METHOD FOR FEATURE PREPROCESSING FOR PATTERN MATCHING

Abstract: A pattern matching preprocessing system which comprises: a) means for effecting a frequency domain transform on an input signal; b) means for producing a spectral plot from the output of a); c) means for effecting a spectral reconfiguration permutation and mapping (SRP & M) on the spectral plot as hereinbefore defined; d) means for effecting an inverse frequency domain transform on the output of c); and e) means for applying a Wave Shape Descriptor coding and classification to the output of d).

Description: METHOD FOR FEATURE PREPROCESSING FOR PATTERN MATCHING Field of the Invention The present invention relates to methods and apparatus for signal recognition. The invention has a wide variety of applications including in particular speech recognition and the monitoring of machinery but it is not limited to such applications. The term "signal recognition" is intended to be a generic term covering any situation in which the signal, however generated, is to be detected, monitored or recognised and includes, for example, voice and word recognition systems, speaker verification systems, conditioning monitoring systems e.g. rotating and vibrating machinery, diagnostic instruments, security sensors and false alarm elimination. Background to the Invention The conventional approach to providing signal recognition systems, typically speech recognition systems, has been to operate in the frequency domain and to utilise, typically, Fourier analysis, e.g. Fourier Transform (FT), Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT) and to its inverse transformations. Although the use of the Fourier Transform has numerous advantages it also has significant limitations or drawbacks. These are associated with the fact that the Fourier Transform is a linear transform. An example of the use of the Fourier approach is US Patent No 6,275,619. In the frequency domain in representing signals it is the spectral plot, which includes both frequency and phase vectors that is utilised in order to characterise band limited signals. By "band limited" is meant signals which are limited in bandwidth. The spectral plot is commonly referred to as the "Frequency Spectrum" (FS). In more recent years there has evolved an alternative approach to the frequency domain/Fourier based systems of signal recognition. This alternative approach operates in the time or zero domain and unlike the frequency domain/Fourier approach it relies on extracting from the signal information which is characteristic of the wave shape of the signal as distinct from the fluctuation in the frequency content of the signal, i.e. the Energy Density Spectrum. The wave shape of the signal has many characteristics such as where it crosses the zero axis (known as "real" zeros), the presence of "complex" zeros associated with the "real" zeros and the actual shape of the waveform between the "real" zeros, e.g. the number of extrema. All these various wave shape characteristics can be termed Waveform Shape Descriptors (WSD) and hereinafter the term Waveform Shape Descriptor will be used to identify or define the type of signal recognition system to which the present invention applies. Examples of such WSD systems are to be found in UK Patent Nos. 2,020,517 and 2,145,867. These patents relate to the system which has become known as a Time Encoded Signal (TES) system or as a Time Encoded Signal Processing and Recognition (TESPAR) system. Other examples of a WSD type system are disclosed in UK Patent No 2,306,010 (University of Wales)and US Patent No. 5,847,658 (OMRON). A further example of a system which can be said to utilise Waveform Shaped Descriptors, but also aims at reconciling the frequency domain approach referred to earlier with the time domain approach is that known as the wavelet system. An example of a wavelet system is disclosed in US Patent No 6 173,074. The present invention is applicable to all signals to which the Fourier approach may be applied. However, it depends upon the utilisation of a WSD type system in order to provide an overall system which has the capability of discriminating between certain types of signals in respect of which a Fourier based system would fail to discriminate. The discrimination problem is illustrated in Figure 1. Figures 1A, B and C illustrate three signal waveforms each of which has a significantly different shape from the other two waveforms. However the Energy Density Spectrum of each of these three apparently different waveforms is the same as is indicated in Figure 1D. It is thus not possible for such a frequency domain/Fourier system to distinguish between the three waveforms illustrated in Figure 1A, B and C. However, by utilising WSDs, which as indicated earlier are concerned with the shape of the waveform, it is possible in a WSD based system to make these distinctions. Although a WSD type system has the above indicated advantage over a frequency domain/Fourier type system there are nevertheless waveforms where even with a WSD based system it is difficult, if not in practice impossible, to make this distinction or differentiation. The present invention is concerned with overcoming this problem. A typical WSD system is illustrated in Figure 2 and this will be described later in more detail and with reference to the drawings. Essentially the present invention consists in combining a Fourier system with aspects of a WSD based system together with means in the overall system which is capable of rearranging spectral representations of a signal in such a way as to provide the overall system with an increased ability to differentiate signals which might otherwise be incapable of differentiation or classification utilising the prior art systems. The present invention is applicable to both time-dependent and non- time-dependent signals. An example of the latter is a geographical profile. A further example is a spectrum derived from emissions from chemical materials. Brief Summary of the Invention According to the present invention a signal recognition system comprises: a) means for effecting a frequency domain transform on an input signal; b) means for producing a spectral plot from the output of a); c) means for effecting a spectral reconfiguration permutation and mapping on the spectral plot as hereinbefore defined; d) means for effecting an inverse frequency domain transform on the output of c); and e) means for applying a Wave Shape Descriptor coding and classification to the output of d). Brief Description of the Several views of the Drawings How the invention may be carried out will now be described by way of example only and with reference to the accompanying drawings in which: Figure 1 illustrates three waveforms having the same Energy Density Spectra; Figure 2 is a diagrammatic representation of a prior art WSD system; Figure 3 represents an ideal "System Comparison Ensemble" (SCE); Figure 4 is similar to Figure 2 but incorporates the present invention; Figure 5 illustrates two examples of how the spectral plot of an original signal may be reconfigured according to the present invention; Figure 6 is similar to Figure 5 and illustrates a third example of reconfiguration; Figure 7 is similar to Figure 5 and illustrates a fourth reconfiguration; Figure 8 is in effect a combination of Figures 2 and 4 to provide a comparison between the prior art WSD system of Figure 2 and the present invention system of Figure 4; Figure 9 illustrates both the waveform and the EDS plot of normal and stressed signals derived from rotating machinery; Figure 10 is connected to Figure 9 and is the equivalent to Figure 3 but shows the System Comparison Ensembles for the signals of Figure 9; Figure 11 repeats Figure 9 but in addition includes illustrations of a first reconfiguration of the invention when applied to the signals of Figure 9; Figure 12 is the equivalent to Figure 10 but relates to Figure 11; Figure 13 is similar to Figure 11 but illustrates a second reconfiguration according to the present invention of the signals of Figure 9; Figure 14 is equivalent to Figure 12 but relates to the reconfiguration illustrated in Figure 13; Figures 15, 16 and 17 A to 17B are identical to Figures 9, 10 and Figures 11 A to 11D respectively; Figures 17E to 17H correspond to Figures 11E to 11H but are in respect of a third reconfiguration algorithm to the one illustrated in Figures 11E to 11H; Figure 18A is identical to Figure 12A but relates to the signal of Figures 17 A to 17D; Figure 18B is equivalent to Figure 12B but relates to the transformed or reconfigured signal as shown in Figures 17Eto 17H; Figure 19A is similar to Figure 18A but instead of illustrating S Matrix correlations it illustrates DZ Matrix correlations; Figures 19B is similar to Figure 18B but instead of illustrating S Matrix correlations it illustrates DZ Matrix correlations; Figure 