PITCH DETECTION PROCESS AND SPEECH CODER USING SAID PROCESS

Abstract: A pitch data detecting means to be used to adjust long term predictive means in a pulse excitation speech coder. A residual signal r(n) is first derived from the speech signal s(n) through short term filtering then r(n) is processed to provide a prediction error signal e(n) to be pulse excitation encoded. The generation of e(n) involves predicting a residual through Long Term Prediction operations including measuring a pitch related factor M, through a dual steps process with first step providing a coarse M value through peak clipping and sign transition detection, and then second step for adjusting said M to a finer value through autocorrelations operating about the roughly spaced peaks.

Description: Field of Invention This invention deals with methods for efficiently coding speech signals. Background of Invention A great number of speech coder families are already known among which one may include so called vocoder and Linear Prediction Coder (LPC) families. Briefly stated, the vocoder family is based on deriving from the original speech signal a set of coefficients used to process the original speech signal and derive therefrom a residual signal. A pitch information is then derived from the residual for voiced speech sections, otherwise the residual signal is simply made to be noise. The correlative decoding process involves modulating back a synthesized pitch or noise signal by the coefficients. The relative efficiency (quality versus bit rate) of such a coding scheme is rather poor unless performing a very precise determination of the pitch value. This already shows the significance of any efficient method for determining the pitch. Also with a reasonable increase in the complexity of the coder, the LPC coder family provides valuable improvement to the coding/decoding operation. Needless to mention the importance of any savings into the bit coding rate and or the coder complexity, for the voice processing industry. Saving in computing complexity enables minimizing processor workload, while saving in bit rate is of major importance in voice transmission or in storage facilities. These reasons enable understanding the full meaning of engineers efforts to optimize their coders in order to save a few coding bits, i.e. minimize the bit rate required for coding the speech signal, while keeping the coding quality quite unchanged. The above considerations not only enable appreciating the engineering value of one coding scheme versus the others, but they might be of great significance to business value appreciation of a given coding/compressing scheme. In summary, in the LPC type of coding schemes one may improve the coding/decoding quality considerably by efficiently detecting the pitch and by adding more information than usually done about the residual signal. Significant improvements are made by judiciously architecturing the coder even within a same sub-family of coders such as the ones known as : Voice Excited Predictive Coder (VEPC) as disclosed in IBM Journal of Research and Development Vol. 29, Number 2, March 1985 ; Multi-Pulse Excited Coder (MPE); or Regular Pulse Excited Coder (RPE), as disclosed in the article "Regular Pulse Excitation, a Novel Approach to effective and Efficient Multipulse Coding a Speech "published by P. Kroon et al. in IEEE Transactions on Acoustics Speech and Signal Processing Vol ASSP 34 N05 Oct. 1986; and in a Thesis "Etude, Simulation et mise en oeuvre sur microprocesseur de codeurs predictifs multi-impulsionnels" presented by E. Lançon, on Nov. 22, 1985 before the University of Nice, France. SUMMARY OF INVENTION One object of this invention: is thus to provide an efficient method for determining a voice pitch related information. Another object of this invention is to provide a coder architecture wherein said pitch related information may be used to improve the speech signal coding scheme from an efficiency standpoint. According to the present invention a digital process for determining a number representing pitch or an harmonic thereof and a digital speech coder are as claimed in claims 1 and 8, respectively. The original speech signal is processed to derive therefrom a speech representative residual signal, compute residual prediction signal using long term prediction means adjusted by using pitch detection operations, then combine both current predicted residual to generate a residual error signal and code the latter using Pulse Excitation Coding techniques. A significant improvement to the