JP2009271641A - Audio processing device and audio processing method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an audio processing device and an audio processing method, allowing reproduction of discrete data with good sound quality according to user preferences. <P>SOLUTION: In an audio processing part 2, an interpolation value reflecting with a numerical value of a variable parameter α by which a value of a control sampling function c<SB>0</SB>(t) is multiplied can be calculated, so that an analog signal obtained by performing interpolation processing by a sampling function s<SB>N</SB>(t) can be adjusted according to the variable parameter α by changing the numerical value of the variable parameter α. Thus, the user appropriately changes the variable parameter α according to various conditions of music reproduction environment, a sound source, a melody or the like to reproduce music having the high sound quality of user-desired sound quality wherein frequency properties of the analog signal vary. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to an acoustic processing device and an acoustic processing method, for example, interpolating between discrete data arranged in a time direction sampled at a predetermined sampling frequency, and generating discrete data at a frequency higher than the sampling frequency at the time of input or an analog signal It is suitable for application when generating. In this specification, the generation of signals at high frequency discrete intervals and the generation of analog signals are referred to as “analog signal generation” as the same processing. Further, the case where the value of the function has a finite value other than 0 in the local region and becomes 0 in the other region will be referred to as a “finite platform”.

  Conventionally, when an analog signal is generated from discrete data such as digital data, a Shannon sampling function derived based on the Shannon sampling theorem has been widely used. Here, as shown in FIG. 14, Shannon's sampling function becomes 1 only at the sample position of t = 0, and becomes 0 at all other sample positions. Theoretically, the vibration from −∞ to + ∞. Indicates a waveform that continues indefinitely. For this reason, when an interpolation process between discrete data is actually performed by various processors using Shannon's sampling function, the process is forcibly terminated in a finite interval, and as a result, errors due to the truncation occur. There was a problem that occurred.

  In addition, when such a Shannon sampling function is used, the analog signal to be reproduced is band-limited, so that, for example, discrete data recorded on a CD (Compact Disc) or DVD (Digital Versatile Disc) When the signal is converted into an analog signal and reproduced, there is a problem that an ultrasonic wave of 22.05 kHz or higher cannot be reproduced and a natural sound generated by the difference sound of the ultrasonic wave cannot be reproduced.

Therefore, in order to solve such a problem, a sampling function that has no censoring error and can reproduce even higher-order band components and converges in a finite range has been devised (for example, Patent Documents). 1). In this sampling function, since it converges to 0 at two sample positions before and after the origin, it is confirmed that signal reproduction can be performed with a small amount of calculation and that there is a band up to a high frequency.
International Publication No. 99/38090

  However, in an audio apparatus using such a sampling function, it is possible to vary high-frequency band components in accordance with various users such as the hard of hearing and the elderly, and various conditions such as music reproduction environment, sound source, and tone. Therefore, the frequency characteristics cannot be freely adjusted according to the situation. On the other hand, in recent years, it has been desired to provide a tailor-made audio device in which the user himself can freely adjust the sound quality including high-frequency band components according to the preference of each user, the type of music, and the like.

  The present invention has been made in consideration of the above points, and an object of the present invention is to propose an acoustic processing apparatus and an acoustic processing method capable of reproducing discrete data with good sound quality according to user preferences.

  In order to solve such a problem, the sound processing apparatus according to claim 1 of the present invention provides a basic sampling function that is finitely differentiable and has a finite stage value, and a finite number of differentiable and finite stage value. The discrete data is obtained by performing a convolution operation on a plurality of discrete data arranged in the time direction and linear addition using a sampling function including a control sampling function indicating a waveform different from the waveform indicated by the basic sampling function. The function processing means includes a coefficient multiplying means for multiplying the control sampling function by a variable parameter that can be set to an arbitrary numerical value by a user.

  In the acoustic processing device according to claim 2 of the present invention, the function processing means performs the convolution operation on the discrete data using the basic sampling function and the control sampling function, respectively, The interpolation value is calculated by linearly adding the calculation results obtained by the convolution calculation using a sampling function.

  According to a third aspect of the present invention, there is provided an acoustic processing device comprising: discrete data extraction means for extracting a predetermined number of the discrete data existing across a point of interest for which the interpolation value is calculated, and the function processing means includes: The value of the basic sampling function is calculated using the distance to the point of interest obtained for each discrete data extracted by the discrete data extracting means, and the basic sampling corresponding to each of the discrete data A basic term calculation means for calculating a basic interpolation value at the point of interest by performing a convolution operation on the value of the function, and a distance to the point of interest obtained for each discrete data extracted by the discrete data extraction means Is used to calculate the value of the control sampling function, and the control sampling value corresponding to each of the discrete data is convolved to calculate the control interpolation value at the point of interest. Control term computing means, and linear addition means for computing the interpolation value by linear addition of the basic interpolation value calculated by the basic term computing means and the control interpolation value calculated by the control term computing means. It is characterized by that.

  Further, in the acoustic processing device according to claim 4 of the present invention, the function processing means uses the sampling function obtained by linearly adding the basic sampling function and the control sampling function in advance to the discrete data. The interpolation value is calculated by performing the convolution operation.

  The acoustic processing device according to claim 5 of the present invention further includes discrete data extraction means for extracting a predetermined number of the discrete data existing across the point of interest where the interpolation value is calculated, and the function processing means includes: The sampling function obtained by linearly adding the basic sampling function and the control sampling function is stored in advance, and the sampling function is calculated using the distance to the point of interest obtained for each discrete data. A function calculating means for calculating a value; and a convolution calculating means for calculating an interpolated value at the point of interest by performing a convolution operation on the value of the sampling function corresponding to each of the discrete data. And

  In the acoustic processing device according to claim 6 of the present invention, the basic sampling function is a piecewise polynomial that can be differentiated only once in the interval [-1, 1] of the sample position of the discrete data, and the other intervals are constant. The control sampling function is a piecewise polynomial function that can be differentiated only once in the interval [−2, 2] of the sample position of the discrete data, and is identical in other intervals. In other words, the function is zero.

  In the acoustic processing device according to claim 7 of the present invention, when the sample position of the discrete data is t, the basic sampling function is f (t), and the basic sampling function is

The control sampling function is C ₀ (t) = C _r (t) + C _r (−t), and Cr (t) is expressed by the following equation:

It is represented by.

  In the acoustic processing device according to claim 8 of the present invention, a plurality of the variable parameters having different numerical values are stored in advance, and any one of the plurality of variable parameters is multiplied by the control sampling function. A selector for selecting the variable parameter is provided.

  According to a ninth aspect of the present invention, there is provided a sound processing apparatus programmed to a field programmable gate array forming a circuit configuration having a control form desired by a user based on program data designated by a user.

  In the acoustic processing apparatus according to claim 10 of the present invention, the function processing means is programmed in a programmable signal processing device that forms an arithmetic configuration in a control form desired by the user based on program data designated by the user. It is characterized by.

  In the acoustic processing device according to claim 11 of the present invention, the basic sampling function and the control sampling function may table the calculation values calculated in advance according to a predetermined number of sections between the discrete data of interest. In addition, a convolution operation between the table value that is tabulated and the discrete data, multiplication of the variable parameter, and linear addition are calculated for each input of the discrete data, and the interpolation value is output. Features.

  The acoustic processing device according to claim 12 of the present invention, when the number of divisions between the discrete data is plural, the table value is calculated in advance by the number of divisions of the least common multiple of the division numbers, and the discrete data The table value is selected according to the number of sections set at the start of input, and a convolution operation between the table value and the discrete data is executed.

  The acoustic processing method according to claim 13 of the present invention includes a parameter setting step in which a variable parameter is set to an arbitrary numerical value by a user, a basic sampling function that can be differentiated a finite number of times and has a finite value. Using a sampling function consisting of a control sampling function that is multiplied by the variable parameter and can be differentiated a finite number of times, has a value of a finite stage, and has a waveform different from the waveform indicated by the basic sampling function, And a function processing step of calculating an interpolated value between the discrete data by performing a convolution operation on a plurality of discrete data arranged in a linear manner and linear addition.

  The acoustic processing method according to claim 14 of the present invention is the convolution operation step in which the function processing step executes the convolution operation on the discrete data using the basic sampling function and the control sampling function, respectively. And a linear addition step of calculating the interpolated value by linearly adding the operation results obtained by the convolution operation using the sampling function.

