Method and device for simulating artificial mastoid path function for bone conduction

Abstract

The invention discloses a method and device for simulating an artificial mastoid path function for bone conduction, and the method comprises the steps: obtaining a first mastoid impedance curve corresponding to a target age, and carrying out the calculation of the first mastoid impedance curve and a mastoid impedance average value curve, and obtaining a second mastoid impedance curve; processing the second mastoid impedance curve; acquiring a bone conduction excitation signal, and performing superposition processing on the bone conduction excitation signal and the second mastoid impedance curve to obtain a target excitation signal curve; and processing the target excitation signal curve, and inputting the target excitation signal curve into the bone conduction earphone to obtain a simulated mastoid response signal. The target excitation signal curve is obtained by combining the mastoid impedance curves of different age groups and the bone conduction excitation signal, so that the influence of age on mastoid impedance is considered, mastoid impedance errors of different nationality people caused by fixed artificial mastoid can be avoided, and the accuracy and universality of a test result are guaranteed.

Description

Technical Field

[0001] This application belongs to the technical field of signal processing, and particularly relates to a method and device for simulating the path function of an artificial mastoid for bone conduction. Background Art

[0002] Hearing is an important sense for humans to perceive and judge the surrounding environment and is also crucial for communication. Generally, there are two ways for people to perceive sound. One is air conduction, which is the process by which sound waves reach the cochlea through the outer ear and middle ear. The other is bone conduction, which is the process by which sound wave vibrations stimulate the cochlea by vibrating the skull. Bone conduction technology is widely used in fields such as hearing tests, hearing rehabilitation, and consumer electronics. Bone conduction headphones have the advantages of not blocking the ears and being comfortable to wear. However, the output characteristics of bone conduction headphones are affected by the force impedance at the skull excitation position.

[0003] In clinical audiology, the excitation position of bone conduction devices is mainly the mastoid. Currently, the main bone conduction standard test method is to use a B&K 4930 artificial mastoid to simulate the human mastoid. However, there are significant differences in mastoid impedance among people of different nationalities. When using the existing test method to test people of different nationalities, there will be significant differences in the output force of bone conduction devices at the excitation position. Moreover, since the influence of different age groups on mastoid impedance is not considered, the prediction results are not universal. Summary of the Invention

[0004] To solve the problem that when using the existing test method to test people of different nationalities, there will be significant differences in the output force of bone conduction devices at the excitation position, and the prediction results are not universal because the influence of different age groups on mastoid impedance is not considered, this application proposes a method and device for simulating the path function of an artificial mastoid for bone conduction. The technical solutions are as follows: In a first aspect, an embodiment of this application provides a method for simulating the path function of an artificial mastoid for bone conduction, including: Obtain a first mastoid impedance curve corresponding to the target age, and calculate the difference between the first mastoid impedance curve and a preset average mastoid impedance curve to obtain a second mastoid impedance curve; Determine whether the second mastoid impedance curve is a mirror curve, and when it is detected that the second mastoid impedance curve is not a mirror curve, perform mirror processing on the second mastoid impedance curve; Obtain a bone conduction excitation signal, and perform superposition fitting processing on the bone conduction excitation signal and the second mastoid impedance curve after mirror processing based on frequency points to obtain a target excitation signal curve; Perform inverse Fourier transform processing on the target excitation signal curve, and input the processed target excitation signal curve into a bone conduction headphone to obtain a simulated mastoid response signal.

[0005] In an alternative solution of the first aspect, before obtaining the first mastoid impedance curve corresponding to the target age and calculating the difference between the first mastoid impedance curve and the preset mastoid impedance average curve to obtain the second mastoid impedance curve, it further includes: Collect at least two sample mastoid impedance curves corresponding to different age groups; wherein, each age group corresponds to at least two sample mastoid impedance curves; Extract the mastoid impedance values corresponding to each frequency point from all the sample mastoid impedance curves, and sort all the mastoid impedance values corresponding to each frequency point according to the preset arrangement order; When the number of all the mastoid impedance values corresponding to the frequency point is 2N, calculate the average value of the Nth mastoid impedance value and the (N + 1)th mastoid impedance value among all the sorted mastoid impedance values, and use the average calculation result as the mastoid impedance average value corresponding to the frequency point; When the number of all the mastoid impedance values corresponding to the frequency point is 2N + 1, use the (N + 1)th mastoid impedance value among all the sorted mastoid impedance values as the mastoid impedance average value corresponding to the frequency point; Generate the preset mastoid impedance average curve according to the mastoid impedance average value corresponding to each frequency point.

[0006] In another alternative solution of the first aspect, before obtaining the target excitation signal curve by performing superposition fitting processing on the bone conduction excitation signal and the second mastoid impedance curve after mirror processing based on the frequency point after obtaining the bone conduction excitation signal, it further includes: Perform Fourier transform processing on the bone conduction excitation signal to obtain the bone conduction excitation signal transformation curve; Performing superposition fitting processing on the bone conduction excitation signal and the second mastoid impedance curve after mirror processing based on the frequency point to obtain the target excitation signal curve includes: Perform superposition fitting processing on the bone conduction excitation signal transformation curve and the second mastoid impedance curve after mirror processing based on the frequency point to obtain the target excitation signal curve.

[0007] In another alternative solution of the first aspect, performing superposition fitting processing on the bone conduction excitation signal transformation curve and the second mastoid impedance curve after mirror processing based on the frequency point to obtain the target excitation signal curve includes: Extract the excitation values corresponding to each frequency point from the bone conduction excitation signal transformation curve; Extract the mastoid impedance values corresponding to each frequency point from the second mastoid impedance curve after mirror processing; Perform superposition processing on the excitation value and the mastoid impedance value corresponding to each frequency point to obtain the target value of each frequency point; Generate a target excitation signal curve according to the target value of each frequency point.

[0008] In another alternative of the first aspect, before superimposing and fitting the bone conduction excitation signal and the mirrored second mastoid impedance curve based on the frequency points to obtain the target excitation signal curve, it further includes: Obtain the mastoid phase curve corresponding to the second mastoid impedance curve; Superimposing and fitting the bone conduction excitation signal and the mirrored second mastoid impedance curve based on the frequency points to obtain the target excitation signal curve, including: Superimpose and fit the bone conduction excitation signal, the mastoid phase curve, and the mirrored second mastoid impedance curve based on the frequency points to obtain the target excitation signal curve.

