linear function, and i is a sub-band index of i.sup.th frequency sub-band.

8. The method according to claim 7, wherein the variable α(i) of the adaptive function is calculated through the function g(i, lsnr) by:
α(i)=g(i, lnsr)=a(i)lsnr+b(i) wherein a(i) and b(i) are real numbers depending on the sub-band index i (1≦i≦N).

9. A method for detecting a voice activity in an input audio signal for use by an audio signal encoder, wherein the audio signal encoder comprises an input/output interface and an audio signal processor, and wherein the input audio signal is composed of frames, the method comprising: receiving one or more frames of the input audio signal; determining a noise attribute of the input audio signal based on the received frames of the input audio signal; dividing the received frames of the input audio signal into one or more frequency sub-bands; obtaining a signal to noise ratio of each of the one or more frequency sub-bands; calculating a sub-band specific parameter of each frequency sub-band based on the signal to noise ratio of the frequency sub-band using an adaptive function, wherein at least one parameter of the adaptive function is selected based on the noise attribute of the input audio signal; deriving a modified segmental signal to noise ratio (mssnr) by summing up the calculated sub-band specific parameters of the frequency sub-bands; comparing the mssnr with a threshold value to generate a voice activity detection decision (VADD); and providing the VADD to an entity, for controlling a discontinuous transmission (DTX) mode of the entity; wherein deriving the mssnr by summing up the calculated sub-band specific parameters of the frequency sub-bands comprises: summing up the calculated sub-band specific parameters (sbsp) of the frequency sub-bands as follows: $\mathrm{mssnr}=\underset{i=1}{\overset{N}{.Math.}}\mathrm{sbsp}\left(i\right)$ wherein N is the number of frequency sub-bands into which the frames of the input audio signal is divided, and sbsp(i) is the sub-band specific parameter of the i.sup.th frequency sub-band calculated based on the signal to noise ratio of the i.sup.th frequency sub-band using the adaptive function; and wherein the sub-band specific parameter of the i.sup.th frequency sub-band sbsp(i) is calculated as follows:
sbsp(i)=(f(snr(i))+α(i)).sup.62 wherein snr(i) is the signal to noise ratio of the i.sup.th frequency sub-band, (f(snr(i))+α(i)).sup.62 is the adaptive function, and α(i), β are configurable variables of the adaptive function.

10. The method according to claim 9, wherein the variable α(i) of the adaptive function depends on a long term signal to noise ratio (lsnr) of the input audio signal as follows:
α(i)=g(i,lsnr) wherein g(i, lsnr) is a linear or non-linear function, and i is a sub-band index of i.sup.th frequency sub-band.

11. The method according to claim 10, wherein the variable α(i) of the adaptive function is calculated through the function g(i, lsnr) by:
α(i)=g(i,lnsr)=a(i)lsnr+b(i) wherein a(i) and b(i) are real numbers depending on a sub-band index i (1≦i≦N).

12. The method according to claim 9, wherein comparing the mssnr with a threshold value to generate a voice activity detection decision (VADD) comprises: comparing the mssnr with the threshold value (thr) that is the following: $\mathrm{thr}=\{\begin{array}{cc}{k}_1& \mathrm{lsnr}>e_2\\ k_2& e_1<\mathrm{lsnr}\le e_2\\ k_3& \mathrm{lsnr}\le e_1\end{array}$ wherein k.sub.1 >k.sub.2 >k.sub.3 and e.sub.1 <e.sub.2 are integer or floating numbers, and lsnr is a long term signal to noise ratio of the input audio signal; and generating the voice activity detection decision (VADD) according to: $VADD=\{\begin{array}{cc}1& \mathrm{mssnr}>\mathrm{thr}\\ 0& \mathrm{mssnr}\le \mathrm{thr}\end{array}$ wherein VADD =1 indicates that the voice activity is present in the received frames of the input audio signal, and VADD=0 indicates that the voice activity is absent in the received frames of the input audio signal.