20 illustrates the signals derived from rotating machinery when that machinery is in good operating condition (Figures 20A and 20C) and that machinery when it is faulty (Figures 20B and 20D); Figure 21 is equivalent to Figure 3 but illustrates the System Comparison Ensemble between the "good" and "faulty" signals of Figure 20; Figure 22 includes Figures 22A to 22D and these are identical to Figure 20, the remaining 22B to 22H being the equivalent of Figures v 7£ to 17H of Figure 17 and illustrating the reconfiguration achieved by utilising the present invention; Figure 23A is identical to Figure 21; Figure 23B shows the DZ Matrix System Comparison Ensemble for the transformed or reconfigured signals of Figures 22E to 22H; Figure 24 illustrates at Figures 24A and 24B signals representative of the word "six" and "fix" respectively, Figures 24C and 24D representing their DFTs respectively; Figure 25 illustrates the S and DZ Matrices of the System Comparison Ensemble of the signals of Figure 24, i.e. without the application of the present invention; Figure 26 is identical at Figures 26A to 26D to Figures 24, Figures 26E to 26H representing the application of the present invention to the signals of Figures 26A to 26D; Figure 27 has Figure 27 A identical to Figure 25A and Figure 27B equivalent to Figure 25B but illustrating the S matrix instead of the DZ matrix; Figure 28 is similar to Figure 27 but illustrating the DZ matrix instead of the S matrix; Figure 29 has Figures 29 A to 29D identical to Figures 17 A to 17 D; Figure 30 has Figures 30A and 30B identical to Figures 13G and 13H and has Figures 30C and 30D identical to Figures 13E and 13F respectively. Figure 31 illustrates normal and stressed signals in relation to the WSD method disclosed and claimed in UK Patent No 2 306 010; Figure 32 illustrates the transformation of the signals of Figure 31 in accordance with the present invention; Figure 33 illustrates untransformed signals in relation to a Wavelet WSD system; Figure 34 is of the same type as Figure 3 but illustrating the untransformed S matrix and DZ matrix with respect to a Wavelet WSD system; Figure 35 illustrates the transformed signal after application of the present invention; Figure 36 illustrates the S matrix for the untransformed signal and the S matrix for the transformed signal in relation to a wavelet system; Figure 37 illustrates another example of the present invention applied to transform the signal of a wavelet system; Figure 38 illustrates an untransformed and transformed system comparison ensembles in respect of untransformed and transformed data in relation to the application of the present invention; Figures 39 to 42 are enlarged views of Figures 11C to 11 F respectively; Figures 43 to 46 are similar to Figures 39 to 42 but relate to a nonlinear method in relation to producing an SRP&M; Figure 47 is a flow diagram illustrating how the SRP&M optimisation process may be carried out; and Figure 48 is a diagrammatic representation of one practical application of the present invention. The following description and drawings illustrates how the present invention may be put into practice. Figures 2 to 42, explain the concept underlying the present invention. The second section involving Figures 43 and 44 explains how the concept may be implemented as a commercial product. The commercially relevant output of the signal treatments illustrated in and described with reference to Figures 2 to 42 would be SRP&M algorithms which have been found to produce a desired level of signal differentiation for a particular commercial application e.g. in the field of voice recognition, or machinery fault detection. These SRP&M algorithms would then be incorporated into a mini-computer or custom chip integrated circuit which would then form part of a signal classification system such as is illustrated diagrammatically in Figure 48. Figures 2 and 3 Figure 2 shows a block diagram for a typical exemplar WSD coding system which, for illustration, may be a TESPAR coding system. The input Signals Under Test (SUTs) 21, will consist of a number of different sets of data, each different set representing a separate condition to be characterised. For simplicity in this disclosure, and in all the subsequent description, the comparison of two sets only, viz. Sets A and B, will be used but the invention is applicable to any plurality of sets. Each individual SUT is WSD coded at 22, and then classified by WSD means at 23 such as TESPAR as in UK Patent Nos 2 020 517 and 2 145 867. Such WSD coding means may, for example, involve transforming the individual SUTs into TESPAR "S"-matrices, or TESPAR "DZ"-matrices or 'TV- matrices, or into any of the proposed "Wavelet sets", or "UOW Ruler Length" (see UK Patent No 2 306 010) data descriptors or OMRON data representations. The comparison and classification process might, for example involve a mathematical correlation between the WSD data representations derived from each signal, Thus, the individual data entities (e.g. TESPAR "S" or "DZ" matrices or wavelet sets....etc.) derived for each SUT by the WSD coding means may, for example, be correlated against each other by any standard conventional means appropriate to the WSD coding means. The results of such comparisons may be stored as indicated in 4 of Figure 2, as a set of numbers indicating the closeness, or otherwise, of the relationships of the individual signals to each other (see Figure 3 and later description). Exemplar classification results will be presented in the form of a "System Comparison Ensemble", (SCE). This ensemble compares individual signals i.e. individual members, of each set against every other individual member of that set. Also, for comparison by these means, the individual signals of a given set, e.g. set A are compared against each individual signal from a different set, e.g. set B. For an idealised system of two sets, all the signals in set A will be scored as identical when compared against each other. Similarly, all the signals in set B will score as identical when compared against each other. Finally, all the signals of set A when compared against all those of set B, will indicate that there is no similarity between any of the signals in the A and B sets. The outcome of such a perfect classification system would produce the Ideal System Comparison Ensemble, (ISCE) as shown in Figure 3. The table shows the ideal distribution of comparison results between five examples from two different sets of signals, set A and set B. By the comparison method applied, the five signals of set A appear identical when compared against each other, as do the five signals of set B. By the same comparison method, there is no similarity between any of the signals in the A and B sets. The * (asterisk) entries indicate redundant comparisons. It should be noted that in Figure 2, the WSD coder and classifier may take any known form. For example it may use any of the TES or TESPAR coding and classification processes. Alternatively it may use any of the WSD methods described in the present application or elsewhere such as in UK Patent No 2 306 010, or by any Wavelet transform characterisation including wavelet coefficients. In fact any coding procedure may be used which depends in whole or in part on the use of waveform shape descriptors (WSDs) as characterising entities for signal classification and analysis. The classification method used in the WSD classifier may be by any known means such as correlation by angle, or by angle and magnitude in TESPAR embodiments or at various ruler lengths as in UK Patent 2 306 010 or by any known comparison of wavelet coefficients in the wavelet method. Figure 4 Figure 4 illustrates a digital application of the present invention. The only difference between Figure 4 and Figure 2 is that in Figure 4 prior to WSD coding, the signals under test have their spectral entities subjected what will now be referred to as Spectral Reconfiguration Permutation & Mapping (SRP&M) which is the essence of the present invention. The signal under test 41 , first undergoes a DFT (or FFT) 42, which produces a "spectral plot" ie a plot which combines frequency and phase vectors. Subsequent to the DFT/FFT operation, the spectral entities of the signals under test are modified by SRP & M, at 43, in a manner to be described later. The modified spectral plot is then converted back to a waveform, at 44 by means of, for example, the IDFT (or IFFT) or by other means. The resulting waveform is then passed for coding to the WSD coder 45 and then for classification at 46), by the WSD previously described in relation to Figure 3. Thus, the reconstructed waveforms of each of the SUTs, after Spectral Reconfiguration Permutation & Mapping are then used as a new informative representation of the original signals for subsequent WSD coding and improved classification purposes. Spectral