Patent Translate Powered by EPO and Google Notice This translation is machine-generated. It cannot be guaranteed that it is intelligible, accurate, complete, reliable or fit for specific purposes. Critical decisions, such as commercially relevant or financial decisions, should not be based on machine-translation output. DESCRIPTION JP2004040771 The present invention relates mainly to the design of digital speech precomps, in particular digital precomp filters, and proposes a speech precomp filter design scheme using a new class of design criteria. The filter parameters are determined based on the weightings, approximating the pre-compact filter to fixed non-zero filter components, and approximating a pre-compensated model response to a reference system response. Preferably for design, the precomp filter comprises a fixed non-zero filter component and an adjustable compensator component. The tunable compensator component is determined by optimizing a criterion function that includes weightings. Frequency- and / or channel-dependent weighting provides a powerful design tool that provides complete control over the degree and type of compensation performed in different frequency bands and / or in different sub-channels. [Selected figure] Figure 5 Digital speech precomp FIELD OF THE INVENTION The present invention relates mainly to digital speech precomp and in particular to the design of digital precomp filters, producing and compensating one or several input signals to a speech production system For those that change the dynamic response of the system. BACKGROUND OF THE INVENTION Audio production or reproduction systems, including amplifiers, cables and loudspeakers, always have sometimes undesirable effects on the spectral characteristics of the sound. The reverberation in the room where the device is installed further changes the sound. Sound reproduction with very high quality can be achieved by using matching of cables, amplifiers and top quality loudspeakers, but it is cumbersome and very expensive. The increased computing power of personal computers and digital signal processors has created new possibilities for changing the sound characteristics in sound generation or reproduction systems. It is known that the dynamic characteristics of a speech production system can be modeled by measuring and recording its response to known test signals. The precomp 08-05-2019 1 filter (R in FIG. 1) is placed between the original sound source and the audio equipment. This filter is designed and implemented to compensate for the measured characteristics of the speech production system indicated by H in FIG. In particular, it is desirable to approximate the phase and amplitude response of the compensated system, shown by D in FIG. 1, to a preset ideal response. In other words, it is necessary to match the compensated speech reproduction y (t) to the ideal value yref (t) with a given accuracy. The predistortion generated by the precompensator R cancels the distortion caused by the system H. The resulting audio reproduction has a sound feature D. Up to the physical limits of the system, at least in theory, it is possible to achieve excellent sound quality without extreme high-end voice equipment at high cost. The aim of the design is, for example, to cancel the acoustic echoes caused by the poorly built loudspeaker cabinet. Another application is to minimize low frequency echoes due to sound effects at various different locations within the listening room. Digital precomp filters can be applied to multichannel sound generation systems as well as single loudspeakers. As well as producing better sound, it can be an important element of the design to produce a particular effect. For example, the generation of virtual sound sources (sound renderings) is associated with the sound effects of computer games. A device called a graphic equalizer has been around for quite some time. The device aims to compensate for the frequency response of a speech production system by changing its gain in a set of fixed frequency bands. There is a mechanism for automatically adjusting such filters (see, for example, Patent Document 1). Also, as another prior art, there is a technique of dividing an audio frequency range into different frequency bands and configuring different compensators within each of these bands (see, for example, Patent Documents 2 and 3). Such sub-band solutions suffer from inadequate phase compensation, especially at the band edges. A method has been proposed to handle the important audio frequency range as one band. This method requires the use and adjustment of filters with very high adjustable factors. The proposed method is generally based on the adjustment of a FIR (finite impulse response) filter to minimize the least squares criterion, and the compensated signal y (t) and the required response yref (t) The deviation between them is measured (see, for example, non-patent documents 4 to 7, patent documents 8 to 10). This method seems to be attractive, as there is an easy-to-handle matching algorithm as well as an off-line design algorithm that can adjust an IIR filter based on a least squares criterion. There have also been proposals for nonlinear compensators (see, for example, Patent Document 11 and Non-Patent Document 14). A solution that proposes measuring the room acoustics response and the loudspeaker response separately has been used in the design of the precompact inverse filter for the sound reproduction systems of the documents US Pat. This design partially equalizes both responses. In U.S. Pat. No. 5,956,015, a method is disclosed for applying both FIR and IIR (infinite impulse response) filters to the compensation of speech systems. This approach is used to reduce the number of FIR filter parameters required in the compensation filter. [Patent Document 1] US Patent No. 4,739,513 [Patent Document 2] US Patent No. 5,384,856 [Patent Document 3] US Patent No. 5,627,899 [Non-patent document 4] P. M. カールソン（Ｃｌａｒｋｓｏｎ）、Ｊ． 08-05-2019 2 Moorjopoulos and J.A. K. Hammond, 1985 "Spectral phase and transient equalization for audio systems", J. Am. Speech Engineering Society (J. Audio Engineering Society), Vol. 33, pages 127 to 131 [Non-Patent Document 5] P.I. A. ネルソン（Ｎｅｌｓｏｎ）、Ｈ． Hamada, S.I. J. "Adaptive inverse filtering for stereo sound reproduction" in 1992 by Elliot, IEEE Proceedings on Signal Processing, Volume 40, pages 1621 to 1632 [Non- Patent Document 6] P.I. A. ネルソン（Ｎｅｌｓｏｎ）、Ｆ． In 1996, "Multichannel signal processing techniques in the reproduction of sound" by OrduaBustamante, J.A. Speech Engineering Association (J. Audio Engineering Society), Vol. 44, pp. 973 to 989 [Non-patent document 7] P.I. A. ネルソン（Ｎｅｌｓｏｎ）、Ｆ． オルドゥア−ブスタマ ンテ（Ｏｒｄｕａ−Ｂｕｓｔａｍａｎｔｅ）、Ｈ． Hamada (1995) "Inverse filter design and equalization zones in multi-channel sound reproduction systems" (IEEE Proceedings on Speech and Speech Processing, 3rd issue, "Inverse filter design and equalization zones in multichannel sound reproduction systems") Volume, page 185 to page 192 [Patent Document 8] US Patent 4,683, 590 [Patent Document 9] US Patent 5,727, 066 [Patent Document 10] International Patent Publication WO 94/24835 [ Patent Document 11 US Pat. No. 5,438,625 [Patent Document 12] US Pat. No. 5,511,129 [Patent Document 13] Japanese Patent Application No. 080799880 [Patent Document 11] No. 5,600,718 However, all of the existing methods have important problems in putting them into practical use. Compensating filters of design schemes available in the prior art generally have sophisticated operations and severe practical limits. The resulting automatically occurring compensation filters are often even dangerous to the audio equipment. Because of the danger of producing a power compensation signal that is too high. There is a need for design techniques and convenient tools to avoid the aforementioned drawbacks. The present invention overcomes the above problems in the prior art. It is a main object of the present invention to provide an improved design scheme for speech precompilation filters. It is another object of the present invention to provide a flexible and very accurate method for designing such a filter and to obtain a better control range and amount of compensation by a precomp filter . In this regard, it is desirable, inter alia, to provide a filter adjustment technique that provides complete control over the amount of compensation performed in different frequency bands and / or different audio paths. It is also an object of the present invention to provide a speech precompensator design method and system that can be easily handled by existing technology and that achieves excellent compensation capabilities with a limited number of filter parameters. It is. Yet another object of the present invention is to provide a flexible and efficient method, system and computer program for designing a digital speech precomp filter. These and further objects of the present invention are defined by the appended claims. SUMMARY OF THE INVENTION The present invention provides speech precomp filters that use novel design criteria. The present invention provides that dynamic system mathematical model and optimization based on digital precomp filter model provide a 08-05-2019 3 powerful tool for filter design, and various