13. An apparatus for encoding an input audio signal, wherein the input audio signal is composed of frames, the apparatus comprising: a receiver, configured to receive one or more frames of the input audio signal; an audio signal processor, configured to: determine a noise attribute of the input audio signal based on the received frames of the input audio signal; divide the received frames of the input audio signal into one or more frequency sub-bands; obtain a signal to noise ratio of each of the one or more frequency sub-bands; calculate a sub-band specific parameter of each frequency sub-band based on the signal to noise ratio of the frequency sub-band using an adaptive function, wherein at least one parameter of the adaptive function is selected based on the noise attribute of the input audio signal; derive a modified segmental signal to noise ratio (mssnr) by summing up the calculated sub-band specific parameters of the frequency sub-bands; compare the mssnr with a threshold value to provide a voice activity detection decision (VADD); and encode the input audio signal based on the VADD; wherein deriving the mssnr by summing up the calculated sub-band specific parameters of the frequency sub-bands comprises: summing up the calculated sub-band specific parameters (sbsp) of the frequency sub-bands as follows: $\mathrm{mssnr}=\underset{i=1}{\overset{N}{.Math.}}\mathrm{sbsp}\left(i\right)$ wherein N is the number of frequency sub-bands into which the frames of the input audio signal is divided, and sbsp(i) is the sub-band specific parameter of the i.sup.th frequency sub-band calculated based on the signal to noise ratio of the i.sup.th frequency sub-band using the adaptive function; and wherein the sub-band specific parameter of the i.sup.th frequency sub-band sbsp(i) is calculated as follows:
sbsp(i)=(f(snr(i))+α(i)).sup.62 wherein snr(i) is the signal to noise ratio of the i.sup.th frequency sub-band, (f(snr(i)) +(i)).sup.60 is the adaptive function, and α(i), βare configurable variables of the adaptive function.

14. The apparatus according to claim 13, wherein the variable α(i) of the adaptive function depends on a long term signal to noise ratio (lsnr) of the input audio signal as follows:
α(i)=g(i,lsnr) wherein g(i, lsnr) is a linear or non-linear function, and i is a sub-band index of i.sup.th frequency sub-band.

15. The apparatus according to claim 14, wherein the variable α(i) of the adaptive function is calculated through the function g(i, lsnr) by:
α(i)=g(i,lnsr)=a(i)lsnr+b(i) wherein a(i) and b(i) are real numbers depending on the sub-band index i (1≦i≦N).

16. The apparatus according to claim 13, wherein in comparing the mssnr with a threshold value to provide a voice activity detection decision (VADD), the audio signal processor is configured to: compare the mssnr with the threshold value (thr) that is the following: $\mathrm{thr}=\{\begin{array}{cc}{k}_1& \mathrm{lsnr}>e_2\\ k_2& e_1<\mathrm{lsnr}\le e_2\\ k_3& \mathrm{lsnr}\le e_1\end{array}$ wherein k.sub.1>k.sub.2 >k.sub.3 and e.sub.1 <e.sub.2 are integer or floating numbers, and lsnr is a long term signal to noise ratio of the input audio signal; and generate the voice activity detection decision (VADD) according to: $VADD=\{\begin{array}{cc}1& \mathrm{mssnr}>\mathrm{thr}\\ 0& \mathrm{mssnr}\le \mathrm{thr}\end{array}$ wherein VADD=1 indicates that the voice activity is present in the received frames of the input audio signal, and VADD=0 indicates that the voice activity is absent in the received frames of the input audio signal.

