A SYSTEM AND METHOD FOR PROVIDING SPLIT VECTOR QUANTIZATION DATA CODING

Abstract: A method and system for providing split vector quantization for use in determining constrained ordered set values, such as line spectrum pair parameters to determine spectral parameters in a data compression system, utilizes multiple codebooks (22a-22c) containing delta coded constrained ordered set values that are normalized to an upper and lower bound. An LSP reconstructor (34) reconstructs received spectral parameters to decode data, such as speech, based on the normalized delta quantization data of line spectrum pair parameters obtained from the split vector reconstruction codebooks (22a-22c). The LSP reconstructor (34) dynamically generates line spectrum pair parameters based on the normalized delta quantization data. In another embodiment, instead of storing the absolute value of the line spectrum pair parameters in segmented codebooks, the combination of at least two absolute value vectors and at least one normalized delta quantization vector is used for spectral quantization.

Description: A SYSTEM AND METHOD FOR PROVIDING SPLIT VECTOR QUANTIZATION DATA CODING Field of the Invention The invention relates generally to data coding systems and methods using scalar or vector quantization of a constrained ordered set and more particularly to methods for providing split vector quantization for use in determining a set of ordered parameters, such as line spectrum pair parameters, to determine spectral parameters or other parameters, such as used in speech or image coding systems. Background of the Invention Quantization of a constrained ordered set is commonly used in modern speech and audio compression systems for coding a set of ordered parameters, such as line spectrum pair parameters. Line spectrum pair parameters are used to represent coarse spectral information as generated by linear predictive coding analysis. With proper stability constraints, the linear predictive coding analysis to line spectrum pair parameter transformation generates a well behaved rank- ordered set that typically spans in the range of 0.0 to 0.5. The correlation properties of line spectrum pairs make them attractive for various scalar and vector quantization techniques, which are used in several recent speech compression standards. For example, IS- 127 standard entitled "Enhanced Variable Rate Codec, Speech Service Option 3 For Wideband Spread Spectrum Digital Systems" from the Telecommunications Industry Association (TIA) in January 1997, utilizes a technique called "weighted split vector quantization" which is used to quantize the LSP parameters. A ten dimensional line spectrum pair vector is split into multi-subvectors or segments, that are easier to manage from both a codebook memory standpoint and code search complexity perspective. For example, the IS- 127 rate 1/2 codec uses a 22 bit, 3 segment weighted split vector quantization scheme. The ten dimensional LSP vector is split into 3 segments resulting in 3+344 elements. This scheme uses a 7, 7 and 8 bit designation for each of the respective vector segment codebooks. A separate codebook is used for each of the segments. Hence, this scheme requires 3 x 2 7 + 3 x 2 7 + 4 x 2 8 = 1792 words of code book memory (ROM), each of which requires a weighted squared error calculation. This method reduces the amount of codebook memory by splitting the vector into codebook segments, as compared to a brute force 22 bit 10 element code vector design which would require 42 mega words of storage and weighted squared error calculations, however, the weighted split vector quantization scheme does not afford the same performance advantage as the brute force method. One of the reasons is that overlap occurs between vector segments resulting in some combinations of codebook data that do not preserve the LSP ordering property and are therefore not valid for given linear predictive coding coefficients. The effect is that code vectors in the codebook are effectively pruned thereby reducing vector quantization coding gain. For example, FIG. 1 shows a sample distribution plot of LSP parameters. Since LSP's are given to be a strictly ascending, rank ordered set, the first element in the second code vector must be greater than the last element in the first code vector. If an optimum code vector for the first segment of a 3-3-4 WSVQ was found to be { , ffiζ , 6%}:{0.10,0.15,0.20}, then the first element of the second code vector, ω , must be greater than 0.20 (ύ; 4 >0.20). But due to statistics in the LSP training database and the independent training of each segment's codebook, there may be a large number of vectors (entries) in the codebook that do not meet this criteria. The effect is that code vectors in the codebook that do not meet this criteria are invalid and rejected as possible candidates by the encoder and the codebook is effectively pruned. A known method that attempts to address this problem involves a weighted split vector quantization scheme in which the codebook values are based on the difference between a current and previously quantized line spectrum pair. In effect, the system constructs a current quantized line spectrum pair value ώ i by adding a specified codebook value Δά, to the previously