[0009] In another alternative of the first aspect, after inputting the processed target excitation signal curve into the bone conduction headset to obtain the simulated mastoid response signal, it further includes: Obtain the simulated mastoid response signal based on the acceleration sensor and calculate the similarity between the simulated mastoid response signal and the sample mastoid response signal; When it is detected that the similarity between the simulated mastoid response signal and the sample mastoid response signal exceeds a preset threshold, send a prompt message indicating that the verification of the simulated mastoid response signal is successful.

[0010] In a second aspect, an embodiment of the present application provides a device for simulating an artificial mastoid path function for bone conduction, including: A data calculation module, configured to obtain a first mastoid impedance curve corresponding to a target age, and perform a difference calculation on the first mastoid impedance curve and a preset average mastoid impedance curve to obtain a second mastoid impedance curve; A first processing module, configured to determine whether the second mastoid impedance curve is a mirrored curve, and when it is detected that the second mastoid impedance curve is not a mirrored curve, perform a mirroring process on the second mastoid impedance curve; A second processing module, configured to obtain a bone conduction excitation signal, and perform a superimposing and fitting process on the bone conduction excitation signal and the mirrored second mastoid impedance curve based on the frequency points to obtain a target excitation signal curve; A data response module, configured to perform an inverse Fourier transform process on the target excitation signal curve, and input the processed target excitation signal curve into the bone conduction headset to obtain a simulated mastoid response signal.

[0011] In an alternative of the second aspect, the device further includes: Before obtaining the first mastoid impedance curve corresponding to the target age and calculating the difference between the first mastoid impedance curve and the preset average mastoid impedance curve to obtain the second mastoid impedance curve, at least two sample mastoid impedance curves corresponding to different age groups are collected; wherein, each age group corresponds to at least two sample mastoid impedance curves; Extract the mastoid impedance values corresponding to each frequency point from all the sample mastoid impedance curves, and sort all the mastoid impedance values corresponding to each frequency point in a preset order; When the number of all the mastoid impedance values corresponding to a frequency point is 2N, calculate the average value of the Nth mastoid impedance value and the (N + 1)th mastoid impedance value among all the sorted mastoid impedance values, and use the average calculation result as the average mastoid impedance corresponding to the frequency point; When the number of all the mastoid impedance values corresponding to a frequency point is 2N + 1, use the (N + 1)th mastoid impedance value among all the sorted mastoid impedance values as the average mastoid impedance corresponding to the frequency point; Generate a preset average mastoid impedance curve according to the average mastoid impedance corresponding to each frequency point.

[0012] In another optional solution of the second aspect, the second processing module further includes: Before obtaining the target excitation signal curve by performing superposition fitting processing on the bone conduction excitation signal and the second mastoid impedance curve after mirror processing based on the frequency point after obtaining the bone conduction excitation signal, perform Fourier transform processing on the bone conduction excitation signal to obtain the bone conduction excitation signal transformation curve; Performing superposition fitting processing on the bone conduction excitation signal and the second mastoid impedance curve after mirror processing based on the frequency point to obtain the target excitation signal curve includes: Performing superposition fitting processing on the bone conduction excitation signal transformation curve and the second mastoid impedance curve after mirror processing based on the frequency point to obtain the target excitation signal curve.

[0013] In another optional solution of the second aspect, the second processing module further includes: Extract the excitation values corresponding to each frequency point from the bone conduction excitation signal transformation curve; Extract the mastoid impedance values corresponding to each frequency point from the second mastoid impedance curve after mirror processing; Perform superposition processing on the excitation value and the mastoid impedance value corresponding to each frequency point to obtain the target value corresponding to each frequency point; Generate a target excitation signal curve according to the target value corresponding to each frequency point.

[0014] In another optional solution of the second aspect, the device further includes: Before obtaining the target excitation signal curve by performing superposition fitting processing on the bone conduction excitation signal and the mirrored second mastoid impedance curve based on frequency points, obtain the mastoid phase curve corresponding to the second mastoid impedance curve; Performing superposition fitting processing on the bone conduction excitation signal and the mirrored second mastoid impedance curve based on frequency points to obtain the target excitation signal curve, including: Performing superposition fitting processing on the bone conduction excitation signal, the mastoid phase curve, and the mirrored second mastoid impedance curve based on frequency points to obtain the target excitation signal curve.

[0015] In yet another alternative solution of the second aspect, the device further includes: After inputting the processed target excitation signal curve into the bone conduction headset to obtain the simulated mastoid response signal, acquire the simulated mastoid response signal based on the acceleration sensor, and calculate the similarity between the simulated mastoid response signal and the sample mastoid response signal; When it is detected that the similarity between the simulated mastoid response signal and the sample mastoid response signal exceeds a preset threshold, send a prompt message indicating that the verification of the simulated mastoid response signal is successful.

[0016] In a third aspect, an embodiment of the present application further provides a device for simulating an artificial mastoid path function for bone conduction, including a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the method for simulating an artificial mastoid path function for bone conduction provided in the first aspect or any implementation manner of the first aspect of the embodiments of the present application.

[0017] In a fourth aspect, an embodiment of the present application provides a computer storage medium, the computer storage medium stores a computer program, the computer program includes program instructions, and when the program instructions are executed by a processor, the method for simulating an artificial mastoid path function for bone conduction provided in the first aspect or any implementation manner of the first aspect of the embodiments of the present application can be implemented.

