Method for diagnosing digital MEMS microphone abnormality and apparatus therefor

Abstract

A method according to an embodiment of the present invention is for diagnosing a digital MEMS microphone abnormality and is performed by an acoustic device including a plurality of digital MEMS microphones, the method comprising the steps of: collecting time-series output decibel data for the respective array positions of each of a plurality of digital MEMS microphone arrays provided in the acoustic device when a pure sound of a preset first frequency is reproduced by a speaker in close contact with the acoustic device; evaluating whether the digital MEMS microphone arrays are abnormal by using the standard deviation between the time-series output decibel data; and outputting a diagnosis result based on the abnormality evaluation.

Description

Digital MEMS microphone abnormality diagnosis method and device thereof

[0001] The present invention relates to a method and device for diagnosing digital MEMS microphone abnormalities. More specifically, the present invention relates to a method and device for evaluating array sensitivity and diagnosing abnormalities for calibration of a digital MEMS microphone.

[0002] To accurately measure physical values ​​like voltage, current, and acceleration using a sensor, the sensor's sensitivity must first be calibrated using an appropriate calibrator. Specifically, for microphones measuring acoustic signals, IEC standards define transducer units, and calibration methods are also detailed.

[0003] Recently, digital MEMS microphones, which are relatively inexpensive compared to general condenser microphones, have been widely used. However, since digital MEMS microphones do not meet the specifications handled by the IEC, their specifications are not defined, and there are no relevant regulations regarding their calibration methods.

[0004] One approach attempted to address this issue involved fabricating a suitable case to ensure that the MEMS microphone meets IEC specifications and conforms to existing standards. However, this approach fails to accurately represent the characteristics of the environments in which MEMS microphones are actually used, resulting in a gap between the calibration achieved in the field and actual use.

[0005] Alternatively, calibration methods using standard microphones have been proposed. The first of these conventional methods is to calibrate the sensitivity of the MEMS microphone by positioning the reference microphone and the MEMS microphone very closely (~2 mm) on the same axis as the center of the speaker installed inside a semi-anechoic chamber, and then measuring the sound pressure level multiple times (G D'Emilia, et al., 2018, Validated data mining technique: a case study on pressure-field digital MEMS microphone calibration, Journal of Physics: Conf. Series, 1065, 072028)).

[0006] In addition, a second method was proposed that uses the pressure reciprocity technique after mounting the MEMS microphone on an appropriate adapter (Randall P. Wagner and Steven E. Fick, 2017, Pressure reciprocity calibration of a MEMS microphone, J Acoust Soc Am., 142(3), EL251).

[0007] However, these conventional methods have the problem of high costs in establishing suitable experimental facilities because they require special environments such as anechoic chambers and expensive microphones.

[0008] Therefore, there is still no appropriate standard for calibrating digital MEMS microphones, making it difficult to determine whether they are operating normally or not. In addition, many methods proposed in the literature require specialized equipment, making them difficult for general users to access.

[0009] The present invention has been devised to solve the problems described above, and the purpose of the present invention is to provide a digital MEMS microphone abnormality diagnosis method and device that enable easy abnormality diagnosis even in general living and office environments rather than professional environments such as anechoic chambers by performing sensitivity evaluation and abnormality diagnosis of a digital MEMS microphone and array from the standard deviation of the magnitude (decibels) of a measured acoustic signal.

[0010] According to an embodiment of the present invention for solving the above-described problem, a method for diagnosing a digital MEMS microphone abnormality performed by an acoustic device including a plurality of digital MEMS microphones comprises the steps of: when a pure tone of a first frequency preset is reproduced from a speaker in close contact with the acoustic device, collecting time-series output decibel data for each array position of a plurality of digital MEMS microphone arrays provided in the acoustic device; evaluating whether the digital MEMS microphone array is abnormal using a standard deviation between the time-series output decibel data; and outputting a diagnosis result based on the abnormality evaluation.

[0011] In addition, a device according to an embodiment of the present invention for solving the above-described problem includes, in an acoustic device including a plurality of digital MEMS microphones, a time series data collection unit for collecting time series output decibel data for each array position of a plurality of digital MEMS microphone arrays provided in the acoustic device when a pure tone of a first frequency preset in a digital MEMS microphone abnormality diagnosis mode is reproduced from a speaker in close contact with the acoustic device; an abnormality diagnosis unit for diagnosing whether the digital MEMS microphone array is abnormal using a standard deviation between the time series output decibel data; and a diagnosis result output unit for outputting a diagnosis result based on the abnormality evaluation.

