CN116707446A - White noise signal generation method and device - Google Patents

Abstract

The application discloses a method and a device for generating a white noise signal, wherein the method comprises the following steps: performing Fourier transform on the generated random data set to obtain an initial complex set; modifying the initial complex group into a deformed complex group; performing inverse Fourier transform on the deformed complex group to obtain a white noise time domain signal; wherein, the step of modifying the initial complex group into a deformed complex group specifically comprises: the phase of each complex number of the current index value in the initial complex number group in the range of the appointed area is kept unchanged, and the module value is converted into m completely, so that the complex number is the complex number in the deformed complex number group. The method and the device for generating the white noise signal generate the white noise with even small data volume and even frequency distribution, so that the energy distribution is even and is closer to the white noise characteristic, the use requirements of the white noise in multiple fields are met, and the follow-up application and noise detection results are more accurate.

Description

White noise signal generation method and device

Technical Field

The present application relates to the field of signal processing, and in particular, to a method and apparatus for generating a white noise signal.

Background

The ideal white noise is noise having the same noise power spectral density in each of the frequency bands of the same bandwidth in a wide frequency range, that is, the energy distribution of each frequency of the white noise is uniform. White noise is widely applied in a plurality of fields, and is a powerful tool for system analysis, for example, in the test of sound insulation materials, white noise is emitted inside a shielding box, sound is received outside the shielding box, and the isolation performance of the sound insulation materials on different frequencies can be seen through the spectrum analysis of the received data.

At present, a relatively approximate white noise signal is mainly generated by a mode of generating random numbers, but in practice, the obtained signal is not ideal white noise, for example, when the data amount is smaller, the frequency distribution is uneven, so that the energy distribution of the signal at each frequency is uneven, for example, as shown in fig. 1a and 1b, the frequency distribution is uneven, and thus, when judging the isolation performance of a sound insulation material to different frequencies, the judgment result is inaccurate due to the uneven white noise. Therefore, how to generate more ideal white noise is a technical problem to be solved.

Disclosure of Invention

In order to solve at least one of the above problems, an object of the present application is to provide a method and apparatus for generating a white noise signal which can generate a white noise signal having a more desirable and closer characteristic to white noise _。

In order to achieve the above object, an embodiment of the present application provides a method for generating a white noise signal, including the steps of:

performing Fourier transform on the generated random data set to obtain an initial complex set;

modifying the initial complex set into a deformed complex set;

performing inverse Fourier transform on the deformed complex group to obtain a white noise time domain signal;

wherein the step of modifying the initial complex set into a deformed complex set specifically includes:

and keeping the phase of each complex number of the current index value in the initial complex number group within the range of the designated area unchanged, and converting all the modulus values into m to obtain complex numbers, namely complex numbers in the deformed complex number group, wherein m is a real number which is not 0.

As a further improvement of the present application, the method further comprises the steps of:

acquiring the generation time and the sampling rate of a white noise signal to be generated;

calculating sampling points according to the generation time and the sampling rate;

and generating the random data group with the data quantity being the sampling point number.

As a further improvement of the present application, said step of modifying said initial complex set into a modified complex set further comprises:

assigning the real part a of the complex number corresponding to the current index value asThe imaginary part b is assigned +.>The obtained complex number is the complex number in the deformed complex number group.

As a further improvement of the present application, the generated complex number of the position symmetrical to the current index value in the deformed complex number group sets the real part as the real part of the complex number corresponding to the current index value and the imaginary part as the opposite number of the imaginary part of the complex number corresponding to the current index value, wherein the index value of the position symmetrical to the current index value is equal to the sampling point number minus the current index value.

As a further improvement of the present application, the method further comprises the steps of:

acquiring an initial frequency and a termination frequency of a white noise signal to be generated;

calculating a step length according to the sampling rate and the sampling point number;

determining a lowest index value according to the step length and the starting frequency;

determining a highest index value according to the step length and the termination frequency;

judging whether the highest index value is greater than half of the sampling points;

when the judgment is negative, the step is operated to modify the initial complex group into a deformed complex group;

the specified region range is a range between the lowest index value and the highest index value.

As a further improvement of the present application, said step of determining a lowest index value from said step size and said starting frequency comprises:

rounding down the quotient of the starting frequency and the step length to obtain the lowest index value;

said step of determining the highest index value from said step size and said termination frequency comprises:

and rounding up the quotient of the termination frequency and the step length to obtain the highest index value.