[0018] In the embodiment of the present application, when simulating the artificial mastoid path function, a first mastoid impedance curve corresponding to the target age can be obtained, and the difference between the first mastoid impedance curve and the preset average mastoid impedance curve is calculated to obtain a second mastoid impedance curve; it is determined whether the second mastoid impedance curve is a mirror curve, and when it is detected that the second mastoid impedance curve is not a mirror curve, mirror processing is performed on the second mastoid impedance curve; a bone conduction excitation signal is obtained, and based on the frequency points, the bone conduction excitation signal and the second mastoid impedance curve after mirror processing are superimposed and fitted to obtain a target excitation signal curve; an inverse Fourier transform process is performed on the target excitation signal curve, and the processed target excitation signal curve is input into the bone conduction headset to obtain a simulated mastoid response signal. By combining the mastoid impedance curves corresponding to different ages and the bone conduction excitation signal to obtain the target excitation signal curve, not only the influence of age on mastoid impedance is effectively considered, but also the mastoid impedance error of different nationalities caused by the fixed artificial mastoid can be effectively avoided, so as to ensure the accuracy and universality of the test results. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the accompanying drawings required for use in the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present application, and those of ordinary skill in the art can obtain other accompanying drawings without creative efforts based on these drawings.

[0020] Figure 1 It is the overall flowchart of a method for simulating an artificial mastoid path function for bone conduction provided by an embodiment of the present application; Figure 2 It is the effect schematic diagram of a first mastoid impedance curve provided by an embodiment of the present application; Figure 3 It is the effect schematic diagram of a sample mastoid impedance curve provided by an embodiment of the present application; Figure 4 It is the effect schematic diagram of a simulated mastoid response signal and a sample mastoid response signal provided by an embodiment of the present application; Figure 5 It is the structural schematic diagram of a device for simulating an artificial mastoid path function for bone conduction provided by an embodiment of the present application; Figure 6 It is the structural schematic diagram of another device for simulating an artificial mastoid path function for bone conduction provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application.

[0022] In the following description, the terms "first" and "second" are for descriptive purposes only and should not be construed as indicating or implying relative importance. The following description provides multiple embodiments of the present application. Different embodiments can be replaced or combined, so the present application can also be considered to include all possible combinations of the same and / or different embodiments described. Thus, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then the present application should also be considered to include embodiments containing one or more of all other possible combinations of A, B, C, and D, even though such embodiments may not be explicitly described in the following content.

[0023] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes can be made to the functions and arrangements of the elements described without departing from the scope of the present application. Various processes or components can be appropriately omitted, substituted, or added to each example. For example, the methods described can be performed in a different order than the order described, and various steps can be added, omitted, or combined. In addition, the features described for some examples can be combined into other examples.

[0024] Please refer to Figure 1 , Figure 1 which shows the overall flowchart of a method for simulating the path function of an artificial mastoid for bone conduction provided by an embodiment of the present application.

[0025] As Figure 1 shown, the method for simulating the path function of an artificial mastoid for bone conduction can at least include the following steps: Step 102: Obtain a first mastoid impedance curve corresponding to the target age, and perform a difference calculation on the first mastoid impedance curve and a preset average mastoid impedance curve to obtain a second mastoid impedance curve.

[0026] In an embodiment of the present application, the method for simulating the path function of an artificial mastoid for bone conduction can be applied to a control terminal. The control terminal can, but is not limited to, determine the corresponding mastoid impedance curve according to the age input by the user or the age range selected by the user, and process the bone conduction excitation signal pair in combination with the mastoid impedance curve to directly simulate the influence of the human mastoid impedance on the bone conduction excitation signal, and can input the obtained target excitation signal curve after processing into a bone conduction headset. The output result of the bone conduction headset can be used as the test result of the simulated artificial mastoid path function.

[0027] Specifically, when simulating the artificial mastoid path function, the control terminal can first obtain the age or age range input by the user, and determine the corresponding first mastoid impedance curve in the preset database according to the age or age range. The preset database can be pre-stored in the specified storage path of the control terminal, and the preset database can include, but is not limited to, multiple age ranges and the mastoid impedance curves corresponding to each age range. It can be understood that when targeting the Chinese population, the preset database can include multiple age ranges and the mastoid impedance curves corresponding to each age range of the Chinese population; when targeting the foreign population, the preset database can include multiple age ranges and the mastoid impedance curves corresponding to each age range of the foreign population. Of course, in the embodiments of the present application, the preset database can also include different ages and the mastoid impedance curves corresponding to each age. For example, it can include 100 ages and the mastoid impedance curves corresponding to each of the 100 ages, and is not limited thereto.

[0028] Reference can also be made here to Figure 2 the effect schematic diagram of a first mastoid impedance curve provided by the embodiments of the present application shown. As Figure 2 shown, the mastoid impedance curve schematic diagram includes three groups of mastoid impedance curves, and the age ranges corresponding to each group of mastoid impedance curves are different. Among them, the age range corresponding to the first group of mastoid impedance curves can be, but is not limited to, 22 to 29 years old, the age range corresponding to the second group of mastoid impedance curves can be, but is not limited to, 32 to 50 years old, and the age range corresponding to the third group of mastoid impedance curves can be, but is not limited to, 52 to 67 years old. It can be understood that each group of mastoid impedance curves can include at least two frequency points, each frequency point corresponds to a mastoid impedance value in the mastoid impedance curve schematic diagram, and each group of mastoid impedance curves can include frequency points of the same frequency. It should be noted that Figure 2 the mastoid impedance curve schematic diagram shown can be obtained from the mastoid impedance measurement results tested by the Key Laboratory of Noise and Vibration, Chinese Academy of Sciences, but is not limited thereto in the embodiments of the present application.

[0029] Further, after determining the first mastoid impedance curve corresponding to the target age, the control terminal can perform a difference calculation on the first mastoid impedance curve and the preset mastoid impedance average curve. The calculation method can be, but is not limited to, first extracting each frequency point and the corresponding mastoid impedance value in the first mastoid impedance curve, then extracting the same frequency points as those in the first mastoid impedance curve and the corresponding mastoid impedance values in the preset mastoid impedance average curve, and performing a difference calculation on the two mastoid impedance values of the same frequency point to obtain the corrected mastoid impedance value of each frequency point. Finally, the second mastoid impedance curve can be drawn according to the corrected mastoid impedance value of each frequency point.