[0012] Meanwhile, a method according to an embodiment of the present invention for solving the above-described problem can be implemented as a computer program stored in a computer-readable medium for executing the method on a computer.

[0013] According to an embodiment of the present invention, time series output decibel data for each array position of a plurality of digital MEMS microphone arrays provided in an acoustic device is collected, and by using the standard deviation between the time series output decibel data, it is possible to diagnose whether the digital MEMS microphone array is abnormal.

[0014] Accordingly, the present invention provides a digital MEMS microphone abnormality diagnosis method and device that enable easy abnormality diagnosis even in general living and office environments rather than professional environments such as anechoic chambers by performing sensitivity evaluation and abnormality diagnosis of a digital MEMS microphone and array from the standard deviation of the magnitude (Decibel) of a measured acoustic signal.

[0015] Figure 1 is a conceptual diagram schematically illustrating the entire system according to an embodiment of the present invention.

[0016] FIG. 2 is a block diagram illustrating an internal circuit of an acoustic device for microphone abnormality diagnosis according to an embodiment of the present invention.

[0017] FIG. 3 is an exemplary diagram of a jig coupling structure between an acoustic device and a speaker according to an embodiment of the present invention.

[0018] Figure 4 is a flowchart for explaining a diagnostic process using an acoustic device according to an embodiment of the present invention.

[0019] FIGS. 5 and 6 are drawings illustrating a diagnostic mode output interface of an acoustic device according to an embodiment of the present invention.

[0020] The following merely exemplifies the principles of the present invention. Therefore, those skilled in the art will be able to implement the principles of the present invention and invent various devices within the scope and spirit of the present invention, even if not explicitly described or illustrated herein. Furthermore, all conditional terms and embodiments listed herein are expressly intended, in principle, to facilitate understanding of the present invention, and should be understood as being in no way limiting to the specifically enumerated embodiments and conditions.

[0021] Furthermore, all detailed descriptions of the principles, aspects, and embodiments of the present invention, as well as specific embodiments, should be understood to encompass structural and functional equivalents thereof. Furthermore, such equivalents should be understood to encompass not only currently known equivalents but also equivalents developed in the future, i.e., all devices invented to perform the same function, regardless of structure.

[0022] Thus, for example, the block diagrams herein should be understood as representing conceptual views of exemplary circuits embodying the principles of the present invention. Similarly, all flowcharts, state transition diagrams, pseudocode, and the like, which may be substantially represented on a computer-readable medium, should be understood as representing various processes performed by a computer or processor, regardless of whether a computer or processor is explicitly depicted.

[0023] The functions of various components depicted in the drawings, including functional blocks represented by processors or similar concepts, may be provided using dedicated hardware as well as hardware capable of executing software in conjunction with appropriate software. When provided by a processor, the functions may be provided by a single dedicated processor, a single shared processor, or multiple individual processors, some of which may be shared.

[0024] Furthermore, any explicit use of terms such as processor, controller, or similar concepts should not be construed as exclusively referring to hardware capable of executing software, but should be understood to implicitly include, without limitation, digital signal processor (DSP) hardware, read-only memory (ROM), random access memory (RAM), and non-volatile memory for storing software. Other commonly used hardware may also be included.

[0025] In the claims of this specification, a component expressed as a means for performing a function described in the detailed description is intended to include any method for performing the function, including, for example, a combination of circuit elements performing the function, or any form of software including firmware / microcode, combined with appropriate circuitry for executing said software to perform the function. The invention defined by these claims should be understood to be equivalent to any means found in this specification for providing the functions provided by the various enumerated means, as long as they are combined and combined in the manner required by the claims.

[0026] The above-described purposes, features, and advantages will become more apparent through the following detailed description, taken in conjunction with the accompanying drawings. Accordingly, those skilled in the art will be able to readily implement the technical concepts of the present invention. Furthermore, in describing the present invention, detailed descriptions of known technologies related to the present invention will be omitted if they are deemed to unnecessarily obscure the gist of the invention.

[0027] Hereinafter, a preferred embodiment of the present invention will be described in detail with reference to the attached drawings.

