AN ADAPTIVE ECHO CANCELLATION METHOD

Abstract: An adaptive echo cancellation method determines a long time aver age (12) of an input signal and a short time average (14) of the same signal, and prevents updating of an adaptive filter if the short time average (14) falls below (24) the long time average.

Description: AN ADAPTIVE ECHO CANCELLATION METHOD TECHNICAL FIELD The present invention relates to an adaptive echo cancellation method in which adaption of an echo canceller is prevented in an environment with low signal to background noise ratio. BACKGROUND OF THE INVENTION Echo is a problem related to the perceived speech quality in telephony systems with long delays, e.g. telephony over long distances or telephony systems using long processing delays, like digital cellular systems. The echo arises in the four-to-two wire conversion in the PSTN/subscriber interface. To remove this echo, echo cancellers are usually provided in transit exchanges for long distance traffic, and in mobile services switching centers for cellular applications. Due to the location of the echo canceller it iε made adaptive,- the same echo canceller iε used for many different subscriberε in the PSTN. This adaption is necesεary not only between different calls, but also during each call, due to the non-fixed nature of the tranεmisεion network, e.g. phaεe εlipε, three-party calls, etc . A problem with this filter adaption procesε iε that the filter may diverge if the input εignal decreaseε to a level that approaches the background noise level. To prevent the filter from diverging in situations where the background noise level is comparable to the signal level, it haε been suggeεted [1] to inhibit updating of the filter when the power of the input signal iε leεs than a given threshold. To overcome problems related to uεing a fixed threshold, the threshold is made adaptive in [1] . The method of [1] is based on comparison of the power of the input signal and the background noise level . The adaption is prevented if the power of the echo (power of input εignal - echo path attenuation ERL) is less than the background noise level plus a margin of 1 to 5 dB. A problem with the described approach is its dependence on an accurate estimate of the echo path attenuation ERL. If a large value of ERL is estimated, the adaption may be completely inhibited. Hence, the filter coefficients are frozen, and, assuming ERL is estimated from the filter coefficients, no new estimate of ERL will be found. If the characteristics of the echo generating system now change, the filter will not be able to adapt to the new situation. Thus, the method suggested in [1] is too conservative, i.e. filter updating is inhibited also in situations where this should be avoided. SUMMARY OF THE INVENTION An object of the present invention iε to provide an adaptive echo cancellation method in which adaption of an echo canceller iε prevented in an environment with low εignal to background noise ratio only when absolutely necesεary. This object is solved by the featureε of claim 1. BRIEF DESCRIPTION OF THE DRAWINGS The invention, together with further objects and advantageε thereof, may best be understood by making reference to the following description taken together with the accompanying drawingε, in which: FIGURE 1 is a block diagram of an echo generating syεtem,- FIGURE 2 iε a block diagram of an echo cancellation system; FIGURE 3 iε a time diagram of the input εignal power to an echo canceller; and FIGURE 4 is a flow chart of a preferred embodiment of the method in accordance with the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Fig. 1 illustrates the echo generating process in a telephony system. A subscriber A, called the far end subscriber below, is connected to a hybrid (a hybrid forms the interface between a four-wire and a two-wire connection, as is well known in the art) over a two-wire line. Similarly a subscriber B, called the near end subscriber below, is connected to another hybrid over a two- wire line. The two-wire lines transfer both incoming and outgoing speech signals. Outgoing speech from far end subscriber A iε transferred to near end subscriber B over the upper two-wire line in Fig. 1. Similarly outgoing speech from near end subεcriber B is transferred to far end subscriber A on the lower two-wire line in Fig. 1. However, the lower two-wire line from εubεcriber B to subscriber A also contains an echo of outgoing speech from subscriber A, which the hybrid at subεcriber B waε not able to εuppreεs completely. Similarly the upper two-wire line in Fig. 1 contains echo from outgoing speech from subscriber B. Fig. 2 illustrateε how the echo back to subscriber A is cancelled at the near end side (a similar arrangement iε provided at the far end side) . Input signal x(n) , where n denotes diεcrete time, represents speech from subscriber A. The input εignal x(n) is attenuated by the hybrid (the attenuation is represented by the echo path attenuation ERL (ERL = Echo Return Loss) ) , and the resulting echo signal s(n) is combined with the near end εignal v(n) , which may or may not contain near end speech. Thuε, the resulting output signal y(n) contains both the near end signal and echo from the far end signal. Furthermore, input εignal x(n) is also forwarded to an adaptive filter, which models the impulse response of the hybrid by adjusting its filter coefficients (a typical filter length iε 512 coefficientε) . The reεulting eεtimate of echo signal ε (n) iε denoted s(n) . This estimate is subtracted from output signal y(n) , and the resulting error signal e(n) is forwarded to the adaptive filter for adjustment of the filter coefficients and to the two-wire line back to far end subscriber A. Often the echo s(n) is modelled using an FIR (Finite Impulse Response) model, and the estimate s(n) is determined by the normalized least mean square (NLMS) method (see e.g. [2]) . For time invariant signals it can be shown that the steady-state misadjustment, i.e. the power of the error of the estimated echo, E(s(n) -s(n) ) 2 , for a constant step-size μ of the NLMS method equals (see e.g. [2]) E( § (n) - s (n) ) 2 = —H_.sv 2 (n) (1) 2-μ where Ev 2 (n) is the variance of the near end noise v(n) (signal from near end subscriber B during time periods without speech) . However, the error in estimate s (n) is due to errors in the estimated FIR coefficients {b k } . These FIR filter coefficient errors may be approximated by (based on eq. (45) in [2] ) B S > - b s k J, * . - rX . l . Bv XnL (2) * 2-μ N Ex 2 (n) where Ν is the filter length. Assuming that the filter haε converged in a stationary scenario, the variance of the filter coefficientε is given by (2) . Now, if the power of input signal x(n) decreases, (2) yields an increased steady-state variance of the filter coefficients, and the filter will diverge. If the power of x(n) increases again, the filter will re-converge, but the estimation error E(ε (n) -ε (n) ) 2 may be undesirably high before the filter has re-converged. Hence, some sort of control of the update process of the filter is desirable in order to prevent the estimation error from increasing too drastically in situations with non-stationary input signal characteristics. Fig. 3 illustrates the above described situation in time diagram form. Curve 1 in Fig. 3 represents the input power R x of input signal x(n) . In this case the signal is rather strong and well above the noise level NL (v(n) with no near end speech), represented by the dash-dotted line in Fig. 3. In this case the filter would converge even in the valleys of the curve. However, if the distance between noise level NL and the signal is reduced, either due to a lower signal power as represented by curve 2 in Fig. 3 or by a higher noise level NL, the filter will diverge in the valleys. As mentioned above, to overcome this problem the method described in [1] compares the power of the input signal x(n) to the background noise level NL. An adaption of the filter is prevented if the power of the echo s (n) is less than the background noise level, with a margin of 1 to 5 dB. That is, — < C'NL (3) ERL where ERL denotes an estimate of the echo path attenuation and C is a constant safety margin (in the range of 1 to 5 dB) . A problem with the approach described above is its dependence on an accurate estimate of the echo path attenuation ERL. If a large value of ERL is estimated, the adaption may be completely inhibited, since R X /ERL may very well be below C-NL for all input signal levels. Hence, the filter coefficients are frozen, and, asεuming ERL iε estimated as the sum of the squares of the filter coefficients, no new estimate of ERL will be formed. Hence, condition (3) is too conservative. The filter may end up in an adaption dead-lock. This may for inεtance be the case for an input signal similar to curve 3 in Fig. 3, which lies completely under the dashed line ERL+NL+C (ERL-NL-C expressed in dB) . The basic idea in accordance with the present invention to overcome this problem is to control updating of the filter by comparing a short time average R x sta of the power of x(n) to a long time average R x lta of the power of x(n) . If the short time average falls below the long time average, filter adaption is inhibited. This basic idea may be formalized as follows: the adaption is inhibited if where D is a predefined constant (for example of the order of -45 dBmO) used to inhibit adaption during periods of both low input power and low background noise levels, and where γ is given by The constant or (>0) ensures that adaption of the filter is never completely inhibited (when the estimated value of ERL is large, α will be chosen in equation (5) , since