[0030] As an option in the embodiment of the present application, before obtaining the first mastoid impedance curve corresponding to the target age and calculating the difference between the first mastoid impedance curve and the preset mastoid impedance average value curve to obtain the second mastoid impedance curve, it further includes: Collect at least two sample mastoid impedance curves corresponding to different age groups; where each age group corresponds to at least two sample mastoid impedance curves; Extract the mastoid impedance values corresponding to each frequency point from all the sample mastoid impedance curves, and sort all the mastoid impedance values corresponding to each frequency point according to the preset arrangement order; When it is detected that the number of all the mastoid impedance values corresponding to the frequency point is 2N, calculate the average value of the Nth mastoid impedance value and the (N + 1)th mastoid impedance value among all the sorted mastoid impedance values, and use the average value calculation result as the mastoid impedance average value corresponding to the frequency point; When it is detected that the number of all the mastoid impedance values corresponding to the frequency point is 2N + 1, use the (N + 1)th mastoid impedance value among all the sorted mastoid impedance values as the mastoid impedance average value corresponding to the frequency point; Generate a preset mastoid impedance average value curve according to the mastoid impedance average value corresponding to each frequency point.

[0031] Specifically, the control terminal can also pre-collect at least two sample mastoid impedance curves corresponding to different age groups. Each of these sample mastoid impedance curves can be obtained from, but is not limited to, the mastoid impedance measurement results tested by the Key Laboratory of Noise and Vibration, Chinese Academy of Sciences. And each age group corresponds to at least two sample mastoid impedance curves, and the ages corresponding to each sample mastoid impedance curve are different. For example, in the age group from 22 to 29 years old, there can be 8 sample mastoid impedance curves, which can respectively correspond to the sample mastoid impedance curves of the ages of 22 years old, 23 years old, 24 years old, 25 years old, 26 years old, 27 years old, 28 years old, and 29 years old.

[0032] Further, after the sample mastoid impedance curves are collected, the control terminal can respectively extract the mastoid impedance values corresponding to each frequency point from all the sample mastoid impedance curves, and sort the mastoid impedance values corresponding to each frequency point in a preset ascending order. For example, the mastoid impedance values corresponding to frequency point A are a1, a2, and a3 respectively, where a1 is greater than a2, and a2 is greater than a3. Then, the sorted mastoid impedance values obtained according to the preset ascending order can be expressed as a3, a2, a1; the mastoid impedance values corresponding to frequency point B are b1, b2, and b3 respectively, where b1 is greater than b2, and b2 is greater than b3. Then, the sorted mastoid impedance values obtained according to the preset ascending order can be expressed as b3, b2, b1.

[0033] Further, when it is detected that the number of all the sample mastoid impedance curves (i.e., the number of all the mastoid impedance values corresponding to the frequency points) is 2N, it indicates that the number of all the sample mastoid impedance curves is even. Then, the Nth mastoid impedance value and the (N + 1)th mastoid impedance value among the sorted all mastoid impedance values can be averaged to obtain the mastoid impedance average value corresponding to each frequency point. It can be understood that the calculation method of the mastoid impedance average value corresponding to each frequency point is the same, and it is the result of averaging the Nth mastoid impedance value and the (N + 1)th mastoid impedance value.

[0034] When it is detected that the number of all the sample mastoid impedance curves (i.e., the number of all the mastoid impedance values corresponding to the frequency points) is 2N + 1, it indicates that the number of all the sample mastoid impedance curves is odd. Then, the (N + 1)th mastoid impedance value among the sorted all mastoid impedance values can be directly used as the mastoid impedance average value corresponding to each frequency point. It can be understood that the calculation method of the mastoid impedance average value corresponding to each frequency point is the same, and it is the (N + 1)th mastoid impedance value.

[0035] Further, after determining the mastoid impedance average value corresponding to each frequency point, the control terminal can draw a preset mastoid impedance average value curve according to each frequency point and the mastoid impedance average value corresponding to each frequency point.

[0036] Reference can also be made here to Figure 3 the schematic diagram of the effect of a sample mastoid impedance curve provided by the embodiment of the present application shown in Figure 3 As shown in

[0037] Step 104: Determine whether the second mastoid impedance curve is a mirror curve. When it is detected that the second mastoid impedance curve is not a mirror curve, perform mirror processing on the second mastoid impedance curve.

[0038] Specifically, after obtaining the second mastoid impedance curve, to ensure the balance of the mastoid impedance curve, the control terminal can determine whether the second mastoid impedance curve is a mirror curve. The determination method can be, but is not limited to, determining whether there is a line segment parallel to the Y-axis, and the partial second mastoid impedance curves on both sides of this line segment are in a symmetric relationship. Possibly, when there is no such line segment, it indicates that the second mastoid impedance curve is not a mirror curve; when there is such line segment, it indicates that the second mastoid impedance curve is a mirror curve.

[0039] It can be understood that when it is detected that the second mastoid impedance curve is not a mirror curve, to improve the accuracy of the test results and ensure the balance of the mastoid impedance curve, the control terminal can perform mirror processing on the second mastoid impedance curve along the Y-axis. Here, the Y-axis can be understood as the coordinate axis (i.e., the vertical axis) in the second mastoid impedance curve for representing the mastoid impedance value, and the X-axis can correspondingly be understood as the coordinate axis (i.e., the horizontal axis) in the second mastoid impedance curve for representing the frequency.

[0040] Step 106: Obtain a bone conduction excitation signal, and perform superposition fitting processing on the bone conduction excitation signal and the second mastoid impedance curve after mirror processing based on frequency points to obtain a target excitation signal curve.

[0041] Specifically, after performing mirror processing on the second mastoid impedance curve, to facilitate the fitting between the mastoid impedance curve and the bone conduction excitation signal, the control terminal can perform Fourier transform processing on the obtained bone conduction excitation signal to obtain a bone conduction excitation signal transformation curve. Here, the bone conduction excitation signal can be understood as the excitation signal acting on the bone conduction earphone, which corresponds to a time-domain signal, and the Fourier transform processing can convert this time-domain signal into a frequency-domain signal to facilitate superposition fitting processing. The Fourier transform processing method here is a common audio signal processing technology in this field and will not be elaborated too much here.

[0042] Furthermore, after performing Fourier transform processing on the bone conduction excitation signal to obtain a bone conduction excitation signal transformation curve, superposition fitting processing can be performed on the bone conduction excitation signal transformation curve and the second mastoid impedance curve after mirror processing based on frequency points to obtain a target excitation signal curve. The influence of the mastoid impedance is simulated in the target excitation signal curve, thereby ensuring the reliability of the target excitation signal curve.

