Abstract:
A noise blanker ( 40, 106 ) monitors and removes noise from a sampled signal by adaptive filtering ( 98, 150 ) the sampled signal to generate trained adaptive filter prediction coefficients. The sampled signal is provided as an output signal when the noise blanker is in a training mode. A noise monitor ( 34, 154 ) detects whether noise contained within the sampled signal exceeds a predetermined threshold and provides a control signal in response to the detecting. The noise blanker is placed in a prediction mode for a predetermined amount of time in response to asserting the control signal. A prediction output signal is generated using a plurality of prediction coefficients as an all-pole filter. The prediction output signal has minimal noise content.

Description:
FIELD OF THE INVENTION 
     The present invention relates generally to noise blankers and more specifically to noise blankers using an adaptive all-pole predictor and method thereof. 
     RELATED ART 
     Automobile engines generate ignition noise that can be picked by radio receivers. The ignition noise is typically in the form of broadband spikes that cause audible effects that can be heard on the radio speakers. The frequency of ignition noise can be within a range of about zero hertz (Hz) to about 6 kilo hertz (KHz). When a received RF signal is relatively strong, ignition noise effects are negligible. A noise blanker is included in radios to reduce the effects of ignition noise. When a received RF signal is weak, effects of the ignition noise are more significant. When the RF level is very low, too much miss firing of the FM blanker may also cause audible effects. Non-ideal radio channel effects will affect the performance of an FM noise blanker, such as adjacent interference, multipath echo, etc. Some prior art noise blankers use previously stored samples to substitute for noise-corrupted samples. However, this technique for removing noise spikes reduces fidelity of the audio signal. Therefore, it would be desirable to have a radio receiver that removes noise spikes without affecting the fidelity of the audio signal. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present invention is illustrated by way of example and not limitation in the accompanying figures, in which like references indicate similar elements, and in which: 
         FIG. 1  illustrates, in block diagram form, a noise controller in accordance with one embodiment of the present invention; 
         FIG. 2  illustrates, in block diagram form, the noise blanker of  FIG. 1  in more detail; 
         FIG. 3  illustrates, in block diagram form, a noise controller in accordance with a second embodiment of the present invention; 
         FIG. 4  illustrates, in block diagram form, the noise blanker of  FIG. 3  in more detail; 
     
    
    
     Skilled artisans appreciate that elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help improve the understanding of the embodiments of the present invention. 
     DETAILED DESCRIPTION 
     Generally, the present invention provides a noise controller that reduces the effect of ignition noise on a received FM signal by using an all-pole prediction algorithm to generate a predicted signal segment to replace the noise-corrupted signal segment. The all-pole prediction algorithm takes a sample of a previously demodulated signal and models a new demodulated signal segment using the all-pole prediction algorithm. The all-pole prediction algorithm uses includes a least means squared (LMS) algorithm to minimize error between the received original signal and the predicted signal. Using an all-pole prediction algorithm to predict a new demodulated signal segment to replace the noise corrupted segment provides higher fidelity over prior art noise blankers. Using an LMS algorithm to minimize prediction error allows the all-pole predictor to be more easily implemented compared to the traditional all-pole prediction technique that requires a calculation of second-order statistics and matrix inverse. 
       FIG. 1  illustrates, in block diagram form, noise controller  10  in accordance with one embodiment of the present invention. Noise controller  10  includes modified polyphase filter  12 , decimator  32 , noise detector  34 , low pass filter (LPF)  36 , decimator  38 , and noise blanker  40 . Modified polyphase filter  12  includes decimators  14  and  16 , polyphase components  22 ,  24 , and  26 , delay elements  18  and  20 , and summation elements  28  and  30 . Noise detector  34  includes multiplier  42 , absolute value calculator  44 , LPF  46 , comparator  48 , counter  49 , switch  47 , and delay element  50 . 
     Noise controller  10  receives a multiplex signal MPX at a sample rate of 960 KS/s. The multiplex signal MPX corresponds to a received FM signal that may be corrupted with ignition noise. Within noise controller  10 , the MPX signal is received at an input of decimator  14  which downsamples, or divides, the sample rate of the MPX signal by two. The MPX signal is also provided to decimator  16  via delay element  18 . Decimator  16  also downsamples the MPX signal by two. Polyphase component  22  has an input coupled to an output of decimator  14 , and an output coupled to an input terminal of summation element  28 . Polyphase component  24  has input coupled to the output of decimator  14  via delay element  20 . An output of polyphase component  24  is provided to inputs of both of summation elements  28  and  30 . Polyphase component  26  has an input coupled to an output of decimator  16 , and an output coupled to an input of summation element  30 . As indicated in  FIG. 1 , summation element  28  subtracts the output of polyphase component  24  from the output of polyphase component  22  and provides the result as an input to decimator  32 . Summation element  30  adds the outputs of all of polyphase components  22 ,  24 , and  26  and provides the result to LPF  36 . 
     A conventional polyphase filter for decimation by two is implemented using the following equation: 
     