  The acoustic processing method according to claim 15 of the present invention includes a discrete data extraction step of extracting a predetermined number of the discrete data existing across a point of interest where the interpolation value is calculated, and the function processing step includes: The value of the basic sampling function is calculated using the distance to the point of interest obtained for each discrete data extracted by the discrete data extracting means, and the basic sampling corresponding to each of the discrete data A basic term calculation step for calculating a basic interpolation value at the point of interest by performing a convolution operation on the value of the function, and a distance to the point of interest obtained for each discrete data extracted by the discrete data extraction means Is used to calculate the value of the control sampling function, and convolutionally calculate the value of the control sampling function corresponding to each of the discrete data. A control term calculation step for calculating a control interpolation value, the basic interpolation value calculated by the basic term calculation means, and the control interpolation value calculated by the control term calculation means are linearly added to calculate the interpolation value. A linear addition step.

  Further, in the acoustic processing method according to claim 16 of the present invention, the function processing step uses the sampling function obtained by linearly adding the basic sampling function and the control sampling function in advance to the discrete data. The interpolation value is calculated by performing the convolution operation.

  The acoustic processing method according to claim 17 of the present invention includes a discrete data extraction step of extracting a predetermined number of the discrete data existing across a point of interest where the interpolation value is calculated, and the function processing step includes: The sampling function obtained by linearly adding the basic sampling function and the control sampling function is stored in advance, and the distance to the point of interest obtained for each discrete data is used to calculate the sampling function. A function calculation step for calculating a value, and a convolution calculation step for calculating an interpolated value at the point of interest by performing a convolution operation on the value of the sampling function corresponding to each of the discrete data. And

  In the acoustic processing method according to claim 18 of the present invention, the basic sampling function is a piecewise polynomial that can be differentiated only once in the interval [-1, 1] of the sample position of the discrete data, and the other intervals are constant. The control sampling function is a piecewise polynomial function that can be differentiated only once in the interval [−2, 2] of the sample position of the discrete data, and is identical in other intervals. In other words, the function is zero.

  In the acoustic processing method according to claim 19 of the present invention, when the sample position of the discrete data is t, the basic sampling function is f (t), and the basic sampling function is

The control sampling function is C ₀ (t) = Cr (t) + Cr (−t), and Cr (t) is expressed by the following equation:

It is represented by.

  Further, in the acoustic processing method according to claim 20 of the present invention, in the parameter setting step, any one of the plurality of variable parameters stored in advance that are different in numerical value is multiplied by the control sampling function. A variable parameter is selected.

  According to a twenty-first aspect of the present invention, the acoustic processing method is executed by a circuit configuration having a control form desired by a user programmed in a field programmable gate array based on program data designated by a user.

  According to a twenty-second aspect of the present invention, the acoustic processing method is executed by an arithmetic circuit configuration having a control form desired by a user programmed in a programmable signal processing device based on program data designated by the user. .

  In the acoustic processing method according to claim 23 of the present invention, the function processing step includes: calculating the basic sampling function and the control sampling function calculated in advance according to a predetermined number of sections between the discrete data of interest. The calculation values are tabulated, the convolution calculation of the table values and the discrete data, the multiplication of the variable parameters, and the linear addition are calculated for each input of the discrete data, and the interpolation value is output. It is characterized by that.

  In the acoustic processing method according to claim 24 of the present invention, in the function processing step, when the number of divisions between the discrete data is plural, the table value is calculated in advance by the number of divisions of the least common multiple of the number of divisions. The table value is selected according to the number of divisions set at the start of input of the discrete data, and a convolution operation between the table value and the discrete data is executed.

  According to the acoustic processing device of claim 1 and the acoustic processing method of claim 13 of the present invention, it is possible to calculate an interpolation value reflecting a numerical value of a variable parameter to be multiplied by a control sampling function. By changing the numerical value of, the interpolation value obtained by performing the interpolation process with the sampling function can be adjusted according to the variable parameter, and the user can change the variable parameter according to various conditions such as music playback environment, sound source, music tone, etc. By appropriately changing the frequency characteristics of the signal generated based on the interpolation value, it is possible to reproduce high-quality music having the sound quality desired by the user.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

(1) Basic Concept of the Present Invention FIG. 1 shows waveforms shown by the basic sampling function f (t) and the control sampling function c ₀ (t) used in the interpolation processing of the present invention. Here, a sampling function consisting of a basic sampling function f (t) and a control sampling function c ₀ (t) between the sampling positions [−2, 2] of the discrete data, where t is the sampling position of the discrete data. s ₂ (t) is:

When a general control sampling function is expressed as c _k (t) and c _k (t) = c _r (t−k) + c _r (−t−k), the discrete data The sampling function s _N (t) between sample positions [−N, N] is

Represented by Α _k represents a variable parameter to be described later, and represents an arbitrary numerical value that can be set by the user, and may not be varied by k such as α ₁ = α ₂ = α ₃ .

  Here, the basic sampling function f (t) is a function of a finite stage paying attention to differentiability. For example, the basic sampling function f (t) can be differentiated only once in the entire region, and the sample position t along the horizontal axis is −1 to +1 (Ie, a section [−1, 1]) is a function having a finite value other than 0, and the other sections are represented by 0 uniformly. Specifically, the basic sampling function f (t) is a quadratic expression in a typical function form, shows a convex waveform that can be differentiated only once in the entire range, and is 1 only at a sample position of t = 0. And converges to 0 toward t = ± 1 and reaches 0 as it is until the sample position of t = ± 2.

  The basic sampling function f (t) may be a function of a finite impulse response waveform, and may be a continuous n-order piecewise polynomial function that can be differentiated at least once at an arbitrary position in the sample position section.

  Specifically, such a basic sampling function f (t) is given by

Represented by Then, by performing superposition based on each discrete data using this basic sampling function f (t), it is possible to temporarily interpolate the value between the discrete data using a function that can be differentiated only once.

On the other hand, the control sampling function c ₀ (t) is a function of a finite stage focusing on differentiability, and can be differentiated only once in the entire region, for example, and the sample position t along the horizontal axis is from −2. It is a function that has a finite value other than 0 when it is in +2 (that is, in the interval [−2, 2]), and is uniformly expressed as 0 in other intervals. Further, the control sampling function c ₀ (t) shows a waveform that can be differentiated only once in the entire range, and has a feature that it becomes 0 at each sample position of t = 0, ± 1, ± 2. The control sampling function c ₀ (t) may be a function of a finite number of impulse response waveforms, and may be a continuous n-order piecewise polynomial function that can be differentiated at least once at an arbitrary position in the sample position section.

Here, the control sampling function c ₀ (t) is represented by the control sampling function c ₀ (t) = c _r (t) + c _r (−t) as described above, and this _cr (t) is Specifically,

Represented by Then, by performing superposition based on each discrete data using this control sampling function c ₀ (t), it is possible to temporarily interpolate the values between the discrete data using a function that can be differentiated only once.

The sampling function s _N (t) is a temporary interpolation value (hereinafter referred to as a basic interpolation value) calculated based on the basic sampling function f (t) in this way, and the control sampling function c _0. Interpolating a value between discrete data using a function that can be differentiated only once by linearly adding a temporary interpolation value calculated based on (t) (hereinafter referred to as a control interpolation value). Can do.

Incidentally, the linear combination of the basic sampling function f (t) and the control sampling function c ₀ (t) is a function that satisfies the following six conditions. First, S ₂ (0) = 1, S ₂ (± 1) = S ₂ (± 2) = 0. Secondly, it should be symmetric with respect to the even function, ie the y-axis. Thirdly, the sample position interval [−∞, −2], [2, ∞] should be zero on the identity. Fourthly, each section [n / 2, (n + 1) / 2] (−4 ≦ n ≦ 3) is at most a quadratic polynomial. Fifth, all sections should be C1 class, that is, differentiable continuously once. Sixth, in the sample position section [−1/2, 1/2],

To be. The sampling function s ₂ (t) when N = 2 will be described below simply as the sampling function s _N (t) for convenience of explanation.

