DIFFERENTIAL PULSE CODE MODULATION SYSTEM

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 (fig. 2) employs a whitening filter (218) to generate a filtered reconstructed signal (220) which is utilized to update, or adapt, the prediction coefficients of a pole-based predictor (210).

Description: DIFFERENTIAL PULSE CODE MODULATION SYSTEM CROSS REFERENCE TO RELATED APPLICATIONS [001] The present application claims the benefit of priority from U.S. Provisional Patent Application No. 60/183,280, entitled "Adaptive Differential Pulse Code Modulation System and Method Utilizing Whitening Filter For Updating Of Predictor Coefficients" filed on February 17, 2000, which is incorporated by reference herein. BACKGROUND Field of Invention [002] 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. Description of the Prior Art [003] 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. [004] As described in U. S. Pat. No. 4,317,208, issued February 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, at a time; is typically derived at a transmitter 102 by the general equation: S j = Ai S,-ι + A 2 S J-2 + ... An S j -n ; where Ai, 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. [005] 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. [006] Due to the reality of frequent transmission errors between the transmitter 102 and the receiver 104, the prediction errors E j (which have been inverse quantized) produced at the receiver 104, and thus the reconstructed input signal S- 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: Ar l = A! (\ - δ) + g - F l (S' J _χ - F 2 (E J ); where ; ' =1 to n, δ is a positive value much smaller than 1, g is a proper positive constant, S,-ι is a reconstructed input signal delayed i samples, and Fi 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. [007] 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 fij and the preceding reconstructed input signal Sμ 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. [008] 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. [009] In U. S. Pat. No. 4,593,398, issued June 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. [0010] 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 Sj and S/-.. 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 S j -i, which is dependent upon the predicted signal Sj, which is dependent upon all of the immediate past coefficient values. [0011] 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 Sj, occurs more rapidly and efficiently than in prior art systems. SUMMARY [0012] 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 bi-directional communication with a similarly situated remote device, or may be configured with solely the encoder or decoder. [0013] 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. [0014] 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. [0015] 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 [0016] In the accompanying drawings: [0017] FIG. 1 depicts a prior art ADPCM system; [0018] FIG. 2 depicts an ADPCM system, in accordance with a first embodiment of the invention; and [0019] FIG. 3 depicts another ADPCM system, in accordance with a second embodiment of the invention. DESCRIPTION OF PREFERRED EMBODIMENTS [0020] 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 Tl 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 ) 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. [0021] Numerical representation N j is also applied to a conventional inverse quantizer 214, which derives a regenerated difference signal O } . 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^ a^ + aiS^ +.-.+aiS^ where S^ 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^ to a n ' are the predictor coefficients at sample period ' , 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 = ajS + a 2 S J _ 2 . The predicted signal S- generated by predictor 210 is then applied to adder 216, completing the feedback loop. [0022] Predictor coefficients a^ and a 2 ) are updated in accordance with the generalized equations: ar , = aj(l -δ I ) + g. . F. (X J f ,X r .1 - x;_ 2 ) a J 2 +l = a 2 (l - δ 2 ) + g 2 - F 2 (X J f ,X;_ 1 ,X J f _ 2 ,X;. 