       
         
           
             
               
                 
                   
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             where 
             Ĥ 0 (z)=h 0 +h 2 z −1 + . . . +h 2N z −N    
             H 2 (z)=h 1 +h 3 z −1 + . . . +h 2N+1 z −N    
           
         
       
    
     The 0 th  polyphase component of the decimation filter can be further decomposed as following, 
     
       
         
           
             
               
                 
                   
                     
                       
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             where 
             H 0 (z)=h 0 +h 4 z −1 +h 8 z −1  . . . 
             H 1 (z)=h 2 +h 6 z −1 +h 10 z −1  . . . 
           
         
       
    
     Polyphase filter  12  is modified to obtain a bandpass filter to extract USN information, and the original lowpass filter is modulated by cos(πn/2) in order to frequency-shift this lowpass filter to a high enough frequency. The resultant bandpass filter plus decimation by two can be represented by the following equation:
 
 H   BPF ( z )=Ĥ 0 ( ze   iπn )= H   0 ( z )− z   −1   H   1 ( z )
 
     USN information can be extracted using the modified poly-phase decimation structure with only one more subtraction. This reduces DSP implementation cost compared to the case that an extra bandpass filter has to be implemented to obtain USN (ultrasonic noise) signal. 
     The output of decimator  32  is a 240 KS/s signal having an ultrasonic noise component labeled “USN”. The USN signal is inputted to noise detector  34 . Noise detector  34  uses the USN signal to detect the occurrence of noise above a predetermined threshold. To detect noise, the USN signal is first provided to absolute value calculator  44  and is lowpass filtered by LPF  46 . A noise threshold value labeled THRESHOLD is multiplied by the output of absolute value calculator  44  and provided as an input to comparator  48 . The output of LPF  46  is provided as a second input to comparator  48 . Comparator  48  compares the noise threshold to the absolute value. If the signal output of multiplier  42  is lower than the output of LPF  46 , a noise spike is detected. Otherwise, a noise spike is not detected. 
     The output of summation element  30  is lowpass filtered by LPF  36  and downsampled by two using decimator  38  to provide a signal labeled “MPX 1 ”. The MPX 1  signal is provided as an input to noise blanker  40 . Also, a control signal labeled “CONTROL” is provided by noise detector  34  via delay element  50 . When activated by the control signal CONTROL, noise blanker  40  generates a new predicted signal, that was generated during a training mode of noise blanker  40 , and substitutes the new predicted signal for the noisy signal. This has the effect of removing the noise spike from the MPX 1  signal, and providing a corresponding MPX signal labeled “MPX 2 ” with a relatively small amount of signal distortion. When a spike is detected, the output of comparator  48  will be provided to switch  47 . Switch  47  is controlled by counter  49  and prevents another spike from activating noise blanker  40  before a predetermined time as indicated by a preprogrammed count value in counter  49 . Counter  49  and switch  47  are provided because blanking too frequently will cause significant distortion in the signal. 
       FIG. 2  illustrates, in block diagram form, noise blanker  40  of  FIG. 1  in more detail. Noise blanker  40  includes switches  72 ,  76 , and  82 , delay elements  60 ,  62 , and  64 , coefficient updaters  66 ,  68 , and  70 , summation elements  74  and  78 , and multiplier  80 . Adaptive filter  98  includes delay elements  60 ,  62 , and  64 , coefficient updaters  66 ,  68 , and  70  and summation element  74 . MPX 1  signal is provided as an input to switch  72 . Switch  72  is controlled by the noise detector CONTROL signal to configure noise blanker  40  for a noise training mode when a noise spike is not present, and a predicting mode when a noise spike is present. Note that switches  72 ,  76 , and  82  are set to training mode in  FIG. 2 . MPX 1  signal is provided to a plurality of series-connected delay elements represented by delay elements  60 ,  62 , and  64 . The coefficient updaters, or taps, are each connected to an output terminal of the plurality of delay elements. Coefficient updater  66  includes multipliers  90  and  92 , delay element  94 , and summation element  96 . Input terminals of both of multipliers  90  and  92  are connected to the output of delay element  60 . Another input of multiplier  90  is coupled to switch  76 . An output of multiplier  90  is connected to an input of summation element  96 , and an output of summation element  96  is provided to input terminals of multiplier  92  and delay element  94 . An output of delay element  94  is connected to an input of summation element  96 . Delay element  94  is used to temporarily store each updated coefficient labeled “a 1 ”. Each of the other coefficient updaters  68  and  70  includes elements similar to coefficient updater  66  for updating each of the other coefficients. Note that only 3 coefficient updaters are illustrated in  FIG. 2 , but the illustrated embodiment of noise blanker  40  includes 30 coefficient updaters like coefficient updater  66 . 
     Noise blanker  40  is an adaptive all-pole predictor used to suppress impulse-type noise effects on audio output in a digitized intermediate frequency (DIF) radio. Noise blanker  40  provides the benefit of better audio quality compared to the prior art noise blankers because the all-pole prediction algorithm more closely approximates the received signal. As illustrated in  FIG. 2 , noise blanker  40  is a modified polyphase filter structure used to extract ultrasonic noise from a demodulated FM multiplex signal (MPX 1 ) and is used with noise detector  34  to decide whether a signal is corrupted by impulse noise or not. When the signal is not affected by ignition noise, noise blanker  40  is adaptively trained using the uncorrupted signal itself so that a mean-squared-error between the predicted output and the true signal is minimized. On the other hand, when the signal is affected by ignition noise, noise blanker  40  is switched into a prediction mode to generate a segment of a clean signal based on the previous training mode to replace that segment of corrupted original signal. By doing this, the segment of impulse noise affected audio signal is replaced by a predicted clean signal. Noise blanker  40  is intended to be used in a FM/AM radio automobile receiver, but it can also be used in any other type of radio receiver systems that are subject to being affected by impulse-type of interference. 
     An all-pole model is used to model the MPX signal at 240 KS/s. Different from a traditional method to obtain those model coefficients, an adaptive algorithm is designed to adjust the model coefficients dynamically. 
     The spike-corrupted MPX signal at 240 KS/s is can be represented by
 
