A detection system for detecting abnormal frequency response of an electret microphone and a detection method thereof

Abstract

The application discloses an abnormal frequency response detection system and method of an electret microphone. The system can automatically collect audio data, analyze frequency response curves, extract key features, and match them with features in a pre-stored reference frequency response feature library, ultimately directly outputting specific fault types, greatly reducing the dependence on human professional skills. Moreover, the matching analysis based on the pre-set algorithm and the reference feature library completely eliminates the subjective judgment deviation, so that the same product can obtain highly consistent and repeatable diagnostic conclusions at different times, by different operators or on different equipment, improving the reliability of quality control. At the same time, the three-dimensional feature parameters of frequency response flatness, frequency band average sensitivity and resonance characteristics are introduced for joint analysis, so as to realize high-precision differentiation and accurate positioning of different fault types (such as "high-frequency blockage" and "diaphragm resonance"), and the diagnostic conclusion directly guides production and maintenance.

Description

Technical Field

[0001] This invention relates to the technical field of electret microphone detection, specifically to a detection system and method for abnormal frequency response of electret microphones. Background Technology

[0002] Electret microphones (ECMs) are widely used in consumer electronics, communication equipment, and medical instruments due to their small size, stable performance, and low cost. Frequency response characteristics are one of the core indicators for measuring the acoustic performance of a microphone. Abnormalities in frequency response characteristics can directly reflect design flaws, manufacturing defects, or assembly failures in the product, such as diaphragm defects, blocked sound holes, cavity contamination, and loose components.

[0003] Currently, the frequency response characteristics of electret microphones are mainly detected by manual listening or by using general-purpose audio analyzers to obtain frequency response curves. These traditional methods have the following significant shortcomings: 1. Reliance on subjective experience and low efficiency: Manually listening to or visually interpreting frequency response curves is highly dependent on the operator's experience and skills, lacks objective and unified judgment standards, has poor detection consistency, and is costly and inefficient, making it difficult to meet the needs of modern large-scale production. 2. Limited diagnostic accuracy and inability to pinpoint the root cause of the fault: Although general audio analyzers can plot frequency response curves, their output is only intuitive graphs or data lists. They can usually only determine whether the frequency response is qualified, but cannot automatically and intelligently diagnose the specific physical fault types that cause abnormal frequency response. Therefore, they cannot provide direct and effective guidance for the improvement and maintenance of the production process. Summary of the Invention

[0004] This application provides a detection system and method for abnormal frequency response of electret microphones, which solves the problem mentioned in the background art that the prior art can only provide frequency response curve data, but cannot automatically and accurately associate abnormal curve patterns with specific physical fault types.

[0005] To address the aforementioned technical problems, in a first aspect, this application provides a system for detecting abnormal frequency response of an electret microphone, comprising: A standard acoustic excitation source is used to output a preset test audio signal to the electret microphone under test; The signal acquisition and conditioning module is electrically connected to the output terminal of the electret microphone under test, and is used to acquire and condition the electrical signal generated by the electret microphone under test after responding to the test audio signal, so as to obtain the measured audio data. The frequency response analysis module, whose input is connected to the output of the signal acquisition and conditioning module, is used to process the measured audio data, generate a measured frequency response curve, and extract three characteristic parameters from the measured frequency response curve. The characteristic parameters include a frequency response flatness parameter, a frequency band average sensitivity parameter, and a resonance characteristic parameter. The fault diagnosis module has its input end connected to the output end of the frequency response analysis module. The fault diagnosis module has a pre-stored reference frequency response feature library associated with various typical fault types. The fault diagnosis module is used to perform matching analysis between the extracted feature parameters and the features in the reference frequency response feature library, and output the abnormal frequency response fault type corresponding to the electret microphone under test based on the matching result.

[0006] In one embodiment, the signal acquisition and conditioning module includes: The preamplifier and bias circuit unit has its input terminal connected to the output terminal of the electret microphone under test, and is used to provide a DC operating point for the electret microphone under test and amplify the primary voltage signal output by the electret microphone under test. An anti-aliasing filter unit, the input of which is connected to the output of the preamplifier and bias circuit unit, is used to filter out frequency components in the primary voltage signal that are higher than half of the system set sampling frequency, and generate a filtered analog voltage signal. An analog-to-digital converter unit, the input of which is connected to the output of the anti-aliasing filter unit, is used to convert the filtered analog voltage signal into the measured audio data in digital form.

[0007] In one embodiment, the frequency response analysis module includes: The frequency response curve generation unit is used to perform fast Fourier transform and system error calibration on the measured audio data to generate the measured frequency response curve. The feature parameter extraction unit is connected to the frequency response curve generation unit and is used to extract the feature parameters from the measured frequency response curve.