17. An apparatus for detecting a voice activity in an input audio signal, wherein the input audio signal is composed of frames, the apparatus comprising: an input/output interface, configured to receive one or more frames of the input audio signal; and an audio signal processor, configured to: determine a noise attribute of the input audio signal based on the received frames of the input audio signal; divide the received frames of the input audio signal into one or more frequency sub-bands; obtain a signal to noise ratio of each of the one or more frequency sub-bands; calculate a sub-band specific parameter of each frequency sub-band based on the signal to noise ratio of the frequency sub-band using an adaptive function, wherein at least one parameter of the adaptive function is selected based on the noise attribute of the input audio signal; derive a modified segmental signal to noise ratio (mssnr) by summing up the calculated sub-band specific parameters of the frequency sub-bands; and compare the mssnr with a threshold value to generate a voice activity detection decision (VADD); wherein the input/output interface is further configured to: provide the VADD to an entity, for controlling a discontinuous transmission (DTX) mode of the entity; wherein deriving the mssnr by summing up the calculated sub-band specific parameters of the frequency sub-bands comprises: summing up the calculated sub-band specific parameters (sbsp) of the frequency sub-bands as follows: $\mathrm{mssnr}=\underset{i=1}{\overset{N}{.Math.}}\mathrm{sbsp}\left(i\right)$ wherein N is the number of frequency sub-bands into which the frames of the input audio signal is divided, and sbsp(i) is the sub-band specific parameter of the i.sup.th frequency sub-band calculated based on the signal to noise ratio of the i.sup.th frequency sub-band using the adaptive function; and wherein the sub-band specific parameter of the i.sup.th frequency sub-band sbsp(i) is calculated as follows:
sbsp(i)=(f(snr(i))+α(i)).sup.62 wherein snr(i) is the signal to noise ratio of the i.sup.th frequency sub-band, (f(snr(+α(i).sup.62 is the adaptive function, and α(i), βare configurable variables of the adaptive function.

18. The apparatus according to claim 17, wherein the variable α(i) of the adaptive function depends on a long term signal to noise ratio (lsnr) of the input audio signal as follows:
α(i)=g(i,lsnr) wherein g(i, lsnr) is a linear or non-linear function, and i is a sub-band index of i.sup.th frequency sub-band.

19. The apparatus according to claim 18, wherein the variable α(i) of the adaptive function is calculated through the function g(i, lsnr) by:
α(i)=g(i,lnsr)=a(i)lsnr+b(i) wherein a(i) and b(i) are real numbers depending on the sub-band index i (1≦i≦N).

20. The apparatus according to claim 17, wherein in comparing the mssnr with a threshold value to generate a voice activity detection decision (VADD), the audio signal processor is configured to: compare the mssnr with the threshold value (thr) that is the following: $\mathrm{thr}=\{\begin{array}{cc}{k}_1& \mathrm{lsnr}>e_2\\ k_2& e_1<\mathrm{lsnr}\le e_2\\ k_3& \mathrm{lsnr}\le e_1\end{array}$ wherein k.sub.1>k.sub.2 >k.sub.3 and e.sub.1 <e.sub.2 are integer or floating numbers, and lsnr is a long term signal to noise ratio of the input audio signal; and generate the voice activity detection decision (VADD) according to: $VADD=\{\begin{array}{cc}1& \mathrm{mssnr}>\mathrm{thr}\\ 0& \mathrm{mssnr}\le \mathrm{thr}\end{array}$ wherein VADD =1 indicates that the voice activity is present in the received frames of the input audio signal, and VADD=0 indicates that the voice activity is absent in the received frames of the input audio signal.

21. The method according to claim 9, wherein the noise attribute of the input audio signal is a long term signal to noise ratio.

22. The apparatus according to claim 13, wherein the noise attribute of the input audio signal is a long term signal to noise ratio.

23. The apparatus according to claim 17, wherein the noise attribute of the input audio signal is a long term signal to noise ratio.

Description

BRIEF DESCRIPTION OF DRAWINGS

(1) In the following, possible implementations of different aspects of the present disclosure are described with reference to the enclosed figures in more detail.

(2) FIG. 1 shows a flow chart of a method for adaptively detecting a voice activity in an input audio signal according to a first aspect of the present disclosure;

(3) FIG. 2 shows a simplified block diagram of a voice activity detection (VAD) apparatus for detecting a voice activity in an input audio signal according to a second aspect of the present disclosure; and

(4) FIG. 3 shows a simplified block diagram of an audio signal processing device according to a third aspect of the present disclosure.