coding scheme efficiency is provided by detecting the pitch or an harmonic of said pitch (hereafter simply designated by pitch or pitch representative information or pitch related information) using dual-steps process including first a coarse pitch determination through peak detection, then followed by auto-correlation operations about the detected pitched peaks. The foregoing and other objects, features and advantages of the invention will be made apparent from the following more particular description of a preferred embodiment of the invention as illustrated in the accompanying drawings. Brief Description of the Drawings Figure 1 : Block diagram of a Voice Coder using the invention. Figure 2 : speech representative waveforms. Figures 3 and 4 : illustrations of the pitch detection process of the invention. Figures 5 and 6 : block diagrams of the coder. Figure 7 : block diagram of the decoder. Figure 8 : general block diagram for implementing the pitch determination. Figure 9 : block diagram of the algorithm for the selection of candidate values for pitch. Figure 10 : block diagram of the algorithm for the elimination of unsignificant values and averaging for the determination of the rough pitch value. Figure 11 : block diagram of the algorithm for the fine determination of the pitch value. Description of a preferred embodiment Represented in figure 1 is a block diagram of a coder made to implement the invention. The original speech signal s(n) sampled at Nyquist frequency and PCM encoded with 12 bits per sample is fed into an adaptive short term prediction filter (10) by consecutive blocks 160 samples long. The filter equation in the z domain is of the form Σ a i . z -i (1) In other words the short term prediction filter is made of a conventional transversal digital filter the tap coefficients of which are the a i parameters. The a i are derived by a step-up procedure in device 13 from so called PARCOR coefficients k(i) in turn derived from the original speech signal using a conventional Leroux-Guegen method and then coded with 28 bits using the Un/Yang algorithm. For reference to these methods and algorithm one may refer to: J. Leroux and C. Guegen " A fixed point computation of partial correlation coefficients "IEEE Trans on ASSP pp 257-259 June 1977; C.K. Yun and S.C. Yang "Piecewise linear quantization of LPC reflexion coefficient" Proc. Int. Conf. on ASSP. Hartford, May 1977. J.D. Markel and A.H. Gray : "Linear Prediction of Speech", Springer Verlag 1976, Step up procedure pp.94-95. The short term prediction filter is made to deliver a residual signal r(n) showing a relatively flat frequency spectrum, with some redundancy at a pitch related frequency. A device (12) processes the residual signal to derive therefrom a pitch or harmonic representative data in other words, a pitch related information M and a gain parameter b to be used to adjust a long term prediction filter (14) performing the operations in the z domain as shown by the following equation. b.z -M (2) The device for performing the operation of equation (2) should thus essentially include a delay line whose length should be dynamically adjusted to M (pitch or harmonic) and a gain device b. A more specific device will be described further. Efficiently measuring b and M is of prime interest for the coder since a prediction residual signal output x(n) of the long term predictor filter is subtracted from the residual signal to derive a long term decorrelated prediction error signal e(n), which e(n) is then to be coded into sequences of pulses using any Pulse Excitation (PE) method. In other words, a PE device (16) is used to convert for instance each sub-group of 40 consecutive PCM encoded e(n) samples into a smaller number, say less than 15, of most significant pulses. Either one of the MPE or RPE techniques could be used. Lower the dynamic of e(n) is, more efficient its quantizing/coding at a given bit rate is. These considerations help appreciate the importance of a precise adjustment of filter 14 thus of a good evaluation of b and M. A significant advantage of the coder architecture of figure 1 derives from the fact that M may either be representative of the pitch or of a pitch harmonic, i.e. it needs only be a pitch related parameter. With MPE, say 6 or 8 samples are selected among the e(n) samples for minimizing the mean square error on e(n). These 6 or 8 samples efficiently describe the e(n) signal as long as adequate decorrelation through filter (14) is performed to get a lower signal dynamic. The new samples provided by device (16) are coded using two set of parameters, one characterizing each pulse position with respect to a significant reference, e.g. the beginning of the sub-block of forty samples being processed, the other one representing each pulse amplitude. Characterizing the pulse position is particularly critical and any error on said position would alter considerably the speech coding quality. With RPE, the computing workload to be devoted to the pulses is lowered as compared to MPE but this assumes a slightly higher number of pulses (e.g. 13 to 15) is used to describe each sub-group of e(n) samples. Then a higher protection against line errors could be obtained with a lower number of bits. Briefly stated, when using RPE techniques, each sub-group of 40 samples is split into interleaved sequences. For instance two 13 samples and one 14 samples long interleaved sequences. The RPE device (16), is then made to select the one sequence among the three interleaved sequences again providing the least mean squared error. There is then no need to code each sample position. Identifying the selected sequence with two bits is sufficient. For further information on the RPE coding operation one may refer to the above cited Kroon reference. The long term prediction associated with regular pulse excitation enables optimizing the overall bit rate versus quality parameter, more particularly when feeding the long term prediction filter (14) with a pulse train r′(n) as close as possible to r(n), i.e. wherein the coding noise and quantizing noise provided by device 16 and quantizer 20 have been compensated for. For that purpose decoding operations are performed in device (22) the output of which p′(n) is added to the predicted residual x(n) to provide a reconstructed residual r′(n) . Also, the closed loop structure around the RPE coder is made operable in real time by setting minimal and maximal limits to the pitch detection window as will be explained further. The various signals s(n) and r(n) in time domain are represented in figure 2, in their analog from. One may notice some sort of redundant pitch related information still remaining in the residual r(n) signal. The computation of the Long Term Predictor (LTP) (12) parameters may be represented as follows. First each block of 160 r(n) samples is split into four sub-blocks of N=40 samples using a sub-window to lower the computing complexity within the PE coding device (16) while enabling faster refreshing of the information provided by said coding device (16). For each sub-block of samples, the following data are available: 40 r(n) samples; a set of short term prediction factors a i to be assigned to four consecutive sub-blocks including the current one. b and M are determined four times over each block of 160 samples, using 40 samples (sub-window) and their 120 predecessors. The device (12) fed with these data computes the long Term Prediction coefficient M as will be described later on and uses it to derive the gain coefficient b according to the following equation: The method for determining M is essential not only to make the whole coder efficient from both quality and complexity standpoints, but also to make the long term prediction arrangement operable in real time. This is achieved by forcing M>N and by splitting the M determination process into two steps. A first step enabling a rough determination of a coarse pitch related M value requiring a fairly low computing power, is then followed by a fine M adjustment using auto-correlation methods over a limited number of values. 1. First step Rough determination is based on use of non linear techniques involving variable threshold and zero crossings detections more particularly this first step (to be considered with reference to figure 3) includes: Initializing the variable M by forcing it to an empirically determined value, say M = 40 sample intervals, or to the previous fine M measured loading a block vector of 160 samples, including the 40 samples of current sub-block of 40 samples, and the 120 previous samples (3 previous sub-blocks); detecting the positive (Vmax) and negative (Vmin) peaks within said vector; computing thresholds: positive threshold Th⁺ = alpha x Vmax negative threshold Th⁻ = alpha x Vmin alpha being an empirically selected number (e.g. alpha = 0.5) setting a new vector X(n) representing the current sub-block according to; X(n) = 1 if r(n) ≧ Th⁺ X(n) = -1 if r(n) ≦ Th⁻ X(n) = 0 if Th⁻ <r(n)<Th⁺ This new vector containing only -1,0 or 1 values will be designated as "cleaned vector"; detecting significant