[0028] Figure 1 is a conceptual diagram schematically illustrating the entire system according to an embodiment of the present invention.

[0029] Referring to FIG. 1, the entire system according to an embodiment of the present invention may be configured to include an acoustic device (100) including a plurality of MEMS microphone arrays (101) as a diagnosis target, a conventional speaker (210), a jig (220) connecting the acoustic device (100) and the speaker (210), and an amplifier (230) that applies an acoustic signal for diagnosis to the speaker (210).

[0030] Here, the MEMS microphone array (101) is a microphone based on MEMS (Micro Electro Mechanical Systems), which is an ultra-small electro-mechanical system, and is composed of MEMS microphone element circuits placed on a printed circuit board (PCB), and each microphone element circuit can perform the role of converting sound pressure waves entering through a hole at the top into electrical signals through changes in electrostatic capacity of a capacitor, thereby converting them into sound analog signals or sound data and transmitting them to the sound processing unit of the sound device (100).

[0031] An acoustic device (100) according to an embodiment of the present invention can be configured to measure various acoustic signals, including ultrasonic bands, at each array location by configuring such a MEMS microphone array (101), and thus the acoustic device (100) can be implemented as various acoustic devices that perform functions based on a microphone array, such as an ultrasonic camera, an acoustic camera, a voice monitoring device, and a noise evaluation device.

[0032] However, as described above, the acoustic device (100) based on the MEMS microphone array (101) has a problem in that no calibration or correction standard has been established, and therefore, there is an inconvenience in that specialized equipment or measuring devices are required. However, the acoustic device (100) according to an embodiment of the present invention can perform a diagnostic process that enables a simple judgment of whether the MEMS microphone array (101) is abnormal and a qualitative evaluation equivalent to calibration by simply applying an acoustic signal through an amplifier (230) to a general speaker (210) connected through a jig (220).

[0033] More specifically, referring to FIG. 1, the acoustic device (100) may be equipped with a MEMS microphone array (101) and a display (102) for outputting diagnostic information. The MEMS microphone array (101) may be arranged to face a speaker (210) located at the bottom, and may be configured to be tightly connected to the speaker (210) through a jig (220).

[0034] Here, the speaker (210) is a commonly used ordinary speaker, and does not need to be a separate professional facility, and can output sound in the direction of the opposing MEMS microphone array (101) while being guided by a jig (220).

[0035] In addition, the acoustic device (100) can enter a diagnostic mode for correction or calibration, and in the diagnostic mode, an acoustic signal applied through the amplifier (230) is output through the speaker (210). Here, the amplifier (230) can apply an acoustic signal input from the acoustic device (100) to the speaker (210), or apply an acoustic signal applied from a separately provided external medium to the speaker (210).

[0036] In addition, the sound signal applied from the amplifier (230) to the speaker (210) may be composed of a pure tone of a preset first frequency. This pure tone is intended to facilitate abnormal diagnosis of the sound device (100), and may be composed of a signal that reproduces a preset 2 kHz sound at a size of 1 Pa, for example.

[0037] Accordingly, when a pure tone in diagnostic mode is output from the speaker (210), the plurality of digital MEMS microphone arrays (101) provided in the acoustic device (100) can collect time series output decibel data for each array position.

[0038] In addition, the acoustic device (100) can evaluate whether the digital MEMS microphone array is abnormal by using the standard deviation between the time series output decibel data.

[0039] More specifically, for example, the acoustic device (100) obtains a root mean square (RMS) value from the time series output decibel data, calculates the accumulated standard deviation of the RMS value, and compares the standard deviation with a preset threshold, thereby enabling the evaluation of whether the digital MEMS microphone array (101) is abnormal.

[0040] In addition, the acoustic device (100) can output a diagnosis result based on the above abnormality evaluation through the display (102). Accordingly, the acoustic device (100) manager can check the abnormality of the MEMS microphone array (101) and the diagnosis result, and adjust the detailed setting values ​​to perform calibration.

[0041] In addition, since no separate specialized equipment is required in this diagnostic process, even general users can easily attempt to diagnose and correct the acoustic device (100).

[0042]

[0043] FIG. 2 is a block diagram illustrating an internal circuit of an acoustic device for microphone abnormality diagnosis according to an embodiment of the present invention.

