Abstract:
An improved technique for processing digital audio signals is provided wherein adaptation of predictor coefficients in an ADPCM environment is caused to converge in a rapid and computationally efficient manner. The technique employs a whitening filter to generate a filtered reconstructed signal which is utilized to update, or adapt, the prediction coefficients of a pole-based predictor.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS  
       [0001]    The present application claims the benefit of priority from U.S. Provisional patent application Ser. No. 60/183,280, entitled “Adaptive Differential Pulse Code Modulation System and Method Utilizing Whitening Filter For Updating Of Predictor Coefficients” filed on Feb. 17, 2000, which is incorporated by reference herein. 
     
    
     
       BACKGROUND  
         [0002]    1. Field of Invention  
           [0003]    The present invention relates generally to encoding and decoding of digital audio signals, and more particularly to predictor adaptation in adaptive differential pulse code modulation (ADPCM) systems.  
           [0004]    2. Description of the Prior Art  
           [0005]    [0005]FIG. 1 may be referenced in conjunction with the following discussion. ADPCM is a well-known technique for encoding speech and other audio signals for subsequent transmission over a network. A standard implementation of such a system is described in the International Telecommunication Union (ITU-T) Recommendation G.722, 7 kHz Audio-Coding Within 64 kBit/s, which is incorporated by reference herein.  
           [0006]    As described in U.S. Pat. No. 4,317,208, issued Feb. 23, 1982 to Araseki et al. and incorporated by reference herein, a differential pulse code modulation system is a band compression system in which a prediction of each signal sample at a present time period is based on signal samples at past time periods. Such a process is particularly effective with voice and similar band signals due to their high degree of correlation between successive signal samples. A predicted signal S j  at a time j is typically derived at a transmitter  102  by the general equation:  
             S   j   =A   1   S   j−1   +A   2   S   j−2   + . . . A   n   S   j−n ;  
           [0007]    where A 1 , A 2 , . . . A n  are termed the prediction coefficients. The prediction coefficients are selected to minimize the difference between an input signal Y j  and the predicted signal S j  thus minimizing a prediction error E j  which is in turn quantized and transmitted to a receiver  104 , thereby requiring significantly less transmission bandwidth than would the input signal. The receiver  104  works in a manner generally the reverse of the transmitter  102 , thereby reconstructing the input signal.  
           [0008]    The characteristics of a voice or related audio signal vary with time, consequently the optimum coefficient values also vary. One method of attempting to efficiently derive prediction coefficients is to adapt them with the goal of minimizing the prediction error E j  while such error is being observed, which could generally describe an ADPCM system. A common type of predictor employed in these systems is a pole-based predictor, such as predictors  110  and  126 , which utilizes a feedback loop to minimize the energy in the prediction error signal E j , which is sometimes referred to as the difference or residual signal.  
           [0009]    Due to the reality of frequent transmission errors between the transmitter  102  and the receiver  104 , the prediction errors Ê j  (which have been inverse quantized) produced at the receiver  104 , and thus the reconstructed input signal Ś j  depending thereon, has a tendency to diverge from the real input signal Y j  received at the transmitter  102 . To gradually eliminate the adverse effect of the transmission errors, the prediction coefficients are typically derived by the general equation:  
             A   i   j+1   =A   i   j (1−δ)+ g·F   1 (S 1   j−1 )· F   2 (Ê j );  
           [0010]    where j=1 to n, δ is a positive value much smaller than 1, g is a proper positive constant, Ś j−i  is a reconstructed input signal delayed i samples, and F 1  and F 2  are non-decreasing functions. The receiver  104  prediction coefficient values are tracked, or gradually caused to converge to those of the transmitter  102 , by operation of the term (1−δ). The detrimental effect of transmission errors is thus partially overcome.  
           [0011]    Instability or oscillation of the receiver may still occur in pole-based predictor systems due to the feedback loop to the predictor, which uses the prediction error signal Ê j  and the preceding reconstructed input signal Ś j−i  to derive the prediction coefficients as described above. Stability checking is often used to ensure that the prediction coefficients remain in desired ranges, but at the expense of increased complexity as the number of poles, i.e., coefficients, increases.  