[0008] In one embodiment, the feature parameter extraction unit includes: The flatness calculation subunit is used to calculate the fluctuation range of the measured frequency response curve within a preset full frequency band as the frequency response flatness parameter. A frequency band sensitivity calculation subunit is used to calculate the average sound pressure level of the measured frequency response curve within at least one preset diagnostic sub-frequency band as the average sensitivity parameter of the frequency band. The resonance detection and quantization subunit is used to detect the resonance peak in the measured frequency response curve and calculate the center frequency, peak amplitude and quality factor of the resonance peak as the resonance characteristic parameters.

[0009] In one embodiment, the fault diagnosis module includes: The data preprocessing unit is used to format and normalize the feature parameters input by the frequency response analysis module to obtain the processed feature parameters; The feature matching engine unit is connected to the data preprocessing unit and the reference frequency response feature library, and is used to perform matching operations between the processed feature parameters and the reference feature vectors in the reference frequency response feature library, and generate matching results. The diagnostic decision unit, connected to the feature matching engine unit, is used to determine and output the final abnormal frequency response fault type based on the matching results.

[0010] In one embodiment, the feature matching engine unit includes: The distance metric calculation subunit is used to select a preset distance metric formula to calculate the numerical distance between the processed feature parameters and the reference feature vector of each type of fault in the reference frequency response feature library. The result sorting subunit is connected to the distance metric calculation subunit, which sorts all the calculated numerical distances and outputs the fault type corresponding to the baseline feature vector with the smallest distance as the optimal matching result.

[0011] In one embodiment, the diagnostic decision unit includes: The confidence assessment subunit is used to analyze the matching results generated by the feature matching engine and calculate the confidence level of the current diagnosis. The final decision subunit is used to decide whether to accept or reject the fault type corresponding to the matching result based on whether the confidence level reaches a preset threshold, and to generate a final diagnostic conclusion. The result formatting subunit is used to encapsulate the final diagnostic conclusion into structured data containing fault type identifiers and confidence levels, and then output it.

[0012] Secondly, this application also provides a method for detecting abnormal frequency response of an electret microphone, a method for implementing a system for detecting abnormal frequency response of an electret microphone, the method comprising: A preset test audio signal is output to the electret microphone under test through a standard acoustic excitation source, and the response electrical signal output by the electret microphone under test is simultaneously acquired and conditioned through a signal acquisition and conditioning module to obtain actual audio data. The measured audio data is processed by the frequency response analysis module to generate a measured frequency response curve, and three characteristic parameters are extracted from the measured frequency response curve. The characteristic parameters include frequency response flatness parameter, frequency band average sensitivity parameter, and resonance characteristic parameter. The fault diagnosis module performs matching analysis between the extracted feature parameters and the features in the pre-stored reference frequency response feature library, and outputs the abnormal frequency response fault type corresponding to the electret microphone under test based on the matching results.

[0013] In one embodiment, the method of processing the measured audio data through a frequency response analysis module to generate a measured frequency response curve, and extracting three feature parameters from the measured frequency response curve, is as follows: The measured audio data is subjected to Fast Fourier Transform and system error calibration to generate the measured frequency response curve; Calculate the fluctuation range of the measured frequency response curve within a preset full frequency band, and use it as the frequency response flatness parameter; Calculate the average sound pressure level of the measured frequency response curve within at least one preset diagnostic sub-band, and use it as the average sensitivity parameter of the band; The resonant peak in the measured frequency response curve is detected, and the center frequency, peak amplitude, and quality factor of the resonant peak are calculated as the resonant characteristic parameters.

[0014] In one embodiment, the method of matching and analyzing the extracted feature parameters with features in a pre-stored reference frequency response feature library through a fault diagnosis module, and outputting the abnormal frequency response fault type corresponding to the electret microphone under test based on the matching result is as follows: The three extracted feature parameters are used as a combined feature vector, and the distance between them and the reference feature vector of each type of fault in the reference frequency response feature library is calculated. Based on the calculated distance, the fault type that meets the preset matching conditions is selected as the abnormal frequency response fault type output.

[0015] The beneficial effects of the above-mentioned detection system and method for abnormal frequency response of electret microphones are as follows: The system can automatically collect audio data, analyze frequency response curves, extract key features, and match them with features in a pre-stored benchmark frequency response feature library. Ultimately, it directly outputs the specific fault type, significantly reducing reliance on human expertise. Moreover, the matching analysis based on preset algorithms and benchmark feature libraries completely eliminates the bias of subjective judgment, enabling highly consistent and repeatable diagnostic conclusions to be obtained for the same product at different times, by different operators, or on different devices, thus improving the reliability of quality control.