3 ,a;) where X f , is a filtered version of reconstructed input signal X, at sample period ;; δi, δ 2 , g-i and 2 are proper positive constants, and Fi 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. [0023] 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. [0024] 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 ,. To ensure stable operation of whitening filter 218, the f i+l f 1+1 filter coefficients a 2 and a, undergo the clamping set forth below at every other time step (i.e., for odd values of; ' ): a 2 J+ is clamped to a maximum of 12288 and a minimum of -12288; and a J is clamped in magnitude to 15360 - a 2 J . Implementation of this clamping routine is exemplified as: temp = 15360 - a 2 ; if a J+ > temp, then af + is set to equal temp; if a J+ < -temp, then a[ J+ is set to equal -temp. The filtered reconstructed signal X f j output by whitening filter 218 is utilized to update the predictor coefficients ai) +1 and a 2 J +1 , as described above and indicated on FIG. 2 by arrow 220. [0025] According to a preferred implementation, whitening filter 218 has two zeroes, yielding the relation: where a f ι and a f 2 are the first and second order filter coefficients. The filter coefficients a f i and a f 2 are updated at each time step j in accordance with the following equations: 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. [0026] 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 ; ' . 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: for even ' : a J 2 +1 = a J 2 ; and ■ J +1 - = a o J 1 ' else for odd ; ' : 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[ar l ]= ar 1 for -8192 ≤ af 1 < 8191; lim[af' ]= -8192 for a 1 < -8192; and lim[a ' J = 8191 for af > 8191. To ensure stability, a 2 ) +1 and Ά +1 are clamped similarly to a[ J+ and a J+ as described above. That is: a 2 ) +1 is clamped to a maximum of 12288 and a minimum of -12288; and a^- 1 " 1 is clamped in magnitude to 15360 - a 2 1+ . Implementation of this clamping routine is exemplified as: temp = 15360 - a 2 J +1 ; if a^ +1 > temp, then aιJ +1 is set to equal temp; if aι) +1 < -temp, then is set to equal -temp. [0027] 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 . [0028] 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-, 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. [0029] 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. [0030] 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. [0031] 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. [0032] 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 Sj. Predicted signal Sj 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 Dj. [0033] 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 Sj Z . Adder 312 combines the zero-based partial predicted signal Sj Z with pole-based partial predicted signal Sj P provided by pole-based predictor 226 to produce the predicted signal Sj. [0034] 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. [0035] 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. [0036] 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. [0037] 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.

Claims: CLAIMS What is claimed is: 1. An adaptive differential pulse code modulation system comprising: an encoder including: a subtractor configured for deriving a difference signal E j , the difference signal E, being the difference between an input signal Y j and a predicted signal S j , j representing a sample period; a quantizer configured for quantizing the difference signal E j to obtain a numerical representation N j for transmission to an encoder inverse quantizer for deriving a regenerated difference signal D j , and to a decoder inverse quantizer coupled to the quantizer through a network for deriving the regenerated difference signal D,, an encoder adder configured for deriving a reconstructed input signal Xj, the reconstructed input signal X j being the sum of the regenerated difference signal D j and the predicted signal S j ; an encoder whitening filter F e configured for receiving the reconstructed input signal X j and for generating a filtered reconstructed signal X f j , the filtered reconstructed signal X f j being generated according to the equation: XJ = X J - a{X J _ l - al X J _ 2 -... m 'X J _ l , X j -n being a value of reconstructed input signal X, at sample period - n, and; n being a number of filter tap coefficients a f n corresponding to the whitening filter F e ; an encoder predictor P ep configured for receiving the reconstructed input signal X j and for generating a predicted signal S )P , the predicted signal S ]P being at least constituent to predicted signal S j and being generated according to the equation: jp = a j i + a 2 j • • • aJrφ i-tj-rtp S j -np being a value of the predicted