CA1240396A - Relp vocoder implemented in digital signal processors - Google Patents
Relp vocoder implemented in digital signal processorsInfo
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- CA1240396A CA1240396A CA000494448A CA494448A CA1240396A CA 1240396 A CA1240396 A CA 1240396A CA 000494448 A CA000494448 A CA 000494448A CA 494448 A CA494448 A CA 494448A CA 1240396 A CA1240396 A CA 1240396A
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
- G10L19/04—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using predictive techniques
- G10L19/06—Determination or coding of the spectral characteristics, e.g. of the short-term prediction coefficients
Abstract
RELP VOCODER IMPLEMENTED IN DIGITAL SIGNAL PROCESSORS
ABSTRACT OF THE DISCLOSURE
A residual-excited linear prediction (RELP) vocoder implemented in two digital signal processors, one for a transmitter system and the other for a remotely located receiver system. The transmitter digital signal processor is adapted for processing digital speech data signal samples to provide a formatted transmission signal including (a) a quantized residual signal generated by inverse filtering of the samples in accordance with linear predictive coding (LPC) coefficients generated from the samples, (b) quantized LPC
coefficients and (c) pitch and gain parameters generated during quantization of the residual signal from the inverse filtered samples, all of which are generated by the processor from the digital speech data signal samples. The receiver digital signal processor is adapted for processing the for-matted transmission signal to synthesize reconstructed digital speech data signals. Transmitter and receiver systems that are commonly located may be included in a single digital signal processor.
ABSTRACT OF THE DISCLOSURE
A residual-excited linear prediction (RELP) vocoder implemented in two digital signal processors, one for a transmitter system and the other for a remotely located receiver system. The transmitter digital signal processor is adapted for processing digital speech data signal samples to provide a formatted transmission signal including (a) a quantized residual signal generated by inverse filtering of the samples in accordance with linear predictive coding (LPC) coefficients generated from the samples, (b) quantized LPC
coefficients and (c) pitch and gain parameters generated during quantization of the residual signal from the inverse filtered samples, all of which are generated by the processor from the digital speech data signal samples. The receiver digital signal processor is adapted for processing the for-matted transmission signal to synthesize reconstructed digital speech data signals. Transmitter and receiver systems that are commonly located may be included in a single digital signal processor.
Description
REP VQCODER IMPLEMENTED IN DIGITAL SIGNAL PROCESSORS
BACKGROUND OF THE INVENTION
The present invention generally pertains to voice coders (vocoders) and is particularly directed to Residual-Excited Linear Prediction (REP) vocoders. Vocoders convert speech signals into digital form for transmission and synthesize speech signals from these digital signals upon reception. Vocoders typically operate at flexible binary data rates varying from 32 kbps (kilobytes per second) down to about 2.4 kbps.
Vocoders traditionally are divided into two basic types, waveform coders and pitch-excited source coders. Waveform coders operate at high data rates (above 16 kbps) and produce good quality natural sounding speech which is robust against both acoustic and transmitted noise. Source coders operate at low data rates (less than 4.8 kbps) in an analysis/synthesis mode governed by a mathematical model of the human vocal " apparatus. Source vocoders typically sound robotic and do not perform well under poor acoustic conditions.
The REP vocoder was originally proposed by Us and McGill, "The Residual-Excited Linear Prediction Vocoder with Transmission Rate Below 9.6 kbits/s", IEEE Trans. COMMA, 1975 pp. 1466-1473;
and an enhanced REP vocoder was proposed by Dank berg and Wrong, "Development of a 4.8-9.6 kbps REP vocoder", ICASSP-i9. The 12~396 purpose of the REP vocoder was to provide satisfactory perform-ante in the gap between the operating ranges of waveform coders and source coders, to wit: I kbps to 16 kbps. The REP
vocoder contains some features of both waveform coders and source coders.
In prior art REP vocoders, digital speech data signal samples are analyzed over relatively short time segments (typically in the range of 10-30 my.) by a linear predictive coding (LPC) vocal tract modeling technique to provide LPC
coefficients for each block of samples. The LPC coefficients represent the vocal tract, glottal flow and radiation of the speech represented by the digital signal samples. Using the LPC coefficients, the digital speech data signal samples are inverse filtered by a time-variant, all-pole recursive digital filter over each short time segment to provide residual signal (prediction error signal samples. The time-variant character of speech is handled by a succession of such filters with different parameters.
.... .
The residual signal and the LPC coefficients are encoded (quantized) and formatted for transmission. Upon reception, speech is synthesized by processing the residual signal in accordance with the LPC coefficients.
In prior art REP vocoders, the residual signal samples are band limited and down sampled prior -to quantization in order ~2~0396 to provide residual signal samples at a reduced data rate.
The upper band harmonics are generated during synthesis of the speech signal when the down sampled residual signal is unsampled and zeros are inserted between data points.
In the Us and McGill REP vocoder the residual signal is quantized prior to transmission by adaptive delta modulation.
Dank berg and Wrong considered various other quantization tech-piques and concluded that pitch predictive adaptive differential pulse code modulation (PPADPCM) provided the best signal-to-quantizing noise ratio.
In accordance with the PPADPCM technique, the residual signal samples are processed by pitch analysis to determine the pitch delay, are processed by pitch predictor gain analysis to determine the pitch predictor gain in accordance with the deter-mined pitch delay, processed by gain analysis to provide a maximum deviation quantize gain, and are further processed by PPADPCM in accordance with the quantize gain, pitch predictor -gain and delay parameters to thereby provide the quantized residual signal. The quantize gain, pitch predictor gain and the pitch delay parameters are combined with the quantized residual signal and the quantized LPC coefficients for transmission.
REP vocoders of the prior art have required complex hardware and have been so expensive to implement as to be commercially impractical.
aye SUMMARY OF THE INVENTION
The present invention provides a commercially practical REP vocoder that is implemented by two digital signal processors, one for a transmitter system and one for a remotely located no-sever system. Thy transmitter digital signal processor is adapted for processing digital speech data signal samples to provide a formatted transmission signal including (a) a quantized residual signal generated by inverse filtering of the samples in accordance with linear predictive coding (LPC) coefficients generated from the samples, (b) quantized LPC coefficients and (c) pitch and gain parameters generated during quantization of the residual signal from the inverse filtered samples, all of which are generated by the processor from the digital speech data samples.
The receiver digital signal processor is adapted for processing the formatted transmission signal to synthesize reconstructed digital speech data signal samples.
The transmitter digital signal processor is adapted for performing a routine for generating the LPC coefficients; a routine for generating the residual signal; and a routine for quantizing the residual signal and the LPC coefficients. The routine for generating the LPC coefficients includes a subroutine for reemphasizing the samples in order to emphasize the high frequencies of speech, a subroutine for defining an auto-correlation -4- j 12~0~96 function (ACT) from the reemphasized samples in order to generate ACT coefficients; and a subroutine for generating the LPC coefficients from the generated ACT coefficients. The routine for generating the residual signal includes a subroutine for inverse filtering the reemphasized samples in accordance with the generated LPC coefficients; a subroutine for band limiting the residual signal by low-pass filtering in a manner which will reduce the effects of quantization; and a subroutine for down sampling the band limited residual signal to reduce the number of residual signal samples that are quantized and formatted for transmission. The routine for quantizing the residual signal and LPC coefficients includes a subroutine for quantizing the LPC coefficients; a subroutine for estimating the pitch period of the down sampled residual signal by ACT analysis of the current down sampled residual signal frame in accordance with the ACT
coefficients generated for the previous frame to thereby provide a pitch delay parameter-for the current frame; a subroutine for providing a pitch predictor gain parameter for each residual signal frame in accordance with the estimated pitch delay pane-meter for each corresponding frame; a subroutine for providing aquantizer gain parameter for each residual signal frame in accord-ante with the pitch delay and pitch predictor gain parameters for each corresponding frame; and a subroutine for quantizing each residual signal frame by pitch predictive adaptive differential --S--. . , pulse code modulation (PPADPCM) in accordance with the pitch delay, pitch predictor gain and quantize gain parameters for each corresponding frame.
The receiver digital signal processor is adapted for processing the formatted transmission signal to synthesize reconstructed digital speech data signal samples by performing synthesisroutinethat includes a subroutine for regenerating the LPC coefficients from the quantized LPC coefficients included in the transmission signal;
a subroutine for decoding the quantized residual signal included lo in the transmission signal in accordance with the pitch delay, pitch predictive gain and quantize gain parameters included in the transmission signal to thereby provide a decoded down sampled residual signal; a subroutine for spectrally regenerating-a full-band residual signal from the decoded down sampled residual signal; a subroutine for regenerating reemphasized digital speech data signal samples by auto-regressively filtering the regenerated full-band residual signal in accordance with the regenerated LPC coefficients; and a subroutine for de-emphasizing the regenerated reemphasized samples in order to de-emphasize the high frequencies of speech, to thereby provide the reconstructed digital speech data signal samples. The decoding subroutine includes a subroutine for scaling quantize coefficients for each quantized residual signal frame in accordance with the quantize gain parameter included in the transmission signal;
--6-- .
., , _ 12~039~i a subroutine for providing data samples from the quantized residual signal included in the transmission signal in accord-ante with the scaled quantize coefficients; and a subroutine for providing the decoded down sampled residual signal from the data samples by pitch excitation in accordance with the pitch delay and pitch predictor gain parameters.
Additional features of the present invention are discussed in relation to the description of the preferred embodiment.
BRIEF DESCRIPTION OF THE DRAWING
Figure 1 is a functional block diagram illustrating the process implemented by the transmitter digital signal processor to code an input signal sample for transmission.
Figure 2 is a functional block diagram illustrating the process implemented by the receiver signal processor to decode a sample which is coded in accordance with the process illustrated in Figure 1.
Figure 3 is a flow chart of the LPC coefficient generation routine performed by the transmitter digital signal processor.
Figure 4 is a flow chart of the residual signal generation routine performed by the transmitter digital signal processor.
Figure 5 is a flow chart of the quantization routine per-formed by the transmitter digital signal processor.
I
03~;
Figure 6 is a diagram of a quantization filter implemented during the PPADPCM quantization subroutine included in the routine of Figure 3.
Figure 7 is a flow chart of the synthesis routine performed by the receiver digital signal processor.
.. .
DESCRIPTION OF THE PREFERRED EMBODIMENT
In the preferred embodiment of the present invention, the transmitter digital signal processor and receiver digital signal processor respectively are each Texas Instruments Model TMS32010 10 Digital Signal Processors. The TMS32010 processor is a 16-bit, 200 no cycle time, stand-alone processor with a 32-bit ALUM and Accumulator. The processor has a four level stack for nested subroutines; and arithmetic performance is enhanced by a hardware 16*16-bit parallel multiplier, which performs a pipeline 15 multiply/accumulate operation in 400 no. The TMS32010 processor has 144 16-bit words available as internal RAM which may be augmented by addressing external RAM, for buffer storage, via TBLR/TBLW ( table read/write) commands. These commands allow a trade-off between data memory requirements and speed of operation.