[0016] Meanwhile, by automating the process to replace manual listening and curve analysis, the detection speed has been increased by orders of magnitude. Furthermore, by introducing three dimensions of characteristic parameters—frequency response flatness, average frequency band sensitivity, and resonance characteristics—for joint analysis, the essential characteristics of frequency response anomalies can be captured more comprehensively and profoundly. This enables high-precision differentiation and accurate location of different fault types (such as "high-frequency attenuation" and "diaphragm resonance"), and the diagnostic conclusions directly guide production and maintenance. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the structure of an electret microphone abnormal frequency response detection system shown in an embodiment of this application; Figure 2 for Figure 1 This application embodiment shows a schematic diagram of the signal acquisition and conditioning module structure of an electret microphone abnormal frequency response detection system; Figure 3 for Figure 1 This application embodiment shows a schematic diagram of the structure of the frequency response analysis module of the abnormal frequency response detection system for electret microphones; Figure 4 This is a flowchart illustrating a method for detecting abnormal frequency response of an electret microphone according to an embodiment of this application. Detailed Implementation

[0018] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0019] It should be noted that when a component is considered to be "connected" to another component, it can be directly connected to the other component or there may be an intermediary component present. Conversely, when a component is said to be "directly" connected to another component, there is no intermediary component.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0021] like Figure 1 As shown, this application provides a detection system for abnormal frequency response of an electret microphone, comprising: Standard acoustic excitation source 1 is used to output a preset test audio signal to the electret microphone under test; The standard acoustic excitation source 1 is an active transmitting device that precisely applies sound waves to the acoustic inlet of the microphone under test. It does not emit sound randomly, but outputs a pre-designed standard signal with completely known characteristics. This signal serves as the reference for all subsequent analysis and comparison. For example, suppose the test audio signal is preset to "1kHz sine wave, sound pressure level 94dB". Then the task of the standard acoustic excitation source 1 is to precisely generate a pure sound wave with a frequency of 1kHz and a sound pressure level of 94dB at the microphone's acoustic inlet. If this excitation source is not "standard," for example, actually emitting a 97dB sound wave, or mixed with 2kHz harmonics, then any abnormality in the microphone output cannot be determined whether it is caused by a microphone malfunction or a distortion of the excitation signal, rendering the entire testing system meaningless.

[0022] Signal acquisition and conditioning module 2, which is electrically connected to the output terminal of the electret microphone under test, is used to acquire and condition the electrical signal generated by the electret microphone under test after responding to the test audio signal, so as to obtain the measured audio data; like Figure 2 As shown, in one embodiment, the signal acquisition and conditioning module 2 includes: The preamplifier and bias circuit unit 21 has its input terminal connected to the output terminal of the electret microphone under test, and is used to provide a DC operating point for the electret microphone under test and amplify the primary voltage signal output by the electret microphone under test. The preamplifier and bias circuit unit 21 provides a suitable power supply voltage (e.g., 3V) to the microphone through a resistor (e.g., 2.2kΩ) and incorporates an internal bias circuit to ensure that the microphone's internal FET operates in the linear portion of the amplification region. This provides the microphone output with a stable DC voltage (e.g., half the power supply voltage, 1.5V), around which the AC voltage signal will fluctuate. The core of the preamplifier and bias circuit unit 21 is a transimpedance amplifier circuit composed of operational amplifiers. It draws the microphone's microamplitude current I_mic through a precision feedback resistor R_f, directly converting the current into a voltage according to Ohm's law V_out = I_mic * R_f. For example, suppose a 1kHz sound wave generates a peak current of 1µA in the microphone. After conversion by R_f = 1kΩ, a peak voltage signal of 1µA * 1kΩ = 1mV is obtained. This voltage signal is still very small and contains circuit noise. The preamplifier and bias circuit unit 21 typically includes a subsequent voltage amplification stage to amplify the 1mV signal by 100 times, resulting in a 100mV primary voltage signal. At this point, the preamplifier and bias circuit unit 21 outputs a 1kHz sinusoidal analog voltage signal with an appropriate amplitude (e.g., peak value 100mV) and a center at a certain DC level (e.g., 1.5V), but it may contain high-frequency noise.

[0023] Anti-aliasing filter unit 22, the input terminal of which is connected to the output terminal of the preamplifier and bias circuit unit, is used to filter out frequency components in the primary voltage signal that are higher than half of the system set sampling frequency, and generate a filtered analog voltage signal. Assuming the system's sampling frequency is set to fs = 48kHz, then fs / 2 = 24kHz. The anti-aliasing filter unit 22 is a sharp-cutoff low-pass filter with a cutoff frequency slightly below 24kHz (e.g., 20kHz). It acts like a smart sieve, allowing only frequency components below 20kHz to pass through, while attenuating all high-frequency noise, interference, and even ultra-high-frequency components that shouldn't be present in the audio signal we need above 20kHz. Finally, it outputs a clean analog voltage signal, in which high-frequency noise that could contaminate digital sampling has been effectively suppressed.