signal S j at sample period j-n p , and n p being a number of predictor coefficients ai np corresponding to the predictor P ep ; and an encoder feedback loop configured for applying the predicted signal S j to the adder; transmission means configured for transmitting the numerical representation N j from the encoder to a decoder; and the decoder including: the decoder inverse quantizer coupled to the quantizer through a network and configured for receiving the numerical representation N, and for deriving the regenerated difference signal D j therefrom, a decoder adder configured for deriving the reconstructed input signal X j , the reconstructed input signal X ) being the sum of the regenerated difference signal D, and the predicted signal S } ; a decoder whitening filter Fd configured for receiving the reconstructed input signal X j and for generating the filtered reconstructed signal X f j , the filtered reconstructed signal X f ; being generated according to the equation: X f , = X, - ah X j -i - a f 2 X ]-2 - ... a f n Xj-n X j -n being a value of reconstructed signal X j at sample period ; ' - n, and n being the number of filter tap coefficients a f n corresponding to the whitening filter Fa; a decoder predictor Pd P configured for receiving the reconstructed input signal X j and for generating a predicted signal S )P , the predicted signal S JP being at least constituent to predicted signal S- and being generated according to the equation: jp = 3 | θ i + a 2 j • • • a^n P Ϊ3 j -n P S j -iφ being a value of the predicted signal S j at sample period j-n p , and n p being the number of predictor coefficients a ) np corresponding to the predictor Pd p ; and a decoder feedback loop configured for applying the predicted signal S j to the decoder adder. 2. The system of claim 1, further comprising: a second encoder predictor P ez configured for receiving the regenerated difference signal D j and for generating a predicted signal S, z ; a second encoder adder configured for deriving the predicted signal S- at the encoder, the predicted signal S- being the sum of the predicted signal S jP and the predicted signal S* z ; a second decoder predictor Pd Z configured for receiving the regenerated difference signal D j and for generating a predicted signal S* z ; and a second decoder adder configured for deriving the predicted signal S j at the decoder, the predicted signal S, being the sum of the predicted signal S jP and the predicted signal S* z . 3. The system of claim 1 wherein: n p is 2; the predictor coefficient a is updated according to the equation: af* =aKl-δ,) + g 1 -F 1 (X J f ,X J f _ I ,X;_ 2 ) b\ and gi being proper positive constants, and Fi being a nonlinear function; and the predictor coefficient a J 2 is updated according to the equation: a J 2 +l =a 2 (l-δ 2 ) + g 2 -F 2 (X J f ,X J f _ 1 ,X J f _ 2 ,a0; δ 2 and g 2 being proper positive constants, and F 2 being a nonlinear function. 4. The system of claim 1 wherein: n is 2; the filter tap coefficient a is updated at each sample period; according to the generalized equation: a t, =a I β (l-δ 1 ) + g 1 .F I (X J f ,x ι ,X^ 2 ) δi and gi being proper positive constants, and Fi being a nonlinear function; and the filter tap coefficients a 2 is updated at each sample period; according to the generalized equation: a« +l = a 2 Q (l - δ 2 ) + g 2 - F 2 (X J f ,X J f _ 1 ,x;. 2 ,a ) δ 2 and g2 being proper positive constants, and F 2 being a nonlinear function. 5. The system of claim 4 wherein: the filter tap coefficient af J is updated according to the equation: the filter tap coefficient a( is updated according to the equation: ' sgn[N/ ]sgn[N _, ]+ 128 * sgn[N/ ]; sgn[ ] being a sign function that returns a value of 1 for a nonnegative argument and a value of -1 for a negative argument. 6. The system of claim 5 wherein at every other sample period ; ' , the filter tap coefficient a f ι +1 2 is maintained in a range -12288 < a f ) +1 2 < 12288; and the filter tap coefficient ^ + \ is maintained in a range -(15360 - a f ι +1 2) a^ < (15360 - aΗ 2 ); whereby and whereby < -(15360 - a*H 2 ). 7. The system of claim 5, further comprising: a second encoder predictor P ez configured for receiving the regenerated difference signal D, and for generating a predicted signal S, z ; a second encoder adder configured for deriving the predicted signal S j at the encoder, the predicted signal S j being the sum of the predicted signal S, p and the predicted signal S JZ ; a second decoder predictor Pd z configured for receiving the regenerated difference signal D j and for generating a predicted signal S, z ; and a second decoder adder configured for deriving the predicted signal S j at the decoder, the predicted signal S j being the sum of the predicted signal S ]p and the predicted signal S JZ . 