Reconfiguration Permutation & Mapping is essentially any rearrangement of the elements of the signal in such a way as to bring out differences between two signals the waveforms of which would otherwise appear inseparable. The means for achieving this rearrangement may be linear or non-linear, digital or analogue. The result of this procedure is S 0 to modify all the signals under test that by subsequent WSD coding means, the sets of manipulated data under test will, when compared by WSD means, be consistently more similar within their set and consistently more different across the differing sets than they were prior to SRP&M. In other words the Ideal System Comparison Ensemble (ISCE)of Figure 3 will be more nearly approached. For any given characterisation task, a large number of different SRP&Ms will enable the ideal SCE of Figure 3 to be approached more closely than would be the case without the application of the present invention. Even where the application of SRP&M still leaves errors of classification it is likely that these will be differently distributed within each SRP&M application. It would then be possible to employ well known data fusion classification procedures in order to diminish the significance of the individual errors. Thus 'P' different Spectral Reconfiguration Permutation & Mappings may be applied successively to each individual member of the sets of signals under test to provide 'P' different manipulated versions of each individual member of each set of signals under test. This will enable known data fusion analysis to be employed across each of the "P" such sets. Thus signal characterisation may be enhanced by reducing the errors involved. Figure 5 Figure 5 illustrates a digital embodiment. Figure 5A shows the structure of a typical spectral plot resulting from the DFT of Figure 4. The spectral locations in List 1 of Figure 5A indicate individual locations which, in the digital domain, relate numerically to the samples of the original digitised signal. At each spectral location, numerical values of magnitude (List 2) are associated with each of the elements of the spectral location (List 1). Associated with each of the (List 2) magnitude elements is phase element, shown as (List 3). If such a list were to be presented for processing to the IDFT without change, the waveform at the input to the WSD coder would be practically identical to the original unmodified signal under test. Figure 5B illustrates one simple implementation of the present inventive concept. There is a reordering which consists of a permutation of linked "location magnitude and phase" elements of the DFT numerical data. By this means, the numerical identities of location, magnitude and phase (hereafter referred to as a "triad") are linked together and then each such "triad" is reconfigured by, for example, permutation, to provide a common permutation and a "one to one" mapping, applied to all three linked spectral entities. Figure 5C shows a similar Spectral Reconfiguration Permutation & Mapping where the permutation 'P1' is applied only to the "magnitude" entities of the DFT with the location and phase elements remaining unmodified. Such procedures may provide a virtually unlimited variety of Spectral Reconfiguration Permutation & Mappings. These will include those related to all the entities of the lists and also those related to less than all the entities of the lists. For example, permutations may be applied to subsets only of individual spectral elements. Alternatively, or in addition , spectral elements may be grouped into specified predetermined preferred blocks of such entities and the subset blocks may themselves then be permutated or otherwise rearranged. A preferred selection from such a wide variety of options is determined by exhaustive search procedures and/or by choices based on the knowledge, skill and judgement of the skilled person in relation to the intended application. Examples of methods of carrying out the search procedures will be described later in relation to Figure 43. Figure 6 Figure 6 exemplifies a Spectral Reconfiguration Permutation & Mapping whereby the elements of the original spectral plot may be "expanded" from, for example, a 1-N spectral plot to a 1-M spectral plot where M is greater than N. It should be noted that on such expansion individual elements of individual lists may be independently and arbitrarily assigned to specified locations of the expanded plot. By means of a Spectral Reconfiguration Permutation & Mapping involving such expansion, spectral elements may, for example, be assigned to a number of vacant slots of the expanded plot to reduce the number of vacant i.e. zero elements in the expanded plot. Alternatively they may be excluded, or overiapped and further manipulated by selective summation or by multiplication or by selective squaring or square rooting, or by any other function or combination of functions to provide more, or fewer vacant (zero) elements in the expanded plot. All such Spectral Reconfiguration Permutation & Mappings may be applied to individual lists or part of lists independently. Figure 7 In Figure 7 the method includes Spectral Reconfiguration Permutation & Mappings whereby the manipulated spectral plot is a reduced version of the original in that it contains fewer spectral entities "R" than the original "N". This may result from an exclusion of elements from the original or by an overlapping of a number of elements in the original onto the limited number in the reduced plot. Such reductions may be produced by exclusion or overlapping or by summation or by multiplication or by any other function or combination of means to provide a consistent subset of "R" spectral elements which will, in this example, be less than the number of elements "N" in the original spectral plot. Such procedures may also result in a reduced spectral plot with less than "R" occupied elements. As previously described, such Spectral Reconfiguration Permutation & Mappings may be applied to individual lists of spectral entities independently. Such processes may be applied in a linear fashion, or a non-linear fashion. The relationship between the original spectral plot and the manipulated spectral plot may be a mapping such as a "one to one and into" injection mapping. The mapping could be a well-known 'surjection' or a 'bijection' as disclosed at page 169 of R.H.Atkin, Mathematical Structure in Human Affairs ISBN 0435 820257, Appendix A, 1974. for example. This is a well known mathematical procedure. The Spectral Reconfiguration Permutation & Mapping could embody one or any number of mathematical relations such as typified at pages 163-172 of the Atkin reference immediately above. Alternatively, for example, sub sets of spectral elements from one data set may be averaged, by any weighted or non-weighted means and the resulting set of averaged spectral entities may then be ranked to provide any defined ranking order, (e.g. a ranking in an increasing or decreasing order of magnitude, or any other prescribed order) associated with the averaged set. This defined ranking order may then be applied as a prescribed permutation for application across all sets of signals taking part in the classification exercise. In the case of two sets of signals "A" and "B", sub set elements of set A may be averaged and ranked in spectral location in terms, for example, of decreasing magnitude. The permutation of locations resulting from such ranking, being applied to the spectral elements of both set A and set B prior to classification by WSD means. By this means the individual signals within each set may be more closely correlated and the important differences between sets A and B may be more apparent. The Spectral Reconfiguration Permutation & Mapping of the spectral elements may be, linear, non-linear, random, or structured, or functional, or any combinations of such. The key requirement of any such Spectral Reconfiguration Permutation & Mapping is that after SRP&M, the resulting reconstructed waveform shape descriptors (WSDs) classify more effectively than before for the purpose required. Once a preferred selection of SRP&Ms has been made that selection is then used as a basis for constructing a signal recognition system or product designed for a particular application such as voice recognition, for example. Figure 8 Figure 8 which is a combination of Figures 2 and 4, exemplifies a typical procedure and data architecture for comparing the performance of simple conventional WSD coders without the present invention against such WSD coders and classifiers incorporating the present invention of Spectral Reconfiguration Permutation & Mapping. The analysis and classification data exemplified at Figure 2 (5) , at Figure 4 (7) and at Figure 8 (11) shall, for the purpose of this disclosure, be illustrated by means of "System Comparison Ensembles" ("SCEs") of the type