In addition, at this time, the control sampling function c ₀ (t) can be multiplied by a variable parameter α set by an arbitrary numerical value by the user. As a result, the control sampling function c ₀ (t) remains 0 at the sample positions of t = 0, ± 1, ± 2, depending on the value of the variable parameter α between the sample positions −2 and +2. The amplitude of the waveform can be modified. As a result, the control sampling function c ₀ (t) can change the calculation result by the convolution operation with the basic sampling function f (t). As described above, the variable parameter α can change the frequency characteristic of the analog signal calculated by the sampling function s _N (t) and adjust the signal level of the high frequency component by changing the numerical value. It is made like that.

Therefore, according to the present invention, when the calculation result of the basic sampling function f (t) and the calculation result of the control sampling function c ₀ (t) are convolutionally calculated to obtain the interpolation value, the control sampling function c _{Since the} interpolation value can be adjusted by the variable parameter α multiplied by ₀ (t), the frequency characteristic of the analog signal obtained by interpolating between these discrete data with the interpolation value can be freely adjusted by the variable parameter α.

(2) Overall Configuration of Audio Device Next, an audio device that performs interpolation processing using the sampling function s _N (t) described above will be described below. In FIG. 2, reference numeral 1 denotes an audio apparatus, and an acoustic processing unit 2 is provided by being programmed in a field programmable gate array (hereinafter referred to as FPGA) 3. Incidentally, in this FPGA 3, a plurality of circuit blocks and wiring blocks are regularly arranged on the chip, and a lot of devices that can program the electrical connection or non-connection of the circuit are arranged in the circuit blocks and the wiring blocks. In addition, the user can design these devices in a field (use site) by programming (defining) these devices and connecting the blocks.

  In this connection, the audio apparatus 1 receives various other functions such as the Shannon sampling function from the personal computer 5 via the external interface 4, the above-described sampling function of Equation 9 according to the present invention, and a sampling function that is completely different from these. When the sampling function is programmed in the FPGA 3, the connection state between the circuit block and the wiring block of the FPGA 3 is changed, and the circuit configuration is changed to hardware capable of executing the interpolation processing by various sampling functions. Has been made. Thus, in this audio apparatus 1, since the circuit configuration desired by the user can be changed by simply programming the FPGA 3, when searching for an optimal sampling function, the circuit board is actually used for each sampling function. Therefore, the cost can be reduced accordingly.

  In the embodiment described above, FIG. 2 applies FPGA 3 and shows an implementation method in FPGA 3. However, the present invention is not limited to this, and a programmable signal such as a DSP (digital signal processor) is used. For example, a control unit configured with a CPU (Central Processing Unit) and a memory may be applied.

Here, in the audio apparatus 1 of the present invention, the basic sampling function f (t) of Formula 11 that satisfies the condition of the sampling function s _N (t) of Formula 9 described above from the personal computer 5 via the external interface 4. And a control sampling function c ₀ (t) where c _r (t) is expressed by Equation 12 can be programmed into the FPGA 3.

  As a result, the audio apparatus 1 performs overall control of the FPGA 3 according to a predetermined program, thereby reproducing various recording media such as CDs and DVDs by the input unit 6, and a plurality of lines arranged in the time direction obtained as a result. Discrete data is sequentially sent to the acoustic processing unit 2. Incidentally, the discrete data is discrete data obtained by, for example, sampling a continuous signal that changes smoothly at regular time intervals and quantizing the sampling data obtained as a result.

Here, the FPGA 3 is connected with a parameter setting unit 7 that allows the user to freely set the numerical value of the variable parameter α. When the user sets the variable parameter α to an arbitrary numerical value by the parameter setting unit 7, the setting is performed. Information indicating numerical values can be sent from the parameter setting unit 7 to the sound processing unit 2. The acoustic processing unit 2 as the acoustic processing device interpolates between discrete data using the sampling function s _N (t), and performs so-called oversampling processing to increase the sampling frequency in a pseudo manner. An interpolation value that reflects the numerical value is calculated and sent to the output unit 8.

  When the interpolation value is input from the acoustic processing unit 2 at a predetermined cycle, the output unit 8 can convert the analog value into a corresponding analog signal and emit music based on the analog signal. As described above, the audio apparatus 1 can generate a high-quality analog signal desired by the user reflecting the numerical value of the variable parameter α by changing the numerical value of the variable parameter α.

The FPGA 3 is connected to a selector 10 having a plurality of selector buttons 9a, 9b, 9c. The selector 10 is associated with a variable parameter α having a different numerical value in advance for each of the selector buttons 9a, 9b, 9c, and a variable corresponding to one of the selector buttons 9a, 9b, 9c is selected. The numerical value of the parameter α is multiplied by the control sampling function c ₀ (t) so that an interpolation process using the sampling function s _N (t) can be executed.

Specifically, in the case of this embodiment, for example, when the selector button 9a is selected, interpolation processing is executed by a sampling function s _N (t) with the variable parameter α set to −1.5, and other selectors are selected. When the button 9b is selected, interpolation processing is executed by a sampling function s _N (t) with the variable parameter α set to −0.25, and when another selector button 9c is selected, the variable parameter α is set to 1. Interpolation processing can be executed with a sampling function s _N (t) of .5.

  Thereby, in this audio apparatus 1, the user can set the numerical value of the variable parameter α to any numerical value in the parameter setting unit 7, and on the other hand, simply select one of the selector buttons 9a, 9b, 9c. Thus, the interpolation processing using the desired variable parameter α can be easily executed without finely setting the variable parameter α by the parameter setting unit 7 each time.

(3) Circuit configuration of acoustic processing unit (3-1) Schematic description of interpolation processing in acoustic processing unit In practice, acoustic processing unit 2 having a circuit configuration as shown in FIG. obtain. The acoustic processing unit 2 includes a discrete data extraction unit 15 that sequentially extracts and holds a predetermined number (in this case, four) of discrete data, and a predetermined number of discrete data extracted and held by the discrete data extraction unit 15 at a time. The function processing unit 14 receives and executes interpolation processing using these discrete data, and is capable of interpolating data between discrete data sequentially input from the input unit 6 at a predetermined time interval. .

The function processing unit 14 includes a basic term calculation unit 16 that performs calculation processing on the term of the basic sampling function f (t) in the sampling function s _N (t) based on the discrete data, and sampling based on the discrete data. A control term computing unit 17 for computing the term of the control sampling function c ₀ (t) in the function s _N (t), and a coefficient multiplying unit 18 for multiplying the calculation result of the control term computing unit 17 by the variable parameter α, The linear term addition unit 19 linearly adds the calculation result of the fundamental term calculation unit 16 and the calculation result of the coefficient multiplication unit 18.

  In the case of this embodiment, the discrete data extraction unit 15 extracts the last four discrete data from the sequentially input discrete data, and uses the four discrete data until new discrete data is input next. The four discrete data are sent to the basic term calculation unit 16 and the control term calculation unit 17, respectively.

  The basic term calculation unit 16 stores a basic sampling function f (t) in a predetermined storage means (not shown), and when an interpolation position between discrete data is designated, the interpolation position and the discrete data The value of the basic sampling function f (t) is calculated based on the distance between. The basic term calculation unit 16 can calculate the value of the basic sampling function f (t) for each of the four discrete data transmitted from the discrete data extraction unit 15. The basic term calculation unit 16 multiplies the values of the four basic sampling functions f (t) obtained for each discrete data by corresponding discrete data values, and then corresponds to these four discrete data. A convolution operation is performed, and the calculation result of this convolution operation is sent to the linear adder 19.

At the same time, the control term calculation unit 17 stores the control sampling function c ₀ (t) in a predetermined storage means (not shown), and when the interpolation position is designated, the interpolation position, discrete data, Calculate the value of the control sampling function c ₀ (t) based on the distance between. The control term calculation unit 17 can calculate the value of the control sampling function c ₀ (t) for each of the four discrete data transmitted from the discrete data extraction unit 15. In addition, the control term calculation unit 17 multiplies the values of the discrete data corresponding to the values of the four control sampling functions c ₀ (t) obtained for each discrete data, and then adds them to obtain 4 A convolution operation corresponding to two discrete data is performed, and a calculation result of the convolution operation is sent to the coefficient multiplication unit 18.