8. The system of claim 1 wherein at every other sample period ; ' , the predictor coefficient a) np corresponding to the predictors P ep and Pd P is maintained unchanged. 9. The system of claim 8, such that if for even j: d 2 — d 2 , then for odd j: sgn[ ] being a sign function that returns a value of 1 for a nonnegative argument and a value of -1 for a negative argument, and lim[< " ' ] = af for -8192 < af < 8191, lim[i ■ ' ] = -8192 for af ' < -8191, and lim[< ■ ' ] = 8192 for af > 8191. 10. An encoder for encoding digital audio signals, comprising: a subtractor configured for deriving a difference signal E j , the difference signal E j being the difference between an input signal Y j and a predicted signal S,, j representing a sample period; a quantizer configured for quantizing the difference signal E j to obtain a numerical representation N j for transmission to an encoder inverse quantizer for deriving a regenerated difference signal D j , and to a decoder inverse quantizer coupled to the quantizer for deriving the regenerated difference signal D,; an adder configured for deriving a reconstructed input signal X j , the reconstructed input signal X, being the sum of the regenerated difference signal D j and the predicted signal S j ; a whitening filter configured for receiving the reconstructed input signal X j and for generating a filtered reconstructed signal X f j , the filtered reconstructed signal X f j being generated according to the equation: X f , = X, - a'ι Xj-i - a< 2 X,. 2 - ... a'„ X f j-n X f )-n being a value of filtered reconstructed signal X f j at sample period ; ' - n, and n being a number of filter tap coefficients a f n corresponding to the whitening filter; a predictor configured for receiving the reconstructed input signal X, and for generating a predicted signal S jP , the predicted signal S ]p being at least constituent to predicted signal S j and being generated according to the equation: Sjp = ah Sj-i - a ) 2 S--2 - ... aJ np S )-np S j-np being a value of the predicted signal Sj at sample period j-n p , and n p being a number of predictor coefficients a) np corresponding to the predictor; and a feedback loop configured for applying the predicted signal S j to the adder. 11. The system of claim 10, the encoder further comprising: a second predictor configured for receiving the regenerated difference signal D, and for generating a predicted signal S- z , the predicted signal S, z being at least constituent to predicted signal S j ; and a second adder configured for deriving the predicted signal S-, the predicted signal S, being the sum of the predicted signal S* p and the predicted signal S, z . 12. The system of claim 10 wherein: n is 2; the filter tap coefficient af is updated at each sample period; according to the generalized equation: a,^ 1 = a (l - δ 1 ) + g 1 - F 1 (X J f ,X J f _ 1 ,X;_ 2 ) δi and gi being proper positive constants, and Fi being a nonlinear function; the filter tap coefficients a 2 is updated at each sample period j according to the generalized equation: af 1 = a (l -δ 2 ) + g 2 - F 2 (X J f ,X J f _ 1 ,X J f _ 2 ,a ) 62 and g2 being proper positive constants, and F2 being a nonlinear function. 13. The system of claim 12 wherein: the filter tap coefficient a[ is updated according to the equation: f 128 v, = a 1 - + 192 * sgn[N ]sgn[ f _. ] and 32768 J) the filter tap coefficient a 2 is updated according to the equation: ], sgn[ ] being a sign function that returns a value of 1 for a nonnegative argument and a value of -1 for a negative argument. 14. The system of claim 13 wherein at every other sample period ; ' , the filter tap coefficient aΗ∑ is maintained in a range -12288 < a f J +1 2 < 12288; and the filter tap coefficient is maintained in a range -(15360 - aΗ 2 ) < a ≤ (15360 - a fj+1 2 ); whereby a^ + is set equal to (15360 - aΗ∑) when a^ > 15360 - afl +1 2 ; and whereby afl +1 ι is set equal to -(15360 - afl +1 2) when aΗi < -(15360 - ^ ). 15. The system of claim 10 wherein at every other sample period ; ' , the predictor coefficient ai np corresponding to the predictor is maintained unchanged. 