           [0012]    In U.S. Pat. No. 4,317,208, Araseki et al. describe a system that also employs zero-based predictors, such as predictors  120  and  128 , which do not utilize a feedback loop but which are known to provide less predictor gain than pole-based predictors and consequently inhibit or slow down the adaptation process. They propose that such a combination of pole-based and zero-based predictors may overcome the instability issues described above, and gain the advantages of each type of predictor.  
           [0013]    In U.S. Pat. No. 4,593,398, issued Jun. 3, 1986 to Millar and incorporated by reference herein, it is suggested that a pole-based predictor, even coupled with a zero-based predictor, is still vulnerable to mistracking if the input signal contains two tones of equal amplitude but different frequencies. Millar notes that certain input signals may cause the pole-based predictor adaptation driven by the feedback loop to have multiple stable states, thus the receiver  104  may stabilize with its prediction coefficients at values different than the transmitter  102 . This in turn is likely to cause a distorted frequency response at the receiver  104  and its associated audio output device.  
           [0014]    The Millar patent proposes to mitigate the problems associated with lower predictor gain in zero-based predictors and mistracking in pole-based predictors. The system described by Millar and depicted in FIG. 1 is such that the predictor means in the transmitter  102  and the receiver  104  derive the prediction coefficients based on an algorithm including a non-linear function having no arguments comprising the value of the reconstructed input signal, such as signals Ś j  and Ś j−i . This coefficient adaptation is depicted by arrows  119  and  127 . This is in contrast to the Araseki system wherein the prediction coefficients are partially derived from a reconstructed input signal such as signal Ś j−i , which is dependent upon the predicted signal S j , which is dependent upon all of the immediate past coefficient values.  
           [0015]    It is postulated that the Millar system and method may be computationally expensive to implement. Therefore what is needed is a system and methods in which the convergence to the optimal prediction coefficients, and thus to the predicted signal S j , occurs more rapidly and efficiently than in prior art systems.  
         SUMMARY  
         [0016]    An improved adaptive differential pulse code modulation (ADPCM) system and method comprises an encoder and a decoder linked together by a network connection and configured for processing digital audio signals. More particularly, the technique described is related to adaptation of predictor coefficients in an ADPCM environment. The components of the system may be implemented in software form as instructions executable by a processor or in hardware form as digital circuitry. Furthermore, devices implementing the system and method described are preferably configured to include both an encoder and a decoder for bidirectional communication with a similarly situated remote device, or may be configured with solely the encoder or decoder.  
           [0017]    At the encoder, a digitized input signal is applied to a subtractor, which derives a difference signal by subtracting from the input signal a predicted signal generated by a pole-based predictor. After quantizing, transmitting to a decoder, and inverse quantizing, the difference signal is added to the predicted signal by an adder to provide a reconstructed input signal, which is fed back to the predictor and to the subtractor. The encoder is additionally provided with a whitening filter for receiving the reconstructed input signal and applying thereto a filtering algorithm to generate a filtered reconstructed signal. The filtered reconstructed signal is utilized to update, or adapt, the prediction coefficients of the pole-based predictor, thus providing more rapid and computationally efficient convergence to optimal prediction coefficients.  
           [0018]    The decoder operates in an inverse manner to the encoder, receiving the quantized difference signal from an encoder and processing it to reconstruct the input signal for delivery to sound reproducing means. It is noted that devices employing the ADPCM techniques described herein are interoperable with devices employing prior art techniques, for example, those described in ITU-T G.722. It is further noted that the techniques described herein may be adapted for various implementations, one example being the employment of a plurality of encoders and/or decoders for frequency sub-band processing.  
           [0019]    Other embodiments of the invention comprise additional predictors at the encoder and the decoder, operating to maximize the signal-to-noise ratio for certain input signals. The additional predictors are preferably zero-based predictors, the output therefrom being summed with the pole-based predictor output to produce the predicted signal.  
       