zero crossings (i.e sign transitions) between two values of the cleaned vector, i.e. zero crossings close to each other; computing M' values representing the number of r(n) sample intervals between consecutive detected zero crossings; comparing M' to the previously rough M by computing ΔM=|M'-M| and dropping any M' value whose ΔM is larger than a predetermined value K (e.g. K=5); computing the coarse M value as the mean value of the M' values not dropped. Figure 3 shows an example of coarse M determination over a residual signal waveform . For convenience sake, the residual signal as well as cleaned vector are represented as operating over analog waveforms. In practice, one would consider the PCM sampled representation instead. Dashed zones on the cleaned vector represent one or several consecutive residual samples above Th⁺ or below Th⁻, said samples being coded respectively by + 1 and - 1. The cleaned vector is then scanned to locate zones of transition from + 1 to - 1 over a limited number of samples. Five transitions zones noted TR1-TR5 have been located on the considered example. The number of samples between consecutive TR locations are computed and noted as M' value with M' = 35; 34; 35 and 34 for a whole block of 160 samples. Assuming the previously measured M value be equal to 35, ΔM = 0; 1; 0 and 1 respectively, then none of the M' values would be far enough from 35 to be dropped. The final (coarse) rough value of M would then be : M is then considered equal to 35. It should be noted that the experimentally selected value of alpha is equal to 0.5, which guarantees in practice that at least 1 value of M' would be selected. Also, once a significant transition zone is detected, a few samples are ignored before starting to locate next significant transitions. This enables minimizing the effect of noisy peaks about the pitch as may be seen on the samples located close to n = 60 and n = 90. The number of ignored samples corresponds to the minimal detectable pitch. And finally, the maximum acceptable ΔM value should be high enough to ascertain computing the mean M value over a significant number of M'. 2. - Second step: fine M determination is based on the use of autocorrelation methods but is operated over a low number of samples taken around the samples located in the neighbourhood of the pitched pulses. In other words, a set of R(k') values is derived from: for k' = K.M ± Delta , locating the sample within the block, with : n = 1 refering to r(1) of sub-block "k" (see figure 4) and K = 1,2,3. K being the sample rank index locating the peaks at multiples of rough M rate, and Delta = 5 for instance defining a number of sample locations about said pitched peaks. In other words, the autocorrelation operation of equation (4) is operated between the 40 samples of sub-block (k) and 40 samples, the first of which is one of the autocorrelation zones samples, then jumping to the next autocorrelation zone. This enables thus saving on computing load. The second step illustrated in figure 4, includes: Initializing the M value either as being equal to the rough (coarse) M value just measured assuming it is different from zero otherwise as being equal to the last measured fine M; locating the autocorrelation zones based on the roughly located pitch and Delta; eliminating from these zones the non significant index values k' i.e., keeping only the values such that: 40 ≦ k' ≦ 120 For instance, the example shown on figure 4 would result in a partial elimination of zone 1. computing the autocorrelation coefficients R(k′) using equation 4; locating the maximum R(k') = autocorrelation peak, to detect the fine M value; and, computing the gain factor b according to equation (3). The value of Delta has been set to 5 and the autocorrelation zones limited to the three first coarse M spaced peaks. A saving on data storage is achieved by using reconstructed shifted samples r'(n-k') instead of samples r(n-k') in relation (4) and by using samples r'(n) instead of samples r(n) in relation (3), as shown in figure 5. In figures 8, 9, 10 and 11 are flow charts representing the algorithms used to implement the above described M pitch determination. The flowcharts are self explanatory with the following definitions: Main Subroutine = HPITCH deals with fine pitch and gain b determination through autocorrelation operations for fine pitch (Figure 8). Input parameters: XWORK Table of N samples r(n), n=1,40 MMIN Minimum assigned to M MMAX Maximum assigned to M Out parameters: MPITCH Fine pitch M value Beta Gain coefficient b. Other