 y ( n )= d ( n )+Spike( n ),  (1)
 
where d(n) is the original MPX signal from the transmitter, and Spike(n) is the impulse noise caused by ignition noise.
 
     The function of noise detector  34  is to detect when the original MPX signal is corrupted by ignition noise and the function of noise blanker  40  is to replace the spike-distorted signal with samples predicted from previously received good samples. 
     If we use an all-pole model to model the MPX signal, we have 
                         y   ^     ⁡     (   n   )       =         b   ⁡     (   0   )       ×     δ   ⁡     (   n   )         +       ∑     i   =   1     p     ⁢       a   ⁡     (   i   )       ×       y   ^     ⁡     (     n   -   i     )               ,           (   2   )               
where {a(i), i=1, 2, . . . p} are the model coefficients that are updated by coefficient updaters  66 ,  68 , and  70 . Minimized Mean Squared Error (MMSE) is used in noise blanker  40 ,
 
                       {       a   ⁡     (   i   )       ,     i   =   1     ,   2   ,     …   ⁢           ⁢   p       }     ⁢           ⁢   to   ⁢           ⁢   minimize   ⁢           ⁢   J     =       1   2     ⁢     E   ⁡     (                y   ^     ⁡     (   n   )       -     y   ⁡     (   n   )              2     )                 (   3   )               
where y(n) is the MPX signal as shown above. To find the solution, set the partial derivative with respect to a(i) to zero as shown in equations 4 and 5.
 
                               ∂   J       ∂     a   ⁡     (   i   )           =         ∂     E   ⁡     (                y   ^     ⁡     (   n   )       -     y   ⁡     (   n   )              2     )           ∂     a   ⁡     (   i   )           =   0       ,             i   =   1     ,   2   ,     …   ⁢           ⁢   p                   (   4   )                           ∂   J       ∂     a   ⁡     (   i   )           =       E   ⁡     [       (         y   ^     ⁡     (   n   )       -     y   ⁡     (   n   )         )     ×       y   ^     ⁡     (     n   -   i     )         ]       =   0       ,             i   =   1     ,   2   ,     …   ⁢           ⁢   p                   (   5   )               
To simplify the digital signal processor (DSP) calculations, an adaptive algorithm is used to adjust the model by updating the coefficients as illustrated in the following equation 6.
 