kinds of voice equipment by changing the input signal Based on the recognition that it will improve In essence, the filter parameters approximate the precomp filter to fixed non-zero filter components while on the other hand, weighting between approximating the precompensate model response to the reference system response It is determined based on For design purposes, the precomp filter preferably comprises a fixed nonzero filter component and a tunable compensator component. The fixed filter component is usually configured by the filter designer or set as a default configuration, and the adjustable compensator component is determined by optimizing the criterion function including the above weighting. Similar to fixed filter components, the weighting is usually configured by the filter designer or set as a default configuration. Once the fixed filter component is configured and the adjustable compensator component is determined, the filter parameters of the precomp filter can be computed and implemented. In many practical cases, it is beneficial to include at least one selectable delay element and a bypass component in the fixed filter component. [0015] By making the weighting frequency-dependent and / or channel-dependent, it is powerful to provide complete control over the degree and type of compensation performed in different frequency bands and / or in different sub-channels. Design tools are obtained. Preferably, the criterion function comprises penalty periods for frequency-dependent and / or weighted channels. This period penalizes the compensation portion of the precompensator. This kind of frequencydependent and / or channel-dependent weighting simplifies avoiding dangerous overcompensation and achieves good compensation in the frequency bands and channels reached without problems. The optimization of the weighted criterion function can be performed on-line, for example by using recursive optimization or adaptive filtering or as an off-line design based on a model. This is similar to traditional on-line optimization. Based on optimization to adjust a stable and causal realizable infinite impulse response (IIR) compensation filter while using a limited number of filter parameters to provide good compensation capability A methodology is proposed. While including a limited number of filter parameters, these digital filters can cause long impulse responses. A compensation filter so designed may have several inputs and may output an audio channel. And it can be used to compensate not only multiple channels but also one channel audio equipment. The proposed design principles and structures are particularly useful for linear dynamic design models and linear precomp filters, but can be generalized to non-linear design models and non-linear precomp filters. According to another aspect of the present invention, there is provided a method of designing a precomp filter for speech, a system and computer program, the precomp filter designed thereby, and the digital speech signal generated by the precomp filter as well as the signal. An audio system comprising such a precomp filter. The invention offers the following advantages:-Tight control of the range and amount of 08-05-2019 4 compensation performed by the precomp filter, providing complete control over the resulting acoustic response-Problem Avoiding dangerous overcompensation while achieving good compensation that can be achieved without-achieving good compensation capability while using a limited number of filter parameters-and-Optimal to produce superior sound quality and experience results Get an audio system that is pre-compensated. Other advantages and features provided by the present invention will be appreciated by the following description of embodiments of the present invention. Embodiments of the present invention will be described with reference to the drawings. Further objects of the present invention and their advantages will be further understood by the following description. Sections 1 to 3 describe the linear case, and Section 4 generalizes the structure and design principles to the problem with nonlinear and preferably variable system models as well as nonlinear and preferably variable compensators, and Section 5 finally describes some of the most primitive aspects. Section 1: Linear Model and Filter Design For a better understanding of the present invention, it may be useful to begin with describing a general approach to speech precomp filter design. . The speech production or reproduction system to be modified is usually represented by a linear invariant dynamic model H. This model describes the discrete time relationship between a set of p output signals u (t) to a set of m output signals y (t). Where t represents a discrete time index, and ym (t) (index m is a “measurement value”. Is an m-dimensional column vector representing the time series of sound at m different locations, e (t) is the noise, reflection in the unmodeled room, the effect of incorrect model structure, Show non-linear distortion and other unmodeled contributions. The operator H is an m × p-matrix, the elements of which are stable linear dynamic operators or transformations implemented as eg FIR filters or IIR filters. These filters determine the response y (t) for any input time series vector u (t) in p dimension. A linear filter or model is represented by such a matrix. This matrix is in the following called transfer function matrix or dynamic matrix. The transfer function matrix H represents the effects of the whole or part of the sound generation or reproduction system, and any existing digital compensators, digital to analog converters, analog amplifiers, loudspeakers, cables and some applications Also includes room acoustic response. In other words, the transfer function matrix H represents the dynamic response of the relevant part of the speech production system. The input signal u (t) to this system is a p-dimensional column vector, which can represent the input signal to the chain of p amplifiers and loudspeakers in the speech production system. The measurement sound ym (t) is, by definition, considered to be a superposition of the response y (t) = Hu (t) and the unmodeled contribution e (t), which are objects to be modified and controlled. Of course, as a practical matter, the requirements for good results include modeling and system design such that the magnitude | e (t) | is not greater than the magnitude | y (t) | in critical frequency bands It is to The main purpose is to change the dynamics of the speech production system represented by Equation 11 in certain reference dynamics. For this purpose, we introduce a criterion matrix D. Where w (t) is a set of raw or recorded sounds, or an artificial including the test signal used in the filter design. <Img class = "EMIRef" id = "197718648-000004" Is an r-dimensional vector 08-05-2019 5 that also includes the digital audio signal generated by. The elements of the vector w (t) may represent, for example, a channel of digitally recorded sound or a sampled and digitized analog source. D in Equation 12 is a transfer function matrix of dimension m × r assumed to be known. The linear system D is variable in design and generally represents the reference dynamics of the vector y (t) in Eq. An example of a possible design objective is the complete inversion of the dynamics and the separation of the channels. In the case of r = m, the matrix D is set equal to a square diagonal matrix with the d-step delay operator as the diagonal component. The nominal response of y (t) is defined as being a delayed version of the original sound vector w (t), w (t) With a delay d of equal sampling period for all elements of. In more complex designs, in addition to introducing delays, reference dynamics may be added to the speech production system in the form of a stable filter. With such a design of D, it is also possible to add new sound features to the system. For example, it is possible to obtain excellent sound quality in low quality audio equipment. For example, a more complex design may be important to emulate the specific sound of one type of speech production system. The desired bulk delay (d) introduced through design matrix D is an important parameter and affects the achievable capacity. The causality compensation filter achieves better compensation than this delay is acceptable. The precomp is generally obtained by a precomp filter indicated by R, and the speech reproduction system equation based on w (t) of the signal. 11. Generate an input signal vector u (t) for. In the prior art, the superior tendency of digital speech precomp is to generate an input signal vector u (t) to the speech reproduction system equation 11, whereby its compensated output y ( It is said that t) approximates the reference vector yref (t) in a certain sense. This objective can be achieved by the signal u (t) in equation 11 being generated by a linear precompact filter R. The filter R operates on the signal w (t) such that the element consists of a stable linear causal dynamic filter p × r matrix and the signal y (t) approximates yref (t). <Img class = "EMIRef" id = "197718648000007" /> Within the system general range, the condition for accurate compensation is that R multiplies the dynamic model H (the right side of D It is equal to the right inverse matrix with stable causality in). Where H <−R> represents the right inverse matrix of the transfer function matrix of the model. <Img class = “EMIRef” id = “197718648-000008” /> Such right-handed inverse matrix by definition has the property HH <-R> = Im (an identity matrix of size m × m). したがって、 ＨＲ＝ＨＨ<−ＲＤ>＝Ｄである。 