Program memory may be redefined as external data memory but its access time is 600 no. External program memory may be expanded to OK bytes at full speed. The two processors must perform -B-12~1D3~!6 all operations of the REP vocoder in real time. The processor choice is constrained by two key factors: operating speed and available internal RAM (especially important because frame storage is required). The TMS32010 processor is chosen based on its fast operating speed (5 MHz), data storage capabilities, and extensive development tools.
The principal functions of the-transmitter processor are described with reference to Figure 1. Digital speech data signal samples 10 are reemphasized 11 to improve the representation of high frequencies during the subsequent LPC analysis. Reemphasized samples 12 are subjected to LPC analysis 13 to provide LPC reflect lion coefficients 14.
The LPC reflection coefficients 14 are antacid 15 to provide quantized LPC reflection coefficients 16. The LPC reflection coefficients 14 are quantized to minimize distortion during sub-sequent transmission to the receiver. LPC coefficients 17 are generated 18 from the quantized LPC reflection coefficients 16.
The reemphasized samples 12 are inverse filtered 19 in accordance with the LPC coefficients 17 to provide a residual signal 20. The residual signal 20 is band limited 21 and down-sampled 22 to provide a base band residual signal 23.
The base band residual signal 23 is quantized by PPADPCM
quantization 24 in order to minimize the effects of distortion during subsequent transmission of the quantized residual signal 25.
I
Three of the parameters of the PPADPCM quantization 24 are pitch delay, pitch predictor gain and quantize gain. These three parameters are generated during PPADPCM quantization 24 and are necessary to decode to the quantized residual signal received by the receiver system. Accordingly, a pitch delay signal is provided on line 26, a pitch predictor gain signal is provided on line 27 and a quantize gain signal is provided on line 28 incident to the PPADPCM quantization 24 of the base band residual signal 23.
The quantized residual signal 25, the quantize, the pitch delay signal on line 26, the pitch predictor gain signal 27, the quantize gain signal 28 and the quantized LPC reflection coefficients 16 are combined linearly by formatting 32 to provide a transmission frame 34.
The principal functions of the receiver processor are de-scribed with reference to Figure 2. The format of each received data transmission frame 36 is decoded 37 to provide the quantized residual signal 39, the pitch delay parameter 40, the pitch predictor gain parameter 41, the quantize gain parameter 42 and ` the quantized LPC reflection coefficients 43.
The quantized residual signal 39 is decoded by PPADPCM
decoding 46 in accordance with the pitch delay 40, pitch predict ion gain or and quantize gain 42 to provide a decoded base band residual signal 47. The decoded base band residual signal 47 is spectrally regenerated 48 to provide a full-band residual signal 49.
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I
The quantized LPC reflection coefficients 43 are processed 50 to generate the LPC coefficients 51.
The full-band residual signal 49 is filtered 52 in accord-ante with the generated LPC coefficients 51 to synthesize a decoded speech data signal samples 53. The decoded speech data signal samples 53 are de-emphasized 54 to provide a regenerated digital speech data signal samples 55.
The processing routines performed by the transmitter processor to perform the above-described signal processing functions are described below with reference to the flow charts of Figures 3, 4 and S.
The processing routine represented by the flow chart of Figure 3 generally pertains to LPC analysis. This routine goner-ales the LPC coefficients from a buffered frame of reemphasized speech data signal samples. The routine of Figure 4 is generally directed to generation of the residual signal; and the routine of - Figure 5 is generally directed to quantization of the residual signal and the LPC coefficients.
The LPC analysis routine includes the subroutines of initialization 58, sample input So, reemphasis 61, ACT
generation 63, ACT normalization 65 and LPC analysis 66.
The sample input subroutine 59 reads in digital speech data signal samples from an external data memory buffer.
The reemphasis subroutine 61 applies first-order digital reemphasis to the input speech data signal samples. The input to the algorithm is the input speech sample Sun and the output is the reemphasized speech sample Sun, both located in internal RAM. Eirst-order digital reemphasis is applied to the input speech signal to emphasize the high frequencies of speech. This leads to a more accurate estimate of the vocal tract frequency response, which is controlled by the LPC parameters. Reemphasis uses a single-delay high-pass filter. Experimentation shows that the choice of the reemphasis constant (a) is not critical and it is normally set to 0.9375. The difference equation for the filter is:
Sun = Sun a Snowily (En. 1) The reemphasis function is complemented at the receiver system by applying a de-emphasis function.
The reemphasized samples are stored in an external data memory for use in the residual signal generation routine of Figure 4.
The ACT generation subroutine 63 iteratively updates a correlation buffer for each input speech data signal sample.
This buffer must be zeroed prior to the first call to the sub-routine. The output of this subroutine is a 32-bit precision auto-correlation function (ACT) for delays between zero and ten points.
I
3~6 In order to generate the LPC coefficients, an auto-correlation function (ACT) must be defined from a windowed buffer of reemphasized speech samples (so). The ACT of a sequence is defined as:
Ok = L j-o Xj Xj + k KIWI.......... No (En. 2) where xj = we so (En. 3) The window (w;) is chosen to be rectangular for ease of implementation.
Won = 1 n = 0,............ No - 0 elsewhere (En. 4) A tenth-order LPC analysis requires the ACT coefficients Rural. These coefficients may be updated iteratively for each input speech data signal sample.
Rk(n~l) = Ok + on ink (En. 5) where Ok is the nth iteration of the kth ACT coefficients.
This equation is implemented by the ACT generation subroutine 63. The coefficients Ok are maintained with 32-bit accuracy to remove round-off error problems. The algorithm is imply-minted by creating a delay buffer that is initialized to zero and ripples after each iteration. This implementation also ensures that the 32-bit result will not overflow. The maximum ~03~6 value of the ACT is the zero-delay element. If, for example, each input sample has a maximum of 12-bit resolution, the maxim mum value attained by the accumulator, for a data buffer of 180 samples, is:
log2t2ll * 211 * 180] = 29.492 bits (En. 6) Upon completion of sample input, the 32-bit ACT result must be converted to 16-bit coefficients. The ACT normalization subroutine 65 performs all operations required to convert the 32-bit ACT to a 16-bit result. The LPC analysis subroutine 66 is transparent to a scaled ACT input. Therefore, to obtain the maximum dynamic range of the 16-bit ACT, the 32-bit results are scaled to the maximum, Row prior to truncation to 16-bits. The optimal procedure for this would be to divide all coefficients by Row However, execution efficiency is greatly improved by simply left-shifting the 32-bit numbers to remove leading zeros in the Row value.
A decision 67 that the 32-bit correlation frame is complete - enables the processor to proceed to the ACT normalization subroutine 65.
The LPC analysis subroutine 66 implements the Durbin algorithm to generate the ten LPC coefficients and ten LPC
reflection coefficients 36. The Durbin algorithms input is the normalized 16-bit ACT.
~L2~3~
The Durbin algorithm is an extremely efficient algorithm for generating the LPC coefficients. See J. Meekly, "Linear Prediction: A Tutorial Review", Pro IEEE, Vol. 63, pup 561-80, 1975. The algorithm is suitable for fixed-point arithmetic implementation and also generates, as a by-product, the reflect lion coefficients, which may used for quantization and coding prior to transmission to the receiver.
Alternatively the LPC coefficients may be generated by the lo Roux-Gueguen (LUG) recursion, which is described in J. lo Rout and C. Gauguin, "A Fixed Point Computation of Partial Correlation Coefficients in Linear Prediction", Pro ICASSP-77, pup 742-3. The LUG recursion, although faster than the Durbin algorithm, generates only the LPC reflection coefficients and not the LPC coefficients, per so which must be generated separately.
Durbin's recursive procedure is as follows:
Initialization.
(En. 7) Al = -R1/Ro ten. 8) E = [l-kl2]Eo (En. 91 Recursion imp k = -(Rip + joy 1 a Rowley (En. 10) at = kit (En. 11) 0;~9~ii aji = a 1 + kiwi jig 1 lull (En. 12) Hi = ski ] Neil (En. 13) Symbols defined:
Hi is the prediction error energy Rip is the ilk auto-correlation function kit is the ilk reflection coefficient all is the ilk I.PC coefficient (Thea iteration) The order of the LPC analysis, P, is determined experimentally and a Thea order analysis is sufficient to adequately model the vocal tract frequency response.
The LPC parameters must be quantized and coded prior to transmission and resynthesis of the digital speech data signal at the receiver. However, the LPC coefficients, a, are sense-live to quantization noise an* introduce significant distortion to the signal. A solution is to quantize and code the LPC
reflection coefficients, kit which are much less sensitive to quantization noise. This operation is performed by a LPC
_ . .
coefficient quantization subroutine 68, which is a part of the quantization routine of Figure 5. At the receiver the LPC
coefficients may be recovered from the quantized reflection coefficients using a subset of the recursion above.
i -1-6-1.2~03~i The initialization subroutine 58 and the sample input subroutine 59 are both contained in the main program for the transmitter processor. The main program controls the calling of the other subroutines in the LPC analysis routine of -Figure 3 in accordance with the following hierarchy:pre-emphasis 61, ACT generation 63, ACT normalization I and LPC analysis 66. The main program implements the-LPC-analysis routine of Figure 5 to generate a frame of a predetermined number of reemphasized speech data signal samples and the ten LPC coefficients. The term "LPC coefficients" as used herein refers to either LPC coefficients or LPC reflection coefficients unless the latter is specified.
The residual signal generation routine is represented by the flow chart of Figure 4. This routine includes the subroutines 15 of initialization 70, sample input 71, inverse filter 72, band limit 73 and down sample 74.
The initialization subroutine 70 transfers second-order section filter coefficients from external data memory to the internal RAM of the transmitter processor for use during the band limit subroutine 73.
The sample input subroutine 71 inputs the reemphasized samples from a speech data buffer located in the external data memory to the zero-delay position of a speech delay buffer, which is located in the internal RAM of the transmitter processor.
I. :
~2g~0396 the delay buffer is used for the implementation by the inverse filter subroutine 72 of the all-zero Finite-Impulse-Response (FIR
filter in accordance with the LPC coefficients.
The inverse filter subroutine 72 implements an all-zero inverse filter in accordance with the LPC coefficients to generate the residual signal lo (Figure 1). The output from this sub-routine 72 is provided to a residual signal data buffer which is located in the external data memory.