                               a     n   +   1       ⁡     (   i   )       =         a   n     ⁡     (   i   )       -     μ   ×       ∂   J       ∂     a   ⁡     (   i   )                 ,             i   =   1     ,   2   ,     …   ⁢           ⁢   p                   (   6   )               
In the above equation 6, μ is a constant. Also, to reduce the costs, we can use sample-mean instead of statistic mean to approximate equation (5),
 
                               ∂   J       ∂     a   ⁡     (   i   )           ≈       (         y   ^     ⁡     (   n   )       -     y   ⁡     (   n   )         )     ×       y   ^     ⁡     (     n   -   i     )           ,             i   =   1     ,   2   ,     …   ⁢           ⁢   p                   (   7   )               
So updating equation (6) with equation (7) becomes
   a   n+1 ( i )= a   n ( i )−μ×( ŷ ( n )− y ( n ))× ŷ ( n−i )  (8) 
When there is not any impulse interference present to cause noise blanker  40  to operate in prediction mode, control signal CONTROL causes noise blanker  40  to operate in training mode. In training mode, the original MPX signal is used to train the All-Pole Model as implemented in  FIG. 2 . The training equation (9) is shown below.
 
                               y   ^     ⁡     (   n   )       =       ∑     i   =   1     p     ⁢         a   n     ⁡     (   i   )       ×     y   ⁡     (     n   -   i     )             ,                   a     n   +   1       ⁡     (   i   )       =         a   n     ⁡     (   i   )       -     μ   ×     [         y   ^     ⁡     (   n   )       -     y   ⁡     (   n   )         ]     ×     y   ⁡     (     n   -   i     )                         (   9   )               
In equation 9, function {y(n)} is the original received MPX signal at 240 KS/s, and {a n (i), i=1, 2, . . . p} are the model coefficients at the time index n. When spikes are detected by the noise detector  34 , the trained All-Pole model starts to generate predictions to replace the originally corrupted MPX signal samples, where
 
     
       
         
           
             
               
                 
                   