As a further improvement of the present application, said step of modifying said initial complex set into a modified complex set further comprises:

the modulus value of the complex number of the index value in the deformation complex number group in the appointed interval is 0, wherein the appointed interval comprises the following steps: a section smaller than the lowest index value, a section larger than the highest index value and smaller than the sampling point number minus the highest index value, and a section larger than the sampling point number minus the lowest index value.

As a further improvement of the present application, the method further comprises the steps of:

the method for converting the white noise time domain signal into the white noise signal with the specified waveform specifically comprises the following steps:

obtaining a target voltage value;

calculating the root mean square value of the white noise time domain signal;

determining a deformation coefficient according to the target voltage value and the root mean square value;

and combining each number in the white noise time domain signal with the deformation coefficient to transform the white noise time domain signal into a new number, so as to obtain the white noise signal with the specified waveform.

To achieve one of the above objects, an embodiment of the present application provides a white noise signal generating apparatus, including:

the transformation module is used for carrying out Fourier transformation on the generated random data set to obtain an initial complex set;

the deformation module is used for modifying the initial complex group into a deformed complex group;

the inverse transformation module is used for carrying out inverse Fourier transformation on the deformed complex group to obtain a white noise time domain signal;

wherein, the deformation module is further for:

and keeping the phase of each complex number of the current index value in the initial complex number group within the range of the designated area unchanged, and converting all the modulus values into m to obtain complex numbers, namely complex numbers in the deformed complex number group, wherein m is a real number which is not 0.

To achieve one of the above objects, an embodiment of the present application provides an electronic device including:

a storage module storing a computer program;

the processing module can realize the steps in the method for generating the white noise signal when executing the computer program.

To achieve one of the above objects, an embodiment of the present application provides a readable storage medium storing a computer program which, when executed by a processing module, performs the steps in the above-described white noise signal generating method.

Compared with the prior art, the application has the following beneficial effects: according to the method and the device for generating the white noise signal, the phase of each complex number is unchanged, the mode value is completely converted into m, so that even if the generated white noise is small in data quantity, the frequency distribution is uniform, the energy distribution is uniform, the white noise signal is closer to the white noise characteristic, the use requirements of the white noise in multiple fields are met, and the follow-up application and noise detection results are more accurate.

Drawings

FIG. 1a is a time domain waveform diagram of a white noise signal generated by the background art;

FIG. 1b is a spectrum diagram of a white noise signal generated by the background art;

FIG. 2 is a flow chart of a method of generating a white noise signal according to an embodiment of the present application;

FIG. 3 is a flow chart of a method of generating a white noise signal according to another embodiment of the present application;

FIG. 4 is a schematic diagram of a method for evaluating the lowest index value and the highest index value according to an embodiment of the present application;

FIG. 5 is a flow chart of generating a set of deformation coefficients based on a current index value according to one embodiment of the present application;

FIG. 6 is an amplitude-frequency plot of a generated deformation complex group according to an embodiment of the present application;

FIG. 7a is a time domain waveform diagram of a generated white noise signal according to an embodiment of the present application;

FIG. 7b is a spectrum diagram of a generated white noise signal according to an embodiment of the present application;

fig. 8 is a block diagram of a white noise signal generating apparatus according to an embodiment of the present application.

Detailed Description

The present application will be described in detail below with reference to specific embodiments shown in the drawings. These embodiments are not intended to limit the application and structural, methodological, or functional modifications of these embodiments that may be made by one of ordinary skill in the art are included within the scope of the application.

An embodiment of the application provides a method and a device for generating a white noise signal which is more ideal and more similar to the white noise characteristic.

Next, referring to fig. 2 to fig. 7b, a method for generating a white noise signal according to an embodiment of the present application is described, wherein a flowchart is partially shown in fig. 2 to fig. 5. Although the present application provides the method operational steps as shown in the following embodiments or flowcharts, the method is based on conventional or non-creative labor, and the order of execution of these steps is not limited to the order of execution provided in the embodiments of the present application in steps where logically no necessary causal relationship exists. The following steps S10 and S30, steps S33 and S35, steps S331 and S341, and steps S42 and S44 may be arbitrarily adjusted or performed simultaneously, without distinguishing the chronological order.

Specifically, the method for generating a noise signal of the present embodiment includes the steps of:

step S10: a random data set is generated.

The step S10 specifically includes the following steps S11 to S13:

step S11: the generation time t and the sampling rate FS required to generate the white noise signal are acquired.