The residual signal 20 is generated by inverse filtering the reemphasized speech data signal samples 12 in accordance with the LPC coefficients 17. (See Figure 1). The LPC Coffey-clients are formulated mathematically to estimate the transfer function of the vocal tract. This function is represented by the polynomial Ho Ho = [1 -~kP=l a Z ] (En. 14) where a is the kth LPC coefficient. The residual signal 19 is obtained by filtering the speech data signal samples 12 by the all-zero filter Ho 1. If on represents the input speech sample at time n and Yin represents the corresponding output sample, the filter can be represented by the following difference equation:
n n 1 n-l + a xn_2 + -- + Alex 10 ' ~2'~3~6 The simplest way to implement this structure is to place the coefficients a in a fixed register and to implement the delay buffer using a shift register. The TMS32010 micro-code is optimized to perform this operation using the LTD/MPY commands:
the processor has a pipeline Multiply/Accumulate instruction that executes in ~00 no.- . .. .; -. - .
The band limit subroutine 73 low-pass filters the residual - signal 20 by implementing an eighth-order elliptic half-band filter, which in turn is implemented by using a cascade of four second-order sections. The transfer function of the elliptic filter is:
Ho = A / By (En. 16) where I KIWI a Z (En. 17) By =~k=0 by z k bowl . (En. 18) It is important to implement this filter in a manner which will reduce the effects of coefficient quantization and finite register length effects which are described in L. A. Rabiner and B. Gold, "Theory and Application of Digital Signal Processing", Prentice-Hall, 1975. This is best achieved by factorizing the polynomial Ho into second order polynomials:
Ho = Hi Ho H3(z) Ho (En. 19) where: H (z) = a + at z + a Z
1 + by . z 1 b2.z 2 (En. I
~240396 The second-order polynomial Hum is implemented by a second-order filter section. The second-order section is implemented by an internal subroutine that is called four times to provide a cascade of four second-order sections. A cascade of four sections is equivalent to an eighth-order elliptic low-pass filter. Each section uses a set of filter coefficients and requires its own delay buffer, which must be shifted at each iteration.
The down sample subroutine 74 implements down sampling by discarding predetermined samples. The down sample algorithm uses the frame counter to alternate between discarding the input data point or scaling it to maintain the energy per frame. The down sampling function reduces the filtered residual signal sample data rate. This function is executed by a frame position pointer. The sample is either discarded or magnitude-scaled (multiplied by a predetermined factor to maintain the average frame energy of the residual signally If, for example, the down sampling ratio is two, the scaling factor is also two.
A decision 75-that the frame is complete concludes the residual signal generation routine of Figure 4 The sample input 71 and inverse filter 72 subroutines and the decision 75 are integrated together and control the calling hierarchy for the other subroutines in the residual signal generation routines of Figure 4. The order of such ~2~03~6 calling hierarchy is band limit 73 and downsamp~e 74.
The quantization routine represented by the flow chart of Figure 5 includes the following subroutines: LPC coefficient quantization 68 (discussed above in relation to the LPC analysis subroutine 66), pitch delay 78, pitch predictor gain 80, qua-titer gain 81, CRC 82, PPADPCM quantization 83 and data format 84.
The LPC coefficient quantization subroutine 68 quantizes the ten LPC reflection coefficients 14. This subroutine obtains its input data from the LPC reflection coefficients 14 and quantize look-up subroutine 68 during the operation of the LPC
analysis subroutine 66. this subroutine 68 is called by the LPC analysis subroutine 66.
The reflection coefficients are quantized with a variable number of bits per coefficient compatible with DOD standard LPC-10 coding, which is described in T. E. Remain, "The Government Standard Linear Predictive Coding Algorithm: LPC-10", - Speech Technology, April 1982.
Data management is necessary because of the limited avail-ability of internal RAM in the TMS32010. Additional data buffers may be located in external data memory, which has a very slow access time (800 no). A data management algorithm performs buffer transfers between internal RAM and external data memory to enable all routines to execute using internal RAM
memory.
)39~
The pitch delay subroutine 78 estimates the pitch period to determine the pitch delay parameter T of the down sampled residual signal 22 (Figure l) used for the PPADPCM quantization using an auto-correlation function (ACT) analysis of the signal 22. The inputs to the algorithm are the partial ACT of the previous frame and the current residual signal frame. The out-put from the algorithm is the estimated pitch delay T and the updated partial ACT.
The pitch delay is updated at the frame rate. Pitch analysis uses a simple auto-correlation detector:
I = no Sun Snot (En. 21) The pitch delay, T, is chosen as the maximum value of I, evaluating I between Twin and Tax. To enable an accurate estimate of the pitch Duluth analysis must cover three pitch periods, i.e., N>3TmaX. The limits of the pitch detection are chosen experimentally using FORTRAN simulations of the REP vocoder algorithm; for example, Twin is a 15 sample delay I' and Tax is a 40 sample delay. This corresponds to pitch ire-quenches of 267 Ho and lo Ho respectively if the down sampled residual signal 22 has a sampling rate of 4 kHz. The value N
is therefore chosen to be two down sampled frames. The auto-correlation detector, I is evaluated as two partial-ACF's, Al and RUT where:
BACKGROUND OF THE INVENTION
The present invention generally pertains to voice coders (vocoders) and is particularly directed to Residual-Excited Linear Prediction (REP) vocoders. Vocoders convert speech signals into digital form for transmission and synthesize speech signals from these digital signals upon reception. Vocoders typically operate at flexible binary data rates varying from 32 kbps (kilobytes per second) down to about 2.4 kbps.
Vocoders traditionally are divided into two basic types, waveform coders and pitch-excited source coders. Waveform coders operate at high data rates (above 16 kbps) and produce good quality natural sounding speech which is robust against both acoustic and transmitted noise. Source coders operate at low data rates (less than 4.8 kbps) in an analysis/synthesis mode governed by a mathematical model of the human vocal " apparatus. Source vocoders typically sound robotic and do not perform well under poor acoustic conditions.
The REP vocoder was originally proposed by Us and McGill, "The Residual-Excited Linear Prediction Vocoder with Transmission Rate Below 9.6 kbits/s", IEEE Trans. COMMA, 1975 pp. 1466-1473;
and an enhanced REP vocoder was proposed by Dank berg and Wrong, "Development of a 4.8-9.6 kbps REP vocoder", ICASSP-i9. The 12~396 purpose of the REP vocoder was to provide satisfactory perform-ante in the gap between the operating ranges of waveform coders and source coders, to wit: I kbps to 16 kbps. The REP
vocoder contains some features of both waveform coders and source coders.
In prior art REP vocoders, digital speech data signal samples are analyzed over relatively short time segments (typically in the range of 10-30 my.) by a linear predictive coding (LPC) vocal tract modeling technique to provide LPC
coefficients for each block of samples. The LPC coefficients represent the vocal tract, glottal flow and radiation of the speech represented by the digital signal samples. Using the LPC coefficients, the digital speech data signal samples are inverse filtered by a time-variant, all-pole recursive digital filter over each short time segment to provide residual signal (prediction error signal samples. The time-variant character of speech is handled by a succession of such filters with different parameters.
.... .
The residual signal and the LPC coefficients are encoded (quantized) and formatted for transmission. Upon reception, speech is synthesized by processing the residual signal in accordance with the LPC coefficients.
In prior art REP vocoders, the residual signal samples are band limited and down sampled prior -to quantization in order ~2~0396 to provide residual signal samples at a reduced data rate.
The upper band harmonics are generated during synthesis of the speech signal when the down sampled residual signal is unsampled and zeros are inserted between data points.
In the Us and McGill REP vocoder the residual signal is quantized prior to transmission by adaptive delta modulation.
Dank berg and Wrong considered various other quantization tech-piques and concluded that pitch predictive adaptive differential pulse code modulation (PPADPCM) provided the best signal-to-quantizing noise ratio.
In accordance with the PPADPCM technique, the residual signal samples are processed by pitch analysis to determine the pitch delay, are processed by pitch predictor gain analysis to determine the pitch predictor gain in accordance with the deter-mined pitch delay, processed by gain analysis to provide a maximum deviation quantize gain, and are further processed by PPADPCM in accordance with the quantize gain, pitch predictor -gain and delay parameters to thereby provide the quantized residual signal. The quantize gain, pitch predictor gain and the pitch delay parameters are combined with the quantized residual signal and the quantized LPC coefficients for transmission.
REP vocoders of the prior art have required complex hardware and have been so expensive to implement as to be commercially impractical.
aye SUMMARY OF THE INVENTION
The present invention provides a commercially practical REP vocoder that is implemented by two digital signal processors, one for a transmitter system and one for a remotely located no-sever system. Thy transmitter digital signal processor is adapted for processing digital speech data signal samples to provide a formatted transmission signal including (a) a quantized residual signal generated by inverse filtering of the samples in accordance with linear predictive coding (LPC) coefficients generated from the samples, (b) quantized LPC coefficients and (c) pitch and gain parameters generated during quantization of the residual signal from the inverse filtered samples, all of which are generated by the processor from the digital speech data samples.
The receiver digital signal processor is adapted for processing the formatted transmission signal to synthesize reconstructed digital speech data signal samples.
The transmitter digital signal processor is adapted for performing a routine for generating the LPC coefficients; a routine for generating the residual signal; and a routine for quantizing the residual signal and the LPC coefficients. The routine for generating the LPC coefficients includes a subroutine for reemphasizing the samples in order to emphasize the high frequencies of speech, a subroutine for defining an auto-correlation -4- j 12~0~96 function (ACT) from the reemphasized samples in order to generate ACT coefficients; and a subroutine for generating the LPC coefficients from the generated ACT coefficients. The routine for generating the residual signal includes a subroutine for inverse filtering the reemphasized samples in accordance with the generated LPC coefficients; a subroutine for band limiting the residual signal by low-pass filtering in a manner which will reduce the effects of quantization; and a subroutine for down sampling the band limited residual signal to reduce the number of residual signal samples that are quantized and formatted for transmission. The routine for quantizing the residual signal and LPC coefficients includes a subroutine for quantizing the LPC coefficients; a subroutine for estimating the pitch period of the down sampled residual signal by ACT analysis of the current down sampled residual signal frame in accordance with the ACT
coefficients generated for the previous frame to thereby provide a pitch delay parameter-for the current frame; a subroutine for providing a pitch predictor gain parameter for each residual signal frame in accordance with the estimated pitch delay pane-meter for each corresponding frame; a subroutine for providing aquantizer gain parameter for each residual signal frame in accord-ante with the pitch delay and pitch predictor gain parameters for each corresponding frame; and a subroutine for quantizing each residual signal frame by pitch predictive adaptive differential --S--. . , pulse code modulation (PPADPCM) in accordance with the pitch delay, pitch predictor gain and quantize gain parameters for each corresponding frame.