already illustrated in Figure 3. As previously indicated in Figure 3 by such means, an ideal two set "A vs. B" system characterisation performance would classify and separate all such "A" signals from all such "B" signals, such that; All the (A) signals classify as identical, when compared against each other; All the (B) signals classify as identical when compared against each other, and Each of the (A) signals when compared against each of the (B) signals classify as orthogonal, i.e. having zero similarity. In most real-world situations, such classification criteria represent, by conventional means, an unattainable ideal when using known methods of signal recognition/classification/differentiation. The invention will now be illustrated, by way of example only, and with reference to the following. Three different types of real-world signals namely: a. Two sets of twelve "Normal" and twelve "Stressed" closely similar, short duration, low frequency, shaped sinusoid waveforms (Figures 9 to 19). b. Two sets of six "Good" and six "Faulty" closely similar, continuous waveforms derived from rotating machinery (Figures 20 to 23). c. Two sets of five acoustically similar speech waveforms of the words "six" and five of the word "fix", which waveforms have been massively corrupted by aliasing and spectral fold-over (Figures 24 to 28). To illustrate the advantages of the present invention and its applicability to WSD systems in general three different WSD characterisation systems were applied in turn to the above signals, namely: d. The TESPAR WSD algorithm (UK Patent Nos. 2 020 517 and 2 145 867) using both "S" and "DZ" matrix descriptors (Figures 29, 30); e. The University of Wales (UOW) WSD algorithm (UK Patent No 2 306 010) across fifteen "Ruler Lengths" (Figures 31, 32); f. MATLAB® based Wavelet WSD (US Patent No 6,173,074) decompositions (Figures 33, 34, 35, 36, 37, 38). Five differing illustrative types of Spectral Reconfiguration Permutation & Mapping were selectively applied in respect of each of the WSD systems d, e and f above, namely: g. A simple linear SRP&M (Figures 5A and 5B); h. Two SRP&Ms each involving different "non-linear", "element reducing" "multiplicative" transformations, where spectral "magnitude" only is manipulated and the phase remains unchanged. i. An SRP&M involving a random permutation of the phase elements only of the corrupted signals. In this transform, the locations of the magnitude elements of the decimated signal are preserved unchanged. j. An SRP&M involving re-ordering locations of a given set of spectral plots, by means of a "ranking" of the average spectral magnitudes of a given set, and applying this magnitude ranking to the competitor sets, with the original phase elements preserved unchanged. Methodology k. A classification and comparison of the waveforms of the exemplar SUTs, is undertaken, initially using the known WSD methods, referred to at d, e and f above to produce a System Comparison Ensemble (SCE) without the use of the present invention. I. "Spectral Plot" data of the SUTs, is generated, using known DFT/FFT routines. An identical SRP&M is then applied to the DFT/FFT data of each of the SUTs. m. Waveforms from the manipulated spectral plots produced at "I" above are regenerated, via an IDFT and a classification and comparison is carried out by the same WSD methods and routines as were used in "k" above. n. A SCE is produced, for the SRP&M data, as a performance indicator for comparison between the prior art system and one employing the present invention. o. Classification improvements produced by the application of the present invention are observed and recorded as detailed later. The above described methodology is illustrated, by way of example, with reference Figures 9 to 38. Figures 9 to 19 (TESPAR, Zero-based WSD). In this example, the test signal waveforms are short duration, low frequency, shaped sinusoids, representing the vibration response of a "normal" mechanical structure,(N), and that of a "stressed" mechanical structure (S). Twelve typical examples N1 to N12, and S1 to S12 are examined. Figure 9 illustrates a typical normal (N) signal (Figure 9A) and a typical stressed (S) signal (Figure 9B) together with their respective "base band" spectral plots (Figure 90 and 9D), derived via a conventional MATLAB® DFT algorithm. The spectral values are described as a function of "Pi". This may be converted to Hz by multiplying the frequencies by 250/Pi then by the sampling rate. The number 250 is derived from the number of samples divided by two. This well-known convention will be utilised throughout. Figures 10A and 10B shows the System Comparison Ensemble derived for comparisons of twelve such normal signals, N1 to N12, with twelve such stressed signals, S1 to S12. These signals are correlated using two different TESPAR matrix representations i.e. by TESPAR "S"-matrices (Figure 10A) and by TESPAR "DZ" matrices (Figure 10B). A brief examination of these comparison ensembles indicates that such simple WSD correlation routines applied to these data are likely to be significantly error prone bearing in mind the "ideal" as illustrated in Figure 3. It will be observed that the N vs. S block (Top right hand side) of the Figure 10A ensemble contains a number of high scoring comparisons and the Figure 10B ensemble is only marginally more discriminative. Figures 11 A, B, C and D show identically the same test signals and spectral plots as in Figures 9 A and 9B. Figures 11E and 11F show the spectral changes which result from applying an identical selected SRP&M, to the original Figures 90 and 9D spectral plots. It will be seen that Figures 11E and 11F now appear significantly differently distributed. Figures 11G and 11H show the waveforms which result from a conventional IDFT applied to the Figures 11E and 11F spectral plots to produce the two transformed waveforms. It can be seen that the application of the invention to the original closely similar signals has produced signals which appear to differ significantly in terms of their wave shape (WSDs). In this instance the permutation and mapping deployed was a very simple random reordering of the linked magnitude and phase vectors of the original plot. The numerical permutations involved were generated by a MATLAB® random reordering routine in the manner described later in relation to Figure 43. Figure 12 shows the System Comparison Ensemble (SCE) for the normal signals (Figure 12A) and the reordered signals Figure 12B when compared using TESPAR S-matrix correlations. From Figure 12B it may be seen that the improvement in the consistency within each set is significant. Also, the very low correlation scores across the two sets of data as shown in the top right hand block of the SCE i.e. across the normal and stressed boundaries is approaching the ideal of Figure 3. Figure 13 shows identically the same exemplar signals as in Figures 11 A and 11B but processed by a different random permutation but deploying the same methodology as for Figure 11. It will be seen from Figure 13 that the modified waveforms resulting from the new permutation and mapping of the invention have resulted in different wave shapes compared to those of Figures 11G and 11H. However, as with Figure 11 the application of the invention has resulted in signals which can be seen to differ significantly in terms of their Wave Shape Descriptors (WSD's). Figure 14A records the System Comparison Ensemble applied to the unprocessed signals of Figures 13A and 13B. Also, for comparison, the Figure 14B ensemble illustrates the scores after reordering. To exemplify the application of different correlation routines, DZ matrix correlations have been used here. The improvements in classification performance are again significant. Figure 15. In this example the same pair of signals have been used as in Figure 9A to 9D to illustrate the effects of another completely different Spectral Reconfiguration Permutation and Mapping algorithm when applied to this signal set. Figures 15A to 15D shows the normal and stressed signal with their associated spectra. Figures 16A and 16B repeat Figures 10A and 10B showing comparison ensembles for the unmodified data set against both S 0 matrix and DZ matrix correlation routines. Figures 17 A, B, C and D repeat the four waveforms and spectral plots of Figures 11A i 11H. Figures 17E and 17F illustrate the application of a permutation and mapping procedure incorporating a "many to one" reduction mapping as part of the permutation process. The algorithm reorders each individual spectral element in a structured but non-linear fashion such that spectral entities may, when transformed, overlap and sum. It will be seen from Figures 17E, F, G and H that applying this procedure produces spectral plots with a much reduced spectral occupancy (two only entries) and widely differing reconstructed waveforms. It is of