constructed line spectrum pair : ώ, = Aώ, + ώ,_, This method, known as delta coding, generally eliminates the overlap of codebook elements (as long as the delta omega is strictly positive), however a similar pruning effect can occur when the respective delta values are relatively large. For example, if the last quantized LSP value u},_ x is 0.3, and the values for the current LSP delta codebook Δώ, span (0.05, 0.30), then any codebook value above 0.2 would be invalid since it would result in an LSP value greater than 0.5. Moreover, codebook values that approach 0.2 are effectively "pruned" since the probability of the corresponding LSP's occurring in that space are greatly diminished. Again this pruning may cause degradation of overall quantizer performance in a speech coding system and it can occur at any stage in the process. Other methods also exist that attempt to remove redundancy from line spectrum pair parameters by using interframe prediction, but generally these methods suffer from increased sensitivity to channel errors that are inherent in existing wireless communication systems. Another system of LPC coefficient quantization in speech coding, known as lattice quantization, is disclosed in IEEE Conference Proceedings from the 1995 International Conference on Acoustics, Speech and Signal Processing in an article entitled "Fast and Low-Complexity LSF Quantization using Algebraic Vector Quantizer" authored by Minjie Xie et al. (1995). In this approach, 10 LSP's per frame are quantized using a four segment split vector quantizer with a maximum segment length of three. The components of only one segment represent absolute LSP values and the remaining segments represent LSP differences normalized in such a manner that for each segment the sum of the components add to a value which is not greater than one. A single stored codebook is used to provide codebook entries for the one segment representing absolute LSP values, while codebook entries for the remaining segments are determined in real-time using the codevector indices and an implicit lattice structure. The use of lattice structures typically requires less memory and search complexity than systems using only stored codebooks. Also, the use of normalized LSP differences in such a system can eliminate the occurrence of pruning. However, it has been found that although normalization can help insure that all codebook entries are valid, normalization can realign data so that a change in properties from the original parameters can occur. For example the use of normalized delta quantization values affects the quantization error by changing the distribution and other statistics of the normalized delta quantization values so that they are different from those of the absolute values from which they were derived. With codebooks containing normalized delta entries, the normalized delta LSP values are mapped to a fixed range. Since the values of the lower and upper normalization bounds for a given LSP to be normalized are a function of previously quantized frame data, the mapping from the absolute LSP value to the normalized range is non-linear. This nonlinear mapping (warping) has the affect of changing the distribution and other statistics of the normalized delta value so that they are different from the statistics of the absolute values from which they were derived. The differences can affect the design of the codebook entries and the resulting quantization error. This warping affect can result in either a gain or loss in performance depending upon the statistics of the data being considered. In cases where warping results in a loss, the gain due to the elimination of pruning should be greater than the loss due to warping in order for the use of normalized delta values to provide an overall reduction in quantization error. If loss due to warping negates the gain due to the elimination of pruning, then the use of absolute values may be preferred. Hence determining LSP parameters from normalized vectors can result in lower coding performance. In addition, the normalization applied in such systems is typically restricted such that the sum of the normalized components in each segment is not greater than one. This restriction is required to enable the use of triangular and simplex lattice structures. However, limiting coding systems to this lattice structure can unnecessarily restrict coding performance. Also, the use of codebook entries based on lattice structures of Xie et al, can be suboptimal for quantization of LSP data. By restricting the codebook entries to be points on a regular lattice structure, the codebook entries do not optimally fit the statistical distribution of the data to be quantized, and thus the average quantization error is increased. In contrast, the use of stored codebooks allows the codebook entries to be optimally fit to a set of training data having a statistical distribution which matches that of the data to be quantized, which serves to reduce the average quantization error. Thus, for a given number of codebook entries, the performance of a trained, stored codebook will generally be better than the performance of a codebook based on a highly complex lattice structure system that has severe memory requirements. Consequently there