16. The system of claim 10, wherein the encoder is constituent to or coupled to a videoconferencing device or application. 17. A decoder for decoding digital audio signals encoded by a properly associated encoder, comprising: an inverse quantizer coupled to the encoder and configured for receiving a numerical representation Nj and for deriving a regenerated difference signal Dj therefrom, the numerical representation N j being a quantized representation of a difference signal E j , the difference signal E j being the difference between an input signal Yj and a predicted signal Sj, ; ' representing a sample period; an adder configured for deriving a reconstructed input signal X j , the reconstructed input signal Xj being the sum of the regenerated difference signal Dj and the predicted signal SJ; a whitening filter configured for receiving the reconstructed input signal X, and for generating a filtered reconstructed signal X f ,, the filtered reconstructed signal X f , being generated according to the equation: X f , = X, - a'ι Xj-i - a f 2 X, -2 - ... a' n X f j-n X f ,- n being a value of filtered reconstructed signal X f , at sample period ; ' - n, and n being a number of filter tap coefficients a f n corresponding to the whitening filter; a predictor configured for receiving the reconstructed input signal X, and for generating a predicted signal S JP , the predicted signal S, p being at least constituent to predicted signal S, and being generated according to the equation: S, p = ah S,-i - a ) 2 S, -2 - ... a ) np S, -np S,- np being a value of the predicted signal S, at sample period j-n p , and n p being a number of predictor coefficients a) np corresponding to the predictor; and a feedback loop configured for applying the predicted signal S j to the adder. 18. The system of claim 17, the decoder further comprising: a second predictor configured for receiving the regenerated difference signal D, and for generating a predicted signal S JZ , the predicted signal S JZ being at least constituent to predicted signal S,; and a second adder configured for deriving the predicted signal S,, the predicted signal S, being the sum of the predicted signal S, p and the predicted signal S JZ . 19. The system of claim 17 wherein: n is 2; the filter tap coefficient af is updated at each sample period; according to the generalized equation: af *, = aJ , (l - δ 1 ) + g I - F 1 (X J r ,X^ 1 ,x 2 ) δi and gi being proper positive constants, and Fi being a nonlinear function; the filter tap coefficients a 2 is updated at each sample period j according to the generalized equation: af = a«(l - δ 2 ) + g 2 . F 2 (X J f ,X J f _ 1 ,X J f _ 2 ,a?) δ 2 and g2 being proper positive constants, and; F2 being a nonlinear function. 20. The system of claim 19 wherein: the filter tap coefficient af is updated according to the equation: ,f J+1 * sgn[N ]sgn[N/ , ] and the filter tap coefficient a 2 is updated according to the equation: sgn[N/ ]sgn[N/_, ]+ 128 * sgn[N/ ]sgn[N _ 2 ] sgn[ ] being a sign function that returns a value of 1 for a nonnegative argument and a value of -1 for a negative argument. 21. The system of claim 20 wherein at every other sample period ; ' , the filter tap coefficient a ]+1 2 is maintained in a range -12288 < a f)+1 2 < 12288; and the filter tap coefficient a f J +1 1 is maintained in a range -(15360 - a J +1 2 ) < a f ) +1 ι < (15360 - a^ ); whereby a$ +1 ι is set equal to (15360 - a f ) +1 2 ) when a f)+1 ι > 15360 - a f ) +1 2; and whereby a^ +1 ι is set equal to -(15360 - a f ) +1 2 ) when a f)+1 ι < -(15360 - a f)+1 2 ). 22. The system of claim 17 wherein at every other sample period ; ' , the predictor coefficient a) np corresponding to the predictor is maintained unchanged. 23. The system of claim 17, wherein the decoder is constituent to or coupled to a videoconferencing device or application. 