[0043] As another option of the embodiments of the present application, superimposing and fitting the bone conduction excitation signal transformation curve and the second mastoid impedance curve after mirror processing based on frequency points to obtain a target excitation signal curve, including: Extract the excitation value corresponding to each frequency point from the bone conduction excitation signal transformation curve; Extract the mastoid impedance value corresponding to each frequency point from the second mastoid impedance curve after mirror processing; Perform a superimposing process on the excitation value and the mastoid impedance value corresponding to each frequency point to obtain the target value of each frequency point; Generate a target excitation signal curve according to the target value of each frequency point.

[0044] Specifically, during the process of generating the target excitation signal curve, the control terminal may first extract each frequency point and the excitation value corresponding to each frequency point from the bone conduction excitation signal transformation curve. Here, each frequency point corresponds to a different frequency, and the excitation value corresponding to each frequency point (i.e., the value corresponding to the Y-axis in the curve) may be different.

[0045] Furthermore, the control terminal may extract each frequency point consistent with the bone conduction excitation signal transformation curve and the respective mastoid impedance value corresponding to each frequency point from the second mastoid impedance curve after mirror processing.

[0046] Furthermore, the control terminal may respectively perform a superimposing process on the excitation value and the mastoid impedance value corresponding to each frequency point to obtain the target value of each frequency point, and may generate a target excitation signal curve in combination with the target value of each frequency point.

[0047] As another option of the embodiments of the present application, before superimposing and fitting the bone conduction excitation signal and the second mastoid impedance curve after mirror processing based on frequency points to obtain a target excitation signal curve, it further includes: Obtain a mastoid phase curve corresponding to the second mastoid impedance curve; Superimposing and fitting the bone conduction excitation signal and the second mastoid impedance curve after mirror processing based on frequency points to obtain a target excitation signal curve, including: Superimposing and fitting the bone conduction excitation signal, the mastoid phase curve, and the second mastoid impedance curve after mirror processing based on frequency points to obtain a target excitation signal curve.

[0048] Specifically, in order to further ensure the balance of the mastoid impedance curve, the control terminal may also perform a conversion process on the second mastoid impedance curve based on a preset function to obtain a mastoid phase curve corresponding to the second mastoid impedance curve. The preset function may be, but is not limited to, the Phase function to obtain the minimum phase curve of the second mastoid impedance curve.

[0049] Further, after obtaining the minimum-phase curve of the second mastoid impedance curve, the control terminal can perform superposition fitting processing on the bone conduction excitation signal, the mastoid phase curve, and the second mastoid impedance curve after mirror processing based on frequency points. Each frequency point can respectively correspond to an excitation value, a phase value, and a mastoid impedance value. When performing superposition processing, the excitation value, the phase value, and the mastoid impedance value corresponding to the same frequency point need to be superposed and calculated, and a target excitation signal curve is generated according to the target value of each frequency point. The specific calculation process can refer to the above embodiments and will not be elaborated here.

[0050] Step 108: Perform an inverse Fourier transform on the target excitation signal curve and input the processed target excitation signal curve into the bone conduction headset to obtain an analog mastoid response signal.

[0051] Specifically, after obtaining the target excitation signal curve, since the target excitation signal curve is a frequency-domain signal, in order to quickly input the target excitation signal into the bone conduction headset, an inverse Fourier transform can be performed on the target excitation signal curve to re-convert the target excitation signal curve into a time-domain signal, and the target excitation signal curve converted into a time-domain signal can be input into the bone conduction headset, thereby obtaining an analog mastoid response signal. It can be understood that the analog mastoid response signal can simulate the actual test results of an artificial mastoid. Compared with the B&K4930 artificial mastoid used in the prior art, the embodiment of the present application can use an acceleration sensor to obtain the analog mastoid response signal, which not only greatly reduces the input cost but also effectively guarantees the accuracy of the results.

[0052] Among them, after obtaining the analog mastoid response signal based on the acceleration sensor, in order to further verify the accuracy of the analog mastoid response signal, the control terminal can also calculate the similarity between the analog mastoid response signal and the sample mastoid response signal. The calculation method can be, but is not limited to, inputting the analog mastoid response signal and the sample mastoid response signal into a trained deep learning model to obtain the similarity between the analog mastoid response signal and the sample mastoid response signal according to the prediction result of the deep learning model. Of course, here the similarity between the analog mastoid response signal and the sample mastoid response signal can also be calculated through a similarity calculation formula to obtain the similarity between the analog mastoid response signal and the sample mastoid response signal, and this is not limited here. It can be understood that when it is detected that the similarity exceeds a preset threshold, it indicates that the similarity between the analog mastoid response signal and the sample mastoid response signal is relatively high, that is, it proves that the accuracy of the analog mastoid response signal is relatively high, and then a prompt message for characterizing the successful verification of the analog mastoid response signal can be sent.

[0053] When it is detected that the similarity does not exceed the preset threshold, it indicates that the similarity between the current simulated mastoid response signal and the sample mastoid response signal is low. Then, the simulated mastoid response signal can be obtained again by combining one or more of the above-mentioned embodiments to eliminate uncertainty.

[0054] Reference can also be made here to Figure 4 the effect schematic diagram of a simulated mastoid response signal and a sample mastoid response signal provided by the embodiment of the present application shown. As Figure 4 shown, Figure 4 the upper part of the schematic diagram corresponds to the curve schematic diagram of the simulated mastoid response signal, Figure 4 and the lower part of the schematic diagram can correspond to the curve schematic diagram of the sample mastoid response signal. It can be intuitively seen that the curve schematic diagram of the simulated mastoid response signal has a high similarity with the curve schematic diagram of the sample mastoid response signal. Of course, the similarity between the curve schematic diagram of the simulated mastoid response signal and the curve schematic diagram of the sample mastoid response signal can also be calculated by the above-mentioned similarity calculation formula, and this is not limited here.

[0055] Please refer to Figure 5 , Figure 5 which shows the structural schematic diagram of a device for simulating an artificial mastoid path function for bone conduction provided by the embodiment of the present application.