[0024] Analog-to-digital converter unit 23, the input terminal of which is connected to the output terminal of the anti-aliasing filter unit, is used to convert the filtered analog voltage signal into the measured audio data in digital form.

[0025] For example, a 1kHz, 100mV peak sine wave, after passing through anti-aliasing filter unit 22, enters a 24-bit, 48kHz sampling ADC. The ADC's reference voltage is set to Vref = 3.3V. At a certain sampling moment, the analog voltage value is 1.5V + 0.1V * sin(θ) = 1.55V. The ADC quantizes this voltage value into a 24-bit digital code, for example, (1.55V / 3.3V) * 2^24 ≈ 0.4697 * 16777216 ≈ 7.87 million, and the corresponding binary code is a sample point in the measured audio data at that moment. The ADC continues to work, outputting a series of such digital sample points. For example, in 1 second, it outputs 48,000 24-bit integers. This series of ordered digital sample points constitutes the measured audio data, which is a one-dimensional array that can be directly read and processed by a digital signal processor (DSP) or computer software.

[0026] Frequency response analysis module 3, whose input terminal is connected to the output terminal of signal acquisition and conditioning module 2, is used to process the measured audio data, generate a measured frequency response curve, and extract three feature parameters from the measured frequency response curve; the feature parameters include frequency response flatness parameter, frequency band average sensitivity parameter, and resonance feature parameter; like Figure 3 As shown, in one embodiment, the frequency response analysis module 3 includes: The frequency response curve generation unit 31 is used to perform fast Fourier transform and system error calibration on the measured audio data to generate the measured frequency response curve. The FFT algorithm is applied to the measured audio data array (time-domain waveform). FFT transforms the signal from the time domain (amplitude changes with time) to the frequency domain (amplitude is distributed with frequency).

[0027] For example, input a sweep tone response lasting 1 second with a sampling rate of 48kHz, which is 48,000 sampling points.

[0028] Therefore, a complex array is output, representing the amplitude and phase of each frequency component. The amplitude is then taken to obtain the microphone's output amplitude spectrum. Since the characteristics of the excitation signal (sweep tone) are known, the microphone's output amplitude spectrum is divided by the excitation signal's amplitude spectrum to obtain the microphone's raw frequency response H_raw(f). This eliminates the effect of the excitation signal's inherent flatness. For example, at 1kHz, if the excitation signal amplitude is A and the microphone output amplitude is B, then the raw frequency response at that point is B / A (usually converted to dB: 20*log10(B / A)).

[0029] H_raw(f) contains not only the characteristics of the microphone but also the frequency response of the test system itself. A pre-stored system error response data H_sys(f) is called. This data was pre-measured using a standard reference microphone with a near-perfect frequency response under identical settings. H_sys(f) is approximately equal to the frequency response of the system itself. Therefore, the frequency response of the real microphone H_dut(f) = H_raw(f) / H_sys(f) (in dB, this is a subtraction: H_dut_dB = H_raw_dB - H_sys_dB).

[0030] For example, suppose that at 8kHz, the system itself has a +2dB boost due to speaker characteristics (H_sys(8k) = +2dB). The original response of the microphone under test is -5dB. After calibration, the microphone's true response at 8kHz is -5dB - (+2dB) = -7dB. Therefore, after calibration, we obtain a curve with frequency on the x-axis and sound pressure level (dB) on the y-axis, which is the measured frequency response curve. Assume we obtain the following key points on the curve (in dB): 100Hz: +0.5dB, 1kHz: 0dB, 4kHz: +3.5dB, 8kHz: -7dB.

[0031] The feature parameter extraction unit 32 is connected to the frequency response curve generation unit 31 and is used to extract the feature parameters from the measured frequency response curve.

[0032] In one embodiment, the feature parameter extraction unit 32 includes: The flatness calculation subunit is used to calculate the fluctuation range of the measured frequency response curve within a preset full frequency band as the frequency response flatness parameter. Within a preset full audio frequency band (e.g., 100Hz-10kHz), scan the entire measured frequency response curve to find the maximum and minimum values. Then, flatness = maximum value (dB) - minimum value (dB).

[0033] For example, in our hypothetical curve, within the range of 100Hz-10kHz, the maximum value is at 4kHz (+3.5dB), and the minimum value is at 8kHz (-7dB). Therefore, the frequency response flatness parameter = 3.5 - (-7) = 10.5dB. This value is relatively large, indicating that the overall frequency response fluctuates drastically, suggesting an anomaly in the initial assessment.

