METHOD OF SPEECH COMPRESSION WITHOUT QUALITY DETERIORATION

Abstract: The subject of the invention is a method of speech compression without quality deterioration, during which the input signal is conducted into a filter bank, where they are separated into partial bands. Later the partial bands are transformed and conducted into a compression unit for comparision with a previously stored signal set. The compressed signal is formed as the result of this transformation and comparison. Its specialty is that after the division of input signals into partial bands, the amplitude of each instantaneous signal is generated, the instantaneous amplitudes are averaged with a low pass filter by partial bands. The segments' averages are logarithmically quantized. The sum of squares of instantaneous amplitudes is filtered with high pass filter, than the period time is defined. Finally the compressed signal is generated by linearly or logarithmically quantizing the period time of segments.

Description: Method of speech compression without quality deterioration The subject of the invention is a method of speech compression without quality deterioration, during which the input signal is conducted into a filter bank, and there are separated into partial bands, after which the partial bands' signals are transformed and conducted into a comparison unit for comparison with a previously stored signal set, and the compressed signal is generated as the result of this transformation and comparison. There are several known methods of speech compression. The essence of them is that the elements of real-time speech are submitted to some transformation, and so base signals with determined longitude are generated. The results are compared with a sample stack, and the examined base signal is coded, also if the sample is identical to any element of the sample stack, the signal is corresponded to this element, and this way the compressed signal is generated. Such kind of solution is described in the patent description No. EP 749.110. However, the defectiveness of the known solution is that at the moment of decoding of compressed signals, the quality of speech is deteriorating, and the recognition does not comply with the supposed requirements. The objective of our invention is the elimination of the defectiveness of known methods and the creation of such a method version, which besides its high effectiveness allows speech compression without quality deterioration. The main idea of the invention comes from the recognition, that if the input signal desired to be compressed is separated into partial bands by an accordingly constructed filter bank, and in the partial bands after suitably selected transformations, the voiced and unvoiced elements' output density spectrum characteristics are compared with the characteristics of the spectral density function of an earlier stored signal set, so in case of a suitable concordance, the compression can be done in a way, that at the decoding process no quality deterioration is happening, and this way the task can be completed. Also part of the patenting idea was also that if for the separation, which is part of the compression method, basically we use a non-linear hearing model, consisting of two parts, first of which is a filter bank constructed suitably of a Zwicker-style filter row, and the second part is a non-linear transformation for generating an averaged amplitude and a frequency function, so the separation into partial bands can be done according to the necessary quality. In conformity with the given goal, the patented method of speech compression without quality deterioration, - where the input signal is conducted into a filter bank, and there are separated into partial bands, after which the partial bands' signals are transformed and conducted into a comparison unit for comparison with a previously stored signal set, and the compressed signal is generated as the result of this transformation and comparison - is based on that after the division of input signals into partial bands, the amplitude of each instantaneous signal is generated, the instantaneous amplitudes are averaged with a low pass filter by partial bands. The segments' averages are logarithmically quantized. The sum of squares of instantaneous amplitudes is filtered with high pass filter, than the period time is defined. Finally the compressed signal is generated by linearly or logarithmically quantizing the period time of segments Possible an additional criterion of the invention can be, that after filtering of squares of instantaneous amplitudes with high pass filter, the period time of resulting signals is defined. Than we decide on majority basis, and the period times resulting from the decision, are linearly or logarithmically quantized by segments. Upon realization of the method, after the separation of input signals into partial bands, the signals coming from the filter bank are cleared by partial bands, and only after this the instantaneous amplitude of each signal is generated. In case of a different execution of the method, the signals are voice tested. We define the voice frequency of the segments, and if the change between two segments is less than 8-22%, the segment is considered as voiced. Out of the voiced segments' and delayed signals we find by partial bands, the zero-crossing before the local maximum inside the closest period to the segment limits, and the zero-crossing before the local maximum inside the next period, and the so defined time between the zero-crossings is considered as a corrected period time. In another version of the invention, out of the voiced segments' and delayed signals we cut off corrected period time long parts by partial bands, after which with these and segments of a previously stored signal set, normalized to this longitude we calculate the value of the cross-correlating coefficients. We chose, mark and store or transfer the signal segment giving the highest correspondence, than during the decoding with this we mark the signal element to be used for synthetization. From the standpoint of the method it can be favorable, if we draw together neighboring partial bands and we generate signal sets with the period time longitude. The segments of the signal set will be compared with the cut off parts from the delayed signals of partial bands, or we draw together all channels. For the comparison during the further execution of the method we use the spectral distribution of segment, or its approaching value. This way we apply to each segment the band part's averaged instantaneous amplitudes, and we generate the cross- correlation coefficient with the preliminary stored set of spectral distribution function. In case of a different version for the comparison we use the spectral distribution of the segment or its approaching value. Later, we define the distance from the segments of a preliminary stored set of spectral distribution function, and the relative frequency of overstepping a previously defined threshold. In another execution of this method, the segments of the previously stored signal set are weighted logarithmically or in another way, and the weighted characteristics of the current segment are compared with the weighted characteristics of the previously stored signal set. In an also favorable version of the invention, for the restoration of the unvoiced segments from Gauss dispersed white noise, we use time functions generated with a suitably constructed Zwicker filter row. Under this method we cut out parts with the longitude of the corrected period time, related to the segment. We quantize the characteristics of these period time parts, for example the absolute value and phase of Fourier co-efficients, or its connected sample, after which the quantized values are correlated to the segment. In one possible case of the execution of the invented method, to the corrected period time long parts of a segment we relate the quantity of the connected sample, and the time functions with an equal number of connected sample are classed into the same subset. Or in another version we relate to the corrected period time long parts the row numbers of partial bands giving the local maximums, and the time functions with the same row numbers are classed into the same subset. The invention's most important advantage is that using it, we can effectuate speech compression, in a way that the decoding of the speech can be done without quality deterioration. The principles of the procedure, according to the invention, are the creation and application of a non-linear hearing model on the circuit layout level, in which the filter bank, differently to the others, is constructed suitably of Zwicker filters. Zwicker published in 1957, that the human hearing perceives the loudness differently inside of a critical band, than outside of this band. Zwicker and Feldtkeller determined the steepness of cutting territory of the filters from the concealment occurrence. These filters are approximately a third wide, asymmetrical and of a exceptionally steep characteristic on the upper part. This filtering effect takes place already on the tympanic membrane of the middle ear. The data of the filter row is fixed by international standard. The resultant characteristics of the 25 filters covers the entire audible spectrum, for the processing of speech approximately 14-15 is enough, while for studio quality 18 filters are necessary. The output signals of the filters are transformed, namely we generate the instantaneous parameters, which in principle means the function of the instantaneous amplitude and the instantaneous frequency, which we define as follows: where In the formulas described x(t) is the output signal of the band-pass filter, y(t) is the Hilbert pair, x'(t) and y'(t) are the time differential quotients. This non-linear transformation is called generalized amplitude and frequency transformation. The result or the iterative value of the above transformation can be determined in several ways. The transformation can be interpreted as the simultaneous implementation of a mutually independent ideal AM and FM demodulator, therefore the result of the GAFT can be determined or iterated with every AM and FM demodulator. For the further part of the model, namely that the instantaneous parameters are followed by changer-filters, there is no nervephysiology evidence, it is only supposed, however, the results of the psychophysical studies can be explained well by this model. In addition to the GAFT method, the signal can be given by another non-linear transformation, called connected sampling. It is provable, that if the signal's width is less than 1 octave, than the signal is unambiguously determined by its connected samples, namely by the samples taken at the local extreme values and the time of the sampling. The bandwidth of the filters in the filter bank is around 1/3 octave, so the connected samples determine unambiguously the output signals. The connected samples can be used for creation of subsets, in other words, classifying. As if the number of connected samples (local extreme values) dose not correspond at the output of the filter bank, it does not worth to calculate the cross- correlation function. Basically it is also a part of the principal base of the method, according to the invention, that with the filter bank based on the above described non-linear hearing model, the input signal, desired to be compressed, is divided into partial bands, and in each band part we determine the instantaneous amplitudes, from which determine the period time of the voiced intervals, later by segments, for example 20 ms, we compare the period time long parts with the output density spectrum of a preliminary stored set of segments. The segment with the highest coincidence, the period time, and the value of the logarithmically quantized segment amplitudes are correlated to the voiced segment. In the unvoiced intervals also the spectral density function is compared with the elements of the previously stored spectral density function, but this time we do not correlate period time to the segments, as they are unvoiced. The amplitude of the signal related to the segment is available by channels (in each channel the average of the instantaneous amplitudes), and with these values we can easily approach the signal's output density spectrum. For example, if in the speech spectrum we use 14 partial bands, so we know the output density spectrum value, and it is enough for the approached determination of the spectrum. The segment with the highest coincidence (namely the segment's number), which is selected by its similarity to the output density spectrums. and also the logarithrnically quantized value of the segments amplitude are correlated to the actual unvoiced segment. The method according to the invention in the following will be presented in more details through examples, and drawings: Fig. 1 - the sketch of the simplest variety of speech compression method according to the invention. Fig. 2 - shows the speech compression method for a more complex period time determination case. Fig. 3 - Diagram for interpretation of corrected period time used during speech compression method. The Fig. 1 shows the simplest sketch of speech compression method according to the invention. The speech signal X(t) conducted to the input, with the use of filter bank (1) - suitably a Zwicker filter row - is divided into partial bands or channels X (t), we determine the instantaneous amplitude A k (f) or the amplitude approaching value with the non-linear transformation unit (2), after which we generate the A t (t) averages with the low pass, averaging filter (3). These averages are quantized by the logarithmic quantizer (4). With the help of the squaring, summing and high pass filter (5)we generate from the A^t) signals a voiced P(t) /pitch/ signal, of which with the period time determiner (6) we obtain the signal's period time. If in two consecutive segments, for example of 20 ms, the period time changes preferably lower than 8-22%, we consider the examined segment as voiced. If the period change is higher than the previously determined limit, so the first segment is surely unvoiced. About the voiced - unvoiced state of the second segment we only can decide if we wait for the next segment. The result of voiced - unvoiced decision we correlate to the segments, the same way as the actually detected period time (6) is correlated to the voiced segment. The simplest way of correlation is if the segment is voiced because the period time changes slightly, and in this case we correlate the actual period date. In other cases let the period time related to the segment to be zero, and in this case the segment is unvoiced. After this we quantize the segments period time with the linear quantizer (7). Hereby we should note, that it is also possible to quantize logarithmically, however, the obtainable profit is not considerable. On the Fig. 2 we can observe such scheme of the method, where we use a more complicated period time definer (6). As in such cases the method is more efficient and it lower the possibility of mistake. I this case we generate form the signal A* (t) with the squarer and high pass filter (8) a P k (t) signal for each channel. After it we determine the period times by channels and with the help of majority decision (9) we decide. The resulting period times are quantized linearly (or logarithmically). The period time detector can be further improved if we generate the Ω k (t) signal with the GAFT method or with its approaching, as it s clear that in case of a periodical or quasi periodical signal the instantaneous frequency also includes a periodical component equal to the period time. Considering that the transformation is a simultaneous realization of mutually independent ideal AM and FM demodulators, therefore the instantaneous frequency generated this way is independent from the instantaneous amplitude, and this way also from the period time generated from the instantaneous amplitude. If using a squaring and high pass filter (8) on each channel we generate a periodical signal (pitch) from the Ω k (t) signal, then these signals also can be used for the majority decision (9), and this way we can elevate the correctness of the decision. The compression method can be further improved, if we clear the output signals of the filter bank on each channel. Since in this case the method compresses the speech lifted out of the background noise, further as a result of the cleaning, the period time determiner will produce less mistake. The experience shows that the period time generated in the way described above is not exactly similar to the periodicity of the original signal, what makes necessary the correction of period time. We determine the corrected period time cT;, related to the i- th segment, by searching the zero-crossing (for example positive) prior to the local maximum prior the i-th segment limit and the zero-crossing (for example positive) posterior to the local maximum after the i-th segment limit of the delayed X k (t-?) signal. This way, we consider as the cT; corrected period time of the i-th segment, the time between the two times shown on Fig. 6. As we have mentioned in the introduction, during the compression method we consider the i-th segment as voiced, if the corrected period time between two subsequent segments changes less than a determined threshold. During the compression we determine the information related to the tone, by cutting out corrected period time long parts of the delayed signals of voiced segments (cleared or uncleared) by channels, later using them and the elements of previously stored corrected period time long signal set (or with the elements spectral density) we calculate the value of cross-correlated coefficients, we decide for the signal segment with the highest correspondence, we store or transfer the result of the decision (for example, the number of the signal segment in the signal set), and finally at the decoding we chose the necessary signal element for synthetization with this result. The level of compression can be further improved if we concentrate (sum) subsequent channels, and generate period time long signal set for the concentrated signals, and we compare the elements of the signal set with the parts cut off the delayed signals of the concentrated channels. However, in this case the size of the set increases, and of course it is necessary to make more comparisons. In this case it is more objective, if we compare the spectral density related to the given segment of the signal. By generating the A k (t) values, namely the average of the instantaneous amplitudes of the channels, the spectral density of the signal (or its approaching value) is available for all segment times. Preliminary we make a set out of these data, and the spectral density of the actual segment is compared (the cross- correlated coefficients are generated) with the elements of the spectral function set, or we consider as criteria of similarity the frequency of overpass of a given threshold by the value of absolute deviation. As it is known, it is possible to speed up the searching in a set, if we generate subsets (hierarchical search). We have several possibilities for generating subsets. One possibility is if to the segment related corrected period time long parts we order the number of related samples, and the time functions with equal number of related samples are grouped into the same subset. Other possibility if we determine the location of the formants. These locations are determined by the speech generating organs, as the mouth-, pharynx- and nose cavity (according to the linguists there are 5-7 formants existing, however, out of these only the first 3 are considerable). We can not give the exact location of the formants, as the spectral dispersion is known "only" in the point given by the channels. However, it is enough, it is not clear if our hearing senses in several points. Therefore we get a good usable solution, if we consider as the location of formant, the number of local maximum giving channel (or local maximum giving channels), and we use this information for the generation of subsets. Considering that for the human hearing not all detail correspondence or similarity is of the same importance, it does worth to weight (for example logarithmically) the elements of previously stored sets (corrected period time long signals), and the related values of the channel spectral density functions, and to examine the similarity of weighted density functions. Further saving can be achieved if the characteristics of the corrected period time long parts, for example the absolute values and phases of Fourier coefficients, or there related samples are quantized, and the quantize values are related to the actual segment, of course the similarities are interpreted on the same data. The similarity can be interpreted in several ways. The simplest, if we consider as measuring number the cross-correlation coefficient, or the expected value (average) of deviation quadrant, namely we consider similar two functions (time function and the spectrum of output density) in case, if the cross-correlation coefficient id close to 100% (for example higher than 95%) or the output of error signals is close to zero (for example lower than 5%). Another possible interpretation, if for the measurement of similarity we order the probability of overpass of a given threshold to the absolute value of deviation form the sample (sample function). Of course the probability is approached by relative frequency, namely we determine the relation between that in how many (sampling) points within the threshold the sample function is approached by the other function, and in how many points the absolute value of deviation passes the threshold value. The decoding of unvoiced segments is realized with noise. The dispersion of speech amplitude can be characterized by a so called ? dispersion, but the unvoiced sections can be very good approached by Gauss dispersion. The spectral density of the Gauss dispersed white (constant) noise, should be modified to the spectral density of the current unvoiced segment. The simplest solution, if we generate by channels the JC* (t) signals form the Gauss dispersed white noise (for example with Zwicker filters), they are weighted with the Aι (t) averages related to the channels, and after summing then we generate the restored unvoiced segment. For this solution the knowledge (storage or transmission) of the A k A(t) averages is necessary. A more complex method, but offering a higher compression, if we preliminary prepare a set out of the spectral dispersion functions of unvoiced segments, as were indicated for the compression of voiced segments. Also if we decide for an element with the highest similarity to the spectral dispersion of the actual segment, the result of decision (for example the number of element in the set) is stored or transmitted, and later at the decoding with this we mark the signal elements (spectral dispersion function) to be used for synthetization. During the formation of unvoiced local maximums are generated in the spectral dispersion, too (as an effect of the mouth-, pharynx- and nose cavities) and the location of these maximums are very peculiar to the unvoiced tone. As were described for the case of voiced elements, we can generate subsets on the basis of the channel number with the maximum spectral dispersion (or the numbers of the channels with local maximums) to significantly speed up the search. The method according to this invention can be effectively applied for speech compression.

Claims: Patent Application Items 1. Method of speech compression without quality deterioration, during which the input signal is conducted into a filterbank, where they are separated into partial bands. Later the partial bands are transformed and conducted into a comparison unit for comparison with a previously stored signal set. The compressed signal is formed as the result of this transformation and comparison, characterized by that after the division of input signals into partial bands, the amplitude of each instantaneous signal is generated, the instantaneous amplitudes are averaged with a low pass filter by partial bands. The segments' averages are logarithmically quantized. The sum of squares of instantaneous amplitudes is filtered with high pass filter, than the period time is defined. Finally the compressed signal is generated by linearly or logarithmically quantizing the period time of segments. 2. The procedure as per Application Item 1, characterized by that after filtering of squares of instantaneous amplitudes with high pass filter, the period time of resulting signals is defined. Than we decide on majority basis, and the period times resulting from the decision, are linearly or logarithmically quantized by segments. 3. Any procedure as per Application Items 1-2, characterized by that after the separation of input signals into partial bands, the signals coming out from the filterbank are cleared by partial bands, and only after this the instantaneous amplitude of each signal is generated. 4. Any procedure as per Application Items 1,2 or 3, characterized by that the signals are voice tested, and we define the voice frequency of the segments, and if the change between two segments is less than 8-22%, the segment is considered as voiced. 5. Any procedure as per Application Items 1 through 4, characterized by that out of the voiced segments' and delayed signals we find by partial bands, the zero-crossing before the local maximum inside the closest period to the segment limits, and the zero-crossing before the local maximum inside the next period, and the so defined time between the zero-crossings is considered as a corrected period time. 6. Any procedure as per Application Items 1 through 5, characterized by that out of the voiced segments' and delayed signals we cut off corrected period time long parts by partial bands, after which with these and segments of a previously stored signal set, normalized to this longitude we calculate the value of the cross-correlating coefficients. We chose, mark and store or transfer the signal segment giving the highest correspondence, than during the decoding with this we mark the signal element to be used for synthetization. 7. Any procedure as per Application Items 1 through 6, characterized by that we concentrate (sum) subsequent channels, and generate period time long signal set for the concentrated signals, and we compare the elements of the signal set with the parts cut off the delayed signals of the concentrated channels. 8. The procedure as per Application Item 7, characterized by that all channels are concentrated. 9. Any procedure as per Application Items 1 through 3, characterized by that we use the spectral distribution of segment, or its approaching value. This way we apply to each segment the band part's averaged instantaneous amplitudes, and we generate the cross-correlation coefficient with the preliminary stored set of spectral distribution function. 10. Any procedure as per Application Items 1 through 3, characterized by that we use the spectral distribution of the segment or its approaching value. Later, we define the distance from the segments of a preliminary stored set of spectral distribution function, and the relative frequency of overstepping a previously defined threshold. 11. Any procedure as per Application Items 1 through 10, characterized by that the segments of the previously stored signal set are weighted logarithmically or in another way, and the weighted characteristics of the current segment are compared with the weighted characteristics of the previously stored signal set. 12. Any procedure as per Application Items 9, 10 or 11, characterized by that for the restoration of the unvoiced segments from Gauss dispersed white noise, we use time functions generated with a suitably constructed Zwicker filter row. 13. Any procedure as per Application Items 1 through 7, characterized by that we cut out parts with the longitude of the corrected period time, related to the segment. We quantize the characteristics of these period time parts, for example the absolute value and phase of Fourier co-efficients, or its connected sample, after which the quantized values are correlated to the segment. 14. Any procedure as per Application Items 1 through 7, characterized by that the corrected period time long parts of a segment we relate the quantity of the connected sample, and the time functions with an equal number of connected sample are classed into the same subset. 15. Any procedure as per Application Items 1 through 7, characterized by that the corrected period time long parts the row numbers of partial bands giving the local maximums, and the time functions with the same row numbers are classed into the same subset. Authorized person:

Inventors: FOELDVARI RUDOLF, GYIMESI LASZLO, FOELDVARI, RUDOLF, GYIMESI, LASZLO
Applicants: FOELDVARI RUDOLF, GYIMESI LASZLO, HERTER EBERHARD, FOELDVARI, RUDOLF, GYIMESI, LASZLO, HERTER, EBERHARD
Application year: 2001
Main classification: G10L19
IPC codes: G10L19