note that in this example, the exemplar non-linear reducing mapping is applied to the magnitude elements of the DFT only and that the phase elements of the transformed spectral plot remain identical to those of the original. Figure 18A shows the System Comparison Ensemble for the S matrix correlations of the original data and in Figure 18B the matrix correlations of the same data set after the permutation and mapping of the present invention. The classification improvements provided by the invention can be seen in Figure 18B. Figure 19 shows the comparison ensembles for the normal and permutated data sets using DZ WSD matrix correlations in both cases. The improvements are apparent from Figure 19B. Figures 20 to 23 Figure 20 shows a typical waveform derived from rotating machinery. In Figure 20A the machinery is in a good condition and in Figure 20B the machine is operating in a faulty mode. The corresponding spectral plots of both specimen sets of data are indicated in Figures 200 and 20D respectively. Figure 21 illustrates a comparison ensemble for six such examples of "Good" with six examples of "Faulty" signals. The System Comparison Ensembles correlation scores shown in Figure 21 are for TESPAR DZ WSD correlations. Figure 22 shows the effects of applying a non-linear SRP&M similar to that indicated in Figure 17. In this instance a different multiplicand function was used but, as previously, the phase elements of the original remained unchanged. Here, in Figures 22E and 22F, it may be seen that the original spectral data has, by means of a reduction mapping, been reduced to six spectral elements only. The resulting signals derived via a standard Inverse Fourier Transformation (IFT) are shown in Figures 22G and 22H. Figures 23A and 23B illustrate respectively the ensemble produced without the benefit of the present invention and the ensemble produced by use of the present invention. The improvements are apparent. Figures 24 to 28 Figures 24 to 28 illustrate the present invention applied to a small set of speech waveforms ("six" and "fix") which have been heavily corrupted by means of a massive, e.g. ten fold, under sampling. Figure 24 illustrates the two corrupted waveforms at Figures 24A and 24B. Figures 240 and 24D show the spectral plots of these two signals. The spectral elements have been severely mutilated by aliasing and spectral fold- over. Figures 25 shows the System Comparison Ensembles of five examples of each word. Figure 25A shows the results of S-matrix WSD correlations. Figure 25B shows the equivalent scores using DZ matrix WSD correlations. Without the benefit of the present invention in both cases the results are very poor when compared with the ideal of Figure 3. Figure 26 shows the original specimen signals at Figures 26A and 26B and the original spectral plots at Figures 260 and 26D. For the purpose of this illustration, an SRP&M based on ranking is deployed. By this means the magnitude profiles of all the individual spectral plots of the word "six" are averaged and the average magnitude plot S 0 derived has its magnitude elements reordered e.g. ranked in descending order of magnitude. This new permutation order, based on the averages of the spectral entities of the original set and is then applied across the individual magnitude elements of the spectral entities of the original set, is then applied across the individual magnitude elements of the spectral plots of the signals under test. Figure 26E indicates the averaged and ranked plot derived for the specimen word "six". Figure 26F shows this particular averaged and ranked permutation applied to the spectral elements of the specimen word "fix". In this example the ranking permutations are applied to the magnitude element of the spectral plot only, the phase elements remaining un-transformed. Figures 26G and 26H illustrate the waveforms resulting from the application of the present invention. Figure 27 shows the comparison ensemble for TESPAR WSD S- matrix magnitude correlations with (Figure 27B) and without (Figure 27 A) the SRP&M of the present invention. Figure 28 shows a similar set of measures using TESPAR WSD DZ magnitude correlations Figure 28A without the invention and Figures 28B with the invention. Here again, in both correlation domains the improvements are significant. Figures 29 to 32 (Re UK Patent No 2306010: University of Wales) Figures 29 to 32 show the application of the present invention to the WSD system described in UK Patent No 2 306 010 (University of Wales). Figure 29 is the same as Figure 9 and shows the normal and stressed signals together with their DFT magnitude plots. Figure 30 shows the representations of the transformed signals utilising the transform of Figure 13. The appropriate transformed spectral magnitude plots are shown in Figure 30C and 30D. In this example, specimen waveforms are coded using the algorithm disclosed in UK Patent No 2 306 010 across fifteen "ruler lengths". A differential comparison routine via a conventional angle and magnitude correlation is performed on the data sets derived. Here the examination is limited to the two signals shown in Figures 29A and 29B. Figure 31 shows three plots of the measure "D(r)" referred in UK Patent No 2 306 010. Figure 31 A shows a comparative plot and the conventional angle (a) and magnitude (m) correlation scores for two normal signals. Figure 31 B shows the comparative plot and scores for two stressed signals. The correlation scores are for those individual "D(r)" signals examined differentially over fourteen "ruler lengths" for both angle (a) and magnitude (m) correlations as indicated in UK Patent No 2 306 010 Figure 310 shows a similar plot and correlation scores for a normal signal vs. a stressed signal. Figure 32 shows the same procedure that is described above in relation to UK Patent No 2 306 010 applied to the same set of signals but after SRP&M according to the present invention. From Figures 32A and 32B it may be seen that the two normal signals, when modified by the procedure of the present invention, correlate very highly. Similarly in Figure 32B the two stressed signals when transformed also correlate very highly. Figure 320 compares a normal signal with a stressed signal after transformation according to the present invention and indicates that when correlated the normal and stressed signals have no similarity by this measure, i.e. they are orthogonal. This simple comparison demonstrates that the present invention is applicable to the type of WSD system disclosed in UK Patent No 2 306 010. Figure 33 (Re. Wavelet WSD) Figure 33 Illustrates the application of the present invention to a WSD system of the type known as a "wavelet" system as, for example, disclosed in UK Patent No 6 173 974. Figures 33A and 33B illustrate a normal signal and a stressed signal, respectively. The waveforms shown in Figures 33C and 33D are MATLAB® wavelet representations of these signals derived from the MATLAB® Wavelet handbook by well known means. Figure 33C shows the Level 1 approximation of the normal signal. Figure 33D shows the Level 1 Approximation for the stressed signal. Figures 33E and 33F show the Level 1 detail for the normal and stressed signal respectively. System Comparison Ensembles (SCE) for the wavelet Level 1 Approximation, without the application of the present invention are shown in Figure 34. Both standard S matrix and DZ matrix comparisons of these wavelet Level 1 approximations are shown in Figure 34. The classification or differentiation of the signals is far from the ideal of Figure 3. Figure 35 illustrates the application of the present invention to the signals of Figure 33. In particular Figures 35A and 35B show the transformed signals, Figure 35C and 35D show the Level 1 "approximation" of these transformed signals and Figures 35E and 35F illustrate the Level 1 "Details" of the transformed signals. It may be observed that after the SRP&M procedure of the present invention the Level 1 approximations are significantly more different than before SRP&M. It is clear that their differences are much more apparent than the Level 1 approximations of the original signal. Similarly the Level 1 detail for the processed data sets are significantly more different and therefore more easily discriminated, differentiated or described. Figure 36 illustrates these differences across the twelve examples of both normal and stressed waveforms. Figure 36A shows the comparison scores before SRP&M is applied and Figure 36B after SRP&M is applied. The improvements here again are significant. To confirm that the procedure is relatively insensitive to the permutation and comparison method applied, Figure 37 shows identically the same data after a different permutation from that of Figure 35 is applied together with the appropriate Level 1 Approximation and Level 1 detail for both normal and stressed signals. Figure 38 