[0056] As Figure 5 shown, the device for simulating an artificial mastoid path function for bone conduction can at least include a data calculation module 501, a first processing module 502, a second processing module 503, and a data response module 504, where: The data calculation module 501 is configured to obtain a first mastoid impedance curve corresponding to a target age, and perform a difference calculation on the first mastoid impedance curve and a preset average mastoid impedance curve to obtain a second mastoid impedance curve; The first processing module 502 is configured to determine whether the second mastoid impedance curve is a mirror curve, and when it is detected that the second mastoid impedance curve is not a mirror curve, perform mirror processing on the second mastoid impedance curve; The second processing module 503 is configured to obtain a bone conduction excitation signal, and perform superposition fitting processing on the bone conduction excitation signal and the second mastoid impedance curve after mirror processing based on frequency points to obtain a target excitation signal curve; The data response module 504 is configured to perform an inverse Fourier transform processing on the target excitation signal curve, and input the processed target excitation signal curve into a bone conduction earphone to obtain a simulated mastoid response signal.

[0057] In some possible embodiments, the device further includes: Before obtaining the first mastoid impedance curve corresponding to the target age and calculating the difference between the first mastoid impedance curve and the preset average mastoid impedance curve to obtain the second mastoid impedance curve, at least two sample mastoid impedance curves corresponding to different age groups are collected; wherein, each age group corresponds to at least two sample mastoid impedance curves. Extract the mastoid impedance values corresponding to each frequency point from all the sample mastoid impedance curves, and sort all the mastoid impedance values corresponding to each frequency point in a preset order. When the number of all the mastoid impedance values corresponding to a frequency point is 2N, calculate the average value of the Nth mastoid impedance value and the (N + 1)th mastoid impedance value among all the sorted mastoid impedance values, and use the average calculation result as the average mastoid impedance value corresponding to the frequency point. When the number of all the mastoid impedance values corresponding to a frequency point is 2N + 1, use the (N + 1)th mastoid impedance value among all the sorted mastoid impedance values as the average mastoid impedance value corresponding to the frequency point. Generate a preset average mastoid impedance curve according to the average mastoid impedance value corresponding to each frequency point.

[0058] In some possible embodiments, the second processing module further includes: Before obtaining the target excitation signal curve by performing superposition fitting processing on the bone conduction excitation signal and the second mastoid impedance curve after mirror processing based on the frequency point after obtaining the bone conduction excitation signal, perform Fourier transform processing on the bone conduction excitation signal to obtain a bone conduction excitation signal transformation curve. Performing superposition fitting processing on the bone conduction excitation signal and the second mastoid impedance curve after mirror processing based on the frequency point to obtain the target excitation signal curve includes: Performing superposition fitting processing on the bone conduction excitation signal transformation curve and the second mastoid impedance curve after mirror processing based on the frequency point to obtain the target excitation signal curve.

[0059] In some possible embodiments, the second processing module further includes: Extract the excitation values corresponding to each frequency point from the bone conduction excitation signal transformation curve. Extract the mastoid impedance values corresponding to each frequency point from the second mastoid impedance curve after mirror processing. Perform superposition processing on the excitation value and the mastoid impedance value corresponding to each frequency point to obtain the target value of each frequency point. Generate a target excitation signal curve according to the target value of each frequency point.

[0060] In some possible embodiments, the device further includes: Before superimposing and fitting the bone conduction excitation signal and the mirrored second mastoid impedance curve based on frequency points to obtain the target excitation signal curve, a mastoid phase curve corresponding to the second mastoid impedance curve is obtained; Superimposing and fitting the bone conduction excitation signal and the mirrored second mastoid impedance curve based on frequency points to obtain the target excitation signal curve includes: Superimposing and fitting the bone conduction excitation signal, the mastoid phase curve, and the mirrored second mastoid impedance curve based on frequency points to obtain the target excitation signal curve.

[0061] In some possible embodiments, the device further includes: After inputting the processed target excitation signal curve into the bone conduction earphone to obtain the simulated mastoid response signal, the simulated mastoid response signal is acquired based on the acceleration sensor, and the similarity between the simulated mastoid response signal and the sample mastoid response signal is calculated; When it is detected that the similarity between the simulated mastoid response signal and the sample mastoid response signal exceeds a preset threshold, a prompt message for characterizing the successful verification of the simulated mastoid response signal is sent.

[0062] Those skilled in the art can clearly understand that the technical solutions of the embodiments of the present application can be implemented by means of software and / or hardware. The "units" and "modules" in this specification refer to software and / or hardware that can independently complete or cooperate with other components to complete specific functions, where the hardware can be, for example, a Field-Programmable Gate Array (FPGA), an Integrated Circuit (IC), etc.

[0063] Please refer to Figure 6 , Figure 6 which shows a schematic structural diagram of another device for simulating the artificial mastoid path function for bone conduction provided by the embodiments of the present application.

[0064] As Figure 6 shown, the device 600 for simulating the artificial mastoid path function for bone conduction may include at least one processor 601, at least one network interface 604, a user interface 603, a memory 605, and at least one communication bus 602.

[0065] Among them, the communication bus 602 can be used to realize the connection and communication of the above-mentioned various components.

[0066] Among them, the user interface 603 may include buttons, and the optional user interface may further include a standard wired interface and a wireless interface.

[0067] Among them, the network interface 604 can but is not limited to include a Bluetooth module, an NFC module, a Wi-Fi module, etc.

[0068] Among them, the processor 601 may include one or more processing cores. The processor 601 is connected to various parts within the device 600 for the analog artificial mastoid path function for bone conduction through various interfaces and lines. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 605, and by calling the data stored in the memory 605, it executes various functions of the device 600 for the analog artificial mastoid path function for bone conduction and processes data. Optionally, the processor 601 may be implemented in at least one hardware form of DSP, FPGA, or PLA. The processor 601 may integrate one or a combination of several of CPU, GPU, and modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above modem may not be integrated into the processor 601 and may be implemented separately by a single chip.