[0044] Referring to FIG. 2, the acoustic device (100) is a configuration for microphone abnormality diagnosis, and includes a time series data collection unit (110), an effective value calculation unit (120), a standard deviation calculation unit (130), an abnormality diagnosis unit (140), and a diagnosis result output unit (150).

[0045] The time series data collection unit (110) can collect the time series output decibel data for each microphone of each MEMS microphone array (101) by measuring the size (Decibel) of the sound signal output from the speaker (210) in a time series manner.

[0046] And, the effective value calculation unit (120) calculates the effective value of each individual digital MEMS microphone from the time series output decibel data.

[0047] Here, the root mean square (RMS) calculation unit (120) can calculate the root mean square (RMS) as the root mean square (RMS). In addition, it is preferable that the average of the root mean square (RMS) be adjusted to fall within a preset range over a certain period of time. To this end, the root mean square (RMS) calculation unit (120) according to an embodiment of the present invention can adjust the output of the amplifier (230) connected to the speaker (220) so that the average of the time series output decibel data is adjusted to fall within a preset range, and when the average of the time series output decibel data is maintained within the preset range for a certain period of time or longer, the maintained time series output decibel data is acquired as time series data, and the root mean square (RMS) can be calculated and transmitted to the standard deviation calculation unit (130).

[0048] Thereafter, the standard deviation calculation unit (130) can calculate the standard deviation for abnormality diagnosis using the time series output decibel data and the effective value.

[0049] If the standard deviation is σ here, the calculation algorithm can be illustrated by the following mathematical formula 1.

[0050]

[0051] In mathematical expression 1, N represents the length of the time series data, x_n represents the nth time series data, X represents the average of each microphone RMS (root mean square) value, m represents the mth digital MEMS microphone, and M represents the number of digital MEMS microphones.

[0052] Once this standard deviation is calculated, the abnormality diagnosis unit (140) can perform a diagnostic evaluation, such as pass if it is below the threshold or fail if it is above the threshold, by comparing it with a preset threshold. Furthermore, the abnormality diagnosis unit (140) can identify MEMS microphone channel information in which an abnormality has been confirmed and output it through the diagnostic result output unit (150).

[0053] Here, the evaluation criteria can be exemplified, for example, as shown in Table 1 below. AVG stands for average, and STDEV stands for standard deviation.

[0054] Number AVG [dB] STDEV [dB] Note 192.00~94.00 2.0 or less pass

[0055] The diagnostic result output unit (150) can output the diagnostic process of this diagnostic process through the display (102), and can process the abnormality information and whether or not each MEMS microphone array (101) is abnormal by collecting the abnormality information and converting it into graphic information that can be output through the display (102) after processing it into a diagnostic result interface. Accordingly, even a general user can easily check the abnormality diagnosis result of the MEMS microphone array (101) through the display (102) by connecting the sound device (100) to a general speaker (210) and then applying a signal through an amplifier (230).

[0056] FIG. 3 is an exemplary diagram of a jig coupling structure between an acoustic device and a speaker according to an embodiment of the present invention.

[0057] Referring to FIG. 3, the acoustic device (100) and the speaker (210) can be tightly connected through a jig (220) formed as a guide in the form of a jig that is configured to be sealed from the outside while fixing the digital MEMS microphone array (101) of the acoustic device (100) so as to face the output surface of the speaker (210).

[0058] More specifically, for example, the digital MEMS microphone array (101) may be arranged in a circle, and the speaker (210) may be a circular speaker corresponding to the digital MEMS microphone array, and in this case, the jig (220) may be formed as a cylindrical guide surrounding the circular speaker as a jig-shaped guide.

[0059] By using this type of tight connection, noise from outside can be blocked, which can improve the performance of abnormal diagnosis.

[0060] Additionally, in the diagnosis of abnormalities, not only external noise but also internal natural frequencies must be considered. In other words, it is necessary to control natural frequencies to prevent resonance from occurring.

[0061] Therefore, it is preferable that the first frequency according to the embodiment of the present invention is formed as a pure tone, and it is preferable that the pure tone is set to have a value different from the natural frequency of the combined body in which the cylindrical guide and the circular speaker are combined.