The coefficient multiplication unit 18 multiplies the calculation result of the convolution calculation of the control sample function c ₀ (t) received from the control term calculation unit 17 by the variable parameter α, and the resulting variable parameter multiplication result is the linear addition unit 19. To send. The linear adder 19 linearly adds the calculation result of the convolution calculation of the basic sampling function f (t) received from the basic term calculation unit 16 and the variable parameter multiplication result received from the coefficient multiplication unit 18 to obtain 4 A linear addition result corresponding to two discrete data is obtained. A value obtained by this linear addition is an interpolation value at an interpolation position between two predetermined discrete data. Incidentally, the value of this interpolation position is updated at a preset predetermined time interval, specifically every 1 / N period (= T / N) of the period T corresponding to the input interval of discrete data. .

(3-2) Specific Example of Obtaining Interpolation Value Based on Four Discrete Data Next, an interpolation process for calculating an interpolation value between two predetermined discrete data based on four discrete data arranged successively in time Will be described below with reference to FIG. 4 showing a positional relationship between four continuous discrete data and a point of interest which is an interpolation position. In FIG. 4, each value of discrete data d1, d2, d3, d4 sequentially input corresponding to each of the sample positions t1, t2, t3, t4 is represented by Y (t1), Y (t2), Y (t3 ), Y (t4), and consider a case where an interpolation value y corresponding to a predetermined position between the sample positions t2 and t3 (that is, the interpolation position (distance b from t2)) t0 is obtained.

Since the sampling function s _N (t) used in the present embodiment converges to 0 at the sample position of t = ± 2, if the discrete data d1, d2, d3, d4 up to t = ± 2 are taken into consideration. Good. Therefore, when obtaining the interpolation value y shown in FIG. 4, only four discrete data d1, d2, d3, and d4 corresponding to t = t1, t2, t3, and t4 need to be considered, and the amount of calculation Can be greatly reduced. In addition, discrete data (not shown) of t = ± 3 or more should be considered originally, but is not ignored in consideration of the amount of calculation and accuracy, and should be considered theoretically. There is no truncation error.

  As shown in FIG. 5, the discrete data extraction unit 15 includes three shift circuits 20a, 20b, and 20c. When continuous discrete data is input, the discrete data extraction unit 15 receives the discrete data for each shift circuit 20a, 20b, and 20c. The data can be shifted, for example, at a CD sampling period (44.1 kHz), and the respective discrete circuits d1, d2, d3, d4 can be extracted and held by the shift circuits 20a, 20b, 20c, respectively. That is, when the discrete data extraction unit 15 receives four continuous discrete data d1, d2, d3, and d4, the latest discrete data d4 is directly used as the basic term calculation circuit 21a of the basic term calculation unit 16 and the control term calculation. To the control term calculation circuit 22a of the unit 17.

  Also, the discrete data extraction unit 15 sends a discrete data string composed of four continuous discrete data d1, d2, d3, and d4 to the shift circuit 20a, and shifts the discrete data string by the shift circuit 20b to thereby update the latest discrete data string. The previous discrete data d3 is extracted from the data d4 and sent to the basic term calculation circuit 21b of the basic term calculation unit 16 and the control term calculation circuit 22b of the control term calculation unit 17, respectively.

  Further, the discrete data extraction unit 15 sequentially sends the discrete data string to the remaining shift circuits 20b and 20c, and further shifts the discrete data string by the shift circuit 20b, so that the two previous data from the latest discrete data d4. The discrete data d2 is sent to the basic term calculation circuit 21c and the control term calculation circuit 22c, respectively, and the discrete data string is further shifted by the shift circuit 20c to calculate the discrete data d1 three times before the latest discrete data d4. The data is sent to the circuit 21d and the control term calculation circuit 22d, respectively.

  Here, FIGS. 6 and 7 are diagrams showing an outline of the interpolation process for the predetermined interpolation position t0 in the basic term calculation unit 16 and the control term calculation unit 17 of the present embodiment. As the contents of the interpolation processing, as described above, first, the calculation processing for calculating the basic interpolation value in the basic term calculation unit 16 (hereinafter simply referred to as basic interpolation value calculation processing), the control term calculation unit 17 Then, a calculation process for calculating a control interpolation value in the coefficient multiplication unit 18 (hereinafter simply referred to as a control interpolation value calculation process) is executed. Hereinafter, the basic interpolation value calculation process and the control interpolation value calculation process will be described with reference to FIGS. 6 and 7.

(3-2-1) Basic Interpolation Value Calculation Processing As shown in FIGS. 6A to 6D, the basic interpolation value calculation processing is performed for each sample position t1, t2, t3, t4. The peak heights at t = 0 (center position) of the sampling function f (t) are matched, and the value of each basic sampling function f (t) at the interpolation position t0 at this time is obtained.

  Focusing on the discrete data d1 at the sample position t1 shown in FIG. 6A, the distance between the interpolation position t0 and the sample position t1 is 1 + b. Therefore, the value of the basic sampling function at the interpolation position t0 when the center position of the basic sampling function f (t) is aligned with the sample position t1 is f (1 + b). Actually, in order to match the peak height of the center position of the basic sampling function f (t) so as to match the value Y (t1) of the discrete data d1, the above-described f (1 + b) is multiplied by Y (t1). The value f (1 + b) · Y (t1) is the desired value. Calculation of f (1 + b) is performed by the basic term calculation circuit 21a of the basic term calculation unit 16, and calculation for multiplying f (1 + b) by Y (t1) is performed by the basic term multiplication circuit 23a of the basic term calculation unit 16. (FIG. 5).

  Similarly, focusing on the value Y (t2) of the discrete data d2 at the sample position t2 shown in FIG. 6B, the distance between the interpolation position t0 and the sample position t2 is b. Therefore, the value of the basic sampling function at the interpolation position t0 when the center position of the basic sampling function f (t) is aligned with the sampling position t2 is f (b). Actually, in order to match the peak height of the center position of the basic sampling function f (t) so as to coincide with the value Y (t2) of the discrete data d2, the above-described f (b) is multiplied by Y (t2). The value f (b) · Y (t2) is a desired value. The calculation of f (b) is performed by the basic term calculation circuit 21b of the basic term calculation unit 16, and the calculation of multiplying f (b) by Y (t2) is performed by the basic term multiplication circuit 23b of the basic term calculation unit 16. (FIG. 5).

  Focusing on the value Y (t3) of the discrete data d3 at the sample position t3 shown in FIG. 6C, the distance between the interpolation position t0 and the sample position t3 is 1-b. Therefore, the value of the basic sampling function at the interpolation position t0 when the center position of the basic sampling function f (t) is aligned with the sampling position t3 is f (1-b). Actually, in order to match the peak height of the center position of the basic sampling function f (t) so as to coincide with the value Y (t3) of the discrete data, the above-described f (1-b) is multiplied by Y (t3). The obtained value f (1-b) · Y (t3) is a desired value. The calculation of f (1-b) is performed by the basic term calculation circuit 21c of the basic term calculation unit 16, and the calculation of multiplying f (1-b) by Y (t3) is the basic term multiplication time of the basic term calculation unit 16. Performed on the 23c road (FIG. 5).

Focusing on the value Y (t4) of the discrete data d4 at the sample position t4 shown in FIG. 6D, the distance between the interpolation position t0 and the sample position t4 is 2-b. Therefore, the value of the basic sampling function at the interpolation position t0 when the center position of the basic sampling function f (t) is aligned with the sampling position t4 is f (2-b). Actually, in order to match the peak height of the center position of the basic sampling function f (2-b) so as to coincide with the value Y (t4) of the discrete data d4, the above-described f (2-b) is changed to Y ( t4) The multiplied value f (2-b) · Y (t4) is a desired value. The calculation of f (2-b) is performed by the basic term calculation circuit 21d of the basic term calculation unit 16, and the calculation of multiplying f (2-b) by Y (t4) is the basic term multiplication circuit of the basic term calculation unit 16. Performed in 23d. (Fig. 5)
The basic term calculation unit 16 then obtains four values f (1 + b) · Y (t1), f (b) · Y (t2), and f (1−b) obtained corresponding to the point of interest at the interpolation position t0. ) · Y (t3) and f (2-b) · Y (t4) are convolutionally calculated in the basic term convolution circuit 24, and a basic interpolation value ya corresponding to the point of interest is calculated. Incidentally, in the case of this embodiment, the values f (1 + b) · Y (t1) and f (2-b) · Y (t4) obtained corresponding to the point of interest at the interpolation position t0 are shown in FIG. ) And 0 as shown in (D), the basic interpolation value ya is {f (b) · Y (t2)} + {f (1−b) · Y (t3)}.