[0034] A frequency band sensitivity calculation subunit is used to calculate the average sound pressure level of the measured frequency response curve within at least one preset diagnostic sub-frequency band as the average sensitivity parameter of the frequency band. Based on predefined, preset diagnostic sub-bands for typical faults, calculate the average sound pressure level (SPL) of all data points within each preset diagnostic sub-band. For example, the preset diagnostic sub-bands include: High-frequency diagnostic band (H_Band): 8kHz-16kHz (for detecting blockages); Mid-frequency diagnostic band (M_Band): 2kHz-6kHz (for detecting resonance or broadband anomalies); Low-frequency diagnostic band (L_Band): 100Hz-300Hz (for detecting backhole blockages). For example, the average sensitivity of the high-frequency band is calculated by taking the arithmetic mean of the SPL at 8kHz, 10kHz, 12kHz, 14kHz, and 16kHz. Assuming the average of (-7, -10, -12, -14, -15) is approximately -11.6dB, this value is significantly lower than 0dB, indicating severe high-frequency attenuation.

[0035] The average sensitivity in the mid-frequency band is calculated by averaging the values ​​at 2kHz, 3kHz, 4kHz, 5kHz, and 6kHz. Since there is a +3.5dB peak at 4kHz, the average value may be inflated to approximately +1.8dB, indicating an overall boost in this frequency band.

[0036] The resonance detection and quantization subunit is used to detect the resonance peak in the measured frequency response curve and calculate the center frequency, peak amplitude and quality factor of the resonance peak as the resonance characteristic parameters.

[0037] Slide the detection window on the measured frequency response curve to find the resonant peak that satisfies the local bulge condition. For example, a significant local peak (+3.5dB) is found near 4kHz. Calculate the center frequency (Fc): Use parabolic interpolation to accurately locate the peak frequency, for example, 4.2kHz. Peak amplitude (Ap): Calculate the height of the peak relative to its two baselines. Assuming the baselines (obtained through fitting) at 4.2kHz have a baseline level BL of +0.5dB, then the peak amplitude Ap = 3.5 - 0.5 = +3.0dB. Therefore, the total peak amplitude = BL + Ap = 3.5dB. Quality factor (Q) calculation: The critical dB value is converted to a linear ratio using the formula: Linearity = 10^(dB value / 20). Therefore, the peak linear amplitude is: A_pk_linear = 10^(3.5 / 20) = 10^0.175 ≈ 1.496. The baseline linear amplitude is: A_bl_linear = 10^(0.5 / 20) = 10^0.025 ≈ 1.059.

[0038] Calculate the linear amplitude corresponding to the -3dB point. -3dB means that the amplitude drops to 1 / √2 times the peak value.

[0039] -3dB linear amplitude: A_3dB_linear=A_pk_linear / √2≈1.496 / 1.414≈1.058.

[0040] Using the formula: dB value = 20 * log10 (linear value), we can convert the -3dB linear amplitude back to a dB value. The target amplitude value for the -3dB point is: A_3dB_dB = 20 * log10(1.058) ≈ 20 * 0.0245 ≈ +0.49dB. Therefore, we need to find frequency points on both sides of the resonant peak where the frequency response curve amplitude is approximately +0.49dB. Assuming we find the left frequency F_low = 3.8kHz and the right frequency F_high = 4.6kHz, we calculate the bandwidth BW = 4.6 - 3.8 = 0.8kHz. We then calculate the quality factor Q = Fc / BW = 4.2 / 0.8 = 5.25. This Q value is not particularly high, indicating a relatively flat resonant peak.

[0041] The fault diagnosis module 4 has its input end connected to the output end of the frequency response analysis module 3. The fault diagnosis module has a pre-stored reference frequency response feature library associated with various typical fault types. The fault diagnosis module is used to match and analyze the extracted feature parameters with the features in the reference frequency response feature library, and output the abnormal frequency response fault type corresponding to the electret microphone under test based on the matching result.

[0042] In one embodiment, the fault diagnosis module 4 includes: The data preprocessing unit is used to format and normalize the feature parameters input by the frequency response analysis module to obtain the processed feature parameters; The feature matching engine unit is connected to the data preprocessing unit and the reference frequency response feature library, and is used to perform matching operations between the processed feature parameters and the reference feature vectors in the reference frequency response feature library, and generate matching results. The diagnostic decision unit, connected to the feature matching engine unit, is used to determine and output the final abnormal frequency response fault type based on the matching results.

[0043] In one embodiment, the feature matching engine unit includes: The distance metric calculation subunit is used to select a preset distance metric formula to calculate the numerical distance between the processed feature parameters and the reference feature vector of each type of fault in the reference frequency response feature library. The result sorting subunit is connected to the distance metric calculation subunit, which sorts all the calculated numerical distances and outputs the fault type corresponding to the baseline feature vector with the smallest distance as the optimal matching result.