Step S12: and calculating a sampling point number DataCnt according to the generation time t and the sampling rate FS.

Specifically, the calculation formula of the sampling point number DataCnt is datacnt=fs×t. Here, the sampling point data cnt is a number greater than 0, and if the sampling point data cnt is 0 or less, it is determined that an error occurs in the program, and the generation time t and the sampling rate FS need to be acquired again.

The number of sampling points DataCnt is calculated to determine the time domain length of the generated white noise signal, and the number of random data sets in step S13 below is determined by the number of sampling points DataCnt, which is one of the basic parameters of the white noise signal, so as to influence the amplitude and spectral characteristics of the white noise signal.

Step S13: generating the random data group with the data quantity of the sampling point number DataCnt.

Step S13 generates a random signal independent of time and frequency, as a basis for a white noise signal, the noise corresponding to the random data set being similar to the white noise, the white noise being characterized by equal power density in all frequency ranges, i.e. by equal intensity in all frequencies.

The random data set is generated as a floating point array, the amplitude value range of the random data set can be [ -i, i ], wherein the value of i can be 1, i.e. the value range of the random data set can be [ -1,1]. Since the power of white noise is equal in all frequency ranges, the range of random numbers is also symmetrical, i.e. between plus and minus 1.

The obtained white noise-like data is stored in an array, i.e. a random data set, in step S13, so that subsequent signal processing and operation can be conveniently performed.

Step S20: and carrying out Fourier transform on the generated random data set to obtain an initial complex set.

Here, a Fast Fourier Transform (FFT) or a Discrete Fourier Transform (DFT) may be used to convert random noise data, i.e., a random data set, into a frequency domain, and simultaneously convert a floating-point random data set into a complex type initial complex set, where the initial complex set includes amplitude and phase information of a signal at each frequency, and the number of data in the initial complex set is equal to the number of sampling points DataCnt.

Step S30: an index value is generated.

Step S30 may be illustrated with reference to fig. 3, and specifically includes the following steps:

step S31: the start frequency StartHz and the end frequency EndHz at which the white noise signal needs to be generated are acquired.

The frequency band required to generate the white noise signal is determined by the start frequency StartHz and the end frequency EndHz, wherein the frequency band can be set according to the needs of a user, for example, a 20HZ (StartHz) -20kHZ (EndHz) area, so that only the white noise signal in the frequency interval required by the user can be generated.

Step S32: and calculating a Step length Step according to the sampling rate FS and the sampling point number DataCnt.

The Step calculated in Step S32 is used to convert the frequency into an index value in fourier transform, and the calculation formula of Step is step=fs/DataCnt.

Step S33: and determining the lowest index value LowIndex according to the step length and the starting frequency StartHz.

The step S33 specifically includes a step S331: the quotient of the starting frequency StartHz and the Step size Step is rounded down to obtain the lowest index value LowIndex, that is

Step S34: the highest index value HighIndex is determined from the step size and the termination frequency EndHz.

Step S34 specifically includes step S341: the quotient of the end frequency EndHz and the Step size Step is rounded up to the highest index value HighIndex, that is,

as shown in fig. 4, the method of the step S331 and the step S341 may be modified to be an integer by rounding down or rounding up, since the result is not necessarily an integer due to an accuracy error when calculating the frequency by using fourier transform. Herein, "rounding down" refers to rounding down the decimal number to the nearest integer, and "rounding up" refers to rounding up the decimal number to the nearest integer.

Step S35: it is determined whether the highest index value is greater than half of the sampling point number DataCnt, that is, whether HighIndex is greater than DataCnt/2, and when the determination is no, the subsequent step S40 is executed.

After the fourier transform operation of step S20, the resulting spectrum is a symmetric sequence. Because of symmetry, only the first half of the frequency need be considered here. Thus, if the highest index value HighIndex is greater than DataCnt/2, this can be adjusted by either adjusting the termination frequency EndHz of the user input to be small, or modifying the sampling rate FS.

The present index value ranges from 0 to DataCnt-1, and the section with the value range of [ LowIndex, highIndex ] is a signal for generating a specific frequency, specifically described in step S40.

Step S40: modifying the initial complex set into a deformed complex set;

step S40 may refer to fig. 2 as an embodiment, and if only generation of white noise signals over any frequency interval is considered, step S40 may include performing the following steps in a loop:

step S41: and keeping the phase of each complex number of the current index value in the initial complex number group within the range of the designated area unchanged, and converting all the modulus values into m to obtain complex numbers, namely complex numbers in the deformed complex number group, wherein m is a real number which is not 0.