The receiver digital signal processor is adapted for processing the formatted transmission signal to synthesize reconstructed digital speech data signal samples by performing synthesisroutinethat includes a subroutine for regenerating the LPC coefficients from the quantized LPC coefficients included in the transmission signal;
a subroutine for decoding the quantized residual signal included lo in the transmission signal in accordance with the pitch delay, pitch predictive gain and quantize gain parameters included in the transmission signal to thereby provide a decoded down sampled residual signal; a subroutine for spectrally regenerating-a full-band residual signal from the decoded down sampled residual signal; a subroutine for regenerating reemphasized digital speech data signal samples by auto-regressively filtering the regenerated full-band residual signal in accordance with the regenerated LPC coefficients; and a subroutine for de-emphasizing the regenerated reemphasized samples in order to de-emphasize the high frequencies of speech, to thereby provide the reconstructed digital speech data signal samples. The decoding subroutine includes a subroutine for scaling quantize coefficients for each quantized residual signal frame in accordance with the quantize gain parameter included in the transmission signal;
--6-- .
., , _ 12~039~i a subroutine for providing data samples from the quantized residual signal included in the transmission signal in accord-ante with the scaled quantize coefficients; and a subroutine for providing the decoded down sampled residual signal from the data samples by pitch excitation in accordance with the pitch delay and pitch predictor gain parameters.
Additional features of the present invention are discussed in relation to the description of the preferred embodiment.
BRIEF DESCRIPTION OF THE DRAWING
Figure 1 is a functional block diagram illustrating the process implemented by the transmitter digital signal processor to code an input signal sample for transmission.
Figure 2 is a functional block diagram illustrating the process implemented by the receiver signal processor to decode a sample which is coded in accordance with the process illustrated in Figure 1.
Figure 3 is a flow chart of the LPC coefficient generation routine performed by the transmitter digital signal processor.
Figure 4 is a flow chart of the residual signal generation routine performed by the transmitter digital signal processor.
Figure 5 is a flow chart of the quantization routine per-formed by the transmitter digital signal processor.
I
03~;
Figure 6 is a diagram of a quantization filter implemented during the PPADPCM quantization subroutine included in the routine of Figure 3.
Figure 7 is a flow chart of the synthesis routine performed by the receiver digital signal processor.
.. .
DESCRIPTION OF THE PREFERRED EMBODIMENT
In the preferred embodiment of the present invention, the transmitter digital signal processor and receiver digital signal processor respectively are each Texas Instruments Model TMS32010 10 Digital Signal Processors. The TMS32010 processor is a 16-bit, 200 no cycle time, stand-alone processor with a 32-bit ALUM and Accumulator. The processor has a four level stack for nested subroutines; and arithmetic performance is enhanced by a hardware 16*16-bit parallel multiplier, which performs a pipeline 15 multiply/accumulate operation in 400 no. The TMS32010 processor has 144 16-bit words available as internal RAM which may be augmented by addressing external RAM, for buffer storage, via TBLR/TBLW ( table read/write) commands. These commands allow a trade-off between data memory requirements and speed of operation.
Program memory may be redefined as external data memory but its access time is 600 no. External program memory may be expanded to OK bytes at full speed. The two processors must perform -B-12~1D3~!6 all operations of the REP vocoder in real time. The processor choice is constrained by two key factors: operating speed and available internal RAM (especially important because frame storage is required). The TMS32010 processor is chosen based on its fast operating speed (5 MHz), data storage capabilities, and extensive development tools.
The principal functions of the-transmitter processor are described with reference to Figure 1. Digital speech data signal samples 10 are reemphasized 11 to improve the representation of high frequencies during the subsequent LPC analysis. Reemphasized samples 12 are subjected to LPC analysis 13 to provide LPC reflect lion coefficients 14.
The LPC reflection coefficients 14 are antacid 15 to provide quantized LPC reflection coefficients 16. The LPC reflection coefficients 14 are quantized to minimize distortion during sub-sequent transmission to the receiver. LPC coefficients 17 are generated 18 from the quantized LPC reflection coefficients 16.
The reemphasized samples 12 are inverse filtered 19 in accordance with the LPC coefficients 17 to provide a residual signal 20. The residual signal 20 is band limited 21 and down-sampled 22 to provide a base band residual signal 23.
The base band residual signal 23 is quantized by PPADPCM
quantization 24 in order to minimize the effects of distortion during subsequent transmission of the quantized residual signal 25.
I
Three of the parameters of the PPADPCM quantization 24 are pitch delay, pitch predictor gain and quantize gain. These three parameters are generated during PPADPCM quantization 24 and are necessary to decode to the quantized residual signal received by the receiver system. Accordingly, a pitch delay signal is provided on line 26, a pitch predictor gain signal is provided on line 27 and a quantize gain signal is provided on line 28 incident to the PPADPCM quantization 24 of the base band residual signal 23.
The quantized residual signal 25, the quantize, the pitch delay signal on line 26, the pitch predictor gain signal 27, the quantize gain signal 28 and the quantized LPC reflection coefficients 16 are combined linearly by formatting 32 to provide a transmission frame 34.
The principal functions of the receiver processor are de-scribed with reference to Figure 2. The format of each received data transmission frame 36 is decoded 37 to provide the quantized residual signal 39, the pitch delay parameter 40, the pitch predictor gain parameter 41, the quantize gain parameter 42 and ` the quantized LPC reflection coefficients 43.
The quantized residual signal 39 is decoded by PPADPCM
decoding 46 in accordance with the pitch delay 40, pitch predict ion gain or and quantize gain 42 to provide a decoded base band residual signal 47. The decoded base band residual signal 47 is spectrally regenerated 48 to provide a full-band residual signal 49.
:
I
The quantized LPC reflection coefficients 43 are processed 50 to generate the LPC coefficients 51.
The full-band residual signal 49 is filtered 52 in accord-ante with the generated LPC coefficients 51 to synthesize a decoded speech data signal samples 53. The decoded speech data signal samples 53 are de-emphasized 54 to provide a regenerated digital speech data signal samples 55.
The processing routines performed by the transmitter processor to perform the above-described signal processing functions are described below with reference to the flow charts of Figures 3, 4 and S.
The processing routine represented by the flow chart of Figure 3 generally pertains to LPC analysis. This routine goner-ales the LPC coefficients from a buffered frame of reemphasized speech data signal samples. The routine of Figure 4 is generally directed to generation of the residual signal; and the routine of - Figure 5 is generally directed to quantization of the residual signal and the LPC coefficients.
The LPC analysis routine includes the subroutines of initialization 58, sample input So, reemphasis 61, ACT
generation 63, ACT normalization 65 and LPC analysis 66.
The sample input subroutine 59 reads in digital speech data signal samples from an external data memory buffer.
The reemphasis subroutine 61 applies first-order digital reemphasis to the input speech data signal samples. The input to the algorithm is the input speech sample Sun and the output is the reemphasized speech sample Sun, both located in internal RAM. Eirst-order digital reemphasis is applied to the input speech signal to emphasize the high frequencies of speech. This leads to a more accurate estimate of the vocal tract frequency response, which is controlled by the LPC parameters. Reemphasis uses a single-delay high-pass filter. Experimentation shows that the choice of the reemphasis constant (a) is not critical and it is normally set to 0.9375. The difference equation for the filter is:
Sun = Sun a Snowily (En. 1) The reemphasis function is complemented at the receiver system by applying a de-emphasis function.
The reemphasized samples are stored in an external data memory for use in the residual signal generation routine of Figure 4.
The ACT generation subroutine 63 iteratively updates a correlation buffer for each input speech data signal sample.
This buffer must be zeroed prior to the first call to the sub-routine. The output of this subroutine is a 32-bit precision auto-correlation function (ACT) for delays between zero and ten points.
I
3~6 In order to generate the LPC coefficients, an auto-correlation function (ACT) must be defined from a windowed buffer of reemphasized speech samples (so). The ACT of a sequence is defined as:
Ok = L j-o Xj Xj + k KIWI.......... No (En. 2) where xj = we so (En. 3) The window (w;) is chosen to be rectangular for ease of implementation.
Won = 1 n = 0,............ No - 0 elsewhere (En. 4) A tenth-order LPC analysis requires the ACT coefficients Rural. These coefficients may be updated iteratively for each input speech data signal sample.
Rk(n~l) = Ok + on ink (En. 5) where Ok is the nth iteration of the kth ACT coefficients.
This equation is implemented by the ACT generation subroutine 63. The coefficients Ok are maintained with 32-bit accuracy to remove round-off error problems. The algorithm is imply-minted by creating a delay buffer that is initialized to zero and ripples after each iteration. This implementation also ensures that the 32-bit result will not overflow. The maximum ~03~6 value of the ACT is the zero-delay element. If, for example, each input sample has a maximum of 12-bit resolution, the maxim mum value attained by the accumulator, for a data buffer of 180 samples, is:
log2t2ll * 211 * 180] = 29.492 bits (En. 6) Upon completion of sample input, the 32-bit ACT result must be converted to 16-bit coefficients. The ACT normalization subroutine 65 performs all operations required to convert the 32-bit ACT to a 16-bit result. The LPC analysis subroutine 66 is transparent to a scaled ACT input. Therefore, to obtain the maximum dynamic range of the 16-bit ACT, the 32-bit results are scaled to the maximum, Row prior to truncation to 16-bits. The optimal procedure for this would be to divide all coefficients by Row However, execution efficiency is greatly improved by simply left-shifting the 32-bit numbers to remove leading zeros in the Row value.
A decision 67 that the 32-bit correlation frame is complete - enables the processor to proceed to the ACT normalization subroutine 65.
The LPC analysis subroutine 66 implements the Durbin algorithm to generate the ten LPC coefficients and ten LPC
reflection coefficients 36. The Durbin algorithms input is the normalized 16-bit ACT.
~L2~3~
The Durbin algorithm is an extremely efficient algorithm for generating the LPC coefficients. See J. Meekly, "Linear Prediction: A Tutorial Review", Pro IEEE, Vol. 63, pup 561-80, 1975. The algorithm is suitable for fixed-point arithmetic implementation and also generates, as a by-product, the reflect lion coefficients, which may used for quantization and coding prior to transmission to the receiver.
Alternatively the LPC coefficients may be generated by the lo Roux-Gueguen (LUG) recursion, which is described in J. lo Rout and C. Gauguin, "A Fixed Point Computation of Partial Correlation Coefficients in Linear Prediction", Pro ICASSP-77, pup 742-3. The LUG recursion, although faster than the Durbin algorithm, generates only the LPC reflection coefficients and not the LPC coefficients, per so which must be generated separately.
Durbin's recursive procedure is as follows:
Initialization.
(En. 7) Al = -R1/Ro ten. 8) E = [l-kl2]Eo (En. 91 Recursion imp k = -(Rip + joy 1 a Rowley (En. 10) at = kit (En. 11) 0;~9~ii aji = a 1 + kiwi jig 1 lull (En. 12) Hi = ski ] Neil (En. 13) Symbols defined:
Hi is the prediction error energy Rip is the ilk auto-correlation function kit is the ilk reflection coefficient all is the ilk I.PC coefficient (Thea iteration) The order of the LPC analysis, P, is determined experimentally and a Thea order analysis is sufficient to adequately model the vocal tract frequency response.