(3-2-2) Control Interpolation Value Calculation Processing On the other hand, the contents of the control interpolation value calculation processing are as shown in FIGS. 7A to 7D for each sample position t1, t2, t3, t4. , to match the t = 0 (center position) of the control sampling function c ₀ (t), the discrete data d1 corresponding to each control sampling function _{c 0 (t), d2,} d3, d4 of the value Y (t1 ), Y (t2), Y (t3), and Y (t4), and the value of each control sampling function c ₀ (t) at the interpolation position t0 at this time is obtained.

Focusing on the value Y (t1) of the discrete data d1 at the sample position t1 shown in FIG. 7A, the distance between the interpolation position t0 and the sample position t1 is 1 + b. Therefore, the value of the control sampling function at the interpolation position t0 when the center position of the control sampling function c ₀ (t) is aligned with the sample position t1 is c ₀ (1 + b). Actually, in order to match the waveform height of the control sampling function c ₀ (t) in correspondence with the value Y (t 1) of the discrete data d 1, a value c obtained by multiplying the above c ₀ (1 + b) by Y (t 1) ₀ (1 + b) · Y (t1) is a desired value. The calculation of c ₀ (1 + b) is performed by the control calculation circuit 22a of the control term calculation unit 17, and the calculation of multiplying c ₀ (1 + b) by Y (t1) is performed by the control term multiplication circuit 25a of the control term calculation unit 17. (FIG. 5).

Similarly, focusing on the value Y (t2) of the discrete data d2 at the sample position t2 shown in FIG. 7B, the distance between the interpolation position t0 and the sample position t2 is b. Therefore, the value of the control sampling function at the interpolation position t0 when the center position of the control sampling function c ₀ (t) is aligned with the sample position t2 is c ₀ (b). Actually, in order to match the waveform height of the control sampling function c ₀ (t) in correspondence with the value Y (t 2) of the discrete data d 2, a value c obtained by multiplying the above c ₀ (b) by Y (t 2). ₀ (b) · Y (t2) is a desired value. The calculation of c ₀ (b) is performed by the control calculation circuit 22b of the control term calculation unit 17, and the calculation of multiplying c ₀ (b) by Y (t2) is performed by the control term multiplication circuit 25b of the control term calculation unit 17. (FIG. 5).

Focusing on the value Y (t3) of the discrete data d3 at the sample position t3 shown in FIG. 7C, the distance between the interpolation position t0 and the sample position t3 is 1-b. Therefore, the value of the control sampling function at the interpolation position t0 when the center position of the control sampling function c ₀ (t) is aligned with the sampling position t3 is c ₀ (1-b). Actually, in order to match the waveform height of the control sampling function c ₀ (t) in correspondence with the value Y (t 3) of the discrete data d ₃ , the above c ₀ (1-b) is multiplied by Y (t 3). The value c ₀ (1-b) · Y (t3) is the desired value. The calculation of c ₀ (1-b) is performed by the control calculation circuit 22c of the control term calculation unit 17, and the calculation of multiplying c ₀ (1-b) by Y (t3) is the control term multiplication of the control term calculation unit 17. This is performed by the circuit 25c (FIG. 5).

Focusing on the value Y (t4) of the discrete data d4 at the sample position t4 shown in FIG. 7D, the distance between the interpolation position t0 and the sample position t4 is 2-b. Therefore, the value of the control sampling function at the interpolation position t0 when the center position of the control sampling function c ₀ (t) is aligned with the sampling position t4 is c ₀ (2-b). Actually, in order to match the waveform height of the control sampling function c ₀ (2-b) in correspondence with the value Y (t4) of the discrete data d4, the above c ₀ (2-b) is changed to Y (t4). The multiplied value c ₀ (2-b) · Y (t4) is the value to be obtained. The calculation of c ₀ (2-b) is performed by the control calculation circuit 22d of the control term calculation unit 17, and the calculation of multiplying c ₀ (2-b) by Y (t4) is the control term multiplication of the control term calculation unit 17. This is performed by the circuit 25d (FIG. 5).

Then, four values c ₀ (1 + b) · Y (t1), c ₀ (b) · Y (t2), c ₀ (1−b) · Y () obtained corresponding to the point of interest at the interpolation position t0. t3), c ₀ (2-b) · Y (t4) are subjected to a convolution operation by the control term convolution circuit 26 of the control term operation unit 17, and then multiplied by the variable parameter α in the coefficient multiplication unit 18, thereby focusing on A control interpolation value yb corresponding to the point is calculated.

(3-2-3) Interpolation Value Calculation Processing The linear addition unit 19 is calculated by the basic interpolation value ya corresponding to the target point calculated by the basic term calculation unit 16, the control term calculation unit 17, and the coefficient multiplication unit 18. The interpolation value y at the interpolation position t0 can be output by linearly adding the control interpolation value yb corresponding to the target point.

(3-3) Result of Interpolation Processing when Variable Parameter Values are Changed In addition to this configuration, the acoustic processing unit 2 is configured such that the parameter setting unit 7 changes the value of the variable parameter α of the coefficient multiplier unit 18. The value of the sampling function s _N (t) is changed, and as a result, the interpolated value y can be varied to change the frequency characteristic of the analog signal. Here, regarding how the sampling function s _N (t) changes when the variable parameter α is changed, the waveform shown by the basic sampling function f (t) shown in FIG. The following description will be focused on a waveform obtained by combining the waveform indicated by the function c ₀ (t).

As shown in FIG. 8, the waveform of the sampling function s _N (t) obtained by synthesizing the waveform indicated by the basic sampling function f (t) and the waveform indicated by the control sampling function c ₀ (t) is a variable parameter. It varies greatly depending on the numerical value of α. In this embodiment, when the variable parameter α is sequentially changed to −1.5, −0.25, and 1.5, the region of −2 ≦ t ≦ −1 and the region of 1 ≦ t ≦ 2 Then, it was confirmed that the amplitude of the wavelength of the sampling function s _N (t) gradually increased and the polarity of the waveform was inverted. On the other hand, in the region of −1 ≦ t ≦ 0 and the region of 0 ≦ t ≦ 1, it was confirmed that the amplitude of the wavelength of the sampling function s _N (t) gradually decreased and the waveform polarity was inverted.

Next, a violin song “Zigeunerweisen” recorded on a CD as a test song was played on the audio device 1 for about 23 seconds. At this time, the acoustic processing unit 2 sets the variable parameter α to −0.25, −1.5, and 1.5, respectively, and interpolates the discrete data input during about 23 seconds. Then, when comparing the frequency characteristics of the analog signal interpolated with each sampling function s _N (t) at this time, the result as shown in FIG. 9 was obtained.

As shown in FIG. 9, in the interpolation process using each sampling function s _N (t) in which the numerical value of the variable parameter α is changed, even if the numerical value of the variable parameter α is changed, all of them are in a high frequency range of 20 kHz or more. The signal level increased, and it was confirmed that the high-frequency component could be reproduced compared with the conventional Shannon sampling function. Further, when the variable parameter α is set to 1.5, the signal level is decreased at less than about 26 kHz, but the signal level is increased except in the vicinity of 44.1 kHz in the high frequency range of about 26 kHz or more. It was confirmed that the high frequency component can be reproduced as compared with the case of setting to 0.25 and -1.5.

  On the other hand, when the variable parameter α is set to −1.5, the signal level suddenly decreases around 26 kHz, but the signal level increases as a whole at less than about 26 kHz, and about 26 kHz except for around 44.1 kHz. It was confirmed that the signal level rose even in the higher region, and that the high frequency component could be reproduced with a different signal level compared to the case where the variable parameter α was set to -0.25 and -1.5.

  Furthermore, when the variable parameter α is set to −0.25, the signal level generally increases except for the vicinity of 44.1 kHz, compared to the case where the variable parameter α is set to 1.5 and −1.5. It was confirmed that the high frequency components can be reproduced with different signal levels.

Next, a sound having a reproduction frequency fixed at 10 kHz and 20 kHz as a test song was reproduced by the audio apparatus 1. At this time, the acoustic processing unit 2 sequentially switches the numerical value of the variable parameter α from −5 to 5 and interpolates the discrete data sequentially input from the input unit 6. Then, when the signal levels of the analog signals obtained by performing the interpolation processing with the sampling functions s _N (t) having different variable parameters α at this time were compared, the results as shown in FIG. 10 were obtained.