[0069] Among them, the memory 605 may include RAM and may also include ROM. Optionally, the memory 605 includes a non-transitory computer-readable medium. The memory 605 can be used to store instructions, programs, codes, code sets, or instruction sets. The memory 605 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above various method embodiments, etc.; the data storage area may store the data involved in the above various method embodiments. Optionally, the memory 605 may also be at least one storage device located far from the aforementioned processor 601. As Figure 6 shown, the memory 605, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for the analog artificial mastoid path function for bone conduction.

[0070] Specifically, the processor 601 may be used to call the application program for the analog artificial mastoid path function for bone conduction stored in the memory 605 and specifically perform the following operations: Obtain a first mastoid impedance curve corresponding to the target age, and perform a difference calculation on the first mastoid impedance curve and a preset average mastoid impedance curve to obtain a second mastoid impedance curve; Determine whether the second mastoid impedance curve is a mirror curve, and when it is detected that the second mastoid impedance curve is not a mirror curve, perform mirror processing on the second mastoid impedance curve; Obtain a bone conduction excitation signal, and perform superposition fitting processing on the bone conduction excitation signal and the second mastoid impedance curve after mirror processing based on frequency points to obtain a target excitation signal curve; Perform an inverse Fourier transform on the target excitation signal curve, and input the processed target excitation signal curve into the bone conduction earphone to obtain an analog mastoid response signal.

[0071] In some possible embodiments, before obtaining the first mastoid impedance curve corresponding to the target age and calculating the difference between the first mastoid impedance curve and the preset average mastoid impedance curve to obtain the second mastoid impedance curve, it further includes: Collect at least two sample mastoid impedance curves corresponding to different age groups; where each age group corresponds to at least two sample mastoid impedance curves; Extract the mastoid impedance values corresponding to each frequency point from all the sample mastoid impedance curves, and sort all the mastoid impedance values corresponding to each frequency point according to the preset arrangement order; When it is detected that the number of all mastoid impedance values corresponding to the frequency point is 2N, calculate the average value of the Nth mastoid impedance value and the (N + 1)th mastoid impedance value among all the sorted mastoid impedance values, and use the average calculation result as the mastoid impedance average value corresponding to the frequency point; When it is detected that the number of all mastoid impedance values corresponding to the frequency point is 2N + 1, use the (N + 1)th mastoid impedance value among all the sorted mastoid impedance values as the mastoid impedance average value corresponding to the frequency point; Generate a preset average mastoid impedance curve according to the mastoid impedance average value corresponding to each frequency point.

[0072] In some possible embodiments, after obtaining the bone conduction excitation signal, before performing superposition fitting processing on the bone conduction excitation signal and the second mastoid impedance curve after mirror processing based on frequency points to obtain the target excitation signal curve, it further includes: Perform a Fourier transform on the bone conduction excitation signal to obtain a bone conduction excitation signal transformation curve; Performing superposition fitting processing on the bone conduction excitation signal and the second mastoid impedance curve after mirror processing based on frequency points to obtain the target excitation signal curve includes: Perform superposition fitting processing on the bone conduction excitation signal transformation curve and the second mastoid impedance curve after mirror processing based on frequency points to obtain the target excitation signal curve.

[0073] In some possible embodiments, performing superposition fitting processing on the bone conduction excitation signal transformation curve and the second mastoid impedance curve after mirror processing based on frequency points to obtain the target excitation signal curve includes: Extract the excitation value corresponding to each frequency point from the bone conduction excitation signal transformation curve; Extract the mastoid impedance value corresponding to each frequency point from the second mastoid impedance curve after mirror processing; Perform superposition processing on the excitation value and the mastoid impedance value corresponding to each frequency point to obtain the target value for each frequency point; Generate a target excitation signal curve based on the target value of each frequency point.

[0074] In some possible embodiments, before performing superposition fitting processing on the bone conduction excitation signal and the second mastoid impedance curve after mirror processing based on frequency points to obtain the target excitation signal curve, it further includes: Obtain the mastoid phase curve corresponding to the second mastoid impedance curve; Performing superposition fitting processing on the bone conduction excitation signal and the second mastoid impedance curve after mirror processing based on frequency points to obtain the target excitation signal curve includes: Performing superposition fitting processing on the bone conduction excitation signal, the mastoid phase curve, and the second mastoid impedance curve after mirror processing based on frequency points to obtain the target excitation signal curve.

[0075] In some possible embodiments, after inputting the processed target excitation signal curve into the bone conduction headset to obtain the simulated mastoid response signal, it further includes: Obtain the simulated mastoid response signal based on the acceleration sensor and calculate the similarity between the simulated mastoid response signal and the sample mastoid response signal; When it is detected that the similarity between the simulated mastoid response signal and the sample mastoid response signal exceeds a preset threshold, send a prompt message indicating that the verification of the simulated mastoid response signal is successful.

[0076] This application also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps of the above method are implemented. Among them, the computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0077] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0078] In the above embodiments, the descriptions of the respective embodiments have their own focuses. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0079] In several embodiments provided by this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0080] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0081] In addition, in each embodiment of this application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0082] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present application. The aforementioned memory includes: USB flash drives, read-only memory (ROM), random access memory (RAM), mobile hard disks, magnetic disks, or optical discs, etc., which are various media that can store program codes.

[0083] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program. This program can be stored in a computer-readable memory, and the memory can include: flash drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs, etc.

[0084] The above are only exemplary embodiments of the present disclosure, and the scope of the present disclosure cannot be limited thereby. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. After considering the specification and practicing the present disclosure herein, those skilled in the art will readily think of other embodiments of the present disclosure. The present application aims to cover any variations, uses, or adaptive changes of the present disclosure, which follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for simulating an artificial mastoid path function for bone conduction, characterized in that Including: Obtain a first mastoid impedance curve corresponding to the target age, and perform a difference calculation on the first mastoid impedance curve and a preset mastoid impedance average value curve to obtain a second mastoid impedance curve; Determine whether the second mastoid impedance curve is a mirror curve, and when it is detected that the second mastoid impedance curve is not a mirror curve, perform mirror processing on the second mastoid impedance curve; Obtain a bone conduction excitation signal, and perform superposition fitting processing on the bone conduction excitation signal and the second mastoid impedance curve after mirror processing based on frequency points to obtain a target excitation signal curve; Perform an inverse Fourier transform processing on the target excitation signal curve, and input the processed target excitation signal curve into a bone conduction headset to obtain an analog mastoid response signal.