[0044] In one embodiment, the diagnostic decision unit includes: The confidence assessment subunit is used to analyze the matching results generated by the feature matching engine and calculate the confidence level of the current diagnosis. The final decision subunit is used to decide whether to accept or reject the fault type corresponding to the matching result based on whether the confidence level reaches a preset threshold, and to generate a final diagnostic conclusion. The result formatting subunit is used to encapsulate the final diagnostic conclusion into structured data containing fault type identifiers and confidence levels, and then output it.

[0045] The different types of input data are organized into a unified feature vector. For example: V_input=[flatness, high-frequency sensitivity, mid-frequency sensitivity, resonant frequency, resonant amplitude, resonant Q value]=[10.5,-11.6,1.8,4.2,3.0,5.25]. Assuming that the maximum and minimum values ​​of each dimension are known based on historical data, the "min-max normalization" method is used to scale each dimension to the interval [0,1]. For example, if the flatness range is known to be [2,20], then 10.5 is normalized to (10.5-2) / (20-2)≈0.47. The normalized values ​​of high-frequency sensitivity, mid-frequency sensitivity, resonant frequency, resonant amplitude, and resonant Q value are calculated sequentially using the above method. The normalized feature vector V_input_norm is, for example, [0.47, 0.42, 0.65, 0.35, 0.55, 0.30].

[0046] Access the reference frequency response feature library. Assume the library contains three normalized reference vectors for known faults: Fault A (High-frequency blockage): V_A=[0.44,0.38,0.58,0.30,0.50,0.25], characterized by: poor flatness, extremely low high frequency, normal mid frequency, and no resonance; Fault B (Mid-frequency wide resonance): V_B=[0.30,0.80,0.70,0.40,0.60,0.25], characterized by: moderate flatness, normal high frequency, high mid frequency, mid-frequency resonance, and low Q value; Fault C (Sharp mechanical resonance): V_C=[0.20,0.75,0.80,0.45,0.80,0.70], characterized by: good flatness, normal high frequency, extremely high mid frequency, and sharp resonance with high Q value.

[0047] The Euclidean distance formula, distance = sqrt((Fla_in-Fla_ref)^2+(High_in-High_ref)^2+(Mid_in-Mid_ref)^2+(Freq_in-Freq_ref)^2+(Amp_in-Amp_ref)^2+(Q_in-Q_ref)^2), is used to calculate the numerical distance between the processed feature parameters and the reference feature vector of each type of fault in the reference frequency response feature library.

[0048] Calculate the distance value from Fault A. First, calculate the squared differences: (0.47 - 0.44)^2 = 0.0009, (0.42 - 0.38)^2 = 0.0016, (0.65 - 0.58)^2 = 0.0049, (0.35 - 0.30)^2 = 0.0025, (0.55 - 0.50)^2 = 0.0025, (0.30 - 0.25)^2 = 0.0025. The sum of the squared differences = 0.0009 + 0.0016 + 0.0049 + 0.0025 + 0.0025 + 0.0025 = 0.0149. Then the distance of D_to_A = sqrt(0.0149) ≈ 0.122.

[0049] Calculate the distance value from Fault B. First, calculate the squared differences: (0.47 - 0.30)^2 = 0.0289, (0.42 - 0.80)^2 = 0.1444, (0.65 - 0.70)^2 = 0.0025, (0.35 - 0.40)^2 = 0.0025, (0.55 - 0.60)^2 = 0.0025, (0.30 - 0.25)^2 = 0.0025. The sum of the squared differences = 0.0289 + 0.1444 + 0.0025 + 0.0025 + 0.0025 + 0.0025 = 0.1833. Then the distance of D_to_B = sqrt(0.1833) ≈ 0.428.

[0050] Calculate the distance value from Fault C. First, calculate the squared differences: (0.47 - 0.20)^2 = 0.0729, (0.42 - 0.75)^2 = 0.1089, (0.65 - 0.80)^2 = 0.0225, (0.35 - 0.45)^2 = 0.0100, (0.55 - 0.80)^2 = 0.0625, (0.30 - 0.70)^2 = 0.1600. The sum of the squared differences = 0.0729 + 0.1089 + 0.0225 + 0.0100 + 0.0625 + 0.1600 = 0.4368. Then the distance of D_to_C = sqrt(0.4368) ≈ 0.661.

[0051] Then D_to_A(0.122) < D_to_B(0.428) < D_to_C(0.661), and the processed input feature parameters are closest to Fault A. Therefore, the optimal matching result = Fault A.