sub-routines: (1) Sub-routine PIT: Determination of coarse M value using center clipping, zero crossing operations, and averaging Input parameters: BUF Table of r(n) signal samples (n=1,160) IFEN Buffer length Output parameters: PITCH coarse pitch M value This subroutine includes two steps: 1st step : Selection of candidate pitch values which are stored in a table TAB (1, ..., KMAX). (see flowgraph in figure 9), 2nd step : Elimination of unsignificant values and averaging (see flow graph in Figure 10), to count a coarse estimate PITCH. (2) Subroutine HPITCH: Fine determination of pitch. input parameter : PITCH : coarse pitch M value outputparameter : MPITCH: fine pitch M value Figure 11 represents the detailed flowgraph of this subroutine. An implementation of Long Term Prediction filter (14) is represented in figure 5 (see figure 1 for similar references). The reconstructed residual signal is fed into a 160 samples long delay line (or shift register) D L the output of which is fed into the LTP coefficients computing means(12) for further processing through cross-correlations with r(n). A tap on the delay line DL is adjusted to the previously computed fine M value. A gain factor b is applied to the data available on said tap, before being subtracted from r(n) as a residual prediction x(n) to generate e(n). The long term predicted residual signal is thus subtracted from the residual signal to derive the error signal e(n) to be coded through Pulse Excitation device (16) before being quantized in quantizer (20). An optimal approach to e(n) coding has been implemented using a Regular Pulse Excited (RPE) Coder the principle of which has been described in the above cited Kroon et al reference. Represented in figure 6 is a device implementing the RPE function as considered with the coder of figure 1. The residual is low-pass filtered in (52) to a low bandwidth limited at 1,66 Khz. Then each sub block of 40, x(n) samples is split in device (54) into three interleaved sequences X₀, X₁ and X₂ as represented hereunder: Where "X" represents a non zero pulse taken among the x(n) samples. The three pulse trains X0, X1 and X2 energies are computed, and the pulse train showing the highest energy is selected to represent the residual signal e(n) for the considered 40 samples long operating time window. A two bits long parameter L is used to define the selected sequence X₀, X₁ or X₂. This parameter is thus provided by the coder output four times every block of 160 samples. The pulses selected are quantized into a sequence "X". Therefore both L and "X" parameters define the e(n) coded signal. In practice, block companded PCM techniques are used to encode the X sample sequence. These technique have been presented by A. Croisier et al in a presentation at the International Seminar on Digital Communications, Zurich 1974. Each 40 samples long e(n) sequence is finally encoded into a characteristic term encoded with five bits and 13 or 14 samples each encoded with three bits. Represented in figure 7 is the decoder or synthesizer to be used with this invention. The received data train is first demultiplexed in 70 to separate the various components (C, X, L, b, M and k(i) from each other. C and X are used in a conventional BCPCM decoder to regenerate in (72) the e(n) pulse train the time position of which is adjusted with reference to the block time origin using the parameter L. In other words, L enables setting an additional time delay to either zero, one or two sampling periods depending whether L indicates that the selected pulse train was X⊘, X1 or X2. The decoded pulses p′(n) are then fed into an inverse long term prediction filter (74) the parameters of which are adjusted by b and M. These operations are performed every 40 samples, i.e. one sub-block window duration. The inverse filter provides a decoded residual signal r′(n) fed into an inverse short term prediction filter (76) the coefficients of which are adjusted each 160 samples long period of time using the PARCOR coefficients k(i) (or the corresponding coefficients a(i)). The decoded speech signal s′(n) is provided at the output of inverse short term filter (76). Thanks to the very efficient method for detecting the long term predictor parameters, and more particularly the pitch related M parameter, a very efficient 16 Kbps voice coding is achieved. More particularly, the bits assignment have been made as follows: For each block of 20ms long speech signal section: which corresponds to a rate of 13 Kbps leaving 3 Kbps for error protection for a 16 Kbps coder.