Step S41 is essentially a loop step including a plurality of individual deformations corresponding to each index value, and may specifically include two steps:

step S411: the complex number corresponding to the current index value is kept unchanged in phase, and only the modulus value is replaced by m;

step S412: the current Index value Index is updated.

The modification of step S411 may be to multiplex the current index valueThe real part of the number, a, is assigned asThe imaginary part b is assigned +.>The complex number obtained is the complex number in the deformed complex number group, the phase of the complex number after the substitution is kept unchanged, and the modulus value is changed into m. Here, m may take the value 1, that is, the modulus of the complex number after substitution is all 1.

In step S411, the current Index value Index, that is, index=index+1, is updated. In this way, the amplitude value corresponding to the new frequency can be replaced in the next cycle.

The above steps S411 to S412 are repeated until the cycle end condition appears, thereby obtaining a deformation complex group.

In addition, as another embodiment, as shown in fig. 5, the generated white noise signal is a noise signal having a frequency ranging from the start frequency StartHz to the end frequency EndHz. This embodiment can also be regarded as the cycle end condition of the above embodiment. In combination with step S30, the method further includes the following steps:

step S42: if the current index value is within the lowest index value and highest index value interval, the steps S411 to S412 are executed, and then step S43 is executed: and setting a real part as a real part of the complex number corresponding to the current Index value Index and an imaginary part as an opposite number of the imaginary part of the complex number corresponding to the current Index value in the generated complex number group at a position symmetrical to the current Index value Index, wherein the Index value of the position symmetrical to the current Index value Index is equal to the sampling point DataCnt minus the current Index value Index.

The step S43 still has symmetry in the obtained set of deformation coefficients, and the amplitude-frequency diagram of the obtained set of deformation coefficients is bilaterally symmetrical as shown in fig. 6.

Step S44: if the current index value is within the specified interval, step S45 is executed: the modulus value of the complex number of the index value in the deformation complex number group in the appointed interval is 0, wherein the appointed interval comprises the following steps: a section smaller than the lowest index value, a section larger than the highest index value and smaller than the sampling point number DataCnt minus the highest index value, and a section larger than the sampling point number DataCnt minus the lowest index value.

The specified section here corresponds to a frequency other than the start frequency StartHz to the end frequency EndHz of the finally obtained white noise signal, and is represented by the amplitude-frequency diagram of the deformation complex group shown in fig. 6, in three sections from [0, lowindex), (HighIndex, dataCnt-HighIndex), (DataCnt-LowIndex, dataCnt-1], and the values corresponding to the deformation complex group are all 0.

On the one hand, the current index value may be updated after step S44, i.e. step S412 may be performed; on the other hand, step S44 may be performed first, and after all the numbers to be assigned to 0 are replaced, step S41 may be performed again, with the modulus value being m being replaced one by one.

Step S60: and performing inverse Fourier transform on the deformed complex group to obtain a white noise time domain signal.

Step S60 converts the frequency domain information of the deformed complex group obtained above back to the time domain, so as to obtain a composite signal in the time domain, i.e. the deformed complex group of complex type is converted into a data group of floating point type.

In order to obtain the white noise signal with the specified waveform, to obtain the white noise signal with the target effective power, that is, the energy of the control signal, so that the influence degree of the noise can be conveniently evaluated in the subsequent use of the white noise signal in the fields of audio processing, voice recognition and the like, the method further comprises the step S70: the white noise time domain signal is converted into a white noise signal of a specified waveform.

The step S70 specifically includes steps S71 to S74:

step S71: obtaining a target voltage value p;

step S72: calculating a root mean square value T_RMS of the white noise time domain signal;

step S73: determining a deformation coefficient Amp according to the target voltage value p and the root mean square value t_rms;

step S74: and transforming each number in the noise time domain signal into a new number by combining the deformation coefficient Amp to obtain a white noise signal with a specified waveform.

Wherein amp=p/t_rms, the root mean square value t_rms is converted into a deformation coefficient through the above three steps, for adjusting the waveform of the resulting white noise signal to a proper height and width.

The mode of combining each number of the white noise time domain signals with the deformation coefficient Amp may be that each number of the white noise time domain signals is multiplied by the deformation coefficient Amp, so that the root mean square value of the finally obtained white noise signals reaches the target voltage value.