exists a need for a method and system for providing improved split vector quantization in data compression or coding systems that helps facilitate reduction of processing time, that helps reduce pruning and that can improve coding performance in quantization of constrained ordered sets. Brief Description of the Drawings FIG. 1 is a graph illustrating line spectrum pair parameter distribution for a number of analyzed speech frames. FIG. 2 is a block diagram generally depicting a speech decoder incorporating one embodiment of the invention in a wireless communication system. FIG. 3 is an illustration generally depicting one example of a codebook structure incorporating normalized quantization in accordance with one embodiment of the invention. FIG. 4 is a flow diagram generally depicting a method for providing split vector quantization to determine spectral parameters, in accordance with one embodiment of the invention; FIG. 5 is a flow diagram generally depicting a method for determining normalized delta quantization data for split vector line spectrum pair codebooks in accordance with one embodiment of the invention; and FIG. 6 is a block diagram generally depicting a speech decoder incorporating another embodiment of the invention in a wireless communication system wherein codebook memory contains a combination of both absolute LSP values and normalized delta quantization data. Detailed Description of the Preferred Embodiment A method and system for providing split vector quantization for use in determining constrained ordered set values, such as line spectrum pair parameters to determine spectral parameters in a data compression system, utilizes multiple codebooks containing delta coded constrained ordered set values, such as line spectrum pair values, that are normalized to an upper and lower bound such as an available dynamic range of a constraint space. The values that are quantized in the segmented codebooks represent a percentage of a distance between a last quantized constrained ordered set value, such as a line spectrum pair value, and another higher (or highest possible) constrained ordered set value, such as a line spectrum pair value. Therefore all values stored in codebook memory result in valid LSP values which eliminates codebook pruning. As used herein, split vector quantization includes scalar quantization where the vector length is one. An LSP reconstructor accesses the normalized delta quantization data in a segmented codebook which are used to determine constrained ordered set values such as line spectrum pair parameters to decode speech or other data. The LSP reconstructor reconstructs received spectral parameters based on reconstruction code data, namely the normalized delta quantization data of line spectrum pair parameters obtained from the split vector reconstruction codebooks. The LSP reconstructor dynamically generates line spectrum pair parameters based on the normalized delta quantization data. The reconstructed parameters are then passed to a short term synthesis filter to decode data, such as speech. In a preferred embodiment, instead of storing the absolute values of the line spectrum pair parameters in segmented codebooks, the disclosed system and method stores and utilizes a combination of at least two absolute value vectors and at least one vector of normalized delta quantization data for use in spectral quantization. FIG. 2 illustrates a speech decoder 10 in a communication system, such as a radio telephone communication system which receives encoded input data 12 representing encoded speech through speech parameters 14 such as spectral parameters, to reconstruct speech that has been transmitted over an air interface or channel. The speech parameters 14 are decoded as known in the art by a parameter decoder. The speech decoder 10 includes for example a fixed codebook with associated vectors 16 as known in the art, a long term prediction block 18, as known in the art, and a short term prediction LPC synthesis filter 20. The short term prediction LPC synthesis filter 20 uses LPC information that was generated based on LSP data derived from using split vector LSP reconstruction codebooks 22a-22c. The split vector LSP reconstruction codebooks 22a-22c serve as a split vector reconstruction code source containing reconstruction code data representing normalized delta quantization data for determining line spectrum pair parameters or other constrained ordered set parameters. Each entry of reconstruction code data in the codebooks 22a through 22c correspond to valid data for determining constrained ordered set data, such as line spectrum pair parameters, to facilitate reconstruction of the received encoded input data 12 or other spectral parameter. The speech parameters 14 may include among other parameters, a fixed codebook index parameter 24, a fixed codebook gain parameter 26, a long term delay parameter 28, a long term prediction gain parameter 30, and a normalization delta quantization (NDQ) codebook index parameter 32. The NDQ codebook index parameter 32 indexes a vector in one or more of the split vector LSP reconstruction codebooks 22a-22c. Line spectrum pair parameters are reconstructed by an LSP reconstructor 34 based on the indexed normalized delta quantization