[0062] For example, when a MEMS microphone array (101), a cylindrical jig (220), and a circular speaker (210) are in close contact, a combined body is formed in the form of a double-sided closed tube, and its natural frequency is calculated as in the following mathematical expression 2, where L may correspond to the height of the cylindrical jig, D to the diameter of the speaker, and λ to the wavelength.

[0063]

[0064] For example, if L is 0.06 m and D is 0.116 m, the natural frequency f(=c / λ c=340 m / s) is as follows.

[0065] f = 0.85 kHz (n=1), 2.56 kHz (n=3), 4.27 kHz (n=5), ...

[0066] Accordingly, when applied to the present invention, it is preferable to set a pure tone of 2 kHz, which is different from the above natural frequency, as the evaluation frequency.

[0067] For setting such evaluation frequency, the acoustic device (100) may provide an evaluation frequency setting menu in the diagnostic mode, and the user may adjust the evaluation frequency so as not to affect the natural frequency.

[0068]

[0069] Figure 4 is a flowchart for explaining a diagnostic process using an acoustic device according to an embodiment of the present invention.

[0070] Referring to Fig. 4, first, for diagnosis, an acoustic device (100) and a speaker (210) are tightly connected through a jig (220) (S101).

[0071] Afterwards, when power is applied to the amplifier (230), a pure tone of a preset first frequency is played through the speaker (210) (S103).

[0072] Then, the sound device (100) enters a diagnostic mode, measures the time series output decibel, and adjusts the average value (S105).

[0073] Thereafter, the acoustic device (100) collects time series output decibel data for each array position of the digital MEMS microphone array (S107).

[0074] And, the sound device (100) calculates the standard deviation from the effective value of the collected time series data (S109), and compares the calculated standard deviation with a threshold to determine whether there is an abnormality (S111).

[0075] Afterwards, the sound device (100) outputs the diagnosis result through the display (102) (S113).

[0076]

[0077] FIGS. 5 and 6 are drawings illustrating a diagnostic mode output interface of an acoustic device according to an embodiment of the present invention.

[0078] FIG. 5 illustrates a microphone status check interface of a diagnostic mode output interface according to an embodiment of the present invention. As illustrated in FIG. 5, the acoustic device (100) can display whether there is an abnormality in each microphone channel of each microphone array through the diagnostic mode interface display (102), and if there is an abnormality, can generate an error or warning message and display it for each channel. Accordingly, a user can check the microphone channel with an abnormality and perform corrections, etc. accordingly.

[0079] Meanwhile, Fig. 6 illustrates an individual array abnormality diagnosis interface of a diagnostic mode output interface according to an embodiment of the present invention. The average value (AVG) calculated by calculating the effective value is preferably adjusted to 92 dB to 94 dB, and accordingly, when the adjustment is completed for a preset time, the standard deviation (STDEV) value is calculated and can be compared with the reference value. Since this calculation process can be output for each channel of each individual microphone array as shown in Fig. 6, the user can adjust the output in real time and accurately check whether there is an abnormality.

[0080]

[0081] Meanwhile, the methods according to the various embodiments of the present invention described above can be implemented as programs and stored on various non-transitory computer-readable media and provided to each server or device. Accordingly, the user terminal (100) can access the server or device and download the program.

[0082] A non-transitory readable medium refers to a medium that permanently stores data and can be read by a device, rather than a medium that stores data for a short period of time, such as a register, cache, or memory. Specifically, the various applications or programs described above may be stored and provided on non-transitory readable media, such as a CD, DVD, hard disk, Blu-ray disc, USB, memory card, or ROM.

[0083] In addition, although the preferred embodiments of the present invention have been illustrated and described above, the present invention is not limited to the specific embodiments described above, and various modifications can be made by a person having ordinary skill in the art to which the present invention pertains without departing from the gist of the present invention as claimed in the claims, and such modifications should not be understood individually from the technical idea or prospect of the present invention.

Claims

1. A method for diagnosing a digital MEMS microphone abnormality performed by an acoustic device including a plurality of digital MEMS microphones, A step of collecting time series output decibel data for each array position of a plurality of digital MEMS microphone arrays provided in the acoustic device when a pure tone of a preset first frequency is reproduced from a speaker in close contact with the acoustic device; A step of evaluating whether the digital MEMS microphone array is abnormal by using the standard deviation between the time series output decibel data; and Including a step of outputting a diagnosis result based on the above abnormality evaluation. A method for diagnosing digital MEMS microphone abnormalities in acoustic devices.