DETAILED DESCRIPTION

(5) FIG. 1 shows a flow chart of a possible implementation of a method for adaptively detecting a voice activity in an input audio signal, according to a first aspect of the present disclosure.

(6) The input audio signal is composed of signal frames. In a first step S1, a noise characteristic of the input audio signal is determined based at least on a received frame of the input audio signal.

(7) In a possible implementation, the input signal is segmented into frames of a predetermined length of e.g. 20 ms, and is inputted frame by frame. In other implementations, the length of the input frame may vary. The noise characteristic of the input audio signal determined in the step S1 may be a long term signal to noise ratio (LSNR) calculated by a LSNR estimation unit. In another possible implementation, the noise characteristic determined in the step S1 is formed by a background noise variation, calculated by a background noise variation estimation unit which calculates a stationarity or fluctuation ε of the background noise of the input audio signal. It is also possible that the noise characteristic determined in the step S1 includes both the LSNR and the background noise variation.

(8) In a further step S2, the received frame of the input audio signal is divided into several frequency sub-bands.

(9) In a further step S3, a sub-band specific parameter is calculated for each of the sub-bands based on the signal to noise ratio (SNR) of each sub-band using an adaptive function (AF).

(10) In a possible implementation, a power spectrum is obtained for each input frame through a fast Fourier transformation (FFT), and the obtained power spectrum is divided into a predetermined number of sub-bands with non-linear widths. Energies for each sub-band are calculated, wherein the energy for each sub-band of the input frame can in a possible implementation be formed by a smoothed energy that is formed by a weighted average of the energies for the same sub-band between the input frame and at least one previous frame. In a possible implementation of the first aspect of the present disclosure, the sub-band SNR of i.sup.th sub-band (snr(i)) can be calculated as the modified logarithmic SNR of the frequency sub-band:

(11) $\mathrm{snr}\left(i\right)={\log }_{10}\left(\frac{E\left(i\right)}{E_n\left(i\right)}\right)$
wherein E(i) is the energy of i.sup.th sub-band of the input frame, and E.sub.n(i) is the estimated background noise energy of the i.sup.th sub-band. The estimated background noise can be calculated by a background noise estimation unit where the estimated energy of each sub-band of the background noise is calculated by moving-averaging the energies of each sub-band among background noise frames detected. This can be expressed as:
E.sub.n(i)=λ.Math.E.sub.n(i)+(1−λ).Math.E(i)
where E(i) is the energy of the i.sup.th sub-band of the frame detected as background noise, λ is a “forgetting factor” usually in a range between 0.9-0.99.

(12) After having obtained a SNR (snr) for each sub-band of the input frame in step S3, a sub-band specific parameter (sbsp) is calculated in step S4 based on the respective SNR (snr) of the respective sub-band using an adaptive function (AF). In a possible implementation of the method for adaptively detecting a voice activity, in an input audio signal, at least one parameter of the adaptive function (AF) is selected dependent of the determined noise characteristic of the input audio signal. The noise characteristic determined in step S1 can comprise a long term SNR and/or a background noise variation of the input audio signal. The adaptive function (AF) is a non-linear function.

(13) In a possible implementation of the method for adaptively detecting a voice activity in an input audio signal according to the first aspect of the present disclosure, in step S5, a modified segmental SNR (mssnr) is derived by adding the sub-band's specific parameters (sbsp) as follows:

(14) 0$\mathrm{mssnr}=\underset{i=0}{\overset{N}{.Math.}}\mathrm{sbsp}\left(i\right)$
wherein N is the number of frequency sub-bands into which the input frame is divided, and sbsp(i) is a sub-band specific parameter calculated based on the sub-band's SNR for each sub-band using the adaptive function (AF).