    
    
     BRIEF DESCRIPTION OF THE FIGURES  
       [0020]    In the accompanying drawings:  
         [0021]    [0021]FIG. 1 depicts a prior art ADPCM system;  
         [0022]    [0022]FIG. 2 depicts an ADPCM system, in accordance with a first embodiment of the invention; and  
         [0023]    [0023]FIG. 3 depicts another ADPCM system, in accordance with a second embodiment of the invention.  
     
    
     DESCRIPTION OF PREFERRED EMBODIMENTS  
       [0024]    [0024]FIG. 2 depicts a first embodiment of an ADPCM system  200  in accordance with the invention. ADPCM system  200  comprises an encoder  202  and decoder  204  linked in communication by a network connection  206 , such as an ISDN line, fractional T 1  line, digital satellite link, wireless modems, or like digital transmission service. At encoder  202 , a digitized input signal, typically representative of speech, is applied to a conventional subtractor  208 . The input signal is represented as Y j , signifying a value at sample period j. Subtractor  208  derives a difference signal E j  by subtracting from input signal Y j  a predicted signal S j  generated by a pole-based predictor  210 . The difference signal E j  is quantized by a conventional quantizer  212  to obtain a quantized numerical representation N j  for transmission to decoder  204  over the network connection  206 . Quantizer  112  is preferably of the adaptive type, but a quantizer utilizing fixed step sizes may also be used.  
         [0025]    Numerical representation N j  is also applied to a conventional inverse quantizer  214 , which derives a regenerated difference signal D j . A conventional adder  216  adds regenerated difference signal D j  to a predicted signal S j  (output by the pole-based predictor  210 ) to provide a reconstructed input signal X j . The reconstructed input signal X j  is in turn applied to the pole-based predictor  210 , which calculates the predicted signal S j  in accordance with the following equation:  
         S   j     =         a   1   j          S     j   -   1         +       a   2   j          S     j   -   2         +              …              +       a   n   j          S     j   -   n                                 
 
         [0026]    where S j−1  is a stored value of the predicted signal at sample period j−1, S j−2  is a stored value of the predicted signal at sample period j−2, and so on, and a 1   j  to a n   j  are the predictor coefficients at sample period j, where n corresponds to the total number of poles (i.e., coefficients) of pole-based predictor  210 . In one implementation of ADPCM system  200 , the pole-based predictor  210  is limited to two poles, yielding the relation:  
         S   j     =         a   1   j          S     j   -   1         +       a   2   j            S     j   -   2       .                               
 
         [0027]    The predicted signal S j  generated by predictor  210  is then applied to adder  216 , completing the feedback loop.  
         [0028]    Predictor coefficients a 1   j  and a 2   j  are updated in accordance with the generalized equations:  
         a   1     j   +   1       =         a   1   j          (     1   -     δ   1       )       +       g   1     ·       F   1          (       X   j   f     ,     X     j   -   1     f     ,     X     j   -   2     f       )                     a   2     j   +   1       =         a   2   j          (     1   -     δ   2       )       +       g   2     ·       F   2          (       X   j   f     ,     X     j   -   1     f     ,     X     j   -   2     f     ,     X     j   -   3     f     ,     a   1   j       )                                 
 
         [0029]    where X f   j  is a filtered version of reconstructed input signal X j  at sample period j; δ 1 , δ 2 , g 1  and g 2  are proper positive constants, and F 1  and F 2  are nonlinear functions which may consist of correlations, sign-correlations, or other relationships. Calculation of the filtered reconstructed signal X f   j  is discussed below.  
         [0030]    In general, whitening filters modify the spectrum of signals to provide a flatter signal spectrum, so that there is less variation of energy as a function of frequency. It is noted that a perfect white noise signal has equal energy at every frequency. Stochastic gradient adaptive filters generally converge more rapidly with white signals than with non-white signals. Therefore, the use of a whitening filter in the present system and method is preferred at least for its effect on convergence of the adaptive pole-based predictors  210  and  226 .  
         [0031]    Referring back to FIG. 2, a whitening filter  218  receives the reconstructed input signal X j  and applies thereto a filtering algorithm to generate a filtered reconstructed signal X f   j . To ensure stable operation of whitening filter  218 , the filter coefficients a 2   f     j+1    and a 1   f     j+1    undergo the clamping set forth below at every other time step (i.e., for odd values of j):  
         [0032]    a 2   f     j+1    is clamped to a maximum of 12288 and a minimum of −12288; and  
         [0033]    a 1   f     j+1    is i clamped in magnitude to 15360−a 2   f     j+1   .  
         [0034]    Implementation of this clamping routine is exemplified as:  
         temp=15360− a   2   f     j+1   ;  
         [0035]    if a 1   f     j+1   &gt;temp, then a 1   f     j+1    is set to equal temp;  
         [0036]    if a 1   f     j+1   &lt;−temp, then a 1   f     j+1    is set to equal −temp.  
         [0037]    The filtered reconstructed signal X f   j  output by whitening filter  218  is utilized to update the predictor coefficients a 1   j+1  and a 2   j+1 , as described above and indicated on FIG. 2 by arrow  220 .  
         [0038]    According to a preferred implementation, whitening filter  218  has two zeroes, yielding the relation:  
         