                     
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       FIG. 3  illustrates, in block diagram form, noise controller  100  in accordance with a second embodiment of the present invention. Noise controller  100  includes lowpass filters  102  and  116 , decimators  104  and  118 , noise blankers  106  and  120 , blender  108  and summation elements  110  and  112 . In noise controller  100 , a multiplex signal MPX is provided to an input of LPF  102  at a data rate of 240 KS/s (kilo samples per second) and to an input of multiplier  114 . Signal MPX is decimated, or downsampled, by five with decimators  104  and  118  to produce left plus right (LPR) and left minus right (LMR) signals, where “left” and “right” refer to audio channels. The LPR signal is provided as an input to noise blanker  106 . As an option, a noise blanker  120  can be included after decimator  118  for signal LMR 1 , but may be unnecessary in most embodiments because the LMR 2  signal from noise blanker  120  is mostly removed, or attenuated, by blender  108  and has little effect on the final signal. Blender  108  receives signal LPR 2  from noise blanker  106 , and provides signals LPR 3 /LMR 3  as outputs to both of summation elements  110  and  112 . Summation elements  110  and  112  then provide separate left and right output signals labeled “L” and “R”, respectively. 
     Generally, noise blanker  106  requires less complexity to implement than noise blanker  40  because the sampling rate of noise blanker  106  is much lower than the sampling rate of noise blanker  40 . Also, noise blanker  106  provides a multiplex signal with higher fidelity than noise blanker  40  because noise blanker  106  uses lower frequency components than noise blanker  40 , and only the audio signal is predicted having relatively low frequencies. The operation of noise blanker  106  will be described in more detail in the discussion of  FIG. 4 . 
       FIG. 4  illustrates, in block diagram form, noise blanker  106  of  FIG. 3  in more detail. Noise blanker  106  includes adaptive filter  150 , summation element  152 , multiplier  148 , switches  142 ,  144 , and  166 , and noise detector  154 . Adaptive filter  150  includes a plurality of series-connected represented by delay elements  130 ,  132 , and  134 , a plurality of coefficient updaters represented by coefficient updaters  136 ,  138 , and  140 , and summation element  146 . Noise detector  154  includes absolute value calculator  156 , LPF  158 , comparator  160 , counter  164 , and switch  162 . 
     Generally, noise blanker  106  generates updated coefficients in a manner similar to noise blanker  40 . However, instead of detecting ultrasonic noise (USN) to control the training and predicting modes, noise blanker  106  uses a prediction error generated by subtracting an output of the noise blanker from the original input MPX signal. The prediction error is used to detect ignition noise. When there is no ignition noise, the prediction error is a “white noise” type of signal. When ignition noise is present, the noise spikes appear in the error signal because, by their nature, cannot be predicted by noise blanker  40 . 
     Noise blanker  106  generates updated coefficients in the same manner as noise blanker  40 . Noise blanker  106  is an adaptive all-pole predictor used to suppress impulse-type noise effects on audio output in a digitized intermediate frequency (DIF) radio. Noise blanker  106  provides the benefit of better audio quality compared to prior art impulse noise blanker as used in FM/AM radio. As illustrated in  FIG. 4 , noise blanker  106  receives a decimated signal LPR 1  and is used with noise detector  154  to decide whether a signal is corrupted by impulse noise or not. When the signal is not affected by impulse noise, noise blanker  106  is adaptively trained using the uncorrupted signal itself so that a mean-squared-error between the predicted output and the true signal is minimized. Switches  142 ,  144 , and  166  are illustrated in the training mode in  FIG. 4 . On the other hand, when the signal is affected by ignition noise, noise blanker  106  is switched into a prediction mode to generate a segment of a clean signal based on the previous training of the all-pole mode to replace that segment of corrupted original signal. By doing this, an impulse noise affected audio signal is replaced by a predicted clean signal. Noise blanker  106  is intended to be used in a FM/AM radio automobile receiver, but it can also be used in any other type of system affected by impulse-type of interference. 
     An all-pole model is used to model the LPR 1  signal at 48 KS/s. This is different from a traditional method to obtain those model coefficients. An adaptive algorithm is designed to adjust the model, or prediction, coefficients dynamically. 
     The spike-corrupted LPR 1  signal at 48 KS/s is
 
 y ( n )= d ( n )+Spike( n ),  (11)
 
where d(n) is the original LPR 1  signal from the transmitter, and Spike(n) is the impulse noise caused by ignition noise.
 
     The function of noise detector  34  is to detect when the original MPX signal is corrupted by ignition noise and the function of noise blanker  40  is to replace the spike-distorted signal with samples predicted from previously received good samples. 
     If we use an all-pole model to model the MPX signal, we have, 
                         y   ^     ⁡     (   n   )       =         b   ⁡     (   0   )       ×     δ   ⁡     (   n   )         +       ∑     i   =   1     p     ⁢       a   ⁡     (   i   )       ×       y   ^     ⁡     (     n   -   i     )               ,           (   12   )               
where {a(i), i=1, 2, . . . p} are the model coefficients that are updated by coefficient updaters  66 ,  68 , and  70 . Minimized Mean Squared Error (MMSE) is used in noise blanker  40 , and
 
                       {       a   ⁡     (   i   )       ,     i   =   1     ,   2   ,     …   ⁢           ⁢   p       }     ⁢           ⁢   to   ⁢           ⁢   minimize   ⁢           ⁢   J     =       1   2     ⁢     E   ⁡     (                y   ^     ⁡     (   n   )       -     y   ⁡     (   n   )              2     )                 (   13   )               
where y(n) is the MPX signal as shown above. To find the solution, set the partial derivative with respect to a(i) to zero as shown in equations 14 and 15.
 
                         ∂   J       ∂     a   ⁡     (   i   )           =         ∂     E   ⁡     (                y   ^     ⁡     (   n   )       -     y   ⁡     (   n   )              2     )           ∂     a   ⁡     (   i   )           =   0       ,           ⁢     i   =   1     ,   2   ,     …   ⁢           ⁢   p             (   14   )                     ∂   J       ∂     a   ⁡     (   i   )           =       E   (                y   ^     ⁡     (   n   )       -     y   ⁡     (   n   )         )     ×       y   ^     ⁡     (     n   -   i     )         ]     =   0       ,           ⁢     i   =   1     ,   2   ,     …   ⁢           ⁢   p             (   15   )               
To simply the digital signal processor (DSP) calculations, an adaptive algorithm is used to adjust the model by updating the coefficients as illustrated in the following equation 16.
 