relates to Figure 37 and shows the comparison ensembles before and after permutation where the improvements again are similarly significant. Figures 39 to 42 are enlarged versions of Figures 110 to 11F respectively in order to assist in an appreciation of the problem of two different signals looking superficially the same. Figures 39 to 42 A simple linear Spectral Reconfiguration Permutation and Mapping will now be described. Two sets of pcm signals are chosen to illustrate discrimination. These are referred to as "Normal" and "Stressed" and there are twelve examples in each set. First it is necessary to establish general performance criteria against which the range of SRP&Ms can be calibrated. Applying WSD coding to both sets of waveforms does this. The WSD coded data, which can be S 0 , DZ or any other WSD format, is then assembled in an Ideal System comparison Ensemle (ISCE). This ISCE forms the set of "non-optimised" comparison data (ie a baseline performance indicator without SRP&M). The SRP&M Optimisation sequence takes the "normal" and "Stressed" waveform sets and applies a DFT, converting the waveforms into spectral plots. An identical SRP&M manipulation is then applied identically to both sets of spectra. IDFT is applied to convert both sets of spectral plots back into "modified" waveforms. These new waveforms are subjected to processing similar to that applied to the original waveforms to create new WSDs, which are compared in their appropriate ISCEs. The two ISCE (non-optimised and SRP&M modified) are compared, using a set of pre-established rules, to determine if the discrimination performs has been enhanced. If so, then the permutation is recorded in a table of results. A new SRP&M manipulation is then applied and the sequence repeated. A small table of the best results is assembled for further examination prior to embodiment into a working system. In this example the permutation and mapping used was a simple random recording by permutation of the linked magnitude and phase vectors of the original spectral plot. Successive random permutations were investigated to achieve improved classifications. As indicated, advantageous, (improved) permutations were identified by testing the classification obtained across the two sets of data, before and after a succession of randomly produced spectral re-orderings. After each reordering, the latest result was compared with the previously recorded, "n" results and added to the "n" set, replacing an existing member if this latest permutation is "better" than any of the existing members of the set. In this instance, "better" might mean a procedure which: a. increases the correlation scores between individual members of the same set, and b. decreases the correlation scores across individual members of the two different sets. An illustrative example of "better" might be a simple procedure, which accepts and records comparison where, resulting from successive random permutations: a. No correlation between individual elements in the same set is less than or equal to say, 0.8 and b. No correlation between the individual elements of the two different sets (i.e. Normal or Stressed) is higher than any of the "n" permutations saved previously, where "n" may be, as indicated, a number between 4 and 8. c. If the most recent examination is better than any of the "n" previously stored entries, replaces the entry in the "n" list which is furthest away numerically from the most recent version, by the most recent version. d. As a result, the classification errors associated with the members of the set "n" may be progressively reduced. Thus, by such means, the "n" permutations that produce the widest separation of the two sets of signals (See Figure 3, "Ideal System Comparison Ensemble") across the e.g. 10,000 permutations investigated, are saved to identify a small, optimized residual set of "n" for further examination. The effects of SRP&M can clearly be seen from the examples shown in Figures 12 and 14 which demonstrate the improved performance resulting from SRP&M. Thus, for these data: Across the Set of 12 x 12 S 2 Matrices Comparisons:- Before SRP&M: Worst Example Normal vs Normal = 0.45 After SRP&M: Worst Example Normal vs Normal = 0.97 Before SRP&M: Worst Example Stressed vs Stressed = 0.68 After SRP&M: Worst Example Stressed vs Stressed = 0.98 Before SRP&M: Worst Example Normal vs Stressed = 0.45 After SRP&M: Worst Example Normal vs Stressed = 0.97 "MATLAB®" functions deployed: MATLAB® The MathsWorks, Inc. 24 Prime Park Way, Natick, Mass. 017760 Read in pcm using the function "readpcm." Remove dc level from the pcm example using "removedc." iii. Calculate 'L' : (length of signal/2 i.e. length of DFT vector) iv. Set 'p' : the number of permutations to be investigated eg 10,000. v. Reset the random number generator to its initial state. (rand('state',0) vi. Repeat the following steps, vii-xiii, 'p' times:- vii. Use "randperm" function to produce a random permutation ('R) of the numbers 1 to 'L'. viii. Repeat steps ix-xii as indicated:- ix. Obtain the DFT (see later for DFT algorithm) x. Recorder the DFT magnitude and phase vector using 'R' as an index. xi. Reconstruct waveform using an IDFT or other similar procedure, using the recorded DFT vector to produce the transformed pcm. (see later for pcm reconstruction algorithm). xii. Produce S Q and DZ matrixes from this transformed pcm using routines from the TESPAR toolbox. xiii. Calculate correlations using "score" for all S 0 and DZ matrixes. (Normal vs Normal, Stressed vs Stressed and Normal vs Stressed.) xiv. A set of the best permutations after 'p' repetitions using criteria above are saved. (It is likely that the best permutations for S 0 may be different from the best for DZ, or from other WSD coder variants.) As indicated earlier the SRP&M process just described is illustrated in Figures 39 to 42. Figures 39 and 40 show the DFT of a typical Normal signal. Figures 41 and 42 show the DFT of a typical Stressed signal. To aid interpretation, in Figures 39 and 41 the largest eight magnitudes of each spectral plot have been labelled with the pre-transform numerical location (Figures 39 and 41) in the vector so that a comparison can be made with their post-transform numerical locations (Figures 40 and 42). It will be seen that in both the "Normal" and "Stressed" examples, the largest magnitudes appear between positions 30 and 45 in Figures 39 and 41. The top eight magnitudes are not in the same numerical locations in the two examples, nor are the magnitudes the same, although the profiles look similar when clustered in the pre-SRP&M spectral plots. Figures 40 and 42 show the numerical locations of the top eight magnitude spectra after SRP&M. The arrowed numbers represent the new (permuted) locations with their spectral origins indicated by the numbers at the tope of each of the spectral lines. The transformed waveforms produces by reconstructing these spectra are shown in Figure 11G (Normal) and Figure 11H (Stressed). The following table shows the numerical locations of the top eight magnitude spectra from which it can be seen that identically located elements are identically displaced. Figures 17 and 43 to 46 A non-linear, element reducing multiplicative Spectral Reconfiguration Permutation and Mapping, where e.g. spectral magnitude only is manipulated and the phase remains unchanged, will now be described. In this example, as in the example described immediately above with reference to Figures 39 to 42, the same two sets of pcm signals i.e. "Normal" and "Stressed" waveforms were used for comparison, using twelve examples of each. As before, as a basis for comparison, both sets of twelve pcm signals were coded into conventional TESPAR "S 0 " and DZ" coding formats. By standard correlation comparisons, an Ideal System Comparison Ensemble [ISCE], was created for both S 0 and DZ variants. This established a set of conventional baseline classification performance indicators for the unprocessed versions of these WSD coding formats. In preparation for SRP&M processing, the twelve members of the original normal and stressed signal sets were presented to a conventional DFT algorithm to produce individual spectral plots for each of the twelve signals in each of the two sets. As indicated earlier, a single identical specified SRP&M permutation was then applied to each of the individual spectral plots of the twelve members of both sets. The 2 x 12 individual spectra S 0 modified were then converted back to waveforms by an IDFT or by another means of (Frequency to Time) reconstruction. By these examplar "SRP&M" means, the twelve individual members of each of the two original sets of waveform data were thus transformed. Individual members of the two new sets of twelve transformed waveforms generated by this process were then passed to coding to S 0 and DZ coders ie typical WSD coders, using