The LPC parameters must be quantized and coded prior to transmission and resynthesis of the digital speech data signal at the receiver. However, the LPC coefficients, a, are sense-live to quantization noise an* introduce significant distortion to the signal. A solution is to quantize and code the LPC
reflection coefficients, kit which are much less sensitive to quantization noise. This operation is performed by a LPC
_ . .
coefficient quantization subroutine 68, which is a part of the quantization routine of Figure 5. At the receiver the LPC
coefficients may be recovered from the quantized reflection coefficients using a subset of the recursion above.
i -1-6-1.2~03~i The initialization subroutine 58 and the sample input subroutine 59 are both contained in the main program for the transmitter processor. The main program controls the calling of the other subroutines in the LPC analysis routine of -Figure 3 in accordance with the following hierarchy:pre-emphasis 61, ACT generation 63, ACT normalization I and LPC analysis 66. The main program implements the-LPC-analysis routine of Figure 5 to generate a frame of a predetermined number of reemphasized speech data signal samples and the ten LPC coefficients. The term "LPC coefficients" as used herein refers to either LPC coefficients or LPC reflection coefficients unless the latter is specified.
The residual signal generation routine is represented by the flow chart of Figure 4. This routine includes the subroutines 15 of initialization 70, sample input 71, inverse filter 72, band limit 73 and down sample 74.
The initialization subroutine 70 transfers second-order section filter coefficients from external data memory to the internal RAM of the transmitter processor for use during the band limit subroutine 73.
The sample input subroutine 71 inputs the reemphasized samples from a speech data buffer located in the external data memory to the zero-delay position of a speech delay buffer, which is located in the internal RAM of the transmitter processor.
I. :
~2g~0396 the delay buffer is used for the implementation by the inverse filter subroutine 72 of the all-zero Finite-Impulse-Response (FIR
filter in accordance with the LPC coefficients.
The inverse filter subroutine 72 implements an all-zero inverse filter in accordance with the LPC coefficients to generate the residual signal lo (Figure 1). The output from this sub-routine 72 is provided to a residual signal data buffer which is located in the external data memory.
The residual signal 20 is generated by inverse filtering the reemphasized speech data signal samples 12 in accordance with the LPC coefficients 17. (See Figure 1). The LPC Coffey-clients are formulated mathematically to estimate the transfer function of the vocal tract. This function is represented by the polynomial Ho Ho = [1 -~kP=l a Z ] (En. 14) where a is the kth LPC coefficient. The residual signal 19 is obtained by filtering the speech data signal samples 12 by the all-zero filter Ho 1. If on represents the input speech sample at time n and Yin represents the corresponding output sample, the filter can be represented by the following difference equation:
n n 1 n-l + a xn_2 + -- + Alex 10 ' ~2'~3~6 The simplest way to implement this structure is to place the coefficients a in a fixed register and to implement the delay buffer using a shift register. The TMS32010 micro-code is optimized to perform this operation using the LTD/MPY commands:
the processor has a pipeline Multiply/Accumulate instruction that executes in ~00 no.- . .. .; -. - .
The band limit subroutine 73 low-pass filters the residual - signal 20 by implementing an eighth-order elliptic half-band filter, which in turn is implemented by using a cascade of four second-order sections. The transfer function of the elliptic filter is:
Ho = A / By (En. 16) where I KIWI a Z (En. 17) By =~k=0 by z k bowl . (En. 18) It is important to implement this filter in a manner which will reduce the effects of coefficient quantization and finite register length effects which are described in L. A. Rabiner and B. Gold, "Theory and Application of Digital Signal Processing", Prentice-Hall, 1975. This is best achieved by factorizing the polynomial Ho into second order polynomials:
Ho = Hi Ho H3(z) Ho (En. 19) where: H (z) = a + at z + a Z
1 + by . z 1 b2.z 2 (En. I
~240396 The second-order polynomial Hum is implemented by a second-order filter section. The second-order section is implemented by an internal subroutine that is called four times to provide a cascade of four second-order sections. A cascade of four sections is equivalent to an eighth-order elliptic low-pass filter. Each section uses a set of filter coefficients and requires its own delay buffer, which must be shifted at each iteration.
The down sample subroutine 74 implements down sampling by discarding predetermined samples. The down sample algorithm uses the frame counter to alternate between discarding the input data point or scaling it to maintain the energy per frame. The down sampling function reduces the filtered residual signal sample data rate. This function is executed by a frame position pointer. The sample is either discarded or magnitude-scaled (multiplied by a predetermined factor to maintain the average frame energy of the residual signally If, for example, the down sampling ratio is two, the scaling factor is also two.
A decision 75-that the frame is complete concludes the residual signal generation routine of Figure 4 The sample input 71 and inverse filter 72 subroutines and the decision 75 are integrated together and control the calling hierarchy for the other subroutines in the residual signal generation routines of Figure 4. The order of such ~2~03~6 calling hierarchy is band limit 73 and downsamp~e 74.
The quantization routine represented by the flow chart of Figure 5 includes the following subroutines: LPC coefficient quantization 68 (discussed above in relation to the LPC analysis subroutine 66), pitch delay 78, pitch predictor gain 80, qua-titer gain 81, CRC 82, PPADPCM quantization 83 and data format 84.
The LPC coefficient quantization subroutine 68 quantizes the ten LPC reflection coefficients 14. This subroutine obtains its input data from the LPC reflection coefficients 14 and quantize look-up subroutine 68 during the operation of the LPC
analysis subroutine 66. this subroutine 68 is called by the LPC analysis subroutine 66.
The reflection coefficients are quantized with a variable number of bits per coefficient compatible with DOD standard LPC-10 coding, which is described in T. E. Remain, "The Government Standard Linear Predictive Coding Algorithm: LPC-10", - Speech Technology, April 1982.
Data management is necessary because of the limited avail-ability of internal RAM in the TMS32010. Additional data buffers may be located in external data memory, which has a very slow access time (800 no). A data management algorithm performs buffer transfers between internal RAM and external data memory to enable all routines to execute using internal RAM
memory.
)39~
The pitch delay subroutine 78 estimates the pitch period to determine the pitch delay parameter T of the down sampled residual signal 22 (Figure l) used for the PPADPCM quantization using an auto-correlation function (ACT) analysis of the signal 22. The inputs to the algorithm are the partial ACT of the previous frame and the current residual signal frame. The out-put from the algorithm is the estimated pitch delay T and the updated partial ACT.
The pitch delay is updated at the frame rate. Pitch analysis uses a simple auto-correlation detector:
I = no Sun Snot (En. 21) The pitch delay, T, is chosen as the maximum value of I, evaluating I between Twin and Tax. To enable an accurate estimate of the pitch Duluth analysis must cover three pitch periods, i.e., N>3TmaX. The limits of the pitch detection are chosen experimentally using FORTRAN simulations of the REP vocoder algorithm; for example, Twin is a 15 sample delay I' and Tax is a 40 sample delay. This corresponds to pitch ire-quenches of 267 Ho and lo Ho respectively if the down sampled residual signal 22 has a sampling rate of 4 kHz. The value N
is therefore chosen to be two down sampled frames. The auto-correlation detector, I is evaluated as two partial-ACF's, Al and RUT where:
-2-2-Al no Sun 'Snot (En. 22) , RUT no Sun Snot (En. 23) M is a single down sampled frame. I is calculated by adding the current frame's partial-ACF, RUT and the previous frame's partial-~CF, Al, that was stored in external data memory.
The pitch predictor gain subroutine 80 evaluates the pitch predictor gain parameter B for the PPADPCM quantization and updates such evaluation at the frame rate. The pitch predictor gain B is evaluated as:
/ n-0 Sun Snout n-0 Snot Snot (En. 24) where N is a single down sampled frame and T is the pitch delay.
B is constrained between two limits:
B 1.0 Then: B = 1.0 B < 0.1 Then: B = 0.0 The quantize gain subroutine 81 evaluates the quantize gain parameter qgainforthe PPADPCM quantization and updates such evaluation at the frame rate. This parameter is used to scale the quantize to the input signal level; each input and output level of the quantize is multiplied by q at . The parameter is chosen to be the maximum on:
On Is Bunt In = 0,...,M-1 (En. 25) 1;~4~396 where M is a single down sampled frame, T is the pitch delay, and B is the pitch predictor gain.
The CRC subroutine 82 introduces an n-bit cyclic rerun-dandy code (CRC) on part of the transmission frame to enable detection of bit errors during transmission. The code protects the LPC coefficients and PPADPCM parameters. The input to the subroutine is the relevant quantized coefficients. The output from the subroutine is an n-bit CRC to be transmitted.
The PPADPCM subroutine 83 quantizes the down sampled residual signal 22, using Pitch Predictive Adaptive Differential Pulse Code Modulation (PPADPCM). The term "pitch predictive"
is misleading however. The pitch predictor is used to remove the dominant periodic frequency from the residual signal 22 prior to quantization. While this frequency is most commonly the pitch period, the predictor may lock onto an alternate frequency without detrimenting the operation of the quantize.
Therefore a rigorous pitch extraction algorithm is not necessary.
The predictor removes the dominant periodicity of the waveform .....
to generate a "white noise" signal with a Gaussian probability density function (pdf). This signal may then be quantized using a classical Max quantize, as described in J. Max, "Quantizing for Minimum Distortion," IRE Trays on Information Theory, March 1960.
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Figure 6 shows the structure of the PPADPCM antisera.
The quantize is embedded in the predictor loop so that the error spectrum introduced by quantization is uniform. The parameters of the quantize are the pitch delay IT), the quantize gain (gain), the pitch predictor gain (B), and the order of the quantize (Q). Experimentation determines that a
The pitch predictor gain subroutine 80 evaluates the pitch predictor gain parameter B for the PPADPCM quantization and updates such evaluation at the frame rate. The pitch predictor gain B is evaluated as:
/ n-0 Sun Snout n-0 Snot Snot (En. 24) where N is a single down sampled frame and T is the pitch delay.
B is constrained between two limits:
B 1.0 Then: B = 1.0 B < 0.1 Then: B = 0.0 The quantize gain subroutine 81 evaluates the quantize gain parameter qgainforthe PPADPCM quantization and updates such evaluation at the frame rate. This parameter is used to scale the quantize to the input signal level; each input and output level of the quantize is multiplied by q at . The parameter is chosen to be the maximum on:
On Is Bunt In = 0,...,M-1 (En. 25) 1;~4~396 where M is a single down sampled frame, T is the pitch delay, and B is the pitch predictor gain.
The CRC subroutine 82 introduces an n-bit cyclic rerun-dandy code (CRC) on part of the transmission frame to enable detection of bit errors during transmission. The code protects the LPC coefficients and PPADPCM parameters. The input to the subroutine is the relevant quantized coefficients. The output from the subroutine is an n-bit CRC to be transmitted.