[0052] Calculate the confidence level of this diagnosis. Distance-based confidence level: Confidence level = 1 - (optimal distance / total distance). Assume confidence level A = 1 - 0.122 / (0.122 + 0.428 + 0.661) ≈ 0.89. After comprehensive evaluation, a confidence level is given, for example, 89%. Make a decision based on the preset confidence threshold. Assume the system setting: threshold = 80%. If confidence level (89%) > threshold (80%), then the optimal matching result (fault A) is adopted. Since 89% > 80%, fault A is ultimately adopted. Encapsulate the final conclusion into a standard format for display, storage, or transmission to other systems. The system completes an intelligent closed loop from feature parameters to an executable fault diagnosis report. The operator receives not a bunch of incomprehensible data and curves, but a clear diagnosis: "There is an 89% chance that the microphone has high-frequency blockage; cleaning the dust filter is recommended." like Figure 4 As shown, in a second aspect, this application also provides a method for detecting abnormal frequency response of an electret microphone, a method for implementing a system for detecting abnormal frequency response of an electret microphone, the method comprising: S1. A preset test audio signal is output to the electret microphone under test through a standard acoustic excitation source, and the response electrical signal output by the electret microphone under test is simultaneously acquired and conditioned through a signal acquisition and conditioning module to obtain measured audio data. S2. The measured audio data is processed by the frequency response analysis module to generate a measured frequency response curve, and three characteristic parameters are extracted from the measured frequency response curve. The characteristic parameters include frequency response flatness parameter, frequency band average sensitivity parameter, and resonance characteristic parameter. S3. The extracted feature parameters are matched and analyzed with the features in the pre-stored reference frequency response feature library through the fault diagnosis module, and the abnormal frequency response fault type corresponding to the electret microphone under test is output based on the matching result.

[0053] In one embodiment, the method of processing the measured audio data through a frequency response analysis module to generate a measured frequency response curve, and extracting three feature parameters from the measured frequency response curve, is as follows: The measured audio data is subjected to Fast Fourier Transform and system error calibration to generate the measured frequency response curve; Calculate the fluctuation range of the measured frequency response curve within a preset full frequency band, and use it as the frequency response flatness parameter; Calculate the average sound pressure level of the measured frequency response curve within at least one preset diagnostic sub-band, and use it as the average sensitivity parameter of the band; The resonant peak in the measured frequency response curve is detected, and the center frequency, peak amplitude, and quality factor of the resonant peak are calculated as the resonant characteristic parameters.

[0054] In one embodiment, the method of matching and analyzing the extracted feature parameters with features in a pre-stored reference frequency response feature library through a fault diagnosis module, and outputting the abnormal frequency response fault type corresponding to the electret microphone under test based on the matching result is as follows: The three extracted feature parameters are used as a combined feature vector, and the distance between them and the reference feature vector of each type of fault in the reference frequency response feature library is calculated. Based on the calculated distance, the fault type that meets the preset matching conditions is selected as the abnormal frequency response fault type output.

[0055] The options described in the above system embodiments are also applicable to this embodiment, and will not be detailed here. The remaining content of this application's embodiments can be found in the above system embodiments, and will not be repeated in this embodiment.

[0056] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0057] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A detection system for abnormal frequency response of an electret microphone, characterized in that: include: A standard acoustic excitation source is used to output a preset test audio signal to the electret microphone under test; The signal acquisition and conditioning module is electrically connected to the output terminal of the electret microphone under test, and is used to acquire and condition the electrical signal generated by the electret microphone under test after responding to the test audio signal, so as to obtain the measured audio data. The frequency response analysis module, whose input is connected to the output of the signal acquisition and conditioning module, is used to process the measured audio data, generate a measured frequency response curve, and extract three characteristic parameters from the measured frequency response curve. The characteristic parameters include a frequency response flatness parameter, a frequency band average sensitivity parameter, and a resonance characteristic parameter. The fault diagnosis module has its input end connected to the output end of the frequency response analysis module, and the fault diagnosis module has a pre-stored reference frequency response feature library associated with a variety of typical fault types. The fault diagnosis module is used to match and analyze the extracted feature parameters with the features in the reference frequency response feature library, and output the abnormal frequency response fault type corresponding to the electret microphone under test based on the matching result.

2. The system for detecting abnormal frequency response of an electret microphone according to claim 1, characterized in that: The signal acquisition and conditioning module includes: The preamplifier and bias circuit unit has its input terminal connected to the output terminal of the electret microphone under test, and is used to provide a DC operating point for the electret microphone under test and amplify the primary voltage signal output by the electret microphone under test. An anti-aliasing filter unit, the input of which is connected to the output of the preamplifier and bias circuit unit, is used to filter out frequency components in the primary voltage signal that are higher than half of the system set sampling frequency, and generate a filtered analog voltage signal. An analog-to-digital converter unit, the input of which is connected to the output of the anti-aliasing filter unit, is used to convert the filtered analog voltage signal into the measured audio data in digital form.