Through the steps, an ideal white noise signal is finally generated, the time domain waveform diagram is shown in fig. 7a, the frequency spectrum diagram is shown in fig. 7b, and compared with the white noise signals of fig. 1a and 1b in the background art, the frequency distribution of the white noise is uniform, so that the white noise generated by the method is more in line with the characteristics of the white noise.

Compared with the prior art, the embodiment has the following beneficial effects:

(1) According to the method and the device for generating the white noise signal, the phase of each complex number is unchanged, the mode value is completely converted into m, so that even if the generated white noise is small in data quantity, the frequency distribution is uniform, the energy distribution is uniform, the white noise signal is closer to the white noise characteristic, the use requirements of the white noise in multiple fields are met, and the follow-up application and noise detection results are more accurate.

(2) The method can set the initial frequency and the final frequency and set the root mean square value of the signal according to the needs of the clients, thereby controlling the frequency band and the amplitude value contained in the noise and facilitating the users to obtain the expected white noise signal according to the needs.

In one embodiment, a white noise signal generating apparatus is provided, as shown in fig. 8. The white noise signal generating device comprises the following modules and specific functions of each module:

the transformation module is used for carrying out Fourier transformation on the generated random data set to obtain an initial complex set;

the deformation module is used for modifying the initial complex group into a deformed complex group;

the inverse transformation module is used for carrying out inverse Fourier transformation on the deformed complex group to obtain a white noise time domain signal;

wherein, the deformation module is further for:

and keeping the phase of each complex number of the current index value in the initial complex number group within the range of the designated area unchanged, and converting all the modulus values into m to obtain complex numbers, namely complex numbers in the deformed complex number group, wherein m is a real number which is not 0.

In addition, the generating device of the white noise signal may further include an acquiring module, configured to acquire a generating time t and a sampling rate FS for generating the white noise signal, and configured to acquire a start frequency StartHz and a stop frequency EndHz for generating the white noise signal;

it should be noted that, for details not disclosed in the white noise signal generating apparatus according to the embodiment of the present application, please refer to details disclosed in the white noise signal generating method according to the embodiment of the present application.

It will be appreciated by those skilled in the art that the block diagram is merely an example of a white noise signal generating apparatus, and does not constitute a limitation of the terminal device of the white noise signal generating apparatus, and may include more or less components than those illustrated, or may combine some components, or different components, e.g., the white noise signal generating apparatus may further include an input/output device, a network access device, a bus, etc.

The white noise signal generating device may further include a computing device such as a computer, a notebook, a palm computer, a cloud server, and the like, and include, but are not limited to, a processing module, a storage module, and a computer program stored in the storage module and capable of running on the processing module, for example, the noise signal generating method program described above. The processing module, when executing the computer program, implements the steps in the foregoing embodiments of the method for generating a white noise signal, for example, the steps shown in fig. 2 to 5.

In addition, the application also provides an electronic device, which comprises a storage module and a processing module, wherein the processing module can realize the steps in the method for generating the white noise signal when executing the computer program, that is, realize the steps in any technical scheme in the method for generating the white noise signal.

The electronic device may be a part of the white noise signal generating apparatus, or may be a local terminal device, or may be a part of a cloud server.

The processing module may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor, but in the alternative, it may be any conventional processor. The processing module is a control center of the white noise signal generating device, and various interfaces and lines are used for connecting various parts of the whole white noise signal generating device.

The storage module may be used to store the computer program and/or the module, and the processing module may implement various functions of the white noise signal generating device by running or executing the computer program and/or the module stored in the storage module and invoking data stored in the storage module. The memory module may mainly include a memory program area and a memory data area, wherein the memory program area may store an operating system, application programs required for at least one function, and the like. In addition, the memory module may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state memory device.

The computer program may be divided into one or more modules/units, which are stored in a storage module and executed by a processing module to accomplish the present application, for example. The one or more modules/units may be a series of computer program instruction segments capable of performing the specified functions, which instruction segments are used for describing the execution of the computer program in the white noise signal generating means.

Further, an embodiment of the present application provides a readable storage medium storing a computer program, where the computer program when executed by a processing module can implement the steps in the above-mentioned white noise signal generation method, that is, implement the steps in any one of the above-mentioned white noise signal generation methods.

The modules integrated in the method for generating a white noise signal may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as a separate product. Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and the computer program may implement the steps of each of the method embodiments described above when executed by the processing module.

Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U-disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random-access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunication signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

It should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is for clarity only, and that the skilled artisan should recognize that the embodiments may be combined as appropriate to form other embodiments that will be understood by those skilled in the art.

The above list of detailed descriptions is only specific to practical embodiments of the present application, and they are not intended to limit the scope of the present application, and all equivalent embodiments or modifications that do not depart from the spirit of the present application should be included in the scope of the present application.

Claims (10)

1. A method of generating a white noise signal, comprising the steps of:

performing Fourier transform on the generated random data set to obtain an initial complex set;

modifying the initial complex set into a deformed complex set;

performing inverse Fourier transform on the deformed complex group to obtain a white noise time domain signal;

wherein the step of modifying the initial complex set into a deformed complex set specifically includes:

and keeping the phase of each complex number of the current index value in the initial complex number group within the range of the designated area unchanged, and converting all the modulus values into m to obtain complex numbers, namely complex numbers in the deformed complex number group, wherein m is a real number which is not 0.

2. The method of generating a white noise signal according to claim 1, further comprising the step of:

acquiring the generation time and the sampling rate of a white noise signal to be generated;

calculating sampling points according to the generation time and the sampling rate;

and generating the random data group with the data quantity being the sampling point number.

3. The method of generating a white noise signal according to claim 2, wherein said step of modifying said initial complex group into a modified complex group further comprises:

assigning the real part a of the complex number corresponding to the current index value asThe imaginary part b is assigned +.>The obtained complex number is the complex number in the deformed complex number group.

4. A method of generating a white noise signal according to claim 3, wherein the generated complex number of the positions of the deformed complex number group symmetrical to the current index value is set to the real part of the complex number corresponding to the current index value and the imaginary part is set to the opposite number of the imaginary part of the complex number corresponding to the current index value, wherein the index value of the positions of the current index value symmetrical is equal to the sampling point number minus the current index value.

5. The method of generating a white noise signal according to claim 2, further comprising the step of:

acquiring an initial frequency and a termination frequency of a white noise signal to be generated;

calculating a step length according to the sampling rate and the sampling point number;

determining a lowest index value according to the step length and the starting frequency;

determining a highest index value according to the step length and the termination frequency;

judging whether the highest index value is greater than half of the sampling points;

when the judgment is negative, the step is operated to modify the initial complex group into a deformed complex group;

the specified region range is a range between the lowest index value and the highest index value.

6. The method of generating a white noise signal according to claim 5, wherein said step of determining a lowest index value based on said step size and said start frequency comprises:

rounding down the quotient of the starting frequency and the step length to obtain the lowest index value;

said step of determining the highest index value from said step size and said termination frequency comprises:

and rounding up the quotient of the termination frequency and the step length to obtain the highest index value.

7. The method of generating a white noise signal according to claim 5, wherein said step of modifying said initial complex group into a modified complex group further comprises:

the modulus value of the complex number of the index value in the deformation complex number group in the appointed interval is 0, wherein the appointed interval comprises the following steps: a section smaller than the lowest index value, a section larger than the highest index value and smaller than the sampling point number minus the highest index value, and a section larger than the sampling point number minus the lowest index value.

8. The method of generating a white noise signal according to claim 1, further comprising the step of:

the method for converting the white noise time domain signal into the white noise signal with the specified waveform specifically comprises the following steps:

obtaining a target voltage value;

calculating the root mean square value of the white noise time domain signal;

determining a deformation coefficient according to the target voltage value and the root mean square value;

and combining each number in the white noise time domain signal with the deformation coefficient to transform the white noise time domain signal into a new number, so as to obtain the white noise signal with the specified waveform.

9. A white noise signal generating apparatus, comprising:

the transformation module is used for carrying out Fourier transformation on the generated random data set to obtain an initial complex set;

the deformation module is used for modifying the initial complex group into a deformed complex group;

the inverse transformation module is used for carrying out inverse Fourier transformation on the deformed complex group to obtain a white noise time domain signal;

wherein, the deformation module is further for:

and keeping the phase of each complex number of the current index value in the initial complex number group within the range of the designated area unchanged, and converting all the modulus values into m to obtain complex numbers, namely complex numbers in the deformed complex number group, wherein m is a real number which is not 0.

10. A readable storage medium storing a computer program, which when executed by a processing module, performs the steps of the white noise signal generating method according to any one of claims 1 to 8.