24. A method for encoding and decoding digital audio signals, comprising the steps of: deriving a difference signal E, at an encoder, the difference signal E j being the difference between an input signal Y, and a predicted signal S,, ; ' representing a sample period; quantizing the difference signal E j to obtain a numerical representation N j for transmitting to an encoder inverse quantizer for deriving a regenerated difference signal D,, and to a decoder inverse quantizer coupled to the quantizer through a network for deriving the regenerated difference signal deriving a reconstructed input signal X, at a first adder, the reconstructed input signal X j being the sum of the regenerated difference signal D, and the predicted signal S j ; receiving the reconstructed input signal X, at a whitening filter F e ; generating a filtered reconstructed signal X f , by the whitening filter F e , the filtered reconstructed signal X f , being generated according to the equation: X f , = X, - ah X,-ι - ah. Xj-2 - ... a f n X f , -n χ f j- n being a value of filtered reconstructed signal X f , at sample period ; ' - n, and n being a number of filter tap coefficients a f n corresponding to the whitening filter F e ; receiving the reconstructed input signal X, at a predictor P ep ; generating a predicted signal S, p by the predictor P ep ,the predicted signal S, p being at least constituent to predicted signal S j and being generated according to the equation: S, p = ah S j -:. - a> 2 S,-2 - ... a ) np S,- n S j - np being a value of the predicted signal S, at sample period j-n p , and n p being a number of predictor coefficients aJ np corresponding to the predictor P ep ; applying the predicted signal S, to the first adder to provide feedback; receiving the numerical representation N, at a decoder; deriving the regenerated difference signal D, from the numerical representation N,, deriving the reconstructed input signal X } at a second adder, the reconstructed input signal X, being the sum of the regenerated difference signal D, and the predicted signal S,; receiving the reconstructed input signal X, at a whitening filter Fd; generating a filtered reconstructed signal X f , by the whitening filter Fd, the filtered reconstructed signal X f , being generated according to the equation: X f , = X, - a'ι X,-ι - a 2 X, -2 - ... a' n X f j-n X f ,-n being a value of filtered reconstructed signal X f , at sample period ; ' - n; n being a number of filter tap coefficients a f n corresponding to the whitening filter Fd; receiving the reconstructed input signal X j at a predictor Pd P ; generating a predicted signal S, p by the predictor Pd P , the predicted signal S, p being at least constituent to predicted signal S, and being generated according to the equation: S, p = ah Sj-i - aJ 2 S, -2 - ... a) np S )-np S)-n P being a value of the predicted signal S, at sample period j-n p , and n p being a number of predictor coefficients a) np corresponding to the predictor Pd P ; and applying the predicted signal S, to the second adder to provide feedback. 25. The method of claim 24, further comprising the steps of: receiving the regenerated difference signal D, at a predictor P ez at the encoder; generating a predicted signal S, z by the predictor P ez ; deriving the predicted signal S, at the encoder, the predicted signal S, being the sum of the predicted signal S, P and the predicted signal S, z ; receiving the regenerated difference signal D, at a predictor Pd Z at the decoder; generating the predicted signal S, z by the predictor Pd Z ; and deriving the predicted signal S, at the decoder, the predicted signal S, being the sum of the predicted signal S, p and the predicted signal S, z . 26. The method of claim 24 wherein n p is 2, further comprising the steps of: updating the predictor coefficient a\ according to the equation: af = a l - δ 1 ) + g, - F 1 (X J f ,x;_ 1 ,X J f . 2 ) δ\ and gi being proper positive constants, and Fi being a nonlinear function; and updating the predictor coefficient a J 2 according to the equation: af = a 2 (l - δ 2 ) + g 2 - F 2 (X J f ,X J f . 1 ,x;_ 2 ,aj) δ 2 and g2 being proper positive constants, and; F 2 being a nonlinear function. 27. The method of claim 24 wherein n is 2, further comprising the steps of: updating the filter tap coefficient a[ at each sample period j according to the generalized equation: a 1 βt, = af (l - δ 1 ) + g 1 - F 1 (X J f ,Xj. 1 ,x 2 ) δϊ and gi being proper positive constants, and Fi being a nonlinear function; and updating the filter tap coefficients a 2 at each sample period j according to the generalized equation: a < f = a 2 1 (l - δ 2 ) + g 2 - F 2 (X J f ,X J f _ 1 ,X J f _ 2 ,a?) δ 2 and g 2 being proper positive constants, and F 2 being a nonlinear function. 28. The method of claim 27 wherein: the filter tap coefficient a is updated according to the equation: x[ the filter tap coefficient a 2 is updated according to the equation: ]+ 128 * sgn[N ]sgn[ /_ 2 ] sgn[ ] being a sign function that returns a value of 1 for a nonnegative argument and a value of -1 for a negative argument. 