       X 
       j 
       f 
       =X 
       j 
       −a 
       1 
       f 
       X 
       j−1 
       −a 
       2 
       f 
       X 
       j−2  
     
         [0039]    where a f   1  and a f   2  are the first and second order filter coefficients. The filter coefficients a f   1  and a f   2  are updated at each time step j in accordance with the following equations:  
               a   2     f     j   +   1         =                    a   2     f   j            (     1   -     (     256   32768     )       )       -       (     1   32     )          a   1     f   j            sgn              [     X   j   f     ]          sgn        [     X     j   -   1     f     ]         +                                128   *     sgn              [     X   j   f     ]          sgn              [     X     j   -   2     f     ]       ;   and                       a   1     f     j   +   1         =         a   1     f   j            (     1   -     (     128   32768     )       )       +     192   *     sgn        [     X   j   f     ]            sgn              [     X     j   -   1     f     ]                      ;                         
 
         [0040]    where sgn [ ] is the sign function that returns a value of 1 for a nonnegative argument and a value of −1 for a negative argument.  
         [0041]    In accordance with a computationally economical implementation of ADPCM system  200 , the values of the predictor coefficients may be frozen at every other sample interval j. It should be noted that by recalculating predictor coefficients for pole-based predictor  210  only at every other interval, computational resources are conserved. This implementation is described by the following equations:  
         [0042]    for even j:  
         a 2   j+1 =a 2   j ; and  
         a 1   j+1 =a 1   j ;  
         [0043]    else for odd j:  
               a   2     j   +   1       =                    a   2     j   -   1            (     1   -     (     510   32768     )       )       -       (     1016   32768     )          lim              [     a   1     j   -   1       ]          sgn        [     X     j   -   1     f     ]            sgn        [     X     j   -   2     f     ]         +                                127   *     sgn              [     X     j   -   1     f     ]          sgn              [     X     j   -   3     f     ]       -       (     1   32     )          lim              [     a   1     j   -   1       ]          sgn        [     X   j   f     ]            sgn        [     X     j   -   1     f     ]         +                                128   *     sgn              [     X   j   f     ]          sgn              [     X     j   -   2     f     ]       ;                 and                 a   1     j   +   1       =                    a   1     j   -   1            (     1   -     (     127.5   32768     )       )       +     191.25   *     sgn              [     X     j   -   1     f     ]          sgn              [     X     j   -   2     f     ]       +                                192   *     sgn              [     X   j   f     ]          sgn              [     X     j   -   1     f     ]       ;                                 
 
         [0044]    where sgn [ ] is the sign function that returns a value of 1 for a nonnegative argument and a value of −1 for a negative argument, and  
           lim        [     a   1     j   -   1       ]       =           a   1     j   -   1                     for                -   8192     ≤     a   1     j   -   1       ≤   8191       ;               lim              [     a   1     j   -   1       ]     =         -   8192                   for                   a   1     j   -   1         &lt;     -   8192         ;     and               lim              [     a   1     j   -   1       ]     =       8191                 for                   a   1     j   -   1         &gt;   8191.                           
 