Unfortunately, models of speech systems often do not have accurate stability causal right inverse matrices. However, given that the bulk delay d in the range of D (the smallest delay due to any element of D) can be increased, the least squares approximation error | y (t) achieved by the stable causality compensation filter yref (t) | 2 indicates that if the normal rank of H (the rank other than the transfer function matrix in a zero matrix system) is equal to the number of elements in m × y (t), it disappears as a delay d it can. The delay d is determined by the designer. The designer can thereby control the degree of 08-05-2019 6 approximation. In order to be able to work well, the system described by H needs to have at least as many separate inputs as outputs, ie p ≧ m. On the other hand, the rank of H can never be as large as m. In the simplest case, y (t), u (t) and w (t) are all scalar time series, with m = p = r = 1 in scalar model and scalar reference dynamics. モデルＨは。 It can compensate the amplifier and loudspeaker chain. In the prior art and the paper, the most promising way of solving this kind of approximation problem is focused on representing H and R by FIR filters, and then to minimize the scalar type criterion Using the least squares technique, we penalize the sum of the mean squared differences of the elements of y (t) and yref (t). <Img class = "EMIRef" id = "197718648000009" /> In the following, () <T> denotes a transposed matrix of vectors, and E () is a related statistic of the signal to be contained Represents the average on the physical characteristics. Such a least squares design can achieve on-line recursive minimization of Eq. 17 by applying, for example, the LMS algorithm or the filtering xLMS algorithm to the measured signals ym (t) and w (t) (Patent Documents 12 and 13). Also, this design can be performed off-line by solving the Wiener optimization problem for a fixed degree of FIR filter. This is equivalent to solving a set of simultaneous binary linear equations and a Wiener-Hopf equation, including correlation evaluation. The minimization of Equation 17 should take into account not only the amplitude response but also the phase response of the system. This approach is better than a method that only takes into account the amplitude response (see, eg, reference 1). The drawback with the use of FIR filters is that filters with very large coefficients may have to be used. For this reason, the present invention focuses on the adjustment of IIR filters, which generally requires less coefficients. Regardless of the use of FIR or IIR filters, careful analysis by the inventor has shown that all prior art designs based on the minimization of the least squares criterion shown in Equation 17 suffer from a further important drawback. The compensation filter based on the minimization shown in Equation 17 obtains extreme characteristics at the highest and lowest frequencies. In the case of the scalar, this results from the transfer function H, which often has a decreasing gain at the highest and lowest frequencies in the range of speech, and results in a compensator R with high gain at these frequencies. Such a compensator has a long and oscillating impulse response (see FIG. 4). It requires operational adjustment and implementation. This is a potential problem not only at extremely high or low frequencies, but also across all frequencies where excessive compensation is required, if the criteria in Eq. 17 are minimized. Furthermore, a compensation filter R with too high gain at certain frequencies can cause non-linear distortion and have deleterious effects. In the worst case, too high a gain input can damage the audio equipment. It will be appreciated that better control over the range and amount of compensation performed at different frequencies and different subchannels needs to be achieved than with Eq. In the design of the precomp filter for the audio installation according to the invention, the filter additively comprises two components, a fixed and non-zero filter element and an adjustable compensator component determined by optimization. It proves to be useful. Fixed filter components are typically configured by the filter designer or set to a default configuration. 08-05-2019 7 The adjustable compensator approximates the pre-compact filter to a fixed and non-zero filter component while the component is determined by optimizing the criterion function based on the given weighting, and pre-compensating for the reference system response Approximate the model response that has been sunk. Although not required, this weighting is preferably frequency dependent and / or channel dependent. This is illustrated below. In order to further understand the basic concepts of the present invention, the design of such weighting based precomp filters is described. For example, the compensation can be realized by additionally changing the signal path as m (t) = Cw (t). It is usually the direct feedthrough and delay of w (t) of the signal. Where g is a suitable delay, and C is a matrix of FIR or IIR filters. <Img class = "EMIRef" id = "197718648-000010" /> In Equation 18, u (t) and w (t) are assumed to have equal dimensions (m = r). Using the standard backward shift operator notation: <img class = "EMIRef" id = "197718648-000011" />, in Eq. 14, the compensator matrix is such a form for design It is considered to have The design of the compensator component C is preferably such that the additional transform signal m (t) = Cw (t) Based on the minimization of a reference transform that includes frequency weighting periods that penalize magnitude. The penalty period may be of any type of criteria used for filter optimization. In particular, when replacing the second-order criterion function shown in Equation 17, the following equation is obtained: <img class = "EMIRef" id = "197718648-000013" /> Here, W is a first weighting function, and V is an additional optional weighting function. The matrix W is preferably a square m × m matrix and includes stable linear IIR filters and represents a set of design variables. Furthermore, the additional weighting function V is preferably a square p × p matrix, includes a stable linear IIR filter, and can be used as another set of design variables. In a particular embodiment of the invention, the weighting represented by the transfer function matrix W acts as a frequency dependent penalty on the compensation signal m (t) = Cw (t). The effect of weighting by W is best understood in the frequency domain by using the Z-transformed representation of the signal and system. The minimization of Equation 21 occurs at a compensation period C (z) where the W (z) criterion has a small gain at a relatively large frequency z. This is because the last period of equation 21 governs J on the one hand. In such a frequency band, C (z) w (z) is small in Equation 18, and the uncompensated system properties remain unchanged, except for the g sample delay. On the other hand, at frequencies z where the W (z) criterion is negligible, the first period of the criterion of Equation 21 is most important. In the case of V = i, in these frequency bands, y (z) to yref (z) = D (z) w (z) (... indicates that they are roughly equal. same as below. ). This adjustment is to minimize the contribution of the values of the first period of Equation 21. For example, the weighting function represented by W may be implemented as a low pass filter at a given break frequency, in parallel with a high pass filter with a given limiting frequency. By properly choosing the break and limit frequencies, the compensation performed by the precomp filter can be customized to the particular application. 