29. The method of claim 28 wherein at every other sample period ; ' , the filter tap coefficient a f ) +1 2 is maintained in a range -12288 < a ) +1 2 < 12288; and the filter tap coefficient a f ) +1 1 is maintained in a range -(15360 - a f ) +1 2 ) < a f ) +1 ι < (15360 - a f ) +1 2 ); whereby a^h is set equal to (15360 - a ή+1 2 ) when a f ) +1 ι > 15360 - a^ and whereby is set equal to -(15360 - a* + ) when a +1 i < -(15360 - a f ) +1 2 ). 30. The method of claim 28, further comprising the steps of: receiving the regenerated difference signal D, at a predictor P ez at the encoder; generating a predicted signal S, z by the predictor Pd z ; deriving the predicted signal S, at the encoder, the predicted signal S, being the sum of the predicted signal S, p and the predicted signal S, z ; receiving the regenerated difference signal D, at a predictor Pdz at the decoder; generating the predicted signal S JZ by the predictor Pd Z ; and deriving the predicted signal S, at the decoder, the predicted signal S, being the sum of the predicted signal S, p and the predicted signal S* z . 31. The method of claim 28 wherein n p is 2, further comprising the steps of: updating the predictor coefficient a\ according to the equation: being proper positive constants, and Fi being a nonlinear function; and updating the predictor coefficient a J 2 according to the equation: af = a 2 (l - δ 2 ) + g 2 - F 2 (X J f ,X J f _ 1 ,X J f _ 2 ,a δ 2 and g2 being proper positive constants, and; F2 being a nonlinear function. 32. A method for adapting coefficients in a two pole predictor in an adaptive differential pulse code modulation system, comprising the steps of: generating a filtered reconstructed signal X f , by a whitening filter F e , the filtered reconstructed signal X f , being generated according to the equation: X f , - X, - a'ι X,-ι - a f 2 X,- 2 - ... a' n X f ,-n χ f , -n being a value of filtered reconstructed signal X f , at sample period ; ' - n, and n being a number of filter tap coefficients a f n corresponding to the whitening filter F e ; updating a predictor coefficient a according to the equation: af = a l - δ 1 ) + g 1 - F 1 (X J f ,X J f _ 1 ,X J f _ 2 ) δi and gi being proper positive constants, and Fi being a nonlinear function; and updating a predictor coefficient a J 2 according to the equation: af =a J 2 (l-δ 2 ) + g 2 -F 2 (X J f ,X J f _ 1 ,X J f _ 2 ,a δ2 and g2 being proper positive constants, and F2 being a nonlinear function. 33. The method of claim 32, further comprising the steps of: updating the filter tap coefficient af at each sample period; according to the generalized equation: a? +1 =af(l-δ I ) + g 1 -F 1 (X J r ,x ι ,x 2 ) δi and gi being proper positive constants, and Fi being a nonlinear function; and updating the filter tap coefficients a 2 at each sample period j according to the generalized equation: af =a«(l-δ 2 ) + g 2 -F 2 (X J f ,X J f _,,X J f _ 2 ,a ) δ 2 and g2 being proper positive constants, and F2 being a nonlinear function. 34. The method of claim 32 wherein: the filter tap coefficient af is updated according to the equation: * sgn[N ]sgn[N_, ] and the filter tap coefficient a 2 is updated according to the equation: sgn[N/ ]sgn[N/_, ]+ 128 * sgn[N/ ]sgn[N/_ 2 ] sgn[ ] being a sign function that returns a value of 1 for a nonnegative argument and a value of -1 for a negative argument. 35. The method of claim 34 wherein at every other sample period; ' , the filter tap coefficient a J +1 2 is maintained in a range -12288 < a f ) +1 2 < 12288; and the filter tap coefficient a^ 1 ! is maintained in a range -(15360 - a f ) +1 2 ) < f J ÷ < (15360 - a f)+1 2 ); whereby a J +1 ι is set equal to (15360 - a f ) +1 2 ) when a f ) +1 1 > 15360 - a^ and whereby is set equal to -(15360 - a f)+1 2 ) when a f)+1 ι < -(15360 - a f ) +1 2 ). 36. A machine readable medium embodying instructions executable by a machine to perform a method for adapting coefficients in a two pole predictor in an adaptive differential pulse code modulation system, the method steps comprising: generating a filtered reconstructed signal X f , by a whitening filter, the filtered reconstructed signal X f , being generated according to the equation: X f , = X, - a'ι X,-ι - ah Xj-2 - ... a*„ X f , -n X f j -n being a value of filtered reconstructed signal X f , at sample period ; ' - n, and n being a number of filter tap coefficients a f n corresponding to the whitening filter; updating a predictor coefficient a^ according to the equation: af = a{(l - δ 1 ) + g, • F 1 (X J f ,X J r _„X 2 ) δi and gi being proper positive constants, and Fi being a nonlinear function; and updating a predictor coefficient a J 2 according to the equation: af = a 2 (l - δ 2 ) + g 2 - F 2 (X J f ,X J f . 