The PPADPCM subroutine 83 quantizes the down sampled residual signal 22, using Pitch Predictive Adaptive Differential Pulse Code Modulation (PPADPCM). The term "pitch predictive"
is misleading however. The pitch predictor is used to remove the dominant periodic frequency from the residual signal 22 prior to quantization. While this frequency is most commonly the pitch period, the predictor may lock onto an alternate frequency without detrimenting the operation of the quantize.
Therefore a rigorous pitch extraction algorithm is not necessary.
The predictor removes the dominant periodicity of the waveform .....
to generate a "white noise" signal with a Gaussian probability density function (pdf). This signal may then be quantized using a classical Max quantize, as described in J. Max, "Quantizing for Minimum Distortion," IRE Trays on Information Theory, March 1960.
~2~9~
Figure 6 shows the structure of the PPADPCM antisera.
The quantize is embedded in the predictor loop so that the error spectrum introduced by quantization is uniform. The parameters of the quantize are the pitch delay IT), the quantize gain (gain), the pitch predictor gain (B), and the order of the quantize (Q). Experimentation determines that a
3-bit quantize is adequate to ensure good subjective speech quality at the receiver.
The data format subroutine 84 formats a data frame 34 (Figure 1) for transmission. The input to the subroutine 84 is a predetermined number of quantized residual signal samples 25, the pitch delay parameter 26, the pitch predictor gain 27, the quantize gain 28, the quantized LPC coefficients 31 Figure 1) and the CRC. The output from the subroutine 84 is a transmission data frame 34 which is place din the output buffer.
A decision 85 that the frame is complete concludes the quantization routine of Figure 5.
The calling hierarchy of the subroutines in the quantize-lion routine of Figure 5 is under the control of the main program. The following subroutines are integrated together in a subroutine designated PPQNT: pitch predictor gain 80, quantize gain 81 and PPADPCM quantization 83. The calling hierarchy is as follows: pitch 78, PPQNT, CRC 82 and data format 84. The subroutine 68 is called by the LPC analysis subroutine 66 in the LO analysis routine of Figure 3.
i -25--2~039~S
he receiver digital signal processor utilizes a synthesis processing routine, Referring to Figure 7, the synthesis routine includes the following subroutines: initialization 88, data input 89, CRC check 90, LPC coefficient generation 91, PPADPCM
S decoding 92, spectral regeneration 93, LPC synthesis filter 94, de-emphasis 95, and speech output 97.
The initialization subroutine 88 is included in the main program for the receiver processor. The initialization sub-routine 88 initializes all registers and data locations within the processor prior to the execution of each subroutine.
The data input subroutine 89 also is included in the main program for the receiver processor. This subroutine inputs the data transmission frame 36 received from the transmitter by inputting the frame from a frame buffer in external data memory.
lo The CRC check subroutine 90 uses the received transmission data frame to generate an n-bit CRC which it compares to the n-bit CRC in the received transmission data frame to check for transmission errors. If any errors are detected, a subset of .,"~
the LPC and PPADPCM parameters for the current frame are disk carded and a subset of the previous frame's parameters substituted. The input to this subroutine is an-bit CRC word from the data transmission frame. The output from this subroutine is a flag indicating which set of parameters to use during the rest of the subroutine.
.
i to $
The LPC coefficient generation subroutine 91 reads in the transmitted quantized PI parameters, calls a subroutine IQRC to decode the LPC reflection coefficients, and performs a step-up algorithm to transform the LPC reflection coefficients to the LPC coefficients. The input to this subroutine is the transmitted quantized LPC reflection coefficients 43 and the output is the LPC coefficients 51 (Figure 2).
Prior to LPC synthesis filtering 52, the LPC coefficients must be generated from the transmitted quantized LPC reflection coefficients. These quantized LPC reflection coefficients must be unpacked and decoded using the quantize look-up tables described in T. E. Remain, "The Government Standard Linear Predictive Coding Algorithm: LPC-10", Speech Technology, April 1982. The LPC coefficients are then generated from the ' 15 decoded LPC reflection coefficients using the step-up algorithm, a recursive algorithm which is a subset of the Durbin algorithm described in J. Meekly, "Linear Prediction: A Tutorial Review,"
Pro IEEE, Vow 63, pup 561-80, 1975.
,- ,. .
The recursion is as follows:
Initialization: all = Al (En. 26) Recursion imp all = kit ' (En. 27) aji = aji 1 + kiwi jig 1 lCj<i-l (En. 28) 0391~S
where kit is the ilk reflection coefficient and air is the ilk LPC coefficient (jth iteration). The order of the transmitter LPC analysis, P, is ten.
The PP~DPCM decoding subroutine 92 reads in the bit-packed quantized residual signal 39 and quantize parameters I 41, 42 received from the transmitter and generates a decoded base band (down sampled) residual signal 47 (Figure 2). This subroutine 92 must perform the inverse operation of the transmitter's PPADPCM
coding. It therefore divides into three parts: unpacking, quantize look-up, and pitch excitation.
The PPADPCM decoding subroutine first transfers the PPADPCM quantize coefficients to internal RAM and scales them using the quantize gain parameter. The inputs to this operation are the coefficient buffer stored in external data memory and the quantize gain. The output of this operation is the scaled look-up table located in internal RAM.
This subroutine 92 next reads in packed data bytes from a data buffer in external data memory, unpacks the byte, and decodes the data samples using the quantize look-up table.
The input to this operation is the bit-packed data word and the quantize coefficient table. The output from this operation is the set of decoded data samples. The received data bytes are unpacked into individual data samples by masking off each individual data sample, which may then be decoded using the I . j ~L2~3~Ç~
quantize look-up table that is identical to the one used at the transmitter to quantize the data samples.
The PPADPCM decoding subroutine 92 then implements a variable delay first-order difference equation to "pitch excite"
the input data and recover the down sampled residual signal 47.
The input to this operation is the transmitted data sample, the pitch delay parameter and the pitch predictor gain parameter.
The output from this operation is the down sampled residual signal 47. The difference equation for this operation is:
Sun = On + B Snot (En. 29) where Sun is the down sampled residual signal sample, on is the transmitted data sample, B is the pitch predictor gain, and T is the current frame's pitch delay (period).
The spectral regeneration subroutine 93 is included in the main program or the receiver processor. The spectral regeneration subroutine 93 generates a full-band residual .. ..
signal 49 from down sampled residual signal 47. The effect is to convert a 4 kHz down sampled signal 47 to an 8 kHz full-band signal 49.
I -The LPC synthesis filter subroutine 94 implements an auto-regressive LPC synthesis filter governed by the ~PC~coefficients.
The inputs to this subroutine are the LPC coefficients 51 and the regenerated full-band residual signal 49. The output from this subroutine is the regenerated reemphasized speech data signal sample 53. This subroutine 94 generates the speech data signal samples 53 by filtering the residual signal 49 with a tenth-order all-pole filter. The filter is governed by the generated LPC coefficients 51. The transfer function of the filter is:
Ho = [1 - ~kP=l a Z ] (En. 30) where a is the kth LPC coefficient. If on represents the residual signal sample 49 at time n and Yin represents the corresponding regenerated reemphasized speech data signal sample 53, the filter operation can be represented by the following difference equation:
Yin n at Yn-l + a Yn_2 + -- + aye Yn_lo (En. 31) _, .
The simplest way to implement this equation is to place the coefficients a in a fixed register and to implement the delay buffer using a shift register. The-TMS32010 micro-code is optimized to perform this operation using the LTD!MPY
I
commands: the processor has a pipeline Multiply-Accumulate instruction that executes in 400 no.
The de-emphasis subroutine 95 implements a first-order digital de-emphasis filter. The inputs to this subroutine are the current regenerated sample 53, the previous regenerated sample, and the reemphasis constant. The output from this subroutine is the regenerated speech data signal sample 55.
First-order digital de-emphasis is applied to complement the reemphasis function in the transmitter processor.
De-emphasis uses a single-delay low-pass filter. The de-emphasis constant is also set to 0.9375. The difference equation for the filter is:
n On + A Yn_l (En. 32) The speech output subroutine 97 also is included in the main program for the receiver processor. This subroutine out-puts the regenerated speech data signal samples to a data buffer in external data memory from which the samples are provided.
A decision 98 that the frame has been completed concludes the synthesis routine of Figure 7.
The calling hierarchy for the ssTnthesis routine of Figure 7 is controlled by the main program for the receiver processor and ~?40396 calls the following subroutines in the following order: CRC
check 90, PI coefficient generation 91, PPADPCM decoding 92, inverse filter 94 and de-emphasis 95.
Transmitter and receiver systems that are commonly located may be included in a single digital processor.
.. , -32-j
The data format subroutine 84 formats a data frame 34 (Figure 1) for transmission. The input to the subroutine 84 is a predetermined number of quantized residual signal samples 25, the pitch delay parameter 26, the pitch predictor gain 27, the quantize gain 28, the quantized LPC coefficients 31 Figure 1) and the CRC. The output from the subroutine 84 is a transmission data frame 34 which is place din the output buffer.
A decision 85 that the frame is complete concludes the quantization routine of Figure 5.
The calling hierarchy of the subroutines in the quantize-lion routine of Figure 5 is under the control of the main program. The following subroutines are integrated together in a subroutine designated PPQNT: pitch predictor gain 80, quantize gain 81 and PPADPCM quantization 83. The calling hierarchy is as follows: pitch 78, PPQNT, CRC 82 and data format 84. The subroutine 68 is called by the LPC analysis subroutine 66 in the LO analysis routine of Figure 3.
i -25--2~039~S
he receiver digital signal processor utilizes a synthesis processing routine, Referring to Figure 7, the synthesis routine includes the following subroutines: initialization 88, data input 89, CRC check 90, LPC coefficient generation 91, PPADPCM
S decoding 92, spectral regeneration 93, LPC synthesis filter 94, de-emphasis 95, and speech output 97.
The initialization subroutine 88 is included in the main program for the receiver processor. The initialization sub-routine 88 initializes all registers and data locations within the processor prior to the execution of each subroutine.
The data input subroutine 89 also is included in the main program for the receiver processor. This subroutine inputs the data transmission frame 36 received from the transmitter by inputting the frame from a frame buffer in external data memory.
lo The CRC check subroutine 90 uses the received transmission data frame to generate an n-bit CRC which it compares to the n-bit CRC in the received transmission data frame to check for transmission errors. If any errors are detected, a subset of .,"~
the LPC and PPADPCM parameters for the current frame are disk carded and a subset of the previous frame's parameters substituted. The input to this subroutine is an-bit CRC word from the data transmission frame. The output from this subroutine is a flag indicating which set of parameters to use during the rest of the subroutine.