         [0045]    To ensure stability, a 2   j+1  and a 1   j+1  are clamped similarly to a 1   f     j+1    and a 1   f     j+1    as described above. That is:  
         [0046]    a 2   j+1  is clamped to a maximum of 12288 and a minimum of −12288; and  
         [0047]    a 1   j+1  is clamped in magnitude to 15360−α 2   j+1 .  
         [0048]    Implementation of this clamping routine is exemplified as:  
         temp=15360 −a   2   j+1 ;  
         [0049]    if a 1   j+1 &gt;temp, then a 1   j+1  is set to equal temp;  
         [0050]    if a 1   j+1 &lt;−temp, then a 1   j+1  is set to equal −temp.  
         [0051]    Decoder  204  operates in an inverse manner to encoder  202 . Inverse quantizer  222  receives the numerical representation N j  over network connection  206  and derives the regenerated difference signal D j . Adder  224  sums the regenerated difference signal D j  with the predicted signal S j  generated by pole-based predictor  226  to produce the reconstructed input signal X j . The reconstructed input signal X j  is then delivered to sound-reproducing means (which will typically include a D/A converter and loudspeaker) for reproduction of the speech represented by the input signal Y j .  
         [0052]    At the decoder  204 , the reconstructed input signal X j  is additionally applied to whitening filter  230  and pole-based predictor  226 . Pole-based predictor  226  operates in a substantially identical manner to pole-based predictor  210  of encoder  202  and generates as output predicted signal S j , which is applied to adder  224  to complete the feedback loop. Whitening filter  230 , which operates in a substantially identical manner to whitening filter  218  of encoder  202 , provides as output a filtered reconstructed signal X f   j  for use by pole-based predictor  226  in updating the predictor coefficients, as discussed above and indicated on FIG. 2 by arrow  228 .  
         [0053]    Those skilled in the art will recognize that the various components of encoder  202  and decoder  204  will typically be implemented in software form as program instructions executable by a general purpose processor. Alternatively, one or more components of encoder  202  and/or decoder  204  may be implemented in hardware form as digital circuitry.  
         [0054]    Additionally, those skilled in the art will recognize that, although the pole-based predictors  210  and  226  are described above in terms of a two-pole implementation, the invention is not limited thereto and may be implemented in connection with pole-based predictors having any number of poles.  
         [0055]    It is additionally noted that the ADPCM technique embodied in the invention may be adapted in various well-known ways in order to improve the speed and performance of the encoding and decoding processes. For example, a transmitting entity may break the input signal into a plurality of frequency-limited sub-bands, wherein each sub-band is applied to a separate encoder operating in a substantially identical manner to encoder  202 . The sub-banded encoded signals are then multiplexed for transmission to a receiving entity over the network connection. The receiving entity then demultiplexes the received signal into a plurality of sub-banded signals and directs each sub-banded signal to a separate decoder operating in a manner substantially identical to decoder  204 . The sub-banded reconstructed signals are thereafter combined and conveyed to sound-reproducing means.  
         [0056]    In other embodiments of the invention, additional predictors may be combined with the pole-based predictors to maximize the signal-to-noise ratio for certain input signals. Referring now to the FIG. 3 embodiment of an ADPCM system  300 , encoder  302  differs from encoder  202  of the FIG. 2 embodiment by the addition of a conventional zero-based predictor  306 . Zero-based predictor  306  receives the regenerated difference signal D j  and produces a zero-based partial predicted signal S jz , which is added to the partial pole-based predicted signal S jp  (equal to S j  in the FIG. 2 embodiment) by adder  308  to provide predicted signal S j . Predicted signal S j  is in turn applied to the feedback loop of pole-based predictor  210  and to subtractor  208 . It is noted that zero-based predictor  306  does not have a feedback loop, and its predictor coefficients are conventionally updated with dependence on regenerated difference signal D j .  
         [0057]    Similarly, decoder  304  differs from decoder  204  of the FIG. 2 embodiment by the inclusion of zero-based predictor  310 . The regenerated difference signal D j  is applied to zero-based predictor  310 , which generates as output a zero-based partial predicted signal S jz . Adder  312  combines the zero-based partial predicted signal S jz  with pole-based partial predicted signal S jp  provided by pole-based predictor  226  to produce the predicted signal S j .  
         [0058]    Another embodiment of the invention utilizes at least one look-up table in determining the proper coefficients for the predictors, i.e., pole-based predictors  210  and  226  of FIGS. 1 and 2, and/or zero-based predictors  306  and  310  of FIG. 3. For example, the first pole-based predictor coefficient is a function of three quantities: its former value, the sign of the current value of the sum of the quantized prediction error plus the all-zero predictor, and the sign of the past value of the sum of the quantized prediction error plus the all-zero predictor. In this embodiment, no arithmetic is necessary in determining a prediction coefficient value, however, identical input-output characteristics of the predictors are preserved.  
         [0059]    It should be appreciated that devices utilizing the above-described ADPCM techniques, such as audioconferencing or videoconferencing endpoints, will typically be equipped for bi-directional communications over the network connection, and so will be provided with both an encoder (such as encoder  202  or  302 ) for encoding local audio for transmission to a remote endpoint as well as a decoder (such as decoder  204  or  304 ) for decoding audio signals received from the remote endpoint.  
         [0060]    It is further noted that devices employing the above-described ADPCM techniques of the invention are advantageously interoperable with devices employing some prior art ADPCM techniques, such as those described in the aforementioned Millar reference and the ITU-T G.722 reference.  
         [0061]    Finally, it is generally noted that while the invention has been particularly shown and described with reference to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention.