CN108172231B - A Kalman Filter-Based Reverberation Method and System - Google Patents

Abstract

The invention discloses a method and system for de-reverberation based on Kalman filtering. The method includes: preprocessing original signals collected by each microphone to obtain corresponding frequency domain signals, and delaying to form an input signal; using Kalman The filter algorithm and the time-varying multi-channel autoregressive model estimate the reverberation signal, take the original signal collected by each microphone at the current moment as the reference signal, and subtract the reverberation signal to obtain the error signal; use the Kalman gain matrix and the error signal to update the Kalman signal Kalman filter coefficients; use the original signal collected by each microphone at the current moment, the input signal and the updated Kalman filter coefficients to obtain the target signal; finally, use the inverse Fourier transform to convert the frequency domain target signal to the time domain. The method of the invention reduces the complexity of the adaptive multi-channel linear prediction de-reverberation algorithm by diagonalizing the Kalman filter state vector error covariance matrix.

Description

A Kalman Filter-Based Reverberation Method and System

technical field

The present invention relates to the field of speech de-reverberation, in particular to a de-reverberation method and system based on Kalman filtering.

Background technique

As shown in Figure 1, due to the reflection of sound waves by the room boundary and objects in the room, the microphone not only receives the direct sound from the sound source, but also the reflected sound from all directions. Generally, the sound signal whose arrival time is 30-50ms after the direct sound is called the early reflection sound, and the sound signal arriving after this is called the late reflection sound, that is, the reverberation tail. Psychoacoustic studies have found that early reflected sound can enhance the intensity of direct sound and improve speech intelligibility. The reverberation signal will mask the subsequent direct sound signal, resulting in blurred speech. In addition, the reverberation signal will also reduce the speech quality of the signal received by the microphone and the accurate recognition rate of the speech recognition system. In application scenarios such as conference calls and smart speakers in a closed room, the microphone is often in the far field of the sound source. As the distance between the sound source and the microphone increases, the effect of reverberation on the signal received by the microphone becomes more severe. In addition, in the voice communication system, the ambient noise is small, and the signal received by the microphone is mainly affected by the reverberation of the room, which leads to the decline of the accuracy and intelligibility of the voice signal, which seriously affects the communication quality. Therefore, it is necessary to de-reverberate the signal received by the microphone.

Speech de-reverberation is a hot research topic. The current solutions mainly include:

(1) Linear prediction residual enhancement algorithm. The speech model used by the linear prediction residual enhancement algorithm is the sound source filter model. The model treats speech as a sequence of excitations passing through a time-varying all-pole filter. The linear prediction analysis of the reverberated speech signal can obtain the estimated value of the all-pole filter coefficient, that is, the linear prediction coefficient. Then, inverse filtering is performed on the signal received by the microphone, and the corresponding excitation signal, that is, the residual signal, can be obtained. De-reverberation can be achieved by enhancing the residual signal, and the speech signal can be reconstructed through the estimated linear prediction coefficients.

(2) Spectral enhancement method. Spectral enhancement methods are a class of classical de-reverberation algorithms. The method achieves the purpose of enhancing the speech signal by correcting the noise-containing or reverberation-containing signal in the short-time Fourier transform domain. Literature [1] (K.Kinoshita,M.Delcroix,T.Nakatani,and M.Miyoshi,"Suppression of late reverberationeffecton speech signal using long-term multiple-step linear prediction,"IEEETrans.Audio,Speech,Lang.Process. , vol.17, no.4, pp.534–545, May 2009.) estimated late reverberation by delayed linear prediction, followed by spectral subtraction to achieve de-reverberation. Literature [2] (F.Xiong,N.Moritz,R.Rehr,J.Anemuller,B.Meyer,T.G.G.Doclo,and S.Goetze, "Robust ASR inreverberant environments using temporal cepstrum smoothing for speechenhancement and an amplitude modulation filterbank for feature extraction, "inProc. REVERB Challenge Workshop, Florence, Italy, 2014.) uses the least mean square error method to estimate the clean speech signal amplitude spectrum as a preprocessing stage for automatic speech recognition, determined by the power of late reverberation and stationary background noise Spectral Density estimates the power spectral density of a clean speech signal. In general, the spectral enhancement method needs to estimate the reverberation time in order to determine the spectral attenuation level. However, blind reverberation estimation is still a very difficult problem, especially in noisy environments, and research on this problem is still in progress.

(3) Inverse filtering method. Blind de-reverberation algorithms refer to the process of de-reverberation in which prior knowledge of the room impulse response between the sound source and the microphone is unknown. The multi-channel linear prediction algorithm based on the microphone array is a classic blind de-reverberation algorithm. According to the Multiple Input/Output Inverse Theorem (MINT), the multi-channel method can perfectly equalize the time-invariant room impulse response under the condition that the transfer function of each channel does not contain a common zero. However, the MINT algorithm is very sensitive to the system identification error, and the impulse response of the actual room often contains similar zeros, so the MINT algorithm is difficult to apply in practice.

Because the time-domain linear prediction algorithm often requires a long filter length, and there is a problem of whitening the target signal. Recently, some scholars proposed to apply a multi-channel linear prediction algorithm in the short-time Fourier transform domain to process signals independently in each subband. In the STFT domain, the reverberated speech signal is described by an autoregressive model in each frequency band, thereby reducing the filter length of each subband. Since the room impulse response is actually time-varying, time-varying predictive model coefficients are modeled. Recently, some scholars proposed a multi-channel autoregressive (MAR) signal model in the STFT domain, using Kalman filter to estimate the MAR coefficients. This algorithm can be regarded as a generalized recursive least squares (RLS) algorithm.

The computational complexity of the multi-channel linear prediction algorithm based on the STFT domain is squared with the order of each subband filter. This complexity limits the application of the algorithm on many resource-limited system platforms. Literature [3] (Dietzen T, Doclo S, Spriet A, et al. Low-complexity Kalmanfilterformulti-channel linear-prediction-basedblindspeechdereverberarion[C].IEEE Workshop on Applicationsof Signal Processing to Audio and Acoustics.IEEE,2017.) for STFT Domain adaptive multi-channel linear prediction de-reverberation algorithm, a simplified Kalman filter solution method is proposed, which reduces the computational complexity to a linear relationship with the filter order. However, this simplified approach results in a certain degree of speech quality degradation. In addition, the algorithm only estimates one channel signal, and in practice multiple channels need to be calculated.

SUMMARY OF THE INVENTION

The purpose of the present invention is to overcome the above-mentioned defects existing in the current de-reverberation methods, and propose a low-complexity de-reverberation method based on Kalman filtering, which further reduces the STFT domain adaptive multiplication rate while ensuring no loss of speech quality. The complexity of the channel linear prediction de-reverberation algorithm.

In order to achieve the above purpose of the invention, the present invention proposes a Kalman filter-based de-reverberation method, which includes:

The original signal collected by each microphone is preprocessed to obtain the corresponding frequency domain signal, and the input signal is formed after delay;

The reverberation signal is estimated by using the Kalman filter algorithm and the time-varying multi-channel autoregressive model, the original signal collected by each microphone at the current moment is used as the reference signal, and the error signal is obtained by subtracting the reverberation signal;

Update the coefficients of the Kalman filter using the Kalman gain matrix and the error signal;

The target signal is obtained by using the original signal collected by each microphone at the current moment, the input signal and the updated Kalman filter coefficients;

Finally, the frequency domain target signal is converted to the time domain using the inverse Fourier transform.

As an improvement of the above method, the method specifically includes:

Step 1) Framing, windowing and Fourier transform are performed on the signals y _m (n) collected by the M microphones, 1≤m≤M, to obtain the corresponding frequency domain signal Y _m (n),

The frequency domain signal Y _m (n) is:

Among them, k is the frequency subscript, N is the number of Fourier transform points; n is the time frame subscript, w _STFT (l) is the short-time Fourier transform analysis window function, and R represents the frame shift;

Step 2) The input signal matrix Y(n-D) is formed by the frequency domain signals of the M microphones from n-D to n-L, and the reverberation signal vector r(n) is estimated by the Kalman weight vector, where D is the delay and L is the linear prediction length. ;

y(n)=[Y ₁ (n),...,Y _M (n)] ^T (2)

代表Kronecker乘积，Y(n-D)是由麦克风观测信号构成的尺寸为M×L_c的稀疏矩阵，L_c＝M²(L-D+1)；In formula (3), _IM is the unit matrix of M×M,

represents the Kronecker product, Y(nD) is a sparse matrix of size M×L _c formed by the microphone observation signal, L _c =M ² (L-D+1);

Calculate the reverberation signal vector r(n) according to formula (4);

M×M的矩阵C_p(n-1)为时变的卡尔曼权重向量系数，p＝[D,D+1,...,L]，Vec{·}为矩阵列堆叠操作因子；In formula (4),

The M×M matrix C _p (n-1) is the time-varying Kalman weight vector coefficient, p=[D,D+1,...,L], Vec{·} is the matrix column stacking operation factor;

Step 3) use the signal y(n) collected by each microphone at the current moment to subtract the reverberation signal vector r(n) obtained in step 2) to obtain the error signal vector e(n);

e(n)=y(n)-r(n) (5)

Step 4) calculate the Kalman gain matrix K(n);

Step 5) Update the Kalman filter coefficients by the Kalman gain matrix K(n) and the error signal vector e(n)

计算目标信号向量x(n)；Step 6) Use the signal y(n) collected by the microphone at the current moment, the input signal matrix Y(nD) and the updated Kalman filter coefficients

Calculate the target signal vector x(n);

Step 7) Inverse Fourier transform is performed on the frequency domain target signal vector x(n) to obtain the time domain target signal vector _xt (l):

As an improvement of the above method, the step 4) specifically includes:

Step 401) Calculate by first-order smoothing according to formula (6)

为n-1时刻的目标信号方差，

为n-2时刻的目标信号方差，x(n-1)为n-1时刻目标信号向量；α为平滑因子，取值为0.2；in,

is the variance of the target signal at time n-1,

is the variance of the target signal at time n-2, x(n-1) is the target signal vector at time n-1; α is the smoothing factor, which is 0.2;

然后按照式(8)计算先验失调方差

Step 402) First calculate the variance of the disturbance noise w(n) according to formula (7)

Then calculate the prior offset variance according to formula (8)

为n-1时刻的后验失调方差；In formula (7), L _c =M ² (L-D+1), and n is usually 10 ^-5 ;

is the posterior offset variance at time n-1;

和先验失调方差

计算规整化因子δ(n)；Step 403) According to formula (9), the variance of the target signal is calculated by

and a priori offset variance

Calculate the normalization factor δ(n);

Step 404) calculate the covariance matrix S _Y (nD) according to the signal collected by the microphone according to formula (10);

S _Y (nD) = Y (nD) Y ^H (nD) (10)

Step 405) Calculate the Kalman gain matrix K(n) according to formula (11);

K(n)= _{YH(nD)[S Y} ⁽ nD)+δ(n) _IM ] ^-1 (11).

As a kind of improvement of aforesaid method, after described step 7), also comprise:

update posterior offset variance

A Kalman filter-based de-reverberation system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the above method when executing the program A step of.

The advantages of the present invention are:

1. The method of the present invention reduces the complexity of the adaptive multi-channel linear prediction de-reverberation algorithm by diagonalizing the Kalman filter state vector error covariance matrix;

提供了更多的可用信息。2. The simplified Kalman filter algorithm of the present invention can be regarded as a normalized least mean square (Normalized Least Mean Square, NLMS) algorithm with a variable normalization factor. In addition, the error signal vector e(n) and the target signal vector x(n) of the simplified Kalman filtering algorithm proposed by the present invention are both M×1 vectors, which provides convenience for subsequent cascading of other multi-channel algorithms. In addition, it is also used to calculate the variance of the target signal

More available information is provided.

Description of drawings

Figure 1 is a schematic diagram of room reverberation generation;

Fig. 2 is the block diagram of Kalman filter de-reverberation of the present invention;

Fig. 3 is the block diagram of Kalman weight vector update of the present invention;

Fig. 4 is the block diagram of the calculation Kalman gain matrix module of the present invention;

Figure 5 is a block diagram of the estimated prior offset variance of the present invention.

Detailed ways

The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

A low-complexity de-reverberation method based on Kalman filtering, the method comprising:

Step 1) The signal y _m (n) collected by the M microphones, 1≤m≤M is divided into frames, windowed and Fourier transformed to obtain the corresponding frequency domain signal Y _m (k, n), in order to simplify the representation , the frequency subscript k will be omitted below;

The calculation of the frequency domain signal Y _m (k, n) is calculated according to formula (1):

Among them, k is the frequency subscript, N is the number of Fourier transform points; n is the time frame subscript, w _STFT (l) is the short-time Fourier transform analysis window function, and R represents the frame shift;

Step 2) The input signal matrix Y(n-D) is formed by the frequency domain signals of the M microphones from n-D to n-L, and the reverberation signal vector r(n) is estimated by the Kalman weight vector, where D is the delay and L is the linear prediction length. ;

Y(nD) is a sparse matrix of size M×L _c composed of microphone observation signals, L _c =M ² (L-D+1). r(n) stands for late reverberation.

According to equations (2) and (3), the input signal matrix Y(n-D) is obtained;

y(k,n)=[Y ₁ (k,n),...,Y _M (k,n)] ^T (2)

代表Kronecker乘积。In formula (3),

stands for the Kronecker product.

Calculate the reverberation signal vector r(n) according to formula (4);

表示对某一信号的估计值，

M×M的矩阵C_p(n-1)为时变的卡尔曼权重向量系数系数，p＝[D,D+1,...,L]。L为线性预测长度，延迟D＞1的选择与STFT(Short-time Fourier transform,STFT)的帧重叠参数有关，取值要保证x(n)与r(n)的相关可以忽略。Vec{·}为矩阵列堆叠操作因子。In formula (4),

represents the estimated value of a signal,

The M×M matrix C _p (n-1) is the time-varying Kalman weight vector coefficient coefficients, p=[D, D+1, . . . , L]. L is the linear prediction length. The selection of delay D>1 is related to the frame overlap parameter of STFT (Short-time Fourier transform, STFT). The value should ensure that the correlation between x(n) and r(n) can be ignored. Vec{·} is the matrix column stacking operation factor.

Step 3) use the signal y(n) collected by each microphone at the current moment to subtract the reverberation signal vector r(n) obtained in step 2) to obtain the error signal vector e(n);

e(n)=y(n)-r(n) (5)

和先验失调方差

计算卡尔曼增益矩阵K(n)；具体包括：Step 4) by the input signal matrix Y(nD), target signal variance

and a priori offset variance

Calculate the Kalman gain matrix K(n); specifically include:

Step 401) Calculate the variance of the target signal at time n in a first-order smoothing manner according to formula (6).

为n-1时刻的目标信号方差，

为n-2时刻的目标信号方差，x(n-1)为n-1时刻目标信号向量；α为平滑因子，取值为0.2；in,

is the variance of the target signal at time n-1,

is the variance of the target signal at time n-2, x(n-1) is the target signal vector at time n-1; α is the smoothing factor, which is 0.2;

然后按照式(8)计算先验失调方差

Step 402) First calculate the variance of the disturbance noise w(n) according to formula (7)

Then calculate the prior offset variance according to formula (8)

In formula (7), L _c =M ² (L-D+1), η is a small constant, and it is generally recommended to take 10 ^-5 .

和先验失调方差

计算规整化因子δ(n)；Step 403) According to formula (9), the variance of the target signal is calculated by

and a priori offset variance

Calculate the normalization factor δ(n);

Step 404) calculate the covariance matrix S _Y (nD) according to the signal collected by the microphone according to formula (10);

S _Y (nD) = Y (nD) Y ^H (nD) (10)

Step 405) Calculate the Kalman gain matrix K(n) according to formula (11);

K(n)=Y ^H (nD)[S _Y (nD)+δ(n)I _M ] ^-1 (11)

Step 5) Update the Kalman filter coefficients by the Kalman gain matrix K(n) and the error signal vector e(n)

计算目标信号向量x(n)；Step 6) Use the signal y(n) collected by the microphone at the current moment, the input signal matrix Y(nD) and the updated Kalman filter coefficients

Calculate the target signal vector x(n);

Step 7) seek the inverse Fourier transform of the frequency domain signal vector x(n) to obtain the time domain target signal vector _xt (l);

Step 8) Update the posterior offset variance

In formula (15), _IM is an M×M unit matrix, L _c =M ² (L-D+1), and L is the linear prediction length. tr[·] means to find the trace of the matrix.