.
i to $
The LPC coefficient generation subroutine 91 reads in the transmitted quantized PI parameters, calls a subroutine IQRC to decode the LPC reflection coefficients, and performs a step-up algorithm to transform the LPC reflection coefficients to the LPC coefficients. The input to this subroutine is the transmitted quantized LPC reflection coefficients 43 and the output is the LPC coefficients 51 (Figure 2).
Prior to LPC synthesis filtering 52, the LPC coefficients must be generated from the transmitted quantized LPC reflection coefficients. These quantized LPC reflection coefficients must be unpacked and decoded using the quantize look-up tables described in T. E. Remain, "The Government Standard Linear Predictive Coding Algorithm: LPC-10", Speech Technology, April 1982. The LPC coefficients are then generated from the ' 15 decoded LPC reflection coefficients using the step-up algorithm, a recursive algorithm which is a subset of the Durbin algorithm described in J. Meekly, "Linear Prediction: A Tutorial Review,"
Pro IEEE, Vow 63, pup 561-80, 1975.
,- ,. .
The recursion is as follows:
Initialization: all = Al (En. 26) Recursion imp all = kit ' (En. 27) aji = aji 1 + kiwi jig 1 lCj<i-l (En. 28) 0391~S
where kit is the ilk reflection coefficient and air is the ilk LPC coefficient (jth iteration). The order of the transmitter LPC analysis, P, is ten.
The PP~DPCM decoding subroutine 92 reads in the bit-packed quantized residual signal 39 and quantize parameters I 41, 42 received from the transmitter and generates a decoded base band (down sampled) residual signal 47 (Figure 2). This subroutine 92 must perform the inverse operation of the transmitter's PPADPCM
coding. It therefore divides into three parts: unpacking, quantize look-up, and pitch excitation.
The PPADPCM decoding subroutine first transfers the PPADPCM quantize coefficients to internal RAM and scales them using the quantize gain parameter. The inputs to this operation are the coefficient buffer stored in external data memory and the quantize gain. The output of this operation is the scaled look-up table located in internal RAM.
This subroutine 92 next reads in packed data bytes from a data buffer in external data memory, unpacks the byte, and decodes the data samples using the quantize look-up table.
The input to this operation is the bit-packed data word and the quantize coefficient table. The output from this operation is the set of decoded data samples. The received data bytes are unpacked into individual data samples by masking off each individual data sample, which may then be decoded using the I . j ~L2~3~Ç~
quantize look-up table that is identical to the one used at the transmitter to quantize the data samples.
The PPADPCM decoding subroutine 92 then implements a variable delay first-order difference equation to "pitch excite"
the input data and recover the down sampled residual signal 47.
The input to this operation is the transmitted data sample, the pitch delay parameter and the pitch predictor gain parameter.
The output from this operation is the down sampled residual signal 47. The difference equation for this operation is:
Sun = On + B Snot (En. 29) where Sun is the down sampled residual signal sample, on is the transmitted data sample, B is the pitch predictor gain, and T is the current frame's pitch delay (period).
The spectral regeneration subroutine 93 is included in the main program or the receiver processor. The spectral regeneration subroutine 93 generates a full-band residual .. ..
signal 49 from down sampled residual signal 47. The effect is to convert a 4 kHz down sampled signal 47 to an 8 kHz full-band signal 49.
I -The LPC synthesis filter subroutine 94 implements an auto-regressive LPC synthesis filter governed by the ~PC~coefficients.
The inputs to this subroutine are the LPC coefficients 51 and the regenerated full-band residual signal 49. The output from this subroutine is the regenerated reemphasized speech data signal sample 53. This subroutine 94 generates the speech data signal samples 53 by filtering the residual signal 49 with a tenth-order all-pole filter. The filter is governed by the generated LPC coefficients 51. The transfer function of the filter is:
Ho = [1 - ~kP=l a Z ] (En. 30) where a is the kth LPC coefficient. If on represents the residual signal sample 49 at time n and Yin represents the corresponding regenerated reemphasized speech data signal sample 53, the filter operation can be represented by the following difference equation:
Yin n at Yn-l + a Yn_2 + -- + aye Yn_lo (En. 31) _, .
The simplest way to implement this equation is to place the coefficients a in a fixed register and to implement the delay buffer using a shift register. The-TMS32010 micro-code is optimized to perform this operation using the LTD!MPY
I
commands: the processor has a pipeline Multiply-Accumulate instruction that executes in 400 no.
The de-emphasis subroutine 95 implements a first-order digital de-emphasis filter. The inputs to this subroutine are the current regenerated sample 53, the previous regenerated sample, and the reemphasis constant. The output from this subroutine is the regenerated speech data signal sample 55.
First-order digital de-emphasis is applied to complement the reemphasis function in the transmitter processor.
De-emphasis uses a single-delay low-pass filter. The de-emphasis constant is also set to 0.9375. The difference equation for the filter is:
n On + A Yn_l (En. 32) The speech output subroutine 97 also is included in the main program for the receiver processor. This subroutine out-puts the regenerated speech data signal samples to a data buffer in external data memory from which the samples are provided.
A decision 98 that the frame has been completed concludes the synthesis routine of Figure 7.
The calling hierarchy for the ssTnthesis routine of Figure 7 is controlled by the main program for the receiver processor and ~?40396 calls the following subroutines in the following order: CRC
check 90, PI coefficient generation 91, PPADPCM decoding 92, inverse filter 94 and de-emphasis 95.
Transmitter and receiver systems that are commonly located may be included in a single digital processor.
.. , -32-j
Claims (18)
1. A residual-excited linear prediction (RELP) vocoder comprising a digital signal processor adapted for processing digital speech data signal samples to provide a formatted transmission signal including (a) a quantized residual signal generated by inverse filtering of the samples in accordance with linear pre-dictive coding (LPC) coefficients generated from the samples, (b) quantized LPC coefficients and (c) pitch and gain parameters generated during quantization of the residual signal from the inverse filtered samples, all of which are generated by the processor from the digital speech data signal samples.
2. A RELP vocoder according to Claim 1, wherein the digital signal processor is adapted for performing a routine for generating the LPC coefficients;
a routine for generating the residual signal; and a routine for quantizing the residual signal and the LPC coefficients.
a routine for generating the residual signal; and a routine for quantizing the residual signal and the LPC coefficients.
3. A RELP vocoder according to Claim 2, wherein the routine for generating the LPC coefficients comprises a subroutine for pre-emphasizing the samples in order to emphasize the high frequencies of speech;
a subroutine for defining an auto-correlation function (ACF) from the pre-emphasized samples in order to generate ACF coefficients; and a subroutine for generating the LPC coefficients from the generated ACF coefficients.
a subroutine for defining an auto-correlation function (ACF) from the pre-emphasized samples in order to generate ACF coefficients; and a subroutine for generating the LPC coefficients from the generated ACF coefficients.
4. A RELP vocoder according to Claim 3, wherein the routine for generating the residual signal comprises a subroutine for inverse filtering the pre-emphasized samples in accordance with the generated LPC coefficients;
a subroutine for bandlimiting the residual signal by low-pass filtering; and a subroutine for downsampling the bandlimited residual signal to reduce the number of residual signal samples that are quantized and formatted for transmission.
a subroutine for bandlimiting the residual signal by low-pass filtering; and a subroutine for downsampling the bandlimited residual signal to reduce the number of residual signal samples that are quantized and formatted for transmission.
5. A RELP vocoder according to Claim 4, wherein the routine for quantizing the residual signal and LPC coefficients comprises a subroutine for quantizing the LPC coefficients;
a subroutine for estimating the pitch period of the down-sampled residual signal by ACF analysis of the current downsampled residual signal frame in accordance with the ACF
coefficients generated for the previous frame to thereby provide a pitch delay parameter for the current frame;
a subroutine for providing a pitch predictor gain parameter for each residual signal frame in accordance with the estimated pitch delay-parameter for each corresponding frame;
a subroutine for providing a quantizer gain parameter for each residual signal frame in accordance with the pitch delay and pitch predictor gain parameters for each corresponding frame; and a subroutine for quantizing each residual signal frame by pitch predictive adaptive differential pulse code modulation (PPADPCM) in accordance with the pitch delay, pitch predictor gain and quantizer gain parameters for each corresponding frame.
a subroutine for estimating the pitch period of the down-sampled residual signal by ACF analysis of the current downsampled residual signal frame in accordance with the ACF
coefficients generated for the previous frame to thereby provide a pitch delay parameter for the current frame;
a subroutine for providing a pitch predictor gain parameter for each residual signal frame in accordance with the estimated pitch delay-parameter for each corresponding frame;
a subroutine for providing a quantizer gain parameter for each residual signal frame in accordance with the pitch delay and pitch predictor gain parameters for each corresponding frame; and a subroutine for quantizing each residual signal frame by pitch predictive adaptive differential pulse code modulation (PPADPCM) in accordance with the pitch delay, pitch predictor gain and quantizer gain parameters for each corresponding frame.
6. A RELP vocoder according to Claim 5, further comprising a second digital signal processor adapted for processing said formatted transmission signal to synthesize reconstructed digital speech data signal samples, wherein the second processor is adapted for performing a synthesis routine comprising a subroutine for regenerating the LPC coefficients from the quantized LPC coefficients included in the transmission signal;
a subroutine for decoding the quantized residual signal included in the transmission signal in accordance with the pitch delay, pitch predictive gain and quantizer gain parameters included in the transmission signal to thereby provide a decoded downsampled residual signal;
a subroutine for spectrally regenerating a full-band residual signal from the decoded downsampled residual signal;
a subroutine for regenerating pre-emphasized digital speech data signal samples by auto-regressively filtering the regener-ated full-band residual signal in accordance with the regenerated LPC coefficients; and a subroutine for de-emphasizing the regenerated pre-emphasized samples in order to de-emphasize the high frequencies of speech, to thereby provide the reconstructed digital speech data signal samples.
a subroutine for decoding the quantized residual signal included in the transmission signal in accordance with the pitch delay, pitch predictive gain and quantizer gain parameters included in the transmission signal to thereby provide a decoded downsampled residual signal;
a subroutine for spectrally regenerating a full-band residual signal from the decoded downsampled residual signal;
a subroutine for regenerating pre-emphasized digital speech data signal samples by auto-regressively filtering the regener-ated full-band residual signal in accordance with the regenerated LPC coefficients; and a subroutine for de-emphasizing the regenerated pre-emphasized samples in order to de-emphasize the high frequencies of speech, to thereby provide the reconstructed digital speech data signal samples.
7. A RELP vocoder according to Claim 6, wherein the decoding subroutine comprises a subroutine for scaling quantizer coefficients for each quantized residual signal frame in accordance with the quantizer gain parameter included in the transmission signal;
a subroutine for providing data samples from the quantized residual signal included in the transmission signal in accordance with the scaled quantizer coefficients; and a subroutine for providing the decoded downsampled residual signal from the data samples by pitch excitation in accordance with the pitch delay and pitch predictor gain parameters.
a subroutine for providing data samples from the quantized residual signal included in the transmission signal in accordance with the scaled quantizer coefficients; and a subroutine for providing the decoded downsampled residual signal from the data samples by pitch excitation in accordance with the pitch delay and pitch predictor gain parameters.