  As shown in FIG. 10, at the reproduction frequency of 10 kHz, as the variable parameter α is increased, the signal level gradually decreases, and when the variable parameter α is between 2 and 3, the signal level rapidly decreases. After that, it was confirmed that the signal level suddenly increased again. On the other hand, at a reproduction frequency of 20 kHz, as the variable parameter α is increased, the signal level gradually decreases, and when the variable parameter α is near 4, the signal level rapidly decreases, and then the signal level again. It was confirmed that it rose rapidly. As described above, it was confirmed that the acoustic processing unit 2 can reproduce the signal at the same reproduction frequency with different signal levels by changing the variable parameter α.

(4) Operation and Effect In the above configuration, the acoustic processing unit 2 stores the basic sampling function f (t) in the basic term calculation unit 16, and each discrete data d1 extracted by the discrete data extraction unit 15 , D2, d3, d4, the basic sampling function f (t) is calculated by calculating the value of the basic sampling function f (t) with the distance to the interpolation position t0 as t, and corresponding to each of the discrete data d1, d2, d3, d4. The basic interpolation value ya at the interpolation position t0 is calculated by performing a convolution operation on the value of f (t).

Separately from this, in the acoustic processing unit 2, the control sampling function c ₀ (t) is stored in the control term calculation unit 17, and each discrete data d 1, d 2, d 3 extracted by the discrete data extraction unit 15 is stored. , The value of the control sampling function c ₀ (t) is calculated with the distance at the interpolation position t0 as t for each d4, and the control sampling function c ₀ (corresponding to each of the discrete data d1, d2, d3, d4). After performing the convolution operation on the value of t), the control interpolation at the interpolation position t0 is performed by multiplying the convolution operation result of the control sampling function c ₀ (t) by a variable parameter α set to an arbitrary numerical value by the user. The value yb was calculated.

The acoustic processing unit 2 calculates the interpolation value y between the discrete data by linearly adding the basic interpolation value ya calculated in this way and the control interpolation value yb. An interpolation value y reflecting the numerical value of the variable parameter α multiplied by the value of the function c ₀ (t) can be calculated.

Therefore, the acoustic processing unit 2 can adjust the interpolation value y obtained by performing the interpolation process with the sampling function s _N (t) according to the variable parameter α by changing the numerical value of the variable parameter α. By appropriately changing the variable parameter α according to various conditions such as a music playback environment, a sound source, and a tune, the user can reproduce high-quality music having a desired sound quality in which the frequency characteristics of the analog signal are adjusted. it can.

The acoustic processing unit 2 uses a basic sampling function f (t) and a control sampling function c ₀ (t) of a finite stage that can be differentiated only once over the entire area as the sampling function s _N (t). Since the control sampling function c ₀ (t) is multiplied by the variable parameter α, the amount of computation required for the interpolation processing between discrete data is greatly reduced as compared with the conventional Shannon sampling function. In addition, the truncation error that occurs when the Shannon sampling function is used does not occur, and the occurrence of aliasing distortion can be prevented.

In the case of this embodiment, in particular, the value of the waveform of the sampling function s _N (t) is converged to 0 in a range that is the same as or narrower than two sample positions before and after the interpolation position t0. Therefore, when data interpolation or the like is performed using the sampling function s _N (t), it is only necessary to use a total of four discrete data pieces before and after the position of interest. Compared with the case of using, the processing burden can be remarkably reduced.

In the case of this embodiment, the sampling function s _N (t) is separated into the basic sampling function f (t) and the control sampling function c ₀ (t) and stored separately, and each is individually stored. A convolution operation is performed on the discrete data, the convolution operation result of the control sampling function c ₀ (t) and the discrete data is multiplied by a variable parameter α, and this is multiplied by the basic sampling function s _N (t) and Since the output signal is obtained by adding the convolution calculation results with the discrete data, it is sufficient to have one control sampling function c ₀ (t), the mathematical formula can be simplified as much as possible, and control sampling Variable control of the function c ₀ (t) can be easily performed.

(5) Other Embodiments In the above-described embodiment, the basic term calculation unit 16 and the control term calculation unit 17 sequentially calculate a plurality of interpolation values between discrete data one by one. However, the present invention is not limited to this, and a plurality of interpolated values between discrete data may be calculated collectively.

In this case, as shown in FIG. 11 in which the same reference numerals are assigned to the same parts as in FIG. 5, the acoustic processing unit 30 includes a discrete data extraction unit 15 and a conversion function matrix calculation unit 31, and the conversion function matrix In the arithmetic unit 31, the values Y (t1), Y (t2), Y (t3), and Y (t4) of the discrete data d1, d2, d3, and d4 are multiplied by the transformation matrix A (described later). , a plurality of interpolation between discrete data values _{y k-2 (1),} y k-2 (2), ..., and so as to be able to calculate sequentially or collectively y _k-2 (n).

Incidentally, in this embodiment, as shown in FIG. 12 where the corresponding parts to FIG. 4 are given the same reference numerals, the second discrete data d2 in the past among the four consecutive discrete data d1, d2, d3, d4. And the third discrete data d3 in the past are divided into 1 to n and separated by a predetermined number of divisions (in this case, n + 1), and interpolated values y _k-2 (1), y _k-2 at each position The case of calculating (2),..., Y _k-2 (n) will be described below.

  Where the transformation matrix A is:

It is represented by This transformation matrix A calculates a sampling function s _N (t) using four discrete data d1, d2, d3, d4, and n interpolation values y _k-2 (1) between the discrete data d2 and d3. ), Y _k-2 (2),..., Y _k-2 (n) are calculated, and therefore, the sampling function s _N (t) is used as an element, and it has n rows and 4 columns. The transformation matrix A is multiplied by a one-column matrix X whose elements are the values Y (t1), Y (t2), Y (t3), and Y (t4) of the discrete data d1, d2, d3, and d4. Thus, correction values y _k-2 (1), y _k-2 (2),..., Y _k-2 (n) can be obtained. That is, the correction values y _k-2 (1), y _k-2 (2),..., Y _k-2 (n)

It can ask for.

  Here, the transformation matrix A is the sum of the basic term matrix B of the following equation and the control term matrix C of the following equation multiplied by the variable parameter α, and is represented by A = B + αC.

The basic term matrix B has a basic sampling function f (t) as an element, and the control term matrix C has a control sampling function c (t) as an element (t indicates a distance between an interpolation point and a sample position). Therefore, the interpolation values y _k-2 (1), y _k-2 (2),..., Y _k-2 (n) are expressed by the following equations:

It is represented by

  In practice, as shown in FIG. 13, the conversion function matrix calculation unit 31 includes a basic term matrix calculation circuit 32 as basic term calculation means for executing calculation of the basic term matrix B and the matrix X, a control term matrix C and a matrix A control term matrix computing circuit 33 as a control term computing means for performing computation of X, a plurality of coefficient multipliers 18a1, 18a2,..., 18an for multiplying the calculation result of the control term matrix computing circuit 33 by a variable parameter α, , 19an that linearly adds the calculation results from the basic term matrix calculation circuit 32 and the calculation results from the coefficient multipliers 18a1, 18a2,..., 18an.

The basic term matrix calculation circuit 32 calculates a basic term matrix B as a basic sampling function in advance according to the number of sections between discrete data, and generates a basic term matrix B obtained by tabulating the calculated values. It is stored in a predetermined storage means. When the basic term matrix calculation circuit 32 receives the discrete data d1, d2, d3, d4 from the discrete data extraction unit 15, the basic term matrix calculation circuit 32 stores the discrete data in the basic term matrix B as a table value stored in advance in a predetermined storage means. The values Y (t1), Y (t2), Y (t3), and Y (t4) of d1, d2, d3, and d4 are multiplied as a matrix X in one column. Then, the basic term matrix calculation circuit 32 sends the value of each row of the matrix obtained as a result to the corresponding linear adders 19a1, 19a2,. That is, the fundamental term matrix calculation circuit 32 uses {f ₁ (n + 1) · Y (t1)} + {f ₂ (1) · Y (t2)} + {f in the first row of the matrix obtained as the calculation result. ₃ (n−1) · Y (t3)} + {f ₄ (2n−1) · Y (t4)} is sent to the linear adder 19a1, and {f ₁ (n + 2) · Y in the next second row (T1)} + {f ₂ (2) · Y (t2)} + {f ₃ (n−2) · Y (t3)} + {f ₄ (2n−2) · Y (t4)} The data are sent to the linear adder 19a2, and then the values from the third to nth rows are sent to different linear adders 19a3,.