08-05-2019 8 Of course, the weighting W can be realized in any form. The frequency-selective weighting by matrix V can be used for various purposes, such as: * Can use known features of the human ear for weighting of perception. The elimination of compensation errors in the frequency band to which we are more sensitive is emphasized. * H modeling errors can be used for low weighting with capacity deviations in large frequency bands, and the optimization will not focus on unreliable frequency bands. Weighting the errors achieved in different places in space, ie different components of the vector y (t), can also be used. This can be achieved by setting V equal to the diagonal transfer function matrix and by using different filters as the diagonal elements of V. Although design model H describes all relevant frequency ranges, the use of frequency dependent weighting allows for different adjustments in different frequency bands. In this way, it is possible to avoid the solution of dividing the entire frequency band into secondary bands and compensating for each band separately. More complex side band solutions (eg, used in graphic equalizers) are known to cause problems with phase response distortion. It should also be noted that W forms the basis of a weighting filter in the case of multiple channels. It is possible to use a diagonal matrix with each different diagonal element to individually adjust the compensation performed on each input channel for a particular loudspeaker characteristic. This type of channel dependent weighting uses frequency independent weighting or frequency dependent weighting for each channel, and independently enables different types of compensation in different paths of the multi-channel system. It can be implemented. The direct feedthrough (or bypass) delay g of Equation 18 is yet another design variable. In the case of scalars, the appropriate choice is (m = p = r = 1) if d> = k is set to g = d−k. Here, k is a bulk delay of H and d is a bulk delay of D. Thus, the total net delay due to the compensated system is g + k = d in almost all frequency bands. If u (t) ~w (t−g) is obtained in a band that is considerably penalized by W, then the total delay of the compensation model HR is g + k. In the band where W is insignificant, it is HR ~ ~D, and D is assigned a delay d in advance. For multi-channel compensators, different feedthrough delays as well as different bulk delays of D may be required in different paths. Such channel dependent delay helps to generate virtual sound, i.e. to make the sound appear to originate from directions other than the loudspeaker. To include such and other variables of the compensation problem, and to handle cases where the number of signals in w (t) is different from the number of signals in y (t), Equation 18 is generalized as r ≠ m Ru. Where F is an arbitrary m × r matrix of a stable linear dynamic system. <Img class = "EMIRef" id = "197718648-000014" /> This matrix is assumed to be known and not modified by the optimization. The special case that F is zero corresponds to using a penalty on the compensator output u (t) and is identical to m (t). This special case was discussed in the prior art on a quadratic basis with a special weight selection with V = 1 and W equal to the frequency independent weight as a special case of scalar type systems (see reference 4). Such optimized feed-forward regulators have been designed for 08-05-2019 9 process control (see references 5, 6). This type of design has been found to be unsuitable for speech pre-comp and is therefore excluded from proposing solutions. The large penalty W cancels the magnitude of the total signal vector u (t) which is itself a large distortion of the existing system characteristics, for F = 0. The main purpose of the proposed compensator design is to introduce a penalty instead. It may leave the natural response of the invariant system, here to get large W and F = q <-g> I. An important element in the proposed design is to assume that the compensator (Eq. 14) can be additively decomposed into two parts for design purposes. Where F is a fixed non-zero filter component, and C is the goal of the optimization. <Img class = "EMIRef" id = "197718648-000015" /> Equation 18, which is a special case of Equation 23, corresponds to F = q <-g> I for r = m. The fixed non-zero filter component F may be a simple bypass component with such selectable delays. However, it does not prevent F from being configured with one or more additional fixed filtering components. In general, the design principle proposed for obtaining C at compensator 23 is to optimize the criteria that include two objective weights. i) as small as possible the deviation between the overall precompensator filter R and the predetermined dynamic non-zero filter component F, and ii) the compensated design model HR and the predetermined dynamic reference system D Make the deviation between them as small as possible. In particular, when this weighting is frequency dependent and / or dependent on the input channel, an efficient tool for automated computer aided filter design is obtained, which may be at different frequency bands and / or multiple channels. Provides control over the amount of compensation performed on different subchannels in the design. The precomp filter of the present invention is mainly implemented as a digital filter or a set of digital filters in a multi-channel system. The filter and model can represent any operator and can perform transformations suitable for linear systems. For example, it can be represented by a delay operator form, a Z conversion representation, a delta operator representation, a series representation of a function or a frequency representation (see reference 7). The degree of approximation (nearness) can be measured with any reference matrix of linear invariant dynamic systems, such as quadratic reference 21, frequency weighted H∞-references, L1-criteria (see references 8 and 9). In order to better understand the advantages of the present invention, a comparison of the ability of the precomp filter designed according to the present invention and the precomp filter designed according to the prior art is made. In this example, the precomp filter is applied to one loudspeaker coupled to an amplifier. The amplitude response and deviation of the phase response of the modeled speech chain is shown in FIGS. 2A and 2B, respectively. A typical impulse response is also shown in FIG. The sampling frequency is 44.1 kHz. The design model has a zero bulk delay k, but its impulse response is shifted to the right in FIG. 3 to make a simple comparison with the compensation response. As a desired criterion in Equation 12, y ref (t) = w (t-d) was used with d = 300 samples. As can be seen from FIG. 2A, the amplitude response of the uncompensated experimental loudspeaker and amplifier model is by no means ideal: there is ripple in the mid frequency band and low power at low and high frequencies. First, this experimental model was compensated by minimizing (Eq. 21) with the feasible (stable and 08-05-2019 10 causal) IIR compensator (Eq. 18) according to the present invention. Use the multi-term Wiener design described in more detail in Section 2. Complete inversion of the entire audio range from 20 Hz to 20000 Hz requires extreme amplification at the lowest and highest frequencies in FIG. 2A. If the entire speech range is inverted, a compensation signal of too high power may be generated, especially for the highest and lowest frequencies. Such strong signals may damage the audio equipment. Thus, instead, the loudspeaker dynamics (up to delay d = g = 300) are completely inverted in the frequency range from 80 Hz to 15 kHz. Also, the amplification should be less than 20 dB outside this range. The weighting W in this example (equation 21) uses a low pass filter with a 30 Hz break frequency, which is used in parallel with the high pass filter with a 17 kHz limiting frequency (see FIG. 8). The impulse response of the designed IIR precomp filter is shown in FIG. The compensated amplitude response and deviation of the phase response are shown in FIGS. 6A and 6B, respectively. As seen in FIG. 6A, the intermediate frequency ripple in FIG. 2A is removed and the amplitude response in the compensated frequency range (80 Hz to 15 kHz) is close to the required flat response (amplitude response = 0 dB) Do. Also, the deviation of the phase response of the compensated model system (FIG. 6B) was significantly improved compared to the uncompensated deviation of the phase response in FIG. 2B. The compensated impulse response (shown by FIG. 7) is close to the ideal Dirac pulse response yref (t) = w (t-300). The remaining small ripple near the main peak is due to the fact that it has limited the amount of complement at the lowest and highest frequencies. This ripple can be eliminated by designing with W = 0 at the expense of designing the precomp filter at very high gain at the lowest and highest frequencies (see FIG. 9). These results are then compared to the FIR filter precompensator designed by the minimization of the least squares criterion (Eq. 17), using the LMS algorithm as the ideal, with appropriately adjusted step lengths . Such compensators have a long, oscillating impulse response that requires computational tuning. This is a potential problem for all frequencies where excessive compensation is required, not only at the highest and lowest frequencies but also when the criterion (Eq. 17) is to be minimized. The amplitude response and associated phase response of a prior art compensated system are shown in FIGS. 10A and 10B, respectively. The amplitude response of this compensated system shows much more oscillations for the intermediate frequencies and in particular for the highest frequency compared to the system compensated with the filter according to the invention. Thus, the design of the present invention is shorter and provides a better compensation filter and provides more accurate inversion within the range of frequencies for which compensation is required. <Section 2: Scalar-Type Compensator Designed as Causal WIENER Filter> A precompact filter design method in which the scalar type filter is designed as a causal WIENER filter will be described below with reference to FIG. . As an embodiment of the present invention, the problem of precompensating one audio chain 08-05-2019 11 (amplifiers, cables, loudspeakers and if necessary room acoustics) is considered. The scalar model H may represent an average over the measured