data in the split vector LSP reconstruction codebooks 22a-22c. An LSP to LPC transformer 36 transforms the LSP parameters to LPC information as known in the art. The LPC information is then used by the conventional short term prediction LPC synthesis filter 20 along with long term prediction information to generate output speech. More specifically, the fixed codebook index parameter 24 indexes an address to a set of stochastic code vectors in the fixed codebook 16 to determine the stochastic component of the encoded speech waveform. The fixed codebook gain parameter 26 represents how strong the energy is from the encoded speech. The fixed codebook gain parameter 26 is multiplied in multiplier (mixer) 27 with the output vector from the fixed codebook 16 to provide gain to the fixed codebook data. The long term prediction block 18 as known in the art receives the delay parameter 28 which is used to determine the pitch period of the speech wave form. The long term prediction block 18 also receives the gain parameter 30 to determine the amount of pitch as known in the art. The short term prediction LPC synthesis filter 20 may be a code excited linear predictor (CELP) which determines the spectral envelope of the encoded speech as known in the art using short term parameters obtained from linear predictive coding information. The linear predictive coding filter coefficients are converted from and represented by the constrained ordered set of line spectrum pair parameters based on the normalized delta quantization data in the NDQ LSP reconstruction codebooks 22a-22c. However, it will be recognized that the disclosed invention is applicable to non-code excited linear prediction based decoders or any other suitable coding and/or decoding device. The normalized delta quantization data in the segmented LSP reconstruction codebooks 22a-22c is used to determine (reconstruct) the absolute value of line spectrum pair parameter values necessary for the short term prediction LPC synthesis filter 20. Referring to FIGS. 3 and 4, suitable normalized delta quantization data, Aω, entries, (see FIG. 5) are determined for split vector LSP reconstruction codebooks 22a-22c such that each entry can be used for determining a valid LSP parameter, as shown in block 50. This may be done by a computer based on training speech frames or other suitable information that will be decoded. In order to circumvent the effects of pruning, the delta coded LSP is normalized to a range, such as the available dynamic range, of the constraint space. That is, the codebook entries represent the percentage of the distance between a lower bound and an upper bound. The lower bound may be set to a previously obtained quantized value of a smaller LSP from the same speech frame, or to the minimum possible value of 0.0. Likewise, the upper bound may be set to a previously obtained quantized value of a larger LSP from the same speech frame, or to the maximum possible value of 0.5. This can be expressed as: where ω, is the ith unquantized LSP, Cϋ H may be the upper bound and ω L is the lower bound. In block 52, the normalized delta quantization data is stored in the split vector codebooks 22a-22c of the decoder 10. This may be done during decoder manufacture, on demand in the field or any suitable point in time. The decoder 10 receives speech parameters 14 for spectral information relating to the original speech as shown in block 54. The LSP reconstructor 34, based on the NDQ index parameters 32, retrieves a code vector from the split vector LSP reconstruction codebooks 22a-22c. The code vector from the split vector LSP reconstruction codebooks 22a-22c contains a representation of estimated LSP parameters. This is shown in block 56. The LSP reconstructor 34 reconstructs the LSP parameter based on the normalized delta quantization data from the codebooks 22a-22c as shown in block 58. Using the above relation, the LSP ώ l is reconstructed from the codebook value Δώ, and the previously obtained lower and upper bounds by: ^ = ^{θ) H - ω L )+ ω L The reconstruction of the LSP's for each frame should be done in an order such that the desired lower and upper bound values are available for each LSP. Implicit in this method is the fact that at least the first LSP to be reconstructed must be assigned fixed lower and upper bound values which are independent of the frame LSP data. For example, these fixed values for the first LSP to be reconstructed may be set to the minimum possible value of 0.0 and the maximum possible value 0.5, which is equivalent to setting the absolute LSP value equal to the corresponding normalized delta LSP value. After reconstruction, the LSP parameters are used to determine linear predictive coding coefficients, as known in the art, by LSP to LPC transformer 36. The short term prediction LPC synthesis filter 20 then evaluates the next speech parameters as shown in block 60. FIG. 5 shows one method for generating the normalized delta quantization data for the split vector codebooks 22a through 22c. Using the distribution of the LSP values, ffij, for a number of analyzed frames, using a computer, a user may generate all possible values of Acty, for the training set. Using models of frames of, for example, voice, music or other data that are