1 ,X J f _ 2 ,aJ) δ 2 and g2 being proper positive constants, and F2 being a nonlinear function. 37. A digital circuit embodying instructions to perform a method for adapting coefficients in a two pole predictor in an adaptive differential pulse code modulation system, the method steps comprising: generating a filtered reconstructed signal X f , by a whitening filter, the filtered reconstructed signal X f , being generated according to the equation: X f , = X, - a'ι X,-ι - a f X,. 2 - ... a' n X f ,-n X f ,-n being a value of filtered reconstructed signal X f - at sample period ; ' - n, and n being a number of filter tap coefficients a f n corresponding to the whitening filter; updating a predictor coefficient a according to the equation: af = a l - δ 1 ) + g 1 - F 1 (X J f ,X J f _, ,X J f _ 2 ) δi and gι being proper positive constants, and Fi being a nonlinear function; and updating a predictor coefficient a J 2 according to the equation: af = a 2 (l - δ 2 ) + g 2 - F 2 (X J f ,X J f _ 1 ,X J f _ 2 ,a δ 2 and g2 being proper positive constants, and F 2 being a nonlinear function. 38. An adaptive differential pulse code modulation system comprising: at a first instance: means for deriving a difference signal E,, the difference signal E, being the difference between an input signal Y, and a predicted signal S,, ; ' representing a sample period; means for quantizing the difference signal E, to obtain a numerical representation N,; means for deriving a regenerated difference signal D, based on the numerical representation N,, means for transmitting the numerical representation N, to an inverse quantizing means coupled to the quantizing means through a network; means for deriving a reconstructed input signal X,, the reconstructed input signal X, being the sum of the regenerated difference signal D, and the predicted signal S,; means for generating a filtered reconstructed signal X f ,, the filtered reconstructed signal X f , being generated according to the equation: X f , = X, - ah X,-ι - ah Xj-2 - ... a f n X f ,-n X f J-n being a value of filtered reconstructed signal X f j at sample period ; ' - n, and n being a number of coefficients a f n corresponding to the means for generating a filtered reconstructed signal; means for generating a predicted signal S, p , the predicted signal S, p being at least constituent to predicted signal S, and being generated according to the equation: S, p = ah Sj-i - a ) 2 S,-2 - ... a ) np S, -n p S,_np being a value of the predicted signal S, at sample period j-n p , and n v being a number of predictor coefficients a) np corresponding to the means for generating a predicted signal; and feedback means for applying the predicted signal S, to the means for deriving a reconstructed input signal X,; at a second instance: the inverse quantizing means for deriving the regenerated difference signal D, from the numerical representation N,; second means for deriving a reconstructed input signal X j , the reconstructed input signal X, being the sum of the regenerated difference signal D, and the predicted signal S,; second means for generating a filtered reconstructed signal X f j , the filtered reconstructed signal X f , being generated according to the equation: X f , = X, - a f i X - a f 2 X, -2 - ... a f n X f ,-n X f ,- n being a value of filtered reconstructed signal X f j at sample period ' - n, and n being a number of coefficients a f n corresponding to the second means for generating a filtered reconstructed signal; second means for generating a predicted signal Sj P , the predicted signal S jP being at least constituent to predicted signal Sj and being generated according to the equation: Sj P = ah Sj-i - a ) 2 Sj -2 - ... ai np Sj-np Sj-n P being a value of the predicted signal S, at sample period j-n p , and n p being a number of coefficients a) np corresponding to the means for generating a predicted signal; and feedback means for applying the predicted signal Sj to the means for deriving a reconstructed input signal Xj.

Inventors: CHU PETER L, CHU, PETER, L.
Applicants: POLYCOM INC, POLYCOM, INC.
Application year: 2001
Main classification: H03M3
IPC codes: G10L19, H03M3, H03M7