Patent Abstract:
A nonlinear processor (NLP) for selectively removing or reducing residual echo signals from an acoustic echo canceller associated with a telephony terminal is provided. Low level background noise and near end speech signals pass through the NLP structure substantially unaltered. Distortion, background noise above a preset threshold and echo signals including long duration echoes are replaced with a linear combination of previous noise data.

Full Description:
FIELD OF THE INVENTION 
     This invention pertains to the field of adaptive, speech echo cancellation, and more particularly to acoustic echo cancellation for speaker-phones and voice conferencing systems utilizing a nonlinear processor (NLP). 
     BACKGROUND 
     Nonlinear processors (NLPs) are used in echo cancellation generally, and in particular for echo cancellation of acoustic speech signals. Speech echo cancellation can be grouped into two major categories: network echo cancellation and acoustic echo cancellation. The primary difference between acoustic echo signals and network echo signals is that an acoustic echo channel includes both loudspeaker and microphone transducers that convert signals to and from audible (acoustic) sound signals, as opposed to network echo signals that are generated by electric circuits (hybrids). The acoustic type typically has high background noise signals present from the surrounding environment that makes application of prior art nonlinear processors unfavorable. 
     PRIOR ART 
     The term “nonlinear processor” or NLP can be used to describe a signal processing circuit or algorithm that is placed in the speech path after echo cancellation, so as to provide further attenuation or removal of residual echo signals that cannot be cancelled completely by an echo canceller. A non-linearity, a distortion, or an added noise signal are examples of signals that can not be fully cancelled by an echo canceller, and these signals are typically removed or attenuated by a nonlinear processor. One example of a prior art NLP is a “center clipper” in which all signal samples with amplitude less than a threshold value are set to zero. This method has been used for network echo cancellation for many years by many different equipment suppliers. A description of the operation of such an NLP has been included in the appendix of the ITU-T G.165 recommendation as a reference design for an NLP. A known problem with this type of NLP is the so called “noise gating” phenomena wherein a party listening to the resulting speech signals, after a center clipping NLP, hears the background noise signals disappearing and then reappearing during periods of activation and de-activation of the NLP. 
     Improvements upon this center clipper method that reduce or eliminate the “noise gating” problem have been introduced in recent years. These improvements are primarily used for network type echo cancelers in which background noise levels are typically very low in comparison to the noise levels experienced with acoustic echo signals. An example of a prior art NLP improvement is a center clipper method combined with the injection of a matched artificial noise source to mask the removal of noise signals by the center clipper. Yet another example is a variable attenuator that provides a soft-switched transition between on/off states of signal attenuation with complementary soft-switched injection of artificial noise. U.S. Pat. No. 5,274,705, which issued Dec. 28, 1993 to Younce et al, describes another example of an improved NLP using dual thresholds in the NLP transfer function which allows transparent transfer of low level noise signals if below the low threshold, and transparent transfer of large signals if above an upper threshold while removing or modifying any signals in-between the two thresholds. 
     Problems with all of the aforementioned methods arise when dealing with signals from an acoustic environment because of the higher noise levels. Noise injection methods are not typically used because the character of the background noise changes very noticeably if an artificial noise is injected in place of the original noise. Variable attenuation methods without noise injection appear to be most commonly used for the control of residual echo in acoustic echo cancelers. This appears to be an extension of methods used previously by half-duplex speakerphones and network echo suppressors which used complementary attenuators to provide switched loss to control echo. The use of echo cancellation for a “full duplex” hands-free telephone appears to also make use of prior art complementary attenuators with reduced attenuation “depth” to make the connection close to full duplex, or perhaps, subjectively, “full duplex”. Some other implementations appear to allow complete full-duplex communication some of the time (e.g. during double-talk periods), while providing some extra attenuation control of echo residual during other periods of time (e.g. single talk periods). All of these methods cause audible changes in background noise signals producing some degradation of overall subjective performance. 
     The prior art dual threshold method when applied to acoustic background noise signals, produces noticeable levels of extra signal distortion. This distortion is caused by the changes made to signals when the NLP is on. This audible distortion changes the character of the background noise during speech from the far end side, and can best be described as a raspy type noise with some high frequency components that sound different than a typical background noise. Note as used in this description the far end talker is the party who is also listening to the resulting signal after the NLP. 
     Another problem with the prior art NLP is that it has no control over a long echo path environment. To save cost most echo cancelers can only deal with a short echo length (e.g. 128 ms or less). In some acoustic environments, the echo can last for about 0.5 to 1 sec. Although, in most cases, the echo residual is very small after 128 ms, when both sides of telephone line are quiet, even a very small echo residual is noticeable. After the loudspeaker has been quiet for over ½ sec, the echo may still be present at the microphone input. The echo residual is treated as near-end single talk by the speaker-phone, and therefore the NLP will not attenuate this signal. 
     SUMMARY OF THE INVENTION 
     The method used in the present invention builds upon the dual threshold method. The NLP turns on only if both a double talk condition and an echo suppression requirement are met. 
     The present invention further relates to a method of reducing the level of extra signal distortion by processing signals in a different manner than the methods described in prior art NLP designs. The signal will be transparent if it is detected to be noise, otherwise a noise prediction value is sent out. 
     In the present invention, the long echo residual is dealt with by the new NLP structure. In lab tests, the echo residual is significantly reduced with the new NLP structure, even in the case when echo signals last up to 1 sec. and the adaptation algorithm can only deal with 100 ms echo length. 
     Briefly, the NLP structure of the present invention determines whether the residual signal from the echo canceller is greater or less than an estimated noise level. If it is less than the estimated noise level the residual signal is passed through the NLP substantially unchanged. If the residual signal is greater than the estimated noise level it is further evaluated to determine whether or not it represents a near-end speech signal. If it is speech as in near-end single talk or double talk the residual signal is again passed through the NLP unchanged. If, however the incoming signal is echo residual or long term echo the NLP outputs a low level noise signal which represents a prediction based on previous noise samples. 
     Therefore in accordance with a first aspect of the present invention there is provided a non linear processor (NLP) for use with an acoustic echo canceller associated with a telephone terminal to selectively reduce residual signals therefrom. The NLP comprises: a first input to receive the residual signal; a second input to receive a reference signal representing a signal from a far end user; a third input for receiving a near end signal from a microphone in the terminal; an output for delivering a NLP output to a far end user; a NLP switch, switchable between a first position wherein the residual signal is passed directly to the output and a second position wherein a signal representing a previous noise signal is delivered to the output; noise decision means to determine whether the residual signal is above a noise level and if not to switch the NLP switch to the first position; and NLP decision means cooperating with the noise decision means to switch the NLP switch to the first position when the residual signal contains near end speech and to the second position otherwise. 
     In a preferred embodiment the decision means incorporates an echo suppression threshold means which determined whether the residual signal is a long echo which was not cancelled by the echo canceller. If it is a long echo the switch remains in the second position wherein low level noise data is provided to Sout. 
     In accordance with a second aspect of the present invention there is provided a method of selectively reducing a residual signal from an acoustic echo canceller associated with a telephone terminal. The method comprises: providing the residual signal to noise decision means for comparison with an estimated noise level; passing the residual signal directly through the NLP if it is less than the estimated noise level; passing the residual signal to further decision means if it is greater than the estimated noise level whereat the residual signal is caused to be passed through the NLP if it is a near end speech signal otherwise a signal representing a previous noise signal is output from the NLP. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The invention will now be described in greater detail having reference to the attached drawings wherein: 
     FIG. 1 shows a typical acoustical echo canceller with an incorporated NLP; 
     FIG. 2 is a block diagram of the NLP structure according to the present invention; and 
     FIGS. 3A and 3B are flow diagrams illustrating the NLP process of the invention. 
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     FIG. 1 shows an acoustical echo canceller having an incorporated NLP  12 . In FIG. 1, S(n) is the near-end signal, R(n) is the far-end signal used as reference signal for the adaptive filter  14 , and E(n) is the echo residual which is the difference between S(n) and its estimation value S(n). 
     FIG. 2 shows the implementation of the new NLP structure, which is composed of four parts: the main NLP block  20  for signal input and output; the NOISE DECISION circuit  40  to check whether the input signal is noise or an active signal; the NLP CONTROL  60  to turn the NLP “ON” and “OFF”; and the ECHO TAIL CONTROL  80  to check whether the switch of NLP from “ON” to “OFF” is caused by the near end signal or the echo residue being too long to be cancelled by the adaptive filter  14 . 
     As shown in FIG. 2 the echo residual signal E(n) is supplied to the NLP block  20  and to the noise decision circuit  40 . The NLP block  20  includes switch  22 , switch  24  and filter  26 . The activation of switch  22  is controlled by the output of AND Gate  30  such that when the output of Gate  30  is “1” the output of switch  22  is provided by the filter  26  i.e. S=1 and when the output of AND Gate  30  is “0” the output of switch  22  is a direct passthrough of E(n). This is shown in FIGS. 2 as S=0. Switch  24  is controlled by the output of comparator  46  of noise decision block  40  such that a logical “1” to switch  24  causes the switch to provide a “0” input to filter  26  and a logical “0” to switch  24  causes E(n) to be supplied to filter  26 . 
     The noise decision block  40  includes absolute signal detector (ABS)  42 , noise level detector  44  and comparator  46 . The purpose of the noise decision block  40  is to monitor the residual echo E(n) with reference to an estimated noise level. When the level of E(n) is lower than the noise level (T noise ) the output of comparator  46  is a “0”. The noise level can be estimated with any common, noise-level detection algorithm implemented by noise level detector  44  whose output is T noise . The “0” at the output of comparator  46  is supplied to one input  32  of AND gate  30  which switches switch  22  to S=0 and as previously discussed the residual echo signal E(n)is passed directly through the NLP structure. Thus, any residual echo whose level is lower than a predetermined noise threshold is not altered by the NLP. This “0” at the output of comparator  46  is also provided to switch  24  so that in accordance with the previous discussion E(n) is also supplied to the input of filter  26 . Because switch  22  is in position s=0, E(n) is not connected to Sout but rather is the output of filter  26 . The filter  26  is normally a low-cost FIR filter with low-pass characteristics. It takes the noise samples in E(n) signal, smooths them and subsequently outputs them as a noise predicted value. 
     If the value of E(n) is greater than the predetermined noise threshold the output of comparator  46  is a “1” and this “1” appears at input  32  of AND gate  30  as well as to switch  24  thereby switching switch  24  to S=1. In this configuration switch  24  receives the “0” input which is supplied to filter  26 . The operation of switch  22 , in this mode, is now dependent on the NLP decision coming out of OR gate  50 . As illustrated in FIG. 2, OR gate  50  has two inputs, input  52  from NLP control block  60  and input  54  from the echo tail control  80 . 
     Looking first at the NLP control block  60  which has two comparator circuits, one for double talk detection and the other for a situation wherein the echo canceller shown in FIG. 1 does not provide enough echo cancellation. This could be because of long echo, because the adaptive algorithm does not converge sufficiently or because of a small echo with a small double talk. The double talk comparator circuit includes level detector  62 , loss threshold  64 , comparator  66  and hangover timer  68 . When the value of E(n) is greater than the noise threshold but is not near-end speech the value of E(n) will be less than the level of R(n) which is multiplied by a loss threshold. (T loss ) Under these conditions the output of comparator  66  will be a “1” which is supplied to input  67  of AND gate  70 . Under the same conditions, i.e. no near-end speech, the value of E(n) is smaller than the value of S(n) multiplied by a suppression loss T sup  and in this situation the output of comparator  76  is a “1”. Thus the output of AND gate  70  is also a “1” and hence the NLP decision is a “1” which, in turn means that switch  22  is in the position S=1 and the output of the NLP structure is a filtered value of a previous noise sample. Thus any residual echo is reduced or removed from the signal by the NLP before it is sent to a far-end user. 
     If double talk occurs i.e. the far-end speaker is talking and the near-end speaker talks as well, the signal E(n) now represents active voice communication and is to be passed directly through the NLP structure. When there is a double talk situation the signal at the negative input of comparator  66  rises above the level of R(n) multiplied by T loss  and the output of comparator  66  switches to a “0”. Hangover timer  68  simply delays for a preselected interval the switchover from a “0” to a “1” to extend the detect time of double talk. In any event, a “0” on one of the inputs to AND gate  70  results in a “0” being provided to one of the inputs to OR gate  50 . Under normal circumstances the output of AND gate  90  in the echo tail control  80  will also be a “0” so that AND gate  30  will also switch to a “0” output resulting in switch  22  switching to S=0 and Sout=E(n). Thus, the residual echo which now includes speech from a near-end user is passed through the NLP structure unaltered. 
     Another scenario which might arise is when the far-end user is silent but the near-end user is speaking i.e. near-end single talk, again this residual signal is to be passed through the NLP structure without alteration. This situation is covered by the aforementioned structure and the structure comprising level detector  72 , echo suppression threshold  74 , and comparator  76 . In this situation the level of S(n) multiplied with T sup  drops below the level of E(n) and the output of comparator  76  switches from a “1” to a “0” . This “0” on input  71  of AND gate  70  results in a “0” to input  52  of OR gate  50  and again, providing the output of echo tail AND gate  90  is a “0”, switch  22  is switched to S=0 and the value of E(n) is provided to Sout. 
     There is one additional condition which must be considered and that is the situation wherein the near-end signal appears to be near-end speech but is, in fact, a long duration echo such as might occur with a speaker phone or the like. The adaptive filter in the echo canceller shown in FIG.  1  and as discussed previously normally only operates on a short echo length e.g. 128 ms. or less. An echo which lasts longer than this time interval will appear in residual echo signal E(n) and without the benefit of the echo tail control of the present invention would be passed through the structure on the false decision that it represents near-end speech. Thus, when comparator  76  switches from “1” to a “0” output indicating near-end speech, the output from AND gate  70  to OR gate  50  is a “0”. At this time, the echo tail control block  80  comprising residual level delay 82 , threshold  84 , NLP decision delay  86  and comparator  88  determine whether the current value of the level of E(n), i.e. P k  in FIG. 2 is greater or less than a previous value of P k  i.e. P k−1 . If the previous value, P k−1  (with a threshold γ) is greater than P k  which would suggest a decaying signal, i.e. a long-term echo, comparator  88  outputs a “1”. Since the output of OR gate  50  is also a “1” from the previous time, this “1” is supplied through decision delay block  86  to input  91  of AND gate  90 . The other input  93  of AND gate  90  is also a “1” by virtue of the output of comparator  88 . Thus, OR gate  50  continues to output a “1” so that Sout is the filtered noise value rather than E(n) when E(n) is above the noise level. When the value of E(n) rises such that P k  is greater or equal to P k−1  multiplied by γ, comparator  88  switches to a “0” output and as a result NLP decision will become “0” and E(n) will again pass directly through to Sout. This rise in E(N) could, for example, indicate a situation wherein there is a near-end speaker and/or a double talk situation. 
     The echo tail control block  80  provides the added functionality of removing echoes having a long tail which would otherwise be passed through the NLP structure on the basis that it was misinterpreted as being a near-end speech. 
     FIG. 3A and 3B is a flowchart setting out the process steps followed by the NLP structure. 
     According to the present invention various alternatives may be introduced. For example, P k  may choose not to be updated when NLP control is “0” and NLP decision is “1” which means that the NLP is “ON” because of a long echo tail. The advantage of that is that P k−1  will not be decreased during the echo tail and it gives a better chance for NLP to remain “ON” to combat a very long echo tail. The NLP will not be released with an occasional level reduction during the echo tail period. The disadvantage is that it may take a little longer to release NLP when both sides of the telephone line are quiet. 
     Also, all the level calculations can be replaced with energy calculations. The disadvantage of that is that the energy responds slowly in comparison with peak level. 
     The following sets out some of the parameter selections for the NLP configuration. 
     1. Threshold for NLP tail decision (γ): Large γ will make it difficult to release NLP when both sides of telephone are quiet. On the other hand, small γ will make it difficult to detect echo tail because the level of echo tail may not decrease strictly monotonically. In some cases, the residual level can be occasionally increased during the echo tail period and NLP can be turned off by these level increase if γ is too small. A suitable value for γ in the acoustical echo cancellation is 1.05. 
     2. The function of the filter is to replace the missing noise samples. In the acoustic echo environment, the background noise is not white but colored with low pass characteristics. Therefore, a low pass filter should be used to recover noise samples. A simple and efficient filter is a four tap FIR filter with its first coefficient being zero: [0, 0.29469694, 0.34868972, 0.20388524]. 
     3. The double talk threshold (T loss ) should also be chosen carefully. If it is too large, double talk may not be detected efficiently and if it is too small, NLP may not function well because the double talk detector may give a lot of false double talk indications. A suitable value for T loss  is 0.5. 
     4. The chosen criteria for the double talk hangover timer is the same as double talk threshold. If it is too small, the double talk detector may not work well and a lot of near-end speech clipping can be heard by the far-end listeners. If the hangover timer is too large, it takes a long time to release the double talk decision and NLP may not function well to cut the echo residual effectively. A suitable value for the hangover timer is 400 samples. 
     5. The threshold for echo suppression (T sup ) may have a relatively large range. It is a safe protection for the small near end double talk. A very small near-end double talk may not be detected by the double talk detector, but it will seriously deteriorate the echo canceller performance. In such a case, an echo suppression level detection should be employed. A high echo suppression threshold will imply that small double talk in the echo environment may not be detected effectively and a low threshold means that NLP will not turn on easily. With a very low threshold, it will be difficult or at least take a long time to turn the NLP on because the NLP will be activated only when large amount of echo suppression is achieved by the adaptive echo canceller. A suitable value for the threshold is T sup  is 0.2. 
     The following provides some definitions which may assist in an understanding of the invention. 
     NLP: Nonlinear processor, used to remove or further attenuate residual echo signals after echo cancellation. 
     Adaptive Filter: An adaptive algorithm to simulate the echo path so that the echo can be removed by subtracting its estimated value. 
     Double-Talk Detector: detects the condition of double-talk (when both the near-end and the far-end signals exist). 
     Level Detector: A recursive algorithm to detect the peak averaged value of the signal. 
     Noise Level Detector: A recursive algorithm to estimate the level of background noise. 
     While a particular embodiment of the invention has been described and illustrated it will be apparent to one skilled in the art that numerous variations can be made to the basic concept. It is to be understood, however, that such variations will fall within the scope of the invention as defined by the appended claims.

Technology Classification (CPC): 7