dynamics at many points as compared to the loudspeaker, such that the volume of space where good compensation is achieved is large. Some room acoustic responses are ignored so that only the loudspeaker chain is compensated. In this case, linear systems and models are all assumed to be invariant. They are shown using a discrete time backward shift operator denoted q <-1>. The signal s (t) is one sample backward shift by this operator q-1s (t) = s (t-1). Similarly, the forward shift operator is denoted by q, so qs (t) = s (t + 1) (see, eg, reference 10). The scalar design model Equation 11 is represented by a linear invariant difference equation with fixed coefficients. <Img class = "EMIRef" id = "197718648-000016" /> Assuming that b ≠ 0, k before k (t) input affects output y (t), There is a delay of one sample. This delay (k) may, for example, represent an acoustic transport delay. This is called the model bulk delay. The maximum delay n, h may be hundreds or thousands of samples in some models of audio systems. Move all items related to y to the left side. In the shift operator representation, model 24 is equal to: <Img class = "EMIRef" id = "197718648-000017" /> polynomial A (q <-1>) = (1 + a1q <-1> + a2q <-2> +... + Anq <-n> Discrete time dynamic model (Equation 24) is shown in a more compact representation by introducing B) and B (q <−1>) = (bo + b1q <−1> +... + Bhq <−h>) obtain. The polynomial A (q <-1>) is said to be monic because its principal coefficient is one. <Img class = "EMIRef" id = "197718648-000018" /> In particular, A (q <-1>) = 1 in the FIR model. In general, the recursion at the old output y (t-j) represented by the filter A (q <-1>) gives the model an infinite impulse response. Also, the IIR filter shown in Equation 26 is a logical filter. This is because those conversion operators can be represented by the ratio of polynomials q <-1>. <Img class = "EMIRef" id = "197718648-000019" /> [0060] All complex IIR systems, models and filters are assumed to be stable below. When the complex variable z is substituted for the operator q, the stability criterion means that the equation A (z <-1>) = 0 with solution only if it has size | z | <1. In other words, the complex function A (z <-1>) must have all zero matrices within the unit circle in the complex plane. The hypothetical second order statistics (characteristics of the spectrum) of w (t) of the signal to be compensated can be expressed in a stable and stable reversible Auto-Regressive Moving Average (ARMA) model. <Img class = "EMIRef" id = "197718648-000020" /> where v (t) is white noise. Also, the polynomials H (z <-1>) and G (z <-1>) have all zero matrices at | z | <1 since both highest order coefficients are equal to 1, ie stable ing. A design model (Equation 12) that shows the required response for y (t) is given by the stable difference equation. Where the polynomial N (q-1) is monic and the principal polynomial of the main polynomial at D (q <-1>) Since the coefficients are assumed to be non-zero, d represents the required bulk delay. The compensator structure used is as shown in Equation 23, the fixed filter F is (polynomial) F (q <−1>), and the bypass delay g is d − assuming that d ≧ k. Equal to k. Temporarily made sense in the section above. Thus, by minimizing the second-order criterion (Eq. 08-05-2019 12 21), a stable discrete-time scalar logic filter C ( q <-1>) is optimized. Here, Wm (t) is a scalarstabilized dynamic system based on the output f (t), and is regarded as V = 1 for simplification, and is expressed by the differential equation as follows. <Img class = "EMIRef" id = "197718648000023" /> Both polynomials V (z <-1>) and W (z <-1>) are design variables. They are restricted to have all zero matrices at | z | <1. Thus, the criterion (Equation 21) can be expressed as follows. <Img class = “EMIRef” id = “197718648-000024” /> The optimization of the solution is described below. The model described above and shown in FIG. 11 and the filter polynomials V, W, G, H, D, N, B, A and the delays k, d are specified numerically. The stable and causal IIR filter C (q <−1>) of Equation 30 minimized by the criterion (Equation 32) is specified by the difference equation. <Img class = "EMIRef" id = "197718648-000025" /> Here, the monic polynomial (q <1>) has all zero matrices at | z | <1. That is, together with the scalar r, the unique and stable highest-order coefficient of the multiplicative spectrum factorization equation is given as a solution equal to one. Here, Q (q <−1>) in polynomial 33 is polynomial with a non-causal FIR filter L * (q). It is given by the unique solution of the linear scalar Diophantine equation. <Img class = "EMIRef" id = "197718648-000027" /> In the above, the polynomial in the forward shift operator is a noncausal operator that shifts the signal forward in time. Represents These are indicated with an asterisk * as a subscript. For a polynomial P (q <-1>) = (p0 + p1q <-1> + p2q <2> +... + Pnpq <-np>) with real-valued coefficients, let P * (q) = It is defined as (p0 + p1q + p2q <2> +... + Pnpq <np>). While it is assumed that N (q <-1>) and G (q <-1>) have all zero matrices at | z | <1 for formulation of the problem, while β (q <− The filter (Eq. 33) is guaranteed to be stable since 1>) has zero matrix only at | z | <1. Compensators are causal. The reason is that the related filter has only the backward shift operator as an argument, and has the non-zero main coefficient due to the fact that βGN is monic in Eq. This means that m (t) and its output u (t) is a function of the future value of w (t) at time t. The optimal filter structure (Equation 33) and the corresponding design equation (Equation 34, 35) can be derived by the orthogonal principle equation (see, eg, references 6, 10, 11, 16). All eligible alternative filters are considered if the alternative compensator can only achieve a criterion value lower than the criterion value obtained by equation 33. The multinomial spectrum factorization equation (Equation 34) always has a stable solution. When the compound variable number z is substituted for the operator q, the right side of Formula 34 can be considered as a polynomial with zero matrices arranged symmetrically on the inside and outside of the unit circle | z | = 1. The zero matrix can not be exactly located on the unit circle because of the above-mentioned filter and model stability assumptions. The solution of Equation 34 corresponds to a set of unique elements including all zero matrices inside the unit circle forming the polynomial β (q <-1>). The scalar r is just a normalization element that makes up the β (q <-1>) monic expression. The polynomial Diophantine equation (Equation 35) can be easily converted to a linear system, and can be solved for polynomial coefficients Q (q <−1>) and L * (q). 08-05-2019 13 These equations are formed by multiplying the equal power coefficients in the equation q on both sides of Equation 35. General theory for solving multinomial Diophantine equations (see reference 12), Equation 35 can be guaranteed to have a unique solution. This is because the right side of the polynomials β * (z) and A (z <-1>) N (z <-1>) H (z <1>) z can never have a common factor. This is a conjugate polynomial of β * (z) being β (z <-1>), and A (z <-1>), N (z <-1>) and H (z <1>) are According to the design conditions, a zero matrix is provided inside | z | = 1, while all of the zero matrix is outside | z | = 1. As described above, the design problem with a defined condition can always be solved, and the solution is embodied by the compensation filter (Equations 30, 33) and the design equation (Equations 34, 35). Linear invariant filters that minimize quadratic criteria based on quadratic (spectral) signal models are literally referred to as Wiener filters (see, for example, reference 13). Not only in the area of speech precomp, but also in the design of the Wiener filter and the linear quadratic design in general, the compensator design equation of the filter (Equation 30) by minimization of the criterion (Equation 32) shows new results. <Section 3: Multivariate Compensator Implemented in State-Space Form Designed by Linear Quadratic Optimization etc.