(15) In a possible implementation of the first aspect of the present disclosure, the modified segmental SNR (mssnr) is calculated as follows:

(16) $\mathrm{mssnr}=\underset{i=o}{\overset{N}{.Math.}}{\left(f\left(\mathrm{snr}\left(i\right)\right)+\alpha \right)}^{\beta }$

(17) wherein snr(i) is the SNR of the i.sup.th sub-band of the input frame, N is the number of frequency sub-bands into which the input frame is divided and AF=(f(snr(i))+α).sup.β is the adaptive function used to calculate the sub-band specific parameter sbsp(i), and α, β are two configurable variables of the adaptive function (AF).

(18) In a possible implementation of the first aspect of the present disclosure, the first variable α of the adaptive function (AF) depends on a long term SNR (lsnr) of the input audio signal as follows:
α=g(i,lsnr)
wherein g(i, lsnr) is a linear or non-linear function, and the second variable β of the adaptive function (AF) depends on the long term SNR (lsnr) and a value φ as follows:
β=h(lsnr,φ)
wherein h(lsnr, φ) is a non-linear function and
φ=f(snr(i))+α

(19) In a possible implementation of the method according to the first aspect of the present disclosure, the first variable α of the adaptive function (AF) may be calculated by:
α=g(i,lnsr)=a(i)lsnr+b(i)
wherein a(i), b(i) are real numbers depending on a sub-band index i,

(20) and the second variable β of the adaptive function (AF) may be calculated by:

(21) $\beta =h\left(\mathrm{lsnr},\phi \right)=\{\begin{array}{cc}{\beta }_1& \phi \ge d\mathrm{and}\mathrm{lsnr}>e_2\\ {\beta }_2& \phi \ge d\mathrm{and}{e}_1<\mathrm{lsnr}\le e_2\\ {\beta }_3& \phi \ge d\mathrm{and}\mathrm{lsnr}\le e_1\\ {\beta }_4& \mathrm{otherwise}\end{array}$
wherein β.sub.1<β.sub.3<β.sub.3 and β.sub.4 and d as well as e.sub.1<e.sub.2 are integer or floating numbers and wherein lsnr is the long term SNR of the input audio signal.

(22) In a possible specific implementation, β.sub.1=4, β.sub.2=10, β.sub.3=15 and β.sub.4=9. In this specific implementation, d is set to 1, and e.sub.1=8 and e.sub.2=18.

(23) The modified segmental SNR (msnr) is derived in step S5 by adding the sub-band's specific parameters (sbsp). In a further step S6 of the implementation of the method for adaptively detecting a voice activity in an input audio signal as shown in FIG. 1, the derived modified segmental SNR (mssnr) is compared with a threshold value thr to provide a VAD decision (VADD).

(24) In a possible implementation, the derived modified segmental SNR (mssnr) is compared with a threshold value thr which is set to:

(25) $\mathrm{thr}=\{\begin{array}{cc}{k}_1& \mathrm{lsnr}>e_2\\ k_2& e_1<\mathrm{lsnr}\le e_2\\ k_3& \mathrm{lsnr}\le e_1\end{array}$
wherein k.sub.1>k.sub.2>k.sub.3 and e.sub.1<e.sub.2 are integer or floating numbers, and wherein the VAD decision (VADD) is generated as follows:

(26) $VADD=\{\begin{array}{cc}1& \mathrm{mssnr}>\mathrm{thr}\\ 0& \mathrm{mssnr}\le \mathrm{thr}\end{array}$
wherein VADD=1 indicates an active frame with voice activity being present, and VADD=0 indicates a passive frame with voice activity being absent.

(27) In a possible specific implementation, k.sub.1=135, k.sub.2=35, k.sub.3=10 and e.sub.1 is set to 8 whereas e.sub.2 is set to 18.