On the other hand, the control term matrix computing circuit 33 calculates in advance a control term matrix C as a control sampling function in accordance with the number of sections between discrete data, and a control term matrix in which the obtained computation values are tabulated. C is stored in a predetermined storage means. When the control term matrix calculation circuit 33 receives the discrete data d1, d2, d3, d4 from the discrete data extraction unit 15, the control term matrix calculation circuit 33 stores the discrete data in the control term matrix C as a table value stored in advance in a predetermined storage means. The values Y (t1), Y (t2), Y (t3), and Y (t4) of d1, d2, d3, and d4 are multiplied as a matrix X in one column. Then, the control term matrix calculation circuit 33 sends the values of the respective rows of the matrix obtained as a result thereof to the corresponding coefficient multipliers 18a1, 18a2,. In other words, the control term matrix operation circuit 33 obtains {c ₁ (n + 1) · Y (t1)} + {c ₂ (1) · Y (t2)} + {c in the first row of the matrix obtained as the operation result. ₃ (n−1) · Y (t3)} + {c ₄ (2n−1) · Y (t4)} is sent to the coefficient multiplier 18a1, and {c ₁ (n + 2) · Y in the next second row (T1)} + {c ₂ (2) · Y (t2)} + {c ₃ (n−2) · Y (t3)} + {c ₄ (2n−2) · Y (t4)} The values are sent to the coefficient multiplication unit 18a2, and then the values in the third to nth rows are sent to different coefficient multiplication units 18a3,.

Each of the coefficient multipliers 18a1, 18a2,..., 18an multiplies the variable parameter α set by the user in the parameter setting unit 7 by the value of each row of the matrix calculated by the control term matrix calculation circuit 33, and responds to this. To the linear adders 19a1, 19a2,. Each linear adder 19a1, 19a2,..., 19an linearly adds the calculation results received from the basic term matrix calculation circuit 32 and the calculation results received from the coefficient multipliers 18a1, 18a2,. Thus, the interpolation values y _k-2 (1), y _k-2 (2),..., Y _k-2 (n) can be generated.

In the above configuration, the acoustic processing unit 30 stores the basic term matrix B in the basic term matrix calculation circuit 32 and controls the control term matrix calculation circuit 33 in addition to the same effects as those of the above-described embodiment. The term matrix C is stored, and the values Y (t1), Y (t2), Y (t3), and Y (t4) of the discrete data d1, d2, d3, and d4 are multiplied as a one-row matrix X. Thus, the interpolation values y _k-2 (1), y _k-2 (2) between the interpolation positions 1 to n between the predetermined discrete data d2 and d3 among the four continuous discrete data d1, d2, d3, d4. ),..., Y _k-2 (n) can be easily calculated collectively.

  In the above-described embodiment, a case where the basic term matrix B and the control term matrix C that can be applied only to a discrete data string in which the number of sections between discrete data of interest is n + 1 and a constant number is used will be described. However, the present invention is not limited to this, and a basic term matrix and a control term matrix that can be applied to a plurality of discrete data strings having different numbers of sections between discrete data of interest may be used. That is, in this case, since the transformation function matrix calculation unit is applied to a plurality of discrete data strings having different numbers of divisions, the basic term matrix B and the control term matrix C are calculated in advance with the number of divisions of the least common multiple of the number of divisions. Then, according to the number of divisions set at the start of discrete data input, an operation value corresponding to the number of divisions is selected from the basic term matrix B and the control term matrix C as a table value, A convolution operation is performed on the selected table value and discrete data. As a result, the conversion function matrix calculation unit can correspond to a plurality of discrete data strings having different numbers of sections only by storing only one basic term matrix B and control term matrix C in advance. Thus, the storage capacity of the apparatus can be reduced, and the processing load of the entire apparatus can be reduced.

The present invention is not limited to this embodiment, and various modifications can be made within the scope of the gist of the present invention. For example, although the sampling function s _N (t) is a finite function that can be differentiated only once over the entire area, the number of differentiable times may be set to 2 or more.

In the above-described embodiment, the analog signal is generated by performing the interpolation process using the sampling function s _N (t). However, the present invention is not limited to this, and the sample is not limited thereto. It is also possible to simply perform oversampling by performing an interpolation process using the conversion function s _N (t), and then generate an analog credit with an analog-digital converter.

Further, in the above-described embodiment, the case where the sampling function s _N (t) converges to 0 at t = ± 2 has been described, but the present invention is not limited to this, and t = ± 3 or more. May converge to 0. For example, when t = ± 3 and converges to 0, the discrete data extraction unit 15 extracts the previous six discrete data, and the function processing unit 14 samples the six discrete data. The value of s _N (t) can be calculated.

Furthermore, in the above-described embodiment, the basic sampling function f (t) is stored in the basic term calculation unit 16, and separately, the control sampling function c ₀ (t) is stored in the control term calculation unit 17. The basic interpolation value ya and the control interpolation value yb are obtained by performing a convolution operation on the discrete data d1, d2, d3, d4 for each of the basic sampling function f (t) and the control sampling function c ₀ (t). Although the case where the interpolation value y is calculated by the linear sum addition of the basic interpolation value ya and the control interpolation value yb after calculation has been described, the present invention is not limited to this, and the basic sampling function f (t) and control sampling function c ₀ (t) is stored as a single sampling function s _N (t), using the variable parameter sampling function has changed the α s _N (t), the discrete data d1, d2, The interpolation value y is directly calculated by performing a convolution operation on d3 and d4. May be.

In this case, as a specific configuration, the function processing means stores a sampling function s _N (t) obtained by linearly adding the basic sampling function f (t) and the control sampling function c ₀ (t) in advance, Calculation means for calculating the value of the sampling function s _N (t) using the distance to the point of interest obtained for each discrete data, and the value of the sampling function s _N (t) corresponding to each discrete data May be provided with a convolution operation means for calculating an interpolated value at the point of interest. Thereby, since the sampling function s _N (t) is calculated in advance in the function processing means, compared with the case where the basic sampling function f (t) and the control sampling function c ₀ (t) are calculated separately. The number of multiplications is reduced, so that the calculation time can be reduced and the number of multipliers can be reduced.

It is the schematic which shows the relationship between the waveform of the basic sampling function by this invention, and the waveform of a control sampling function. It is a block diagram which shows the circuit structure of an audio apparatus. It is a block diagram which shows the circuit structure of an acoustic process part. It is the schematic which shows the positional relationship of four discrete data and an attention point. It is a block diagram which shows the detailed structure of an acoustic process part. It is the schematic which shows the interpolation process using the basic sampling function by the acoustic process part 2 by this invention. It is the schematic which shows the interpolation process using the control sampling function by the acoustic process part 2 by this invention. It is the schematic which shows the waveform of the sampling function when a variable parameter is changed. It is the schematic which shows the frequency characteristic when a variable parameter is changed. It is the schematic which shows the signal level when fixing the reproduction frequency and changing the numerical value of the variable parameter. It is a block diagram which shows the circuit structure of the acoustic process part by other embodiment. It is the schematic which shows the positional relationship of four discrete data and a view point, and an interpolation position. It is a block diagram which shows the detailed structure of the acoustic process part by other embodiment. It is the schematic which shows the waveform of the sampling function of Shannon in the past.