                         a     n   +   1       ⁡     (   i   )       =         a   n     ⁡     (   i   )       -     μ   ×       ∂   J       ∂     a   ⁡     (   i   )                 ,           ⁢     i   =   1     ,   2   ,     …   ⁢           ⁢   p             (   16   )               
In the above equation 16, μ is a constant. Also, to reduce costs, we can use sample-mean instead of statistic mean to approximate equation (15),
 
                         ∂   J       ∂     a   ⁡     (   i   )           ≈       (         y   ^     ⁡     (   n   )       -     y   ⁡     (   n   )         )     ×       y   ^     ⁡     (     n   -   i     )           ,           ⁢     i   =   1     ,   2   ,     …   ⁢           ⁢   p             (   17   )               
So updating equation (16) with equation (17) provides
   a   n+1 ( i )= a   n ( i )−μ×( ŷ ( n )− y ( n ))× ŷ ( n−i )  (18) 
     When there is not any impulse interference causing noise blanker  106  to operate in prediction mode, control signal CONTROL causes noise blanker  106  to operate in training mode. In training mode, the original LPR 1  signal is used to train the All-Pole Model as implemented in  FIG. 4 . The training equation (19) is shown below. 
                         y   ^     ⁡     (   n   )       =       ∑     i   =   1     p     ⁢           ⁢         a   n     ⁡     (   i   )       ×     y   ⁡     (     n   -   i     )             ,     
     ⁢         a     n   +   1       ⁡     (   i   )       =         a   n     ⁡     (   i   )       -     μ   ×     [         y   ^     ⁡     (   n   )       -     y   ⁡     (   n   )         ]     ×     y   ⁡     (     n   -   i     )                     (   19   )               
where {y(n)} is the original received LPR 1  signal at 48 KS/s, {a n (i),i=1, 2, . . . p} are the model coefficients at the time index n. When spikes are detected by the noise detector  154 , the trained All-Pole model starts to generate predictions to replace the originally corrupted MPX signal samples, where
 
     
       
         
           
             
               
                 
                   
                     
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                   ( 
                   20 
                   ) 
                 
               
             
           
         
       
     
     To detect noise spikes, noise detector  154  takes the prediction error from the output of summation element  152 . An absolute value calculation is performed by absolute value calculator  156 . The result is provided to a first input of comparator  160  via LPF  158  and also directly to a second input of comparator  160  via a threshold multiplier  159 . The threshold value THRESHOLD is a predetermined noise threshold. The output of LPF  158  is compared to the predetermined noise threshold THRESHOLD to detect the present of noise. If the output of LPF  158  is higher than the predetermined threshold, then noise is not present and a control signal labeled CONTROL, having a predetermined logic state, is provided through a switch  162  to control switches  142  and  166 . As illustrated in  FIG. 4 , when noise is not present, switches  142 ,  144 , and  166  are positioned and illustrated in  FIG. 4  and noise blanker  106  is in the training mode. When noise blanker  106  is in the training mode, signal LPR 1  is passed through and is simply output as LPR 2 . However, when noise is detected by noise detector  154 , as determined by the output of LPF  158  being lower than the predetermined noise threshold, control signal CONTROL is provided at a different logic state, causing switches  142  and  166  to switch to the other terminal, and switch  144  is opened placing noise blanker  106  in prediction mode. In prediction mode, noise blanker  106  replaces the noisy segment of LPR 2  with a predicted clean version of LPR 2 . Counter  164  and switch  162  prevent control signal CONTROL from being asserted too frequently. If predicted segments are substituted for original segments too frequently, then output signal LPR 2  becomes too distorted. 
     Note that in the illustrated embodiments the noise controllers are implemented in a combination of hardware and software, where the software is run on a digital signal processor. However, in other embodiments, the noise controllers can be implement in hardware or in software. 
     In the foregoing specification, the invention has been described with reference to specific embodiments. However, one of ordinary skill in the art will appreciate that various modifications and changes can be made without departing from the scope of the present invention as set forth in the claims below. Accordingly, the specification and figures are to be regarded in an illustrative rather than a restrictive sense, and all such modifications are intended to be included within the scope of present invention. 
     Benefits, other advantages, and solutions to problems have been described above with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any element(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or element of any or all the claims. As used herein, the terms “comprises,” “comprising,” or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.