> The form and design of the polynomials in the above section are the polynomials described in reference 14 The matrix representation can be generalized to MIMO (multiple input, multiple output) filters and models. Also, MIMO design can be performed by linear quadratic Gaussian distribution (LQG) optimization based on the state space model and its design. This is described below. For a general introduction of LQG designs based on state space methods, see, for example, document [28]. In the following, the conventional notation of dynamic systems in the state theory field is used to describe the multi-channel function of the precomp filter according to the invention. In the following, a matrix whose elements are realvalued constants (not a filter) is indicated in the text by subscripting the character that is the symbol in front of the character that is the symbol, and the character that is the symbol in the mathematical expression In bold and underlined. The vector-ARMA model of w (t) is introduced as a linear discrete time-invariant state space model, along with the state vector x1 (t) of appropriate dimension. Where w (t) is a column vector with dimension r, as in the case of section 1. <img class = "EMIRef" id = "197718648-000028" /> The r-dimensional vector v (t) represents white noise with a known covariance matrix -R1. The ARMA model (Equation 36) is considered stable and stable reversible. In Equation 36, −D1 is assumed to be a reversible r × r matrix, and is generally set equal to the unit matrix. Assuming that w (t) is white noise, the dimensions of x1 (t) are zero and w (t) =-D1v (t). A stable linear design model H (Equation 11), which describes the audio system to be compensated, is implemented in state space form with a state vector x 2 (t). Here, the vector y (t) is a dimension m, and u (t) is a dimension p. Bulk delay is assumed to occur in the state delay structure. Thus, a larger delay increases the dimension of the state vector x2 (t). A stable demand system (Equation 12) is also realized in state space form with a state vector x3 (t). <Img class = "EMIRef" id = "197718648-00300" /> Here, the bulk delay d is incorporated into the state delay structure. Using the compensator filter structure (Eq. 23), a stable preset linear 08-05-2019 14 filter F is implemented in state space form with a state vector x 4 (t). <Img class = "EMIRef" id = "197718648-000031" /> In Eq. 39, the additional signal m (t) is the criterion here as V = I for simplicity. 21) optimize based on Nevertheless, the stable input penalty filter W in the criterion is implemented as another filter in state space form with an output signal whose vector is shown as f (t). <Img class = "EMIRef" id = "197718648-000032" /> Here, the quadratic criterion to be minimized (Equation 21) is given as follows. <Img class = "EMIRef" id = "197718648-000033" /> Then, the state vector of the entire system is defined as follows. <Img class = "EMIRef" id = "197718648-000034" /> This state update equations 36 to 40 are also combined into a single model. <Img class = "EMIRef" id = "197718648-000035" /> Here, the state transition matrix -F of the joint model and the input matrix -G, -H are secondary models (Equation 36 to Equations 36) It is easily obtained from 40). The criterion (Eq. 41) can then be expressed in the form of a criterion with infinite control horizontals and penalties in the selected state. Add a penalty with penalty matrix -R on the quadratic of m (t) as a normalization period. <Img class = "EMIRef" id = "197718648-000036" /> Here, <img class = "EMIRef" id = "197718648-000037" />. If x (t) is known, then linear state feedback: <img class = "EMIRef" id = "197718648-000038" /> minimizes the infinite horizontal criterion (Eq. 43) It may be possible. The optimal controller gain matrix is given by Where S is a symmetric, semi-positive definite matrix that solves the algebraic matrix Riccati equation. <Img class = "EMIRef" id = "197718648-000039" <Img class = "EMIRef" id = "197718648-000040" /> The overall system is by definition detectable and stabilizable, as all complex systems are stable. This guarantees the existence of a solution to this linear secondary state feedback control problem. This solution corresponds to the semi-positive definite matrix solution -S for Eq. When R is set to a positive positive definite matrix, there is always a p × p matrix appearing as an inverse matrix in Eq. 47 and Eq. If the state vector is not known, it can be evaluated by the observer. The separation principle of linear quadratic optimal control theory is that if this observer is designed as a quadratic optimized linear observer (Kalman Estimator), it is minimized using only the measurement signal (formula 44) It states that it can be obtained by joint optimal design. Such designs are known as linear second-order Gaussian (LQG) designs or H2-optimal designs. In the formulation of the particular problem considered here, the observer in the optimal state is simple in design. The stable subsystems (Eqs. 38 to 40) can be derived without noise with the measurement signal alone, which form part of the compensator and problem formulation. Their state is thus known. The output of the model (Equation 37) is not directly measurable. This is because the design is not designed to solve by feedforward, and does not use feedback from the voice measurement ym (t). The most qualified observer for x2 (t) is a copy of Equation 37 driven by u (t) of the known signal, providing a state estimate x2 (t | t-1). Since -D1 is assumed to be reversible in model 36, the noise input v (t) can be estimated as follows: Therefore, the state estimate for x1 (t) can be updated by the following equation: <img class = "EMIRef" id = 08-05-2019 15 "197718648-000041" /> <Img class = "EMIRef" id = "197718648-000042" /> [0084] This recursion is stable since it is assumed that the ARMA model 36 is stable and invertible. Assuming that w (t) is white noise, Equation 50 is of course excessive. The complete solution is thus given by Eqs. 50, 37, 38, 40 for evaluating the state and Eq. 39 representing the precompensator, and m (t) occurs as follows. <Img class = "EMIRef" id = "197718648-000043" /> Here, <img class = "EMIRef" id = "197718648-000044" /> The compensator (Equation 39) , 51) becomes <img class = "EMIRef" id = "197718648-000045" />, and the r input w (t) and the p output u (t) constitute an IIR filter. The gain matrix-L is optimized by solving equation 48 for -S, which is one of the many existing solvers of Riccati equation. Then use equation 47. Section 4: Nonlinear Models and Compensators The design principles introduced in section 1 can be generalized to the speech precompilation problem. In that case, the design model may be non-linear and / or the required compensator may have a non-linear structure. The simplest example would be a linear system and compensator in series with a non-linear static element (eg, a limiter). Elements like these are in fact always present in real systems but are ignored in linear design and optimization. Other conceivable non-linear models and filter structures include model structures consisting of Volterra and Wiener models, neural networks, series expansions of functions and non-linear physical models of acoustic elements. Define a set of vectors of delayed signals. <Img class = "EMIRef" id = "197718648-000046" /> A non-linear and variable dynamic model corresponding to Equation 11 can be expressed as follows. Where h () denotes a dynamic operator that may be non-linear and variable. <Img class = "EMIRef" id = "197718648-000047" /> Similarly, a nonlinear demand response model that generalizes the structure (Equation 12) can be expressed as: <Img class = "EMIRef" id = "197718648-000048" /> where d () denotes a dynamic operator that may be non-linear and variable. An important feature of the present invention (including the nonlinear case) is the additional degradation of the precompensator. For a compensator that will be non-linear and variable, this is expressed as: <Img class = "EMIRef" id = "197718648-000049" /> where r (), f (), and c () are non-linear, time-dependent stable dynamic operators Indicates The operator f is preset and is also not zero while c is to be adjusted by optimization. Preferably, a parameterization of c, for example c = 0, is allowed for the setting of the parameters, such that in such a case a nominal response r = f is obtained. Also for nonlinear problems, the optimization criterion is weighted between the closeness of r to f (smallness of m (t)) and the closeness of the compensation output y (t) to yref (t) Must be included. If this weighting is frequency dependent, it has to be represented by a linear and stable dynamic weighting matrix V, W as in the linear case. This is because the frequency response is preserved in a meaningful form only by the linear system. The criterion corresponding to Equation 21 depends on the amplitude of the input signal for non-linear systems. A scalar quadratic criterion that weights the response for a given sequence of decision input signals w (t) may be defined and minimized. Possible and appropriate 08-05-2019 16 criteria can be expressed as: Where imgt () denotes the sum over a particular test signal sequence w (t) with an appropriate amplitude range. <Img class = "EMIRef" id = "197718648000050" Minimization of the free parameters of c () in Equation 57 could be performed by a mathematical search routine for nonlinear models and filters. Section 5: The Most Primitive Face In general, the design equations are solved on separate computer systems to create the filter parameters of the precomp filter. The computed filter parameters are typically downloaded to digital filters, such as digital signal processing systems or similar computer systems that perform the actual filtering. The filter design mechanism according to the invention is preferably implemented as software in the form of program modules, functions or equations. The software may be written in any type of computer language (eg, a specialized language for C, C ++ or Digital Signal Processor (DSP)). In practice, the relevant steps, functions and actions of the present invention are mapped to a computer program and executed by the computer system to perform the operations associated with the design of the precomp filter. In the case of personal computer (PC) based systems, the computer program used