3. The system for detecting abnormal frequency response of an electret microphone according to claim 2, characterized in that: The frequency response analysis module includes: The frequency response curve generation unit is used to perform fast Fourier transform and system error calibration on the measured audio data to generate the measured frequency response curve. The feature parameter extraction unit is connected to the frequency response curve generation unit and is used to extract the feature parameters from the measured frequency response curve.

4. The system for detecting abnormal frequency response of an electret microphone according to claim 3, characterized in that: The feature parameter extraction unit includes: The flatness calculation subunit is used to calculate the fluctuation range of the measured frequency response curve within a preset full frequency band as the frequency response flatness parameter. A frequency band sensitivity calculation subunit is used to calculate the average sound pressure level of the measured frequency response curve within at least one preset diagnostic sub-frequency band as the average sensitivity parameter of the frequency band. The resonance detection and quantization subunit is used to detect the resonance peak in the measured frequency response curve and calculate the center frequency, peak amplitude and quality factor of the resonance peak as the resonance characteristic parameters.

5. The system for detecting abnormal frequency response of an electret microphone according to claim 1, characterized in that: The fault diagnosis module includes: The data preprocessing unit is used to format and normalize the feature parameters input by the frequency response analysis module to obtain the processed feature parameters; The feature matching engine unit is connected to the data preprocessing unit and the reference frequency response feature library, and is used to perform matching operations between the processed feature parameters and the reference feature vectors in the reference frequency response feature library, and generate matching results. The diagnostic decision unit, connected to the feature matching engine unit, is used to determine and output the final abnormal frequency response fault type based on the matching results.

6. The system for detecting abnormal frequency response of an electret microphone according to claim 5, characterized in that: The feature matching engine unit includes: The distance metric calculation subunit is used to select a preset distance metric formula to calculate the numerical distance between the processed feature parameters and the reference feature vector of each type of fault in the reference frequency response feature library. The result sorting subunit is connected to the distance metric calculation subunit, which sorts all the calculated numerical distances and outputs the fault type corresponding to the baseline feature vector with the smallest distance as the optimal matching result.

7. The system for detecting abnormal frequency response of an electret microphone according to claim 6, characterized in that, The diagnostic decision unit includes: The confidence assessment subunit is used to analyze the matching results generated by the feature matching engine and calculate the confidence level of the current diagnosis. The final decision subunit is used to decide whether to accept or reject the fault type corresponding to the matching result based on whether the confidence level reaches a preset threshold, and to generate a final diagnostic conclusion. The result formatting subunit is used to encapsulate the final diagnostic conclusion into structured data containing fault type identifiers and confidence levels, and then output it.

8. A method for detecting abnormal frequency response of an electret microphone, used to implement the method of the electret microphone abnormal frequency response detection system as described in any one of claims 1-7, characterized in that, The method includes: A preset test audio signal is output to the electret microphone under test through a standard acoustic excitation source, and the response electrical signal output by the electret microphone under test is simultaneously acquired and conditioned through a signal acquisition and conditioning module to obtain actual audio data. The measured audio data is processed by the frequency response analysis module to generate a measured frequency response curve, and three characteristic parameters are extracted from the measured frequency response curve. The characteristic parameters include frequency response flatness parameter, frequency band average sensitivity parameter, and resonance characteristic parameter. The fault diagnosis module performs matching analysis between the extracted feature parameters and the features in the pre-stored reference frequency response feature library, and outputs the abnormal frequency response fault type corresponding to the electret microphone under test based on the matching results.

9. The method for detecting abnormal frequency response of an electret microphone according to claim 8, characterized in that, The method for processing the measured audio data through a frequency response analysis module to generate a measured frequency response curve, and extracting three feature parameters from the measured frequency response curve, is as follows: The measured audio data is subjected to Fast Fourier Transform and system error calibration to generate the measured frequency response curve; Calculate the fluctuation range of the measured frequency response curve within a preset full frequency band, and use it as the frequency response flatness parameter; Calculate the average sound pressure level of the measured frequency response curve within at least one preset diagnostic sub-band, and use it as the average sensitivity parameter of the band; The resonant peak in the measured frequency response curve is detected, and the center frequency, peak amplitude, and quality factor of the resonant peak are calculated as the resonant characteristic parameters.

10. The method for detecting abnormal frequency response of an electret microphone according to claim 9, characterized in that, The method for matching and analyzing the extracted feature parameters with features in a pre-stored reference frequency response feature library through a fault diagnosis module, and outputting the abnormal frequency response fault type corresponding to the electret microphone under test based on the matching results, is as follows: The three extracted feature parameters are used as a combined feature vector, and the distance between them and the reference feature vector of each type of fault in the reference frequency response feature library is calculated. Based on the calculated distance, the fault type that meets the preset matching conditions is selected as the abnormal frequency response fault type output.

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