(28) In a further possible implementation of the method for adaptively detecting a voice activity in an input audio signal, the first variable α of the adaptive function (AF) may be calculated by:
α=g(i,lsnr,ε)=a(i)lsnr+b(i)+c(ε)
wherein a(i), b(i) are real numbers depending on a sub-band index i, and c(ε) is a real number depending on the estimated fluctuation of the background noise of the input audio signal, and wherein the second variable β of the adaptive function (AF) may be calculated by:

(29) $\beta =h\left(\mathrm{lsnr},\phi ,\mathrm{.Math.}\right)=\{\begin{array}{cc}{\beta }_1& \phi \ge d\mathrm{and}\mathrm{lsnr}>e_2\mathrm{and}\mathrm{.Math.}\le p\\ {\beta }_2& \phi \ge d\mathrm{and}\mathrm{lsnr}>e_2\mathrm{and}\mathrm{.Math.}>p\\ {\beta }_3& \phi \ge d\mathrm{and}{e}_1<\mathrm{lsnr}<e_2\mathrm{and}\mathrm{.Math.}\le p\\ {\beta }_4& \phi \ge d\mathrm{and}{e}_1<\mathrm{lsnr}<e_2\mathrm{and}\mathrm{.Math.}>p\\ {\beta }_5& \phi \ge d\mathrm{and}\mathrm{lsnr}\le e_1\mathrm{and}\mathrm{.Math.}\le p\\ {\beta }_6& \phi \ge d\mathrm{and}\mathrm{lsnr}\le e_1\mathrm{and}\mathrm{.Math.}>p\\ {\beta }_7& \phi <d\end{array}$ wherein φ=f(snr (i))+α and ε is the estimated fluctuation of the background noise and d and e.sub.1<e.sub.2 and p are integer or floating numbers.

(30) In a specific implementation the parameters are set as follows: β.sub.1=3, β.sub.2=4, β.sub.3=7, β.sub.4=10 β.sub.5=8, β.sub.6=15, β.sub.7=15, and d=1, e.sub.1=8, e.sub.2=18, p=40

(31) In an implementation of the method adaptively detecting a voice activity in an input audio signal according to the first aspect of the present disclosure, the derived modified segmental SNR (mssnr) is compared with a threshold value (thr) being set to:

(32) $\mathrm{thr}=\{\begin{array}{cc}{q}_1+r_1.Math.\mathrm{Min}\left[\frac{\mathrm{lsnr}-v_1}{W_1},1\right]& \mathrm{lsnr}>e_2\\ q_2+r_2.Math.\mathrm{Min}\left[\frac{\mathrm{lsnr}-v_2}{W_2},1\right]& e_1<\mathrm{lsnr}\le e_2\\ q_3+r_3.Math.\mathrm{Max}\left[\frac{\mathrm{lsnr}-v_3}{W_3},1\right]& \mathrm{lsnr}\le e_1\end{array}$
wherein q.sub.1, q.sub.2, q.sub.3 and r.sub.1, r.sub.2, r.sub.3 and e.sub.1<e.sub.2 and v.sub.1, v.sub.2, v.sub.3 and W.sub.1, W.sub.2, W.sub.3 are integer or floating numbers.

(33) In a specific implementation of the first aspect of the present disclosure, q.sub.1=20, q.sub.2=30, q.sub.3=9 and r.sub.1=30, r.sub.2=10 and r.sub.3=2. Further, v.sub.1=18, v.sub.2=8 and v.sub.3=5 and W.sub.1=8, W.sub.2=10 and W.sub.3=3. Further, the parameters e.sub.1, e.sub.2 are set to e.sub.1=8 and e.sub.2=18.

(34) Accordingly, in a possible embodiment, not only a background noise estimation and a long term SNR estimation is performed but additionally also a background noise variation estimation is performed to determine a background noise fluctuation ε of the background noise of the input audio signal.

(35) Two factors, α, β of the adaptive function (AF) adjust a trade-off of the discriminating power of the modified segmental SNR parameter. Different trade-offs signify that the detection is more favorable for either active or inactive detection for the received frames. Generally the higher the long term SNR of the input audio signal is the more favorable it is to adjust the modified segmental SNR for active detection by means of adjusting the corresponding coefficients α, β of the adaptive function.

(36) The VAD decision performed in step S6 can further go through a hard hang-over procedure. A hard hang-over procedure forces the VAD decisions for several frames to be active immediately after the VAD decision obtained in step S6 changes from active to inactive.