expected from the encoder, a distribution range for each Δffi| may be generated. This is shown in block 70. The computer then evaluates the values to obtain an equal error probability distribution for each quantization interval, as shown in block 72. The computer then takes the distributed averages for each quantization level and stores the averages as Aώ i in the split vector codebooks 22a through 22c to provide indices to valid LSP parameters for each split vector codebook entry as shown in block 74. It has been found that a preferred system and method should include use of both absolute and normalized delta quantization data. Although normalization as described above ensures that for every target vector all codevectors are valid, in another embodiment, absolute LSP values are included in the split vector NDQ LSP reconstruction codebooks 22a-22c to provide yet further improved spectral quantization. Referring to FIG. 6, hybrid reconstruction codebooks 80a-80c are split vector codebooks. Codebooks 80a and 80c store at least two split vector segments (groups) containing absolute split vector code source data, such as absolute LSP values. Codebook 80b stores at least one segment of reconstruction code data representing normalized delta quantization of a set of ordered parameters, such as LSP parameters. The reconstruction code data representing normalized delta quantization of a set of ordered parameters is optimized based on a set of training data. The hybrid reconstruction codebook 80a-80c can afford advantages over known lattice codebook structures and those shown in FIG. 2 by employing at least two absolute vectors . An LSP reconstructor 82 reconstructs the set of ordered parameters based on both the at least two sets of absolute split vector code source data and the reconstruction code data representing normalized delta quantization of the set of ordered parameters. The LSP reconstructor uses the at least two segments of absolute split vector code source data and the at least one segment of normalized delta quantization data to convert the normalized quantization data to absolute quantization data for use by the LSP to LPC transformer 36. As previously mentioned, the reconstruction code data representing normalized delta quantization of the set of ordered parameters includes split vector codebook values quantized to represent a distance percentage between two absolute quantized line spectrum pair parameter values or other previously obtained upper and lower bounds. By way of example, codebook 80a may contain codebook entries for quantizing one segment or group containing LSP's 1-3, codebook 80c may contain LSP's 7-10 and codebook 80b may contain LSP's 4-6. As such, codebooks 80a and 80c contain absolute LSP values while codebook 80b contains normalized delta quantization data. Further, in computing the normalized delta quantization data used in deriving the codebook values for codebook 80b, the lower bound Cut is set equal the quantized value of LSP 3, and the upper bound ύ is set to the quantized value of LSP 7. From FIG. 1 it can be observed that there is very little overlap in the distributions of LSP 3 and LSP 7. Thus, the loss due to pruning the codebook 80c is small. Although it is possible to substantially eliminate this pruning loss by using normalized delta quantization values for quantizing codebook 80c which are determined by setting the lower bound to the quantized value of LSP 3, and the upper bound to the maximum possible value of 0.5, the loss due to warping for this case is greater than the gain due to eliminating the pruning, so codebook 80c contains absolute LSP values. For codebook 80b, it can be seen from FIG. 1 that there is a significant overlap in the distributions of LSP 6 and LSP 7 and the distributions of LSP 3 and LSP 4. Therefore, there would be a significant loss due to pruning if absolute LSP values were used for codebook 80b. The use of normalized delta quantization data in codebook 80b results in a coding gain due to eliminating the pruning loss which is greater than a loss due to warping. In another example, fourteen LPC's can be used per frame to represent the coarse spectral information. This set of fourteen LPC's is converted to an ordered set of fourteen LSP's, ranging from 0.0 to 0.5. Instead of three codebooks, a four segment split vector quantizer having four codebooks is used. The first codebook contains entries representing the absolute values of LSP's one to three. The second codebook contain entries representing the absolute values of LSP's eleven to fourteen. The third codebook contains entries representing normalized delta LSP values derived from LSP's seven to ten. In computing these normalized delta quantization values, the lower bound is set equal to the quantized value of LSP 3 and the upper bound is set equal to the quantized value of LSP 11. The fourth codebook contains entries representing normalized delta LSP values derived from LSP's four to six. In computing these normalized delta LSP values, the lower bound is set equal to the quantized value of LSP 3, and the upper bound is set to the quantized value of LSP 7. It should be understood that the implementation of other variations and modifications of the invention in its various aspects will