2. In paragraph 1, The above sound device and the above speaker, The digital MEMS microphone array of the above sound device is tightly connected through a jig-shaped guide that is designed to be sealed from the outside while being fixed so as to face the speaker. Digital MEMS microphone fault diagnosis method.

3. In paragraph 2, The above digital MEMS microphone array is arranged in a circle, The above speaker is a circular speaker corresponding to the digital MEMS microphone array, The above jig-shaped guide includes a cylindrical guide surrounding the circular speaker. Digital MEMS microphone fault diagnosis method.

4. In paragraph 3, The pure tone of the first frequency is set to have a different value from the natural frequency of the combined body in which the cylindrical guide and the circular speaker are combined. Digital MEMS microphone fault diagnosis method.

5. In paragraph 1, The steps to evaluate the above abnormalities are: A step of calculating the effective value of each individual digital MEMS microphone from the above time series output decibel data; and A step of determining whether an individual digital MEMS microphone corresponding to the time series output decibel data is abnormal based on a standard deviation obtained using the above effective value and a preset threshold. Digital MEMS microphone fault diagnosis method.

6. In paragraph 5, The steps to determine whether the above is abnormal are: A step of accumulating the standard deviation based on the time series output decibel data, the length of the time series output decibel data, the mean of the effective value of each digital MEMS microphone, and the number of each digital MEMS microphone, and comparing the standard deviation with the threshold to determine the standard deviation. Digital MEMS microphone fault diagnosis method.

7. In paragraph 1, The step of collecting the above time series output decibel data is: A step of adjusting the output of an amplifier connected to the speaker so that the average of the time series output decibel data is adjusted within a preset range; and A step of obtaining the maintained time series output decibel data as data for standard deviation calculation when the average of the above time series output decibel data is maintained within the preset range for a certain period of time or longer. Digital MEMS microphone fault diagnosis method.

8. In an acoustic device including a plurality of digital MEMS microphones, In a digital MEMS microphone abnormality diagnosis mode, when a pure tone of a preset first frequency is played from a speaker in close contact with the acoustic device, a time series data collection unit that collects time series output decibel data for each array position of a plurality of digital MEMS microphone arrays provided in the acoustic device; An abnormality diagnosis unit that diagnoses whether the digital MEMS microphone array is abnormal by using the standard deviation between the time series output decibel data; and Includes a diagnosis result output unit that outputs a diagnosis result based on the above abnormality evaluation. Sound device.

9. In paragraph 8, The above sound device and the above speaker, The digital MEMS microphone array of the above sound device is tightly connected through a jig-shaped guide that is designed to be sealed from the outside while being fixed so as to face the speaker. Sound device.

10. In paragraph 9, The above digital MEMS microphone array is arranged in a circle, The above speaker is a circular speaker corresponding to the digital MEMS microphone array, The above jig-shaped guide includes a cylindrical guide surrounding the circular speaker. Sound device.

11. In paragraph 10, The pure tone of the first frequency is set to have a different value from the natural frequency of the combined body in which the cylindrical guide and the circular speaker are combined. Sound device.

12. In paragraph 8, An effective value calculation unit that calculates the effective value of each individual digital MEMS microphone from the above time series output decibel data; and Includes a standard deviation calculation unit that calculates the standard deviation using the above effective value, The above abnormality diagnosis unit determines whether an individual digital MEMS microphone corresponding to the time series output decibel data is abnormal based on a preset threshold. Sound device.

13. In paragraph 12, The above standard deviation calculation unit accumulates and calculates the standard deviation based on the time series output decibel data, the length of the time series output decibel data, the average effective value of each digital MEMS microphone, and the number of each digital MEMS microphone. The above abnormality diagnosis unit compares the standard deviation with the threshold to determine whether there is an abnormality. Sound device.

14. In paragraph 8, The above time series data collection unit, Adjusting the output of the amplifier connected to the speaker so that the average of the time series output decibel data is adjusted within a preset range, and when the average of the time series output decibel data is maintained within the preset range for a certain period of time or longer, obtaining the maintained time series output decibel data as time series data for standard deviation calculation. Sound device.

15. A computer program stored in a computer-readable medium for executing the method described in any one of paragraphs 1 to 8 on a computer.

Images