2. The method according to claim 1, wherein Before obtaining the first mastoid impedance curve corresponding to the target age, and performing a difference calculation on the first mastoid impedance curve and the preset mastoid impedance average value curve to obtain a second mastoid impedance curve, it further includes: Collect at least two sample mastoid impedance curves corresponding to different age groups; wherein, each age group corresponds to at least two of the sample mastoid impedance curves; Extract the mastoid impedance values corresponding to each frequency point from all the sample mastoid impedance curves, and sort all the mastoid impedance values corresponding to each frequency point in a preset arrangement order; When it is detected that the number of all the mastoid impedance values corresponding to a frequency point is 2N, calculate the average value of the Nth mastoid impedance value and the (N + 1)th mastoid impedance value among all the sorted mastoid impedance values, and use the average calculation result as the mastoid impedance average value corresponding to the frequency point; When it is detected that the number of all the mastoid impedance values corresponding to a frequency point is 2N + 1, use the (N + 1)th mastoid impedance value among all the sorted mastoid impedance values as the mastoid impedance average value corresponding to the frequency point; Generate a preset mastoid impedance average value curve according to the mastoid impedance average value corresponding to each frequency point.

3. The method according to claim 1, wherein After obtaining the bone conduction excitation signal, before performing superposition fitting processing on the bone conduction excitation signal and the second mastoid impedance curve after mirror processing based on frequency points to obtain a target excitation signal curve, it further includes: Perform a Fourier transform processing on the bone conduction excitation signal to obtain a bone conduction excitation signal transformation curve; The performing superposition fitting processing on the bone conduction excitation signal and the second mastoid impedance curve after mirror processing based on frequency points to obtain a target excitation signal curve includes: Perform superposition fitting processing on the bone conduction excitation signal transformation curve and the second mastoid impedance curve after mirror processing based on frequency points to obtain a target excitation signal curve.

4. The method according to claim 3, wherein The performing superposition fitting processing on the bone conduction excitation signal transformation curve and the second mastoid impedance curve after mirror processing based on frequency points to obtain a target excitation signal curve includes: Extract the excitation values corresponding to each frequency point from the bone conduction excitation signal transformation curve; Extract the mastoid impedance values corresponding to each of the frequency points from the second mastoid impedance curve after mirror processing; Perform a superposition process on the excitation value and the mastoid impedance value corresponding to each of the frequency points to obtain the target value for each of the frequency points; Generate a target excitation signal curve based on the target value for each of the frequency points.

5. The method according to claim 1, wherein Before performing the superposition fitting process on the bone conduction excitation signal and the second mastoid impedance curve after mirror processing based on the frequency points to obtain the target excitation signal curve, it further includes: Obtain the mastoid phase curve corresponding to the second mastoid impedance curve; The superposition fitting process on the bone conduction excitation signal and the second mastoid impedance curve after mirror processing based on the frequency points to obtain the target excitation signal curve includes: Perform a superposition fitting process on the bone conduction excitation signal, the mastoid phase curve, and the second mastoid impedance curve after mirror processing based on the frequency points to obtain the target excitation signal curve.

6. The method according to claim 1, wherein After inputting the processed target excitation signal curve into the bone conduction earphone to obtain the simulated mastoid response signal, it further includes: Obtain the simulated mastoid response signal based on the acceleration sensor and calculate the similarity between the simulated mastoid response signal and the sample mastoid response signal; When it is detected that the similarity between the simulated mastoid response signal and the sample mastoid response signal exceeds the preset threshold, send a prompt message indicating that the verification of the simulated mastoid response signal is successful.

7. A device for simulating the path function of an artificial mastoid for bone conduction, characterized in that, It includes: A data calculation module, configured to obtain the first mastoid impedance curve corresponding to the target age, and perform a difference calculation on the first mastoid impedance curve and the preset average mastoid impedance curve to obtain the second mastoid impedance curve; A first processing module, configured to determine whether the second mastoid impedance curve is a mirror curve, and when it is detected that the second mastoid impedance curve is not a mirror curve, perform mirror processing on the second mastoid impedance curve; A second processing module, configured to obtain the bone conduction excitation signal, and perform a superposition fitting process on the bone conduction excitation signal and the second mastoid impedance curve after mirror processing based on the frequency points to obtain the target excitation signal curve; A data response module, configured to perform an inverse Fourier transform process on the target excitation signal curve, and input the processed target excitation signal curve into the bone conduction earphone to obtain the simulated mastoid response signal.

8. The device according to claim 7, characterized in that The apparatus further includes: Before obtaining the first mastoid impedance curve corresponding to the target age, and performing a difference calculation on the first mastoid impedance curve and the preset average mastoid impedance curve to obtain the second mastoid impedance curve, collect at least two sample mastoid impedance curves corresponding to different age groups; wherein, each age group corresponds to at least two of the sample mastoid impedance curves; Extract the mastoid impedance values corresponding to each frequency point from all the sample mastoid impedance curves, and sort all the mastoid impedance values corresponding to each frequency point in a preset arrangement order; When the number of all the mastoid impedance values corresponding to the frequency point is detected to be 2N, calculate the average value of the Nth mastoid impedance value and the (N + 1)th mastoid impedance value among all the sorted mastoid impedance values, and use the average value calculation result as the mastoid impedance average value corresponding to the frequency point; When the number of all the mastoid impedance values corresponding to the frequency point is detected to be 2N + 1, use the (N + 1)th mastoid impedance value among all the sorted mastoid impedance values as the mastoid impedance average value corresponding to the frequency point; Generate a preset mastoid impedance average value curve according to the mastoid impedance average value corresponding to each frequency point.

9. A device for simulating an artificial mastoid path function for bone conduction, characterized in that, It includes a processor and a memory; The processor is connected to the memory; The memory is used to store executable program codes; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the steps of the method according to any one of claims 1-6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, Instructions are stored in the computer-readable storage medium, and when the instructions run on a computer or a processor, the computer or the processor is caused to execute the steps of the method according to any one of claims 1-6.

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