8. A RELP vocoder according to Claim 7, wherein the pitch excitation subroutine comprises processing the data samples in accordance with a variable delay first order difference equation:
Sn = Xn + B Sn-T
where Sn is the provided decoded downsampled residual signal sample, Xn is the provided data sample, B is the pitch pre-dictor gain and T is the pitch delay for the current residual signal frame.
Sn = Xn + B Sn-T
where Sn is the provided decoded downsampled residual signal sample, Xn is the provided data sample, B is the pitch pre-dictor gain and T is the pitch delay for the current residual signal frame.
9. A RELP vocoder according to Claim 5, further comprising a second digital signal processor adapted for processing said formatted transmission signal to synthesize signal samples.
10. A RELP vocoder according to Claim 2, further comprising a second digital signal processor adapted for processing said formatted transmission signal to synthesize reconstructed digital speech data signal samples.
11. A RELP vocoder according to Claim 1, further comprising a second digital signal processor adapted for processing said formatted transmission signal to synthesize reconstructed digital speech data signal samples.
12. A RELP vocoder according to Claim 11,.
wherein the second processor is adapted for performing a synthesis routine comprising a subroutine for regenerating the LPC coefficients from the quantized LPC coefficients included in the transmission signal;
a subroutine for decoding the quantized residual signal included in the transmission signal in accordance with the pitch delay, pitch predictive gain and quantizer gain para-meters included in the transmission signal to thereby provide a decoded downsampled residual signal;
a subroutine for spectrally regenerating a full-band residual signal from the decoded downsampled residual signal;
a subroutine for regenerating pre-emphasized digital speech data signal samples by auto-regressively filtering the regenerated full-band residual signal in accordance with the regenerated LPC coefficients; and a subroutine for de-emphasizing the regenerated pre-emphasized samples in order to de-emphasize the high frequencies of speech, to thereby provide the reconstructed digital speech data signal samples.
wherein the second processor is adapted for performing a synthesis routine comprising a subroutine for regenerating the LPC coefficients from the quantized LPC coefficients included in the transmission signal;
a subroutine for decoding the quantized residual signal included in the transmission signal in accordance with the pitch delay, pitch predictive gain and quantizer gain para-meters included in the transmission signal to thereby provide a decoded downsampled residual signal;
a subroutine for spectrally regenerating a full-band residual signal from the decoded downsampled residual signal;
a subroutine for regenerating pre-emphasized digital speech data signal samples by auto-regressively filtering the regenerated full-band residual signal in accordance with the regenerated LPC coefficients; and a subroutine for de-emphasizing the regenerated pre-emphasized samples in order to de-emphasize the high frequencies of speech, to thereby provide the reconstructed digital speech data signal samples.
13. A RELP vocoder according to Claim 12, wherein the decoding subroutine comprises a subroutine for scaling quantizer coefficients for each quantized frame in accordance with the quantizer gain parameter included in the transmission signal;
a subroutine for providing data samples from the quantized residual signal included in the transmission signal in accordance with the scaled quantizer coefficients; and a subroutine for providing the decoded downsampled residual signal from the data samples by pitch excitation in accordance with the pitch delay and pitch predictor gain parameters.
a subroutine for providing data samples from the quantized residual signal included in the transmission signal in accordance with the scaled quantizer coefficients; and a subroutine for providing the decoded downsampled residual signal from the data samples by pitch excitation in accordance with the pitch delay and pitch predictor gain parameters.
14. A RELP vocoder according to Claim 13, wherein the pitch excitation subroutine comprises processing the data samples in accordance with a variable delay first order difference equation:
Sn = Xn + B Sn-T
where Sn is the provided decoded downsampled residual signal sample, Xn is the provided data sample, B is the pitch predictor gain and T is the pitch delay for the current residual signal frame.
Sn = Xn + B Sn-T
where Sn is the provided decoded downsampled residual signal sample, Xn is the provided data sample, B is the pitch predictor gain and T is the pitch delay for the current residual signal frame.
15. A residual-excited linear prediction (RELP) vocoder comprising a digital signal processor adapted for processing a formatted transmission signal including (a) a quantized residual signal generated by inverse filtering of digital speech data signal samples in accordance with linear predic-tive coding (LPC) coefficients generated from the samples, (b) quantized LPC coefficients and (c) pitch and gain para-meters generated during quantization of the residual signal from the inverse filtered samples, to synthesize reconstructed digital speech data signal samples.
16. A RELP vocoder according to Claim 15, wherein the processor is adapted for performing a synthe-sis routine comprising a subroutine for regenerating the LPC coefficients from the quantized LPC coefficients included in the transmission signal;
a subroutine for decoding the quantized residual signal included in the transmission signal in accordance with the pitch delay, pitch predictive gain and quantizer gain para-meters included in the transmission signal to thereby provide a decoded downsampled residual signal;
a subroutine for spectrally regenerating a full-band residual signal from the decoded downsampled residual signal;
a subroutine for regenerating pre-emphasized digital speech-data signal samples by auto-regressively filtering the regenerated full-band residual signal in accordance with the regenerated LPC coefficients; and a subroutine for de-emphasizing the regenerated pre-emphasized samples in order to de-emphasize the high frequencies of speech, to thereby provide the reconstructed digital speech data signal samples.
a subroutine for decoding the quantized residual signal included in the transmission signal in accordance with the pitch delay, pitch predictive gain and quantizer gain para-meters included in the transmission signal to thereby provide a decoded downsampled residual signal;
a subroutine for spectrally regenerating a full-band residual signal from the decoded downsampled residual signal;
a subroutine for regenerating pre-emphasized digital speech-data signal samples by auto-regressively filtering the regenerated full-band residual signal in accordance with the regenerated LPC coefficients; and a subroutine for de-emphasizing the regenerated pre-emphasized samples in order to de-emphasize the high frequencies of speech, to thereby provide the reconstructed digital speech data signal samples.
17. A RELP vocoder according to Claim 16, wherein the decoding subroutine comprises a subroutine for scaling quantizer coefficients for each quantized frame in accordance with the quantizer gain parameter included in the transmission signal;
a subroutine for providing data samples from the quantized residual signal included in the transmission signal in accord-ance with the scaled quantizer coefficients; and a subroutine for providing the decoded downsampled residual signal from the data samples by pitch excitation in accordance with the pitch delay and pitch predictor gain parameters.
a subroutine for providing data samples from the quantized residual signal included in the transmission signal in accord-ance with the scaled quantizer coefficients; and a subroutine for providing the decoded downsampled residual signal from the data samples by pitch excitation in accordance with the pitch delay and pitch predictor gain parameters.
18. A RELP vocoder according to Claim 17 wherein the pitch excitation subroutine comprises processing the data samples in accordance with a variable delay first order difference equation:
Sn = Xn + B Sn-T
where Sn is the provided decoded downsampled residual signal sample, Xn is the provided data sample, B is the pitch predictor gain and T is the pitch delay for the current residual signal frame.
Sn = Xn + B Sn-T
where Sn is the provided decoded downsampled residual signal sample, Xn is the provided data sample, B is the pitch predictor gain and T is the pitch delay for the current residual signal frame.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US667,446 | 1984-11-01 | ||
| US66744684A | 1984-11-02 | 1984-11-02 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CA1240396A true CA1240396A (en) | 1988-08-09 |
Family
ID=24678262
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA000494448A Expired CA1240396A (en) | 1984-11-02 | 1985-11-01 | Relp vocoder implemented in digital signal processors |
Country Status (7)
| Country | Link |
|---|---|
| EP (1) | EP0203940A4 (en) |
| JP (1) | JPS63500896A (en) |
| AU (1) | AU577641B2 (en) |
| CA (1) | CA1240396A (en) |
| DK (1) | DK311386D0 (en) |
| NO (1) | NO862602L (en) |
| WO (1) | WO1986002726A1 (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4675863A (en) * | 1985-03-20 | 1987-06-23 | International Mobile Machines Corp. | Subscriber RF telephone system for providing multiple speech and/or data signals simultaneously over either a single or a plurality of RF channels |
| JP2626223B2 (en) * | 1990-09-26 | 1997-07-02 | 日本電気株式会社 | Audio coding device |
| US5235670A (en) * | 1990-10-03 | 1993-08-10 | Interdigital Patents Corporation | Multiple impulse excitation speech encoder and decoder |
| US6006174A (en) * | 1990-10-03 | 1999-12-21 | Interdigital Technology Coporation | Multiple impulse excitation speech encoder and decoder |
| ES2143396B1 (en) * | 1998-02-04 | 2000-12-16 | Univ Malaga | LOW RATE MONOLITHIC CODEC-ENCRYPTOR MONOLITHIC CIRCUIT FOR VOICE SIGNALS. |
| US7907977B2 (en) | 2007-10-02 | 2011-03-15 | Agere Systems Inc. | Echo canceller with correlation using pre-whitened data values received by downlink codec |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US3431362A (en) * | 1966-04-22 | 1969-03-04 | Bell Telephone Labor Inc | Voice-excited,bandwidth reduction system employing pitch frequency pulses generated by unencoded baseband signal |
| US3750024A (en) * | 1971-06-16 | 1973-07-31 | Itt Corp Nutley | Narrow band digital speech communication system |
| GB2102254B (en) * | 1981-05-11 | 1985-08-07 | Kokusai Denshin Denwa Co Ltd | A speech analysis-synthesis system |
-
1985
- 1985-11-01 CA CA000494448A patent/CA1240396A/en not_active Expired
- 1985-11-01 WO PCT/US1985/002168 patent/WO1986002726A1/en not_active Application Discontinuation
- 1985-11-01 JP JP50505785A patent/JPS63500896A/en active Pending
- 1985-11-01 EP EP19850905709 patent/EP0203940A4/en not_active Withdrawn
- 1985-11-01 AU AU50198/85A patent/AU577641B2/en not_active Ceased
-
1986
- 1986-06-27 NO NO86862602A patent/NO862602L/en unknown
- 1986-06-30 DK DK311386A patent/DK311386D0/en not_active Application Discontinuation
Also Published As
| Publication number | Publication date |
|---|---|
| DK311386A (en) | 1986-06-30 |
| NO862602D0 (en) | 1986-06-27 |
| EP0203940A1 (en) | 1986-12-10 |
| JPS63500896A (en) | 1988-03-31 |
| AU5019885A (en) | 1986-05-15 |
| DK311386D0 (en) | 1986-06-30 |
| AU577641B2 (en) | 1988-09-29 |
| WO1986002726A1 (en) | 1986-05-09 |
| EP0203940A4 (en) | 1987-04-07 |
| NO862602L (en) | 1986-09-01 |
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