(37) In a possible implementation of the method for adaptively detecting a voice activity in an input audio signal according to the first aspect of the present disclosure, the background noise of the input audio signal is analyzed and a number representing the extent of stationarity or fluctuation of the background noise, denoted by ε, is generated. This fluctuation ε of the background noise can be calculated, for example, by:
ε=ω.Math.ε+(1−ω).Math.ssnr.sub.n
wherein ω is a forgetting factor usually between 0.9-0.99 and ssnr.sub.n is the summation of snr(i) over all sub-bands of the frame detected as a background frame multiplied by a factor of e.g. 10.

(38) FIG. 2 shows a block diagram of a VAD apparatus 1 according to a second aspect of the present disclosure. The VAD apparatus 1 comprises a SNR based VAD parameter calculation unit 2 receiving an input audio signal applied to an input 3 of the VAD apparatus 1. The SNR based VAD parameter calculation unit 2 calculates a SNR to each sub-band of an applied input frame of the input audio signal and a sub-band's specific parameter based on the respective sub-band SNR for each sub-band using an adaptive function and derives a modified segmental SNR by adding the sub-band's specific parameters. The derived modified segmental SNR is applied by the SNR based VAD parameter calculation unit 2 to a VAD decision generation unit 4 of the VAD apparatus 1. The VAD decision generation unit 4 generates a VAD decision (VADD) by comparing the modified segmental SNR with a threshold value. The generated VAD decision (VADD) is output by the VAD apparatus 1 at an output 5.

(39) In a possible implementation of the VAD apparatus 1 according to the second aspect of the present disclosure, the VAD detection apparatus 1 further comprises a noise characteristic determination unit 6 as shown in FIG. 2. The noise characteristic determination unit 6 determines a noise characteristic of the input signal based at least on a received input frame of the input audio signal applied to input 3 of the VAD apparatus 1. In an alternative implementation the noise characteristic is applied to the SNR based VAD parameter calculation unit 2 from an external noise characteristic determination entity. In a possible implementation of the VAD apparatus 1 according to the second aspect of the present disclosure the noise characteristic determination unit 6 as shown in FIG. 2 can comprise a long term SNR estimation unit which calculates a long term SNR of the input audio signal. In a further possible implementation, the noise characteristic determination unit 6 can also comprise a background noise variation estimation unit which calculates a stationarity or fluctuation ε of the background noise of the input audio signal. Accordingly, the noise characteristic provided by the noise characteristic determination unit 6 can comprise a long term SNR of the input audio signal and/or a stationarity or fluctuation of the background noise of the input audio signal. In a possible implementation an adaptive function used by the SNR based VAD parameter calculation unit 2 is selected dependent on at least one noise characteristic determined by the noise characteristic determination unit 6.

(40) FIG. 3 shows a block diagram of an audio signal processing device 7 according to a third aspect of the present disclosure. The signal processing device 7 comprises the VAD apparatus 1 providing a VAD decision (VADD) for an audio signal processing unit 8 within the audio signal processing device 7. The audio signal processing of an input audio signal is performed by the audio signal processing unit 8 depending on the received VAD decision (VADD) generated by the VAD apparatus 1 according to the first aspect of the present disclosure. The audio signal processing unit 8 can perform for example an encoding of the input audio signal based on the VAD decision (VADD). The audio signal processing device 7 can form part of a speech communication device such as a mobile phone. Further, the audio signal processing device 7 can be provided within a speech communication system such as an audio conferencing system, an echo signal cancellation system, a speech noise reduction system, a speech recognition system or a speech encoding system. The VAD decision (VADD) generated by the VAD apparatus 1 can control in a possible implementation a discontinuous transmission DTX mode of an entity, for example an entity in an cellular radio system, for example a GSM or LTE or CDMA system. The VAD apparatus 1 can enhance the system capacity of a system such as a cellular radio system by reducing co-channel interferences. Furthermore, the power consumption of a portable digital device within a cellular radio system can be reduced significantly.