As shown in FIG. 2 , FIG. 2 is a system block diagram of a low-complexity de-reverberation algorithm based on Kalman filtering of the present invention. Among them, Y(nD) is the input signal matrix composed of the frequency domain signals of M microphones from nD to nL, r(n) is the reverberation signal vector estimated by the Kalman filter algorithm, and y(n) is composed of The reference signal vector formed by the signal collected by the microphone at the current moment, x(n) is the final output target signal vector. The Fourier transform module 201 represents performing Fourier transform on the signal collected by the microphone, and the Fourier transform of the mth microphone signal is represented by Y _m (n). The delay module 202 represents performing a delay operation on the signal collected by the microphone. The selection of delay D>1 is related to the frame overlap parameter of STFT, and the value should ensure that the correlation between x(n) and r(n) can be ignored. The Kalman filter module 203 represents that the input signal is filtered by the Kalman filter to estimate the reverberation signal. The target signal vector x(n) is calculated by the summation module 204 . The inverse Fourier transform module 205 transforms the frequency domain signal to the time domain.

FIG. 3 is a block diagram showing the principle of updating the Kalman weight coefficient, which includes a Kalman gain calculation module 303 . The update amount of the weight vector is obtained from the error signal vector and the Kalman gain matrix, and the final output target signal vector x(n) can be calculated from the updated weight vector.

输入信号矩阵Y(n-D)和先验失调误差

计算卡尔曼增益矩阵。

由先验失调方差估计模块403计算得到。卡尔曼增益对滤波器权系数的更新以及先验失调方差的估计至关重要。首先计算R_e(n)，然后计算得到卡尔曼增益矩阵K(n)。FIG. 4 is a schematic block diagram of calculating the Kalman gain matrix, which includes a priori offset variance estimation module 403 . The product module 401 realizes the multiplication of two input variables, and the inversion module 402 indicates the inversion operation of the input signal. Use the variance of the target signal

Input signal matrix Y(nD) and a priori offset error

Compute the Kalman gain matrix.

Calculated by the prior offset variance estimation module 403 . The Kalman gain is crucial for updating the filter weights and estimating the prior offset variance. First calculate _Re (n), and then calculate the Kalman gain matrix K(n).

的计算方法。转置模块501表示对矩阵进行转置操作。模块503表示求矩阵的迹。The prior offset variance estimation module shown in Figure 5 also reflects the posterior offset variance

calculation method. The transpose module 501 represents a transpose operation on the matrix. Block 503 represents finding the trace of the matrix.

From the above analysis and Figure 2, Figure 3 and Figure 4, the following conclusions can be drawn:

First, after adopting the technology of the present invention, the computational complexity of the STFT domain adaptive multi-channel linear prediction de-reverberation algorithm is greatly reduced;

Secondly, after adopting the technology of the present invention, not only the computational complexity is reduced, but the output voice quality is also guaranteed;

Finally, after adopting the technology of the present invention, a good compromise can be obtained between the tracking performance and the convergence performance of the Kalman filter.

The above fully shows that the present invention provides an effective de-reverberation technology, which can well remove the reverberation interference caused by room sound reflection, and improve the speech intelligibility and the accurate recognition rate of the automatic speech recognition system.

对滤波器系数c(n)的估计具有重要作用，较小的

值表征了良好的失调性能及差的跟踪性能，较大的

值表征了良好的跟踪性能及差的失调性能。换句话说，

的取值高度决定了卡尔曼滤波器的跟踪性能和收敛性能。当算法还未收敛时，

和

的差值较大，根据式(7)，

此时也取较大的值，因此提供了快速的收敛性能和跟踪性能。当算法开始收敛到稳态时，

和

的差值减小，导致了较小的

也就是较低的失调。It should be pointed out that the simplified Kalman filter algorithm described in the present invention can be regarded as an NLMS algorithm with a variable normalization factor, wherein δ(n) can be regarded as a variable normalization factor. variance

It plays an important role in the estimation of the filter coefficient c(n), the smaller

values characterize good offset performance and poor tracking performance, larger

The values characterize good tracking performance and poor offset performance. in other words,

The value of the height determines the tracking performance and convergence performance of the Kalman filter. When the algorithm has not converged,

and

The difference is large, according to formula (7),

A larger value is also taken at this time, thus providing fast convergence performance and tracking performance. When the algorithm begins to converge to a steady state,

and

The difference decreases, resulting in a smaller

That is, lower dissonance.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the embodiments, those of ordinary skill in the art should understand that any modification or equivalent replacement of the technical solutions of the present invention will not depart from the spirit and scope of the technical solutions of the present invention, and should be included in the present invention. within the scope of the claims.