identical coding specifications to those applied to the original un-transformed data. By the same standard correlation comparisons, an Ideal System Comparison Ensemble (ISCE) was created for the SRP&M transformed versions using both S 0 and DZ variants. Examplar descriptions and results of this process are shown at Figure 17. The ISCEs of the original un-transformed data were compared with those of the transformed version to assess the improvement, if any, in the classification resulting from that particular SRP&M permutation. This process was then repeated with different permutations to identify a best small subset of "n" permutations from the total number of permutations used in the investigation. In the illustrative example, which follows, the permutation and mapping used records each individual spectral element in a structured, non-linear fashion such that individual spectral entries may, when transformed, overlap and sum. Each spectral location is multiplied by an integer number "m" (the multiplicand) and the new location is the frequency location after multiplying by "m" with wrapping at either side of the sample range. Refer to the multiplicand function in section 3c). Successive permutations were investigated to achieve improved classifications. As indicated previously, advantageous (improved) permutations were identified by testing the classifications obtained across the two sets of data, before and after a succession of spectral re-orderings. After each reordering, the latest result was compared with the previously recorded, "n" results and is added to the "n" set, if this latest permutation is "better" than any of the members of this set. In this instance, "better" might mean a procedure which: a. increases the correlation scores between individual members of the same set, and b. decreases the correlation scores across individual members of the two different sets. By way of example but not limitation, as illustrative example of "better" might be a simple procedure, which accepts and records comparison where, resulting from successive random permutations: a. No correlation between individual elements in the same set is less than or equal to say, 0.8 and b. No correlation between the individual elements of the two different sets is higher than any of the "n" permutations saved previously, where "n" may be, for example a number between 4 and 8. c. If the most recent examination is better than any of the "n" previously stored entries, replace the entry in the "n" list which is furthest away numerically from the most recent version, by this most recent version, d. As a result, the classification errors associated with the members of the set "n" may be progressively reduced. Thus, by such means, the "n" permutations that produce the widest separation of the two sets of signals (see Figure 3, "Ideal System Comparison Ensemble") across the permutations investigated, are saved to identify a small, optimized, residual set of "n" for further examination. The effects of SRP&M can clearly be seen from the examples shown in Figures 18 and 19 which demonstrate the improved performance resulting from SRP&M. Thus, for these data: Across the Set of 12 x 12 Sp Matrices Comparisons:- Before SRP&M: Worst Example Normal vs Normal = 0.45 After SRP&M: Worst Example Normal vs Normal = 0.99 Before SRP&M: Worst Example Stressed vs Stressed = 0.68 After SRP&M: Worst Example Stressed vs Stressed = 1.00 Before SRP&M: Worst Example Normal vs Stressed = 0.99 After SRP&M: Worst Example Normal vs Stressed = 0.18 "MATLAB®" functions deployed: Read in pcm using the function "readpcm." i. Remove dc level from the pcm examples using "removedc." ii. Calculate 'L' : (length of signal/2 i.e. length of DFT vector) v. Set 'm' : the multiplicands to investigate e.g. 10,20,30...400 or 2,4,6...100. v. Repeat the following steps, vi-xii, for each value of "m":- vi. Repeat steps, vii-xi, of each of examples:- vii. Obtain the DFT (see later for DFT algorithm) viii. Calculate the new frequency location of each element of the DFT magnitude vector by using the multiplicand algorithm (Refer to section 3). ix. If, after applying the multiplicand algorithm, a magnitude element arrives at a frequency location that is already occupied, it is summed to the magnitude already there. This results in the "many to one" reduction mapping referred to in the SRP&M patent diagram 10.3 description. x. Reconstruct waveform using the transformed magnitude vector and the original phase vector to produce the transformed pcm. (Refer to section 3 for pcm reconstruction algorithm). xi. Produce S 0 and DZ matrices from this transformed pcm using routines from the TESPAR toolbox. xii. Calculate correlations using "score" for all S 0 and DZ matrices. (Normal vs Normal, Stressed vs Stressed and Normal vs Stressed). xiii. A set of the best permutations using criteria above are saved. (It is likely that the best permutations for S 0 may be different from the best for DZ, or from other WSD coder variants.) As indicated earlier the SRP&M process just described is illustrated in Figures 43 to 46. Figures 43 and 44 show the DFT of a typical Normal signal. Figures 45 and 46 show the DFT of a typical Stressed Signal. To aid interpretation, in Figures 43 and 45, the largest eight magnitudes of each spectral plot have been labelled with their pre- transform numerical location (Figures 43 and 45) in the vector so that a comparison can be made with their post-transform numerical locations (Figures 44 to 46). It will be seen that in both the "Normal" and "Stressed" examples, the largest magnitude appear between positions 30 and 45. The top eight magnitudes are not in the same numerical locations in the two examples, nor are the magnitudes the same, although the profiles look similar when clustered in the pre SRP&M spectral plots. Figures 44 to 46 show the numerical locations of the top eight magnitude spectra after SRP&M. (A multiplicand of 200 is used in this example.). The horizontal scale represents the new (permuted) locations with their spectral origins indicated by the numbers at the top of each of the spectral lines. The transformed waveforms produced by reconstructing these spectra are shown in the SRP&M patent in Figure 17G (Normal) and Figure 17H (Stressed). The following table shows the numerical locations of the top eight magnitude spectra from which it can be seen that identically located elements are identically displaced. It should be noted that the magnitudes at location 1 are not used in the reconstruction of the waveform (dc), so that Figures 44 to 46 do not give magnitudes at location 1. Algorithm Details a. DFT NS = length (sig) get number of samples Nt = fix (Ns/2) X = fft (sig) produce DFT vector X = X (1 :Nt) only need to look at first half of spectrum - don't need mirror image RR = abs(X) produce magnitude vector A = angle (X) produce angle vector b. IDFT Inverse DFT using MATLAB IDFT when 3 vectors not separate X (Nt+1 : Ns) = 0 zero upper half of spectrum rec = 2*real(ifft(X)) perform IDFT on DFT vector X Inverse DFT using MATLAB IDFT when 3 vectors separate Z = RR *exp (sqrt(-1)*A) RR is the DFT magnitude vector, A is the DFT angle vector Z(Nt + 1 :Ns)=0 zero upper half of spectrum rec=2 * real (ifft (Z)) perform IDFT on DFT vector Z Reconstruction of pcm from specified terms of its DFT W=2 * pi * (1 :Nt)/Ns produce frequency location vector t=1:Ns rec = X(1)/2 for K = 2:Nt rec = rec + RR(k)* cos(W(k-1)* t + A(k)) end rec = 2 * rec/Ns c. Multiplicand Function Variables 's' is the number of samples in the magnitude vector 'f is a frequency location 'm' is a spectral multiplier (i.e. the multiplicand) 'newW is the frequency location after multiplying by m with wrapping at either side of the sample range Algorithm q=fix((m * f)/s) z=(-1) Λ q a=(1-z)/2 newW=a*s + z*mod (m*f,s) Set the new frequency location to 1 if it work out as zero: If newW==0 newW=1 end Figure 47 The iterative process by which the optimum SRP&Ms are obtained is illustrated in the flow chart of Figure 47. Figure 48 This illustrates diagrammatically one possible application of the present invention. A machine 481 is fitted with a sensor 482 so that when the machine is operating the sensor generates an electrical signal which is indicative of the operating condition of the machine. This signal is fed into a fault detection system 483 which incorporates the present invention. The system 483 is equivalent to 42 to 47 of Figure 4. As indicated earlier the present invention may be implemented in either a digital or analogue form. Analogue processing by well known methods such as those described by Beker & Piper [J.J. Beker and F.C. Piper. Secure Speech Communications. ISBN 0-12-084780-9. 1985. Chapter 4 - Frequency Domain Scrambling] for frequency domain and time domain re-ordering, by analogue processes associated with classical analogue filter design may be used.