Explanation of symbols

2 Acoustic processing unit (acoustic processing device)
3 FPGA (Field Programmable Gate Array)
10 Selector
14 Function processing section (Function processing means)
15 Discrete data extraction unit (discrete data extraction means)
16 Basic term calculation section (Basic term calculation means)
17 Control term calculation unit (control term calculation means)
18 Coefficient multiplier (coefficient multiplier)
19 Linear adder (linear adder)
32 Basic term matrix arithmetic circuit (Basic term arithmetic means)
33 Control term matrix computing circuit (control term computing means)

Claims (24)

A basic sampling function that is finitely differentiable and has a value of a finite stage, and a control sampling that is finitely differentiable and has a value of a finite stage and shows a waveform different from the waveform indicated by the basic sampling function A function processing means for calculating an interpolated value between the discrete data by a convolution operation on a plurality of discrete data arranged in the time direction using a sampling function consisting of a function and linear addition;
The sound processing apparatus, wherein the function processing means includes coefficient multiplying means for multiplying the control sampling function by a variable parameter that can be set to an arbitrary numerical value by a user. The function processing means includes
After performing the convolution operation on the discrete data using the basic sampling function and the control sampling function, respectively, linearly add the operation results obtained by the convolution operation using the sampling function. The sound processing apparatus according to claim 1, wherein the interpolation value is calculated as described above. A discrete data extraction means for extracting a predetermined number of the discrete data existing across the point of interest for which the interpolation value is calculated;
The function processing means includes
The value of the basic sampling function is calculated using the distance to the point of interest obtained for each discrete data extracted by the discrete data extracting means, and the basic sampling corresponding to each of the discrete data A basic term calculation means for calculating a basic interpolation value at the point of interest by performing a convolution operation on the value of the function;
The value of the control sampling function is calculated using the distance to the point of interest obtained for each discrete data extracted by the discrete data extracting means, and the control sampling corresponding to each of the discrete data A control term calculation means for calculating a control interpolation value at the point of interest by performing a convolution operation on the value of the function;
The linear addition means for calculating the interpolation value by linear addition of the basic interpolation value calculated by the basic term calculation means and the control interpolation value calculated by the control term calculation means. The sound processing apparatus according to 1 or 2. The function processing means includes
The interpolation value is calculated by performing the convolution operation on the discrete data using the sampling function obtained by linearly adding the basic sampling function and the control sampling function in advance. The sound processing apparatus according to 1. A discrete data extraction means for extracting a predetermined number of the discrete data existing across the point of interest for which the interpolation value is calculated;
The function processing means includes
The sampling function obtained by linearly adding the basic sampling function and the control sampling function is stored in advance, and the sampling function is calculated using the distance to the point of interest obtained for each discrete data. A function calculation means for calculating a value;
5. The convolution operation means for calculating an interpolated value at the point of interest by performing a convolution operation on the value of the sampling function associated with each of the discrete data. Sound processing device. The basic sampling function is a piecewise polynomial that can be differentiated only once in the interval [−1, 1] of the sample position of the discrete data, and the other interval is a function that is uniformly represented by 0,
The control sampling function is a piecewise polynomial function that can be differentiated only once in the interval [−2, 2] of the sample position of the discrete data, and is a function that is uniformly 0 in other intervals. The sound processing apparatus according to any one of claims 1 to 5. When the sample position of the discrete data is t,
The basic sampling function is f (t), and the basic sampling function is

Represented by
The control sampling function is C ₀ (t) = C _r (t) + C _r (−t), and Cr (t) is expressed by the following equation:

The sound processing apparatus according to claim 1, wherein the sound processing apparatus is represented by: A plurality of the variable parameters having different numerical values are stored in advance, and a selector for selecting any one of the variable parameters for multiplying the control sampling function from the plurality of variable parameters is provided. The sound processing apparatus according to any one of claims 1 to 7. The function processing means includes
The sound processing device according to any one of claims 1 to 8, wherein the sound processing device is programmed in a field programmable gate array that forms a circuit configuration having a control form desired by a user based on program data designated by a user. . The function processing means includes
The acoustic processing apparatus according to any one of claims 1 to 8, wherein the acoustic processing apparatus is programmed to a programmable signal processing device that forms an arithmetic configuration in a user-desired control form based on program data designated by a user. . The basic sampling function and the control sampling function form a table of operation values calculated in advance according to a predetermined number of divisions between the discrete data of interest, and the table value thus tabulated and the discrete data The convolution operation, the multiplication of the variable parameter, and the linear addition are calculated for each input of the discrete data, and the interpolation value is output. 11. The sound processing apparatus according to the description. When the number of divisions between the discrete data is plural, the table value is calculated in advance with the number of divisions of the least common multiple of the number of divisions, and according to the number of divisions set at the start of input of the discrete data, The sound processing apparatus according to claim 11, wherein the table value is selected, and a convolution operation between the table value and the discrete data is executed. A parameter setting step in which a variable parameter is set to an arbitrary numerical value by the user;
A basic sampling function that is finitely differentiable and has a value of a finite stage and a waveform that is multiplied by the variable parameter and is finitely differentiable and has a value of finite stage and that is different from the waveform indicated by the basic sampling function A function processing step of calculating an interpolated value between the discrete data by performing a convolution operation on a plurality of discrete data arranged in the time direction using a sampling function including a control sampling function indicating a waveform and linear addition. An acoustic processing method comprising: The function processing step includes
Using the basic sampling function and the control sampling function, a convolution operation step for executing the convolution operation on the discrete data, respectively,
The acoustic processing method according to claim 13, further comprising: a linear addition step of calculating the interpolation value by linearly adding the calculation results obtained by the convolution calculation using the sampling function. A discrete data extraction step of extracting a predetermined number of the discrete data existing across the point of interest for which the interpolation value is calculated;
The function processing step includes
The value of the basic sampling function is calculated using the distance to the point of interest obtained for each discrete data extracted by the discrete data extracting means, and the basic sampling corresponding to each of the discrete data A basic term calculation step for calculating a basic interpolation value at the point of interest by performing a convolution operation on the value of the function;
The value of the control sampling function is calculated using the distance to the point of interest obtained for each discrete data extracted by the discrete data extracting means, and the control sampling corresponding to each of the discrete data A control term calculation step for calculating a control interpolation value at the point of interest by performing a convolution operation on the value of the function;
The linear addition step of linearly adding the basic interpolation value calculated by the basic term calculation means and the control interpolation value calculated by the control term calculation means to calculate the interpolation value. Item 15. The acoustic processing method according to Item 13 or 14. The function processing step includes
The interpolation value is calculated by performing the convolution operation on the discrete data using the sampling function obtained by linearly adding the basic sampling function and the control sampling function in advance. 14. The acoustic processing method according to 13. A discrete data extraction step of extracting a predetermined number of the discrete data existing across the point of interest for which the interpolation value is calculated;
The function processing step includes
The sampling function obtained by linearly adding the basic sampling function and the control sampling function is stored in advance, and the distance to the point of interest obtained for each discrete data is used to calculate the sampling function. A function calculation step for calculating a value;
The convolution operation step of calculating an interpolated value at the point of interest by performing a convolution operation on the value of the sampling function corresponding to each of the discrete data. Sound processing method. The basic sampling function is a piecewise polynomial that can be differentiated only once in the interval [−1, 1] of the sample position of the discrete data, and the other interval is a function that is uniformly represented by 0,
The control sampling function is a piecewise polynomial function that can be differentiated only once in the interval [−2, 2] of the sample position of the discrete data, and is a function that is uniformly 0 in other intervals. The sound processing method according to any one of claims 13 to 17. When the sample position of the discrete data is t,
The basic sampling function is f (t), and the basic sampling function is

Represented by
The control sampling function is C ₀ (t) = Cr (t) + Cr (−t), where Cr (t) is

The sound processing method according to claim 13, wherein the sound processing method is expressed by: In the parameter setting step, any one of the variable parameters for multiplying the control sampling function is selected from the plurality of variable parameters having different numerical values stored in advance. The sound processing method according to claim 1. The function processing step includes
The acoustic processing method according to any one of claims 13 to 20, wherein the acoustic processing method is executed by a circuit configuration having a control form desired by a user programmed in a field programmable gate array based on program data designated by a user. . The function processing step includes:
The sound according to any one of claims 13 to 20, wherein the sound is executed by an arithmetic circuit configuration in a user-desired control form programmed in a programmable signal processing device based on program data designated by a user. Processing method. The function processing step includes
The calculation values of the basic sampling function and the control sampling function calculated in advance according to a predetermined number of divisions between the discrete data of interest are tabulated, and a convolution operation between the table value and the discrete data is performed. The acoustic processing according to any one of claims 13 to 22, wherein multiplication of the variable parameter and linear addition are calculated for each input of the discrete data, and the interpolation value is output. Method. The function processing step includes
When the number of divisions between the discrete data is plural, the table value is calculated in advance with the number of divisions of the least common multiple of the number of divisions, and according to the number of divisions set at the start of input of the discrete data, The acoustic processing method according to claim 23, wherein the table value is selected, and a convolution operation between the table value and the discrete data is executed.

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