in this case < will be less than ERL- L/R x lta ) . By choosing o/~l (for example 0.95) , the filter is at least updated when the short time average R x βta of the input signal x(n) exceeds the long time average R x lta . The method in accordance with the present invention is illustra- ted in curve 3. The short double arrow R x sta representε the short time average. The length of the double arrow representε the time interval over which the average iε formed (typical valueε are 60- 70 milliεeconds) . Similarly the double arrow designated R x lca represents the long time average, which typically is computed over a time interval that is at leaεt an order of magnitude longer than the time period for calculating the short time average, for example of the order of 4 seconds. Thuε, only the moεt recent εampleε are uεed for calculating the short time average, while a large number of εampleε are uεed for calculating the long time average. As can be seen from the figure the short time average at sample instant n exceeds the long time average at the same instant (the diεtance above the t-axis representε the correεponding average) . Thuε, in this case (curve 3, sample instant n) the present invention would allow filter updating, while the method in accordance with the prior art would inhibit filter updating. A preferred embodiment of the method in accordance with the present invention will now be described with reference to the flow chart in Fig. 4. In step 10 the next sample of input εignal x(n) iε collected. In step 12 a new long time average R lca including the new sample is calculated. Similarly, in step 14 a new short time average R x sta including the new sample is cal¬ culated. In step 16 γ is calculated in accordance with (5) above. In step 16 a reference level R is calculated in accordance with the right hand side of relation (4) above. In step 20 the short time average is compared to this reference. If the short time average falls below the reference level, filter updating is inhibited in step 24, otherwise the filter is updated in step 22. Thereafter the algorithm returns to step 10 for collecting the next sample. In a simplified embodiment of the present invention D may be set to 0, which corresponds to -∞ dBmO or zero background noise. In this case the right hand side of (4) will always equal γR x lta . It will be understood by those skilled in the art that various modifications and changes may be made to the present invention without departure from the spirit and scope thereof, which is defined by the appended claims. REFERENCES [1] WO93/09608, Nokia Telecommunications OY [2] D.T.M. Slock, "On the convergence behavior of the LMS and the normalized LMS algorithms", IEEE Transactions on Signal Processing, 41 (9) :2811-2825, September 1993.

Claims: CLAIMS 1. An adaptive echo cancellation method in which adaption of an echo canceller is prevented in an environment with low signal to background noise ratio, including the steps of determining an echo path attenuation (ERL) estimate and a noise level estimate (NL) of said environment, characterized by the steps of: determining a long time average (R x lta ) of recent samples of an input signal x(n) to said echo canceller,- determining a short time average (R x sta ) of recent samples of said input signal x(n) ; preventing adaption of said echo canceller if said short term average iε less than the maximum of said long time average multiplied by a predetermined factor (γ) and a first predetermi¬ ned constant (D) . 2. The method of claim 1, characterized by said predetermined factor being the minimum of (a) a second predetermined constant ( ) , and (b) a product of said noiεe level estimate and said echo path attenuation eεtimate (ERL) divided by εaid long term average (R x lta ) . 3. The method of claim 2, characterized by εaid first predetermi¬ ned conεtant corresponding to a background noise level of the order of -45 dBmO . 4. The method of claim 2 or 3 , characterized by said second predetermined constant being approximately 1. 5. The method of claim 4, characterized by said second predeter¬ mined constant being equal to 0.95. 6. The method of any of the preceding claimε, characterized by said short term average being formed over a time period of the order of 60-70 ms . 7. The method of any of the preceding claims, characterized by said long term average being formed over a time period of the order of 4 seconds. 8. The method of claim 2, characterized by said first predetermi¬ ned constant being equal to 0, which corresponds to zero background noise.

Inventors: ERIKSSON ANDERS, KARLSEN JOHNNY, ERIKSSON, ANDERS, KARLSEN, JOHNNY
Applicants: ERICSSON TELEFON AB L M, ERIKSSON ANDERS, KARLSEN JOHNNY, ERIKSSON, ANDERS, KARLSEN, JOHNNY, TELEFONAKTIEBOLAGET LM ERICSSON (PUBL)
Application year: 1996
Main classification: H04B3
IPC codes: H04B3