to design the audio precomp filter is usually a code that is read by a PC readable medium, such as a CD for distribution to users and filter designers. Turn The user or the filter designer may load the program from these media into the computer system for subsequent execution. FIG. 12 is a schematic block diagram showing an example of a computer system suitable for the function of the filter design algorithm according to the present invention. System 100 may be implemented on any known computer system, including a personal computer (PC), mainframe computer, multiprocessor system, network PC, digital signal processor (DSP), and the like. System 100 basically includes a central processing unit (CPU) or digital signal processor (DSP) core 10, a system memory 20 and a system bus 30 interconnecting various system components. The system memory 20 includes read only memory (ROM) 22 and random access memory (RAM) 24. Additionally, system 100 includes peripheral storage device 40, which is typically controlled by one or more drivers. The peripheral storage device 40 is a non-volatile storage device of data and program information of, for example, a hard disk, a magnetic disk, an optical disk, a flexible disk, a digital video disk or a memory card. Each peripheral storage device 40 is associated with a drive for controlling the storage device as well as a drive interface (not shown) for connecting to the system bus 30. The filter design program implementing the design algorithm according to the present invention, together with other related program modules, is stored in the peripheral storage device 40 and loaded into the RAM 22 of the system memory 20 for execution by the CPU 10. The filter design program operates on the filter parameters of the precomp filter given the associated input data (e.g. model representation, fixed filter components, configured weighting and reference system representation). The determined filter parameters are transferred from the RAM 24 in the system memory 20 to the precomp filter system 200, typically via the I / O interface 70 of the system 08-05-2019 17 100. The precomp filter system 200 comprises a signal processor (DSP) or similar central processing unit (CPU) 202, having one or more memory modules 204 for storing filter parameters and samples of required delay signals. Is desirable. The memory 204 also typically includes a filter program, which when executed by the processor 202 performs the actual filtering based on the filter parameters. Instead of transmitting the filter parameters computed directly via the I / O system 70 to the precomp filter system 200, a memory for later distributing the filter parameters to the precomp filter system It is also possible to store it on the card or peripheral storage disk 40 and save the data away from the filter design system 100. A known microphone unit or similar recording facility 80 is connected to the computer system 100, for example via an analog to digital (A / D) converter 80, in order to be able to measure the sound output by the audio facility You may Based on measurements of known test signals obtained by the microphone unit 80, the system 100 can refine the model of the audio system using an application program loaded into the system memory 20. The measurements can also be used to assess the performance of the precomp filter and the combined system of the audio equipment. If the designer is not satisfied with the resulting design, the designer may begin a new optimization of the precomp filter based on the modified set of design parameters. Additionally, system 100 may have a user interface 50 between the filter designer and the user. Several different user interface scenarios are possible. For example, the filter designer may decide to use a particular set of customized design parameters (e.g., a particular fixed filter element or weighting in the operation of the filter parameters of the filter system 200). The filter designer then defines through the user interface 50 fixed filter components and associated design parameters such as weighting. The designer can choose between different pre-set fixed filter compensation and / or weighting sets. These sets may be designed to produce different features of the different audio systems, listening environment and / or sounds. In such cases, preset options are typically stored in ambient storage 40 and loaded into system memory during execution of the filter design program. The filter designer chooses fixed non-zero filter components and / or maximum weighting by testing some of the preset options and / or changing the parameters in the preset options The audio system and listening environment can be adapted. Alternatively, the filter design program may automatically choose fixed and default non-zero filter components and weights, preferably based on the audio equipment for which the precomp filter is to be used. In addition to fixed non-zero filter components and frequency dependent and / or channel dependent weighting, filter designers can also define a reference system by using the user interface 50. For example, the reference system delay can be selected by the user and provided as a default delay. It is also possible to select and introduce more advanced special effects to the reference system. Such special effects may include enabling a cinema-like audio reproduction in a compact stereo system. Instead of determining the system model based on the measurements of the microphones, it is also possible for the filter designer to select a model of the speech system from a set of different pre-set system models. 08-05-2019 18 Such selection is preferably based on the particular audio equipment using the precomp filter according to the invention. As another embodiment, an example of construction in which the filter design is independently executed with or without the participation of the peripheral users will be described below. A typical system includes an input / output monitoring program, system identification software and filter design software. The input / output monitoring program first generates a test signal to measure the acoustic response produced by the voice system. Based on the test signal and the obtained measurements, system identification software determines a model of the audio system. The I / O monitoring program gathers and / or generates the necessary design parameters and sends these design parameters to the filter design program which computes the precomp filter parameters. The input / output monitoring program can optionally evaluate the design capability with the measured signal and, if necessary, determine a new set of filter parameters based on the changed set of design parameters for the filter design program. It can be done. Repeat this procedure until a satisfactory result is obtained. Then download the final set of filter parameters to the precomp filter system. It is also possible to adjust the filter parameters of the precomp filter appropriately, without using a fixed set of filter parameters. Audio conditions may change while using the filter in an audio system. For example, the position of the loudspeaker or the position of an object such as furniture in the listening environment may change. These can affect the acoustics of the room. Also, some equipment in the audio system may be replaced with other equipment to change the characteristics of the overall audio system. In such a case, one or more microphone units or similar recording equipment will perform continuous or intermittent measurement of the sound from the audio system at one or several locations in the listening environment Will. The recorded audio data is provided to a filter design system, such as the system 100 of FIG. The system computes new audio system models and adjusts filter parameters to suit newer audio situations. Of course, the invention is not limited to the example of FIG. As an option, the design of the precomp filter and the actual implementation of the filter may both be done in one and the same computer system 100 or 200. This means that the filter design program and the filtering program run on the same DSP or microprocessor system. A speech production system 300 employing the precomp filter system 200 according to the present invention is schematically illustrated in FIG. The audio signal w (t) from the sound source is transferred to the precomp filter system 200. This transfer occurs through the conventional I / O interface 210. If the audio signal w (t) is an analog signal, such as, for example, a sound from an LP record, an analog recording cassette tape or other analog source, the signal is first digitized in the A / D converter 210 before entering the filter 200 Be done. Digital audio signals from CDs, DAT tapes, DVDs, mini-discs, etc. can be transferred directly to, for example, the filter 200 without conversion. The digital or digitized input signal w (t) is then precompensated by the precomp filter 200, basically taking into account the effects of the subsequent voice system equipment. The compensation of the digital speech signal varies according to the frequency 08-05-2019 19 dependent and / or channel dependent penalty period and penalizes the compensation part of the filter system. The resulting compensated signal u (t) is then sent to the DA converter 240 via the further I / O device 230 and the digital compensation signal u (t) is converted into the corresponding analog signal Ru. This analog signal then enters amplifier 250 and loudspeaker 260. The audio signal ym (t) emitted from the loudspeaker 260 has the required audio features and is approaching the ideal sound empirically. Without overcompensating the system, any unwanted effects of the audio system equipment are removed through the reverse operation of the precomp filter. As mentioned above, special sound effects can also be introduced into the resulting audio signal ym (t). The precomp filter system may be implemented as a stand-alone facility in a digital signal processor or computer, but as mentioned above, has an analog or digital interface to the subsequent amplifier. It can also be aimed at incorporating into digital preamplifiers, computer sound cards, compact stereo systems, home movie systems, computer game consoles or other audio output devices. It is also possible to implement the precomp filter in a more hardware oriented direction with customized arithmetic hardware structures. It will be appreciated that the precomp may be performed separately from sending the audio signal to the actual place of playing the sound. The precomp signal generated by the precomp filter does not necessarily have to be delivered directly and immediately from the speech generation system. It may be recorded on another medium for later sending to the speech generation system. The compensation signal u (t) of FIG. 1 may be representative of the sound recorded on a CD or DVD disc tailored to the specific audio equipment and listening environment. It can also be pre-compensated audio files stored on an Internet server to allow downloading of files from remote locations on the Internet. Finally, the overall flow of the filter design method according to the exemplary embodiment of the present invention will be summarized with reference to the flow chart of FIG. This flow chart shows the pre-steps as well as the actual design steps. This pre-step is preferably used with the present invention, and therefore an example of the general steps starting with the uncompensated audio system for designing the precomp filter of the present invention and ending with the implemented filter and Become. The design starts in step S1. In step S2, a model of the audio system is determined based on methods known to those skilled in the art. For example, by determining a model based on physical rules or by performing measurements on an audio system using known test signals. In step S3, a fixed non-zero filter component is configured. This configuration may be performed, for example, by using default preset filter components. It can also be implemented by selecting from a set of pre-set filter components or by inputting fixed filter components customized by the user's settings. In step S4, weighting is configured. This is the weighting between approximating the precomp filter to the fixed filter component or approximating the precompensated model response to the reference system response. This configuration can be implemented, for example, by using default pre-configured weights, as 08-05-2019 20 in the case of the filter components. It can also be done by choosing from a set of weights or by entering completely new weights. In step S5, which represents a preferred embodiment of the present invention, the criterion function comprising the weightings configured in step S4 is optimized with respect to the adjustable compensator component. This optimization provides a tunable compensator component that is used to determine the filter parameters of the precomp filter in step S6 with the fixed non-zero filter component. In step S7, the determined filter parameters are implemented in the filter hardware or software of the precomp filter. If necessary, filter parameters must be adjusted. The overall design may be repeated as schematically represented by dotted line 400 or certain steps may be repeated as represented by dotted line 500. The embodiments described above are examples, and the present invention is not limited thereto. It is apparent that further modifications, variations and improvements within the spirit of the present invention disclosed in the present application are included in the scope of the present invention. <References> [Reference 1] B. ウィドロウ（Ｗｉｄｒｏｗ）とＳ．Ｄ． Adaptive Signal Processing, 1985, by Stearns, Prentice-Hall [Ref. 2] S. Adaptive Filter Theory, 3rd Edition, Prentice-Hall, 1996 by Haykin, Englewood Cliffs, New Jersey [Ref 3] S. T . Neely and J.A. B. Allen, 1979, "Invertibility of a room impulse response", US Pat. H. Hearing Association (J. Acoustical Society of America), 66, 165-169 [Reference 4] シュテルナド（Ｓｔｅｒｎａｄ）、Ｍ． Johansson and J.A. Rutstroem's 2000 "Inversion of loudspeaker dynamics by polynomial LQ feedforward control" (Inversion of loudspeaker dynamics by polynomial LQ feedforward control), IFAC Symposium on Robust Control Design, Prague, Czech Republic, 2000 6 21st to 23rd of the month [Reference 5] Sternad and T.W. 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Sternad's 1991 "Wiener filter design using polynomials" (Wiener filter design using polynomial equations), IEEE Transactions on Signal Processing, Vol. 39, pp. 2387-2399 [Reference 12] V . Kucera, 1991 "Analysis and Design of Linear Control Systems" (Analysis and Design of Linear Control Systems), Academia in Prague and Prentice Hall International, London [Ref 13] W. Bode and C.I. E. 1950 "Simplified derivation of linear least squares smoothing and prediction theory" by Shannon, I. R. 08-05-2019 21 E. Bulletin 38, pages 417 to 425. [Reference 14] A. Ahlen and M.W. Ctern of 1994 in Sternad. T. レノンデ（Ｌｅｎｏｎｄｅｓｅｄ． Derivation and design of Wiener filters using polynomials, Control and Dynamic Systems, Digital Signal Processing and Applications, Academic Press, New York [Reference 15] B. D. O. Anderson and J.A. B. Moore's 1989 Linear Quadratic Methods, Optimal Control. Linear Quadratic Methods, Prentice Hall International, London [Ref. 16] Sternad and A.I. Ahlen, 1993, "A new derivation methodology for polynomial LQ controller design" (A novel derivation methodology for polynomial LQ controller design), IEEE Transactions on Automatic Control, Vol. 38, pp. 116-121. The present invention offers the following advantages: -Tight control of the range and amount of compensation performed by the precomp filter, providing perfect control over the resulting acoustic response. -Avoid dangerous overcompensation while providing good compensation that can be achieved without problems. Demonstrate good compensation capabilities while using a limited number of filter parameters. Get an optimally pre-compensated audio system that produces superior sound quality and experience results. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a general illustration of a system for producing compensated sound. FIG. 2 is a graph (A) showing the amplitude response of the uncompensated loudspeaker model and the deviation of the phase response of the uncompensated loudspeaker model as compared to the phase shift of a pure delay It is a graph figure (B). FIG. 3 shows the discrete time impulse response of the loudspeaker model of FIGS. 2 (A), (B), sampled at 44.1 kHz and delayed by 250 samples for illustration. 4 shows an example of the impulse response of a scalar FIR compensation filter designed according to the prior art to reverse the dynamics of the loudspeakers of FIGS. 2 (A), (B) and FIG. 3; 5 shows the impulse response of a scalar IIR compensation filter designed based on the loudspeaker models of FIGS. 2A, 2B and 3 according to the invention. 6A is a graph showing the amplitude response of the loudspeaker model of FIG. 2A compensated by the IIR filter of FIG. 5A and the loudspeaker model of FIG. 2B compensated by the IIR filter of FIG. 5; (B) is a graph showing the deviation of the phase response of the signal in comparison with the phase shift of a pure delay. 7 shows the compensated impulse response of the loudspeaker model of FIG. 3 compensated by the IIR filter of FIG. 5; 8 shows the frequency response amplitude of the weighting function used in the design of the IIR filter of FIG. 5; 9 shows the compensated impulse response of FIG. 8 when using compensation without control penalty. FIG. 10 is a graph (A) showing the amplitude response of the prior art FIR filter compensated loudspeaker model of FIG. 2 of FIG. 4 and FIG. 2 (compacted with the prior art FIR filter of FIG. FIG. 7B is a graph (B) showing the deviation of the phase response of the loudspeaker model of B) compared to the phase shift of a pure delay. FIG. 11 is a schematic diagram illustrating a specific embodiment of a filter design structure according to the present invention. FIG. 12 is a block diagram of a computer based system that may be used to practice the present invention. FIG. 13 is a diagram showing an audio system incorporating a precomp 08-05-2019 22 filter set by the design method of the present invention. FIG. 14 is a process flow diagram showing an overall flow of a filter design method according to an exemplary embodiment of the present invention. 08-05-2019 23

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