be apparent to those of ordinary skill in the art, and that the invention is not limited by the specific embodiments described. For example, the system and method may be applied to scalar, vector, matrix, or other suitable quantizers having a variety of dimensions and segmentations. Further, there are other similar methods of normalization which also have the affect of eliminating codebook pruning, and can therefore be used to achieve a similar improvement in the performance of split vector quantization of ordered sets. For example, a normalized LSP may be computed according to the following formula: where ω L < ω, < ω H anά ω L ≤ ω τ ≤ ω H , and where Aω, is the normalized LSP value, ω t is the absolute LSP value, ω H and ω L are previously obtained upper and lower bounds which satisfy the above constraints, ω r is a previously obtained reference value which satisfies the above constraints, and the sign is set apriori to the same polarity at both the encoder and decoder. In this case, the absolute quantized LSP value is obtained from the quantized value of Aω, (or Aω, ) according to: ώ, = ±Aώ ! {ω H - ω L )+ ω r Also, it will be recognized that the combination of absolute and normalized delta quantization values may differ depending upon the statistics of the data to be quantized. It is therefore contemplated to cover by the present invention, any and all modifications, variations, or equivalents that fall within the spirit and scope of the basic underlying principles disclosed and claimed herein.

Claims: CLAIMS 1. A method for providing split vector quantization for use in determining a set of ordered parameters for coding, comprising the steps of: storing a split vector reconstruction code source containing reconstruction code data representing normalized delta quantization data for determining set of ordered parameters wherein each entry of reconstruction code data corresponds to valid data for determining the set of ordered parameters to facilitate reconstruction of received spectral parameters; and reconstructing the received spectral parameters based on reconstruction code data representing normalized delta quantization of the set of ordered parameters from the split vector reconstruction code source. 2. The method of claim 1 wherein the split vector reconstruction code source is a split vector codebook and the set of ordered parameters are determined from the normalized delta quantization data. 3. The method of claim 2 wherein the reconstruction code data representing normalized delta quantization of the set of ordered parameters includes split vector codebook values quantized to represent a distance percentage between a lower bound value and a upper bound value. 4. The method of claim 1 wherein the step of reconstructing received parameters includes dynamically generating line spectrum pair (LSP) parameters based on the reconstruction code data representing normalized delta quantization of the set of ordered parameters and utilizing linear predictive coding coefficients based on the dynamically generated LSP parameters to reconstruct the received parameters. 5. The method of claim 1 including the step of indexing each vector entry of the split vector reconstruction code source to facilitate selection of vector entries from differing splits vectors within the split vector reconstruction code source. 6. The method of claim 1 including the step of operatively receiving encoded spectral parameters from a speech encoder. 7. The method of claim 1 wherein the set of ordered parameters are comprised of line spectrum pair values. 8. The method of claim 1 further including the step of storing at least two groups of absolute split vector code source data and wherein the step of reconstructing received parameters includes reconstructing received parameters based on both the two sets of absolute split vector code source data and the reconstruction code data representing normalized delta quantization of the set of ordered parameters. 9. The method of claim 2 wherein the reconstruction code data representing normalized delta quantization of the set of ordered parameters includes split vector codebook values quantized to represent a distance percentage between two quantized LSP values and absolute quantization values. 10. A coding system for providing split vector quantization for use in determining a set of ordered parameters comprising: means for storing a split vector reconstruction code source containing reconstruction code data representing normalized delta quantization data for determining the set of ordered parameters wherein each entry of reconstruction code data corresponds to valid data for determining the set of ordered parameters to facilitate reconstruction of received parameters; and means, operatively coupled to the means for storing, for reconstructing the received parameters based on reconstruction code data representing normalized delta quantization of the set of ordered parameters from the split vector reconstruction code source.

Inventors: ASHLEY JAMES PATRICK, JASIUK MARK A, PENG WEIMIN, SMITH AARON M, ASHLEY, JAMES, PATRICK, JASIUK, MARK, A., PENG, WEIMIN, SMITH, AARON, M.
Applicants: MOTOROLA INC, MOTOROLA INC.
Application year: 1999
Main classification: G10L
IPC codes: G10L19, H03M7