Claims: A digital process for determining a number M representing the pitch or an harmonic thereof of a sampled speech signal through processing a short term residual signal r(n) derived from said speech signal split into consecutive fixed length blocks of signal samples, said process including : a) measuring a rough value of said pitch or an harmonic thereof, said measuring including : setting signal dependent positive threshold (Th⁺) and negative threshold (Th⁻) defined by (alpha.Vmax) and (alpha.Vmin), where Vmax and Vmin are positive and negative peaks within a block vector and alpha is empirically selected ; locating and storing samples representative of the signal block and having magnitudes above and below said Th⁺ and Th⁻ respectively ; locating significant sign transitions within said stored samples ; computing the numbers of stored samples between consecutively located significant transitions, and computing the mean value of said numbers and storing said mean value as representing the rough value of said pitch or an harmonic thereof ; and, b) deriving a fine measurement value of said pitch or an harmonic thereof, said deriving including : setting auto-correlation zones about the multiples of said rough value spaced peak samples ; splitting the considered block of signal samples into consecutive sub-blocks ; auto-correlating a current sub-block of samples with sub-blocks of samples the first of which is one sample of said zones ; and, locating the auto-correlation peak to determine said fine value (M). A process according to claim 1, wherein said setting auto-correlation zones include : locating auto-correlation zones based on roughly located pitched peaks and a predetermined Delta variation, said zones including the samples whose index k' = K.M± Delta , K being an integer value 1, 2, 3, ; and, eliminating from these located zones, non significant k' index valued ones, i.e. keeping only 40 ≦k'≦ 120 wherein 40 samples represents a sub-block length. A process according to claim 1 or 2 wherein said autocorrelation operations are operated over coded and then reconstructed shifted samples of said speech representative signal. A digital process according to claim 1, 2 or 3 wherein said residual signal r(n) is derived from said speech signal through a short term-filtering operation using a digital filter the a(i) coefficients of which are derived from the speech signal. A digital process according to claim 4 wherein said determined M value is used to adjust a Long Term Prediction (LTP) filter used to generate a predicted residual signal to be subtracted from the current residual signal and derive therefrom a prediction error signal, e(n). A digital process according to claim 5 wherein said prediction error signal e(n) s in turn encoded using Regular Pulse Excitation techniques converting each sub-block of e(n) samples into a shorter sequence selected among a set of sequences of relatively fixed positions samples. A digital process according to anyone of claims 5 or 6 wherein said M value is used to adjust said LTP filter with a gain factor b according to : wherein N is a predetermined integer function of the number of samples within a block of samples. A digital speech coder for coding a speech signal s(n), including : short term adaptive filtering means (10) filtering said s(n) signal and providing a residual signal r(n) ; a subtracting device having a (+) input and a (-) input said (+) input being connected to be fed with said r(n) signal, and said subtracting device providing a prediction error signal e(n) ; a regular pulse excitation (RPE) coder for converting fixed length sub-blocks of e(n) samples into shorter (RPE) sequences of samples ; quantizing means for quantizing said RPE, sequences ; decoding means for decoding the quantized output ; adding means connected to said decoding means ; Long Term Predictive (LTP) coding means including delay means connected to said adding means,for delaying the adder output by a delay equal to M and multiply said delayed output by a gain b, whereby a predicted residual signal x(n) is generated ; LTP coefficient computing means sensitive to said r(n) signal and connected to said delay means for deriving said M according to the process of claim 1, and b gain according to claim 7 ; and, means for applying said predicted residual to both the subtracting (-) input and the second adding means input. A digital speech coder according to claim 8 wherein said LTP coding means include : a one block length shift register having an input connected to the adding means output, an adjustable tap, and an output ; a multiplier connected to said tap and having an output connected to both (-) subtractor input and to second adder input ; LTP coefficients computing means connected to said shift register output and sensitive to the residual r(n) signal to generate a pitch related M data according to claim 1 or 2 and a gain factor b according to claim 5 ; means for shifting said tap to be spaced from the shift register input by a delay M ; and, means for applying said b gain to said multiplier.

Inventors: CREPY, HUBERT, ELIE, PHILIPPE, GALAND, CLAUDE, LANCON, EMMANUEL, LIETHOUDT, THIERRY, ROSSO, MICHELE, Rosso, Michèle
Applicants: INTERNATIONAL BUSINESS MACHINES CORPORATION
Application year: 1987
Main classification: G10L9
IPC codes: G10L19, G01H3, G10L11