Claims: Claims 1. A signal recognition system which comprises: a) means for effecting a frequency domain transform on an input signal; b) means for producing a spectral plot from the output of a); c) means for effecting a spectral reconfiguration permutation and mapping (SRP&M) on the spectral plot as hereinbefore defined; d) means for effecting an inverse frequency domain transform on the output of c); and e) means for applying a Wave Shape Descriptor coding and classification to the output of d). 2. A system as claimed in claim 1 including a process to optimise the selection of suitable SRP&Ms. 3. A system as claimed in claims 1 or 2 in which the frequency domain transform and inverse transform are Fourier transforms and inverse Fourier transforms respectively. 4. A system as claimed in any of claims 1 to 3 in which the means e) comprises a TESPAR coder and classifier. 5. A system as claimed in any of claims 1 to 3 in which the means e) comprises a Wavelet coder and classifier. 6. A system as claimed in any of claims 1 to 3 in which the means e) comprises a coder and classifier as claimed in UK Patent No 2 306 010 (University of Wales). 7. A system as claimed in any of claims 1 to 3 in which the means e) comprises a coder and classifier as claimed in US Patent No 5,847,658 (OMRON). 8. A system as claimed in any previous claim in which the said means are digital means. 9. A system as claimed in any of claims 1 to 7 in which the said means are analogue means. 10. A signal classification method which comprises: a) effecting a frequency domain transform on an input signal; b) producing a spectral plot from the output of a); c) effecting a spectral reconfiguration permutation and mapping on the spectral plot as hereinbefore defined; d) effecting an inverse frequency domain transform on the output of c); and e) applying a Wave Shape Descriptor coding and classification to the output of d). 11. A method as claimed in claim 10 including the step of optimising the selection of suitable SRP&Ms. 12. A method as claimed in claim 10 in which the frequency domain transform and inverse transform are Fourier transforms and inverse Fourier transforms respectively. 13. A method as claimed in any of claims 10 to 12 in which the method e) comprises a TESPAR coding and classifying. 14. A method as claimed in any of claims 10 to 12 in which the step e) comprises a Wavelet coding and classifying. 15. A method as claimed in any of claims 10 to 12 in which the step e) comprises coding and classifying as claimed in UK Patent No 2 306 010 (University of Wales). 16. A method as claimed in any of claims 10 to 12 in which the step e) comprises coding and classifying s claimed in US Patent No 5,847,658 (OMRON). 17. A system as claimed in any of claims 10 to 16 in which the said means are digital means. 18. A system as claimed in any of claims 10 to 16 in which the means are analogue means. 19. A method of analysing a band-limited signal, comprising the steps of i) receiving a band-limited signal; ii) generating a variable dependent electrical signal in response to said band-limited signal; iii) processing said variable dependent electrical signal to generate a set of characteristic values comprising a set of frequency and phase values and an associated set of amplitude values; iv) mapping said set of characteristic values onto a modified set of characteristic values; v) processing the modified set of characteristic values to generate a modified variable dependent signal; vi) encoding said modified variable dependent signal to generate a set of waveform shape descriptors; and vii) comparing said set of waveform shape descriptors with a reference data set. 20. A method as claimed in claim 19 in which the variable is time. 21. A method of analysing a band-limited signal according to claims 19 or 20, wherein said mapping comprises mapping said set of frequency values onto a modified set of frequency values to generate said modified set of characteristic values. 22. A method of analysing a band-limited signal according to any of claims 19 to 21, wherein said mapping comprises mapping said set of amplitude values onto a modified set of amplitude values to generate said modified set of characteristic values. 23. A method of analysing a band-limited signal according any of claims 19 to 22, wherein said set of characteristic values includes a set of phase angle values and said mapping comprises mapping a set of phase angle values onto a modified set of phase angle values to generate said modified set of characteristic values. 24. A method of analysing a band-limited signal according any of claims 19 to 23, wherein said band-limited signal is a time varying signal. 25. A method of analysing a band-limited signal according any of claims 19 to 24, wherein said band-limited signal represents a sound signal. 26. A method of analysing a band-limited signal according any of claims 19 to 24, wherein said set of waveform shape descriptors are generated using a time encoded signal coding process. 27. A method of analysing a band-limited signal according to any of claims 19 to 26, wherein said set of characteristic values is generated by subjecting said time dependent electrical signal to a Fourier transformation, and said modified time dependent signal is generated by subjecting said modified set of characteristic values to an inverse Fourier transformation. 28. A method of analysing a band-limited signal according to any of claims 19 to 27, wherein said time dependent electrical signal is subject to a plurality of band-pass filters thereby generating said set of characteristic values as a plurality of frequency bands in which each frequency band has associated frequencies and associated amplitudes, and said mapping comprises inter changing the associated frequencies of at least one of the frequency bands. 29. Apparatus for analysing ~ a band-limited signal, comprising i) receiving means for receiving a band-limited signal and generating a variable dependent electrical signal in response to said band- limited signal; ii) a signal processing means which processes said time dependent electrical signal to generate a set of characteristic values comprising a set of frequency values and an associated set of amplitude values; iii) a mapping means which maps said set of characteristic values onto a modified set of characteristic values; iv) a signal generating means which processes said modified set of characteristic values to generate a modified variable dependent signal: v) coding means which encoding said modified time dependent signal to generate a set of waveform shape descriptors; and vi) comparison means for comparing said set of waveform shape descriptors with a reference data set. 30. Apparatus as claimed in claim 29, wherein said mapping comprises mapping said set of frequency values onto a modified set of frequency values to generate said modified set of characteristic values. 31. Apparatus as claimed in claim 29, wherein said mapping comprises mapping said set of amplitude values onto a modified set of amplitude values to generate said modified set of characteristic values. 32. Apparatus as claimed in any of claims 29 to 31, wherein said set of characteristic values includes a set of phase angle values and said mapping comprises mapping a set of phase angle values onto a modified set of phase angle values to generate said modified set of characteristic values. 32. Apparatus as claimed in any of claims 29 to 31, wherein said band-limited signal is a time varying signal. 33. Apparatus as claimed in any of claims 29 to 32, wherein said band-limited signal has a variable other than time. 34. Apparatus as claimed in any of claims 29 to 32, wherein said set of waveform shape descriptors are generated using a time encoded signal coding process. 35. Apparatus as claimed in any of claims 29 to 32, wherein said set of characteristic values is generated by subjecting said dependent electrical signal to a Fourier transformation, and said modified time dependent signal is generated by subjecting said modified set of characteristic values to an inverse Fourier transformation. 36. Apparatus as claimed in any of claims 29 to 35, wherein said time dependent electrical signal is subject to a plurality of band-pass filters thereby generating said set of characteristic values as a plurality of frequency bands in which each frequency band has associated frequencies and associated amplitudes, and said mapping comprises changing the associated frequencies of at least one of the frequency bands.

Inventors: KING REGINALD ALFRED, KING, REGINALD, ALFRED
Applicants: DOMAIN DYNAMICS LTD, KING REGINALD ALFRED, DOMAIN DYNAMICS LIMITED, KING, REGINALD, ALFRED
Application year: 2001
Main classification: G06K9
IPC codes: G06K9, G10L15