Claims (3)

1. A Kalman filter-based de-reverberation method, the method comprising: The original signal collected by each microphone is preprocessed to obtain the corresponding frequency domain signal, and the input signal is formed after delay; The reverberation signal is estimated by using the Kalman filter algorithm and the time-varying multi-channel autoregressive model, the original signal collected by each microphone at the current moment is used as the reference signal, and the error signal is obtained by subtracting the reverberation signal; Update the coefficients of the Kalman filter using the Kalman gain matrix and the error signal; The target signal is obtained by using the original signal collected by each microphone at the current moment, the input signal and the updated Kalman filter coefficients; Finally, use the inverse Fourier transform to convert the frequency domain target signal to the time domain; The method specifically includes: Step 1) Framing, windowing and Fourier transform are performed on the signals y _m (n) collected by the M microphones, 1≤m≤M, to obtain the corresponding frequency domain signal Y _m (n), The frequency domain signal Y _m (n) is:

Among them, k is the frequency subscript, N is the number of Fourier transform points; n is the time frame subscript, w _STFT (l) is the short-time Fourier transform analysis window function, and R represents the frame shift; Step 2) The input signal matrix Y(n-D) is formed by the frequency domain signals of the M microphones from n-D to n-L, and the reverberation signal vector r(n) is estimated by the Kalman weight vector, where D is the delay and L is the linear prediction length. ; y(n)=[Y ₁ (n),...,Y _M (n)] ^T (2)

In formula (3), _IM is the unit matrix of M×M,

represents the Kronecker product, Y(nD) is a sparse matrix of size M×L _c formed by the microphone observation signal, L _c =M ² (L-D+1); Calculate the reverberation signal vector r(n) according to formula (4);

In formula (4),

The M×M matrix C _p (n-1) is the time-varying Kalman weight vector coefficient, p=[D,D+1,...,L], Vec{·} is the matrix column stacking operation factor; Step 3) use the signal y(n) collected by each microphone at the current moment to subtract the reverberation signal vector r(n) obtained in step 2) to obtain the error signal vector e(n); e(n)=y(n)-r(n) (5) Step 4) calculate the Kalman gain matrix K(n); Step 5) Update the Kalman filter coefficients by the Kalman gain matrix K(n) and the error signal vector e(n)

Step 6) Use the signal y(n) collected by the microphone at the current moment, the input signal matrix Y(nD) and the updated Kalman filter coefficients

Calculate the target signal vector x(n);

Step 7) Inverse Fourier transform is performed on the frequency domain target signal vector x(n) to obtain the time domain target signal vector _xt (l):

The step 4) specifically includes: Step 401) Calculate by first-order smoothing according to formula (6)

in,

is the variance of the target signal at time n-1,

is the variance of the target signal at time n-2, x(n-1) is the target signal vector at time n-1; α is the smoothing factor, which is 0.2; Step 402) First calculate the variance of the disturbance noise w(n) according to formula (7)

Then calculate the prior offset variance according to formula (8)

In formula (7), L _c =M ² (L-D+1), and n is usually 10 ^-5 ;

is the posterior offset variance at time n-1; Step 403) According to formula (9), the variance of the target signal is calculated by

and a priori offset variance

Calculate the normalization factor δ(n);

Step 404) calculate the covariance matrix S _Y (nD) according to the signal collected by the microphone according to formula (10); S _Y (nD) = Y (nD) Y ^H (nD) (10) Step 405) Calculate the Kalman gain matrix K(n) according to formula (11); K(n)= _{YH(nD)[S Y} ⁽ nD)+δ(n) _IM ] ^-1 (11). 2. the de-reverberation method based on Kalman filtering according to claim 1, is characterized in that, after described step 7) also comprises: update posterior offset variance

3. a kind of de-reverberation system based on Kalman filter, comprising memory, processor and the computer program that can be stored on the memory and can be run on the processor, it is characterized in that, when described processor executes described program, realize The steps of the method of any of claims 1-2.

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