Automatic audio device self-checking method based on embedded telephone equipment

Abstract

The invention discloses an automatic audio device self-checking method based on embedded telephone equipment, and relates to the field of audio signal processing and hardware diagnosis. The method comprises the steps that firstly, a microphone collects first 3S playback signals of a test audio, and frequency similarity detection is carried out by extracting spectrum features; then, continuously collecting the 4s-5s playback signal of the audio, and carrying out single frequency point detection; meanwhile, aiming at the front 3S playback signals, calculating a root-mean-square RMS value according to signal energy, converting the RMS value into loudness, and carrying out loudness anomaly detection; thirdly, continuously collecting the 6s-7s playback signal of the audio for sampling analysis, calculating the root-mean-square of the playback signal, converting the root-mean-square into loudness L2, and when the loudness value L2 is lower than a set threshold value, indicating that the MIC device is abnormal; and finally, through frequency similarity detection, single-frequency detection, loudness anomaly detection and device anomaly detection, results of four rounds of detection are reported in a binary form, and equipment sound anomaly reasons are analyzed. According to the invention, specific abnormal links in the audio link can be effectively positioned.

Description

Technical Field

[0001] The present invention relates to the fields of audio signal processing and hardware diagnosis, in particular to an automatic audio device self-test method based on embedded telephone equipment. Background Art

[0002] In current audio equipment production, factory testing, and on-site maintenance, whether microphones, speakers, and other audio components are functioning properly is primarily determined through subjective listening or spectrum analysis. However, these methods have certain limitations:

[0003] First, human auditory perception is greatly influenced by subjective factors and has low detection efficiency, making it difficult to adapt to automated production testing processes. Although spectrum analysis can provide a certain visual reference, it lacks clear judgment indicators for frequency response distortion, loudness deviation, and frequency offset, and is easily affected by environmental noise and changes in test methods, which greatly affects detection accuracy.

[0004] Embedded devices often feature more complex hardware. Factors such as the spatial layout of microphones and speakers, as well as the structure of the sound cavity, can affect the device's sound reception and playback. This system-level coupling characterization relies solely on testing individual components or offline audio to assess the overall audio circuit's performance, which is cumbersome and inefficient.

[0005] Therefore, an automated audio detection method for embedded hardware platforms is needed. This method utilizes local audio receivers and playback devices to collect test audio playback and echoes. By analyzing the characteristics of the collected digital signals in real time, it can detect spectral similarity, single frequencies, loudness status, and device anomalies. An automated audio self-test algorithm, combining frequency domain characteristics with loudness metrics, can accurately identify and report audio anomalies. Summary of the Invention

[0006] To address the above issues, the present invention proposes an automatic audio device self-test method based on an embedded telephone device. Based on the device's local audio acquisition and playback paths, the method automatically analyzes the working status of audio devices such as speakers and microphones, as well as the software's voice paths, to determine whether there are any abnormalities.

[0007] The automatic audio device self-test method based on the embedded phone device has the following specific steps:

[0008] Step 1: Enter the set IP address, trigger the play command through the web page, play the test audio and perform real-time sampling and analysis;

[0009] Step 2: The microphone collects the first 3 seconds of the test audio playback signal and performs frequency similarity detection;

[0010] First, extract the spectral feature F1 from the signal collected by the microphone; perform standard parsing on the test audio to obtain the spectral feature F2;

[0011] Then, divide the spectral features F1 and F2 into N frequency points respectively. Use the Goertzel algorithm to calculate the relative amplitude value of each frequency point, calculate the difference between the two amplitude values at the same frequency point, and take the Euclidean distance of the amplitude differences at all frequency points as the spectral similarity S. When S is compared with the set threshold TH1, if S < TH1, the frequency similarity detection is qualified; otherwise, the self-check is unqualified.

[0012] The formula for calculating the Euclidean distance between the feature F1 and F 2 at the i-th frequency point is:

[0013]

[0014] is the amplitude value of the i-th frequency point of the feature F1, is the amplitude value of the i-th frequency point of the feature F2; 、 are the vectors composed of the amplitude values of all frequency points of the features F1 and F2 respectively;

[0015] Step 3: The microphone continues to collect the playback signal of the test audio from 4s to 5s for single-frequency point detection;

[0016] First, extract the spectral feature from the signal collected by the microphone and divide it into N frequency points. Use the Goertzel algorithm to analyze the amplitude value of each frequency point and calculate the average amplitude value of the N frequency points.

[0017] Then, for the single frequency point to be measured, calculate the ratio of the amplitude value of this single frequency point to the average amplitude value to obtain the amplitude ratio R;

[0018] The amplitude ratio is calculated as follows:

[0019] <{

[0020] Where is the amplitude value corresponding to the single frequency point to be measured, is the amplitude value corresponding to the i-th frequency point.

[0021] Finally, judge the amplitude ratio R and the set threshold TH2: If R > TH2, the detection of the single frequency point is qualified, and it is confirmed that this frequency point is clearly visible in the playback signal, verifying that there is no abnormal filtering or attenuation in the frequency transmission link; otherwise, the self-check is unqualified.

[0022] Step 4: Calculate the root mean square (RMS) value of the first 3S playback signal collected by the microphone and convert it into loudness for loudness anomaly detection; ]>

[0023] The conversion formula is:

[0024]

[0025] Indicates the signal energy of the nth frequency point; decibels to indicate loudness;

[0026] Judging decibels If it is lower than the set loudness threshold TH3, it indicates that the speaker output power is insufficient or there is gain loss in the signal path, and the loudness abnormality detection fails; otherwise, the loudness abnormality detection passes.

[0027] Step 5: The microphone continues to collect the playback signal of the 6th to 7th second of the test audio for sampling and analysis, calculates its root mean square (RMS) value and converts it into loudness L2, and determines whether the loudness value L2 is lower than the set loudness threshold TH4. If so, it indicates that there is an abnormality in the MIC device and the self-test fails; otherwise, the self-test passes.

[0028] Step 6: Analyze the cause of the device sound anomaly by performing frequency similarity detection, single frequency detection, loudness anomaly detection, and device anomaly detection, and report the results of the four rounds of detection in binary form.

[0029] The specific analysis is as follows: if the frequency similarity and single-frequency detections fail, but the loudness anomaly detection and device anomaly detection succeed, it indicates that the hardware can receive and play sound but may have reversed polarity. If the loudness anomaly detection and MIC device anomaly detection fail, it indicates that the microphone may not be connected or is damaged. Other combinations are further classified and processed through the internal state machine.

[0030] The advantages of the present invention are:

[0031] 1. By simultaneously analyzing the audio playback and microphone recovery paths and combining software and hardware data, the present invention has strong software and hardware coupling detection capabilities and can effectively locate specific abnormal links in the audio chain; such as power amplifiers, audio interfaces, microphones, sound cards, and VDSP modules, facilitating rapid troubleshooting.

[0032] 2. The present invention adopts a multi-dimensional phased self-test process based on spectral feature similarity, single-frequency energy ratio and overall loudness level, covering four key indicators: frequency transmission, signal integrity, volume output and microphone function. It avoids misjudgment caused by a single detection method and improves the comprehensiveness and accuracy of detection.

[0033] 3. The present invention is controlled by web page instructions and IP parameters. The self-test process can be started remotely. The detection frequency and threshold parameters support flexible configuration to meet the customized requirements of different models and different scenarios, which is convenient for batch deployment and later maintenance.

[0034] 4. The entire detection process of the present invention is based on real-time audio sampling inside the device. The detection results can be output in a short time, supporting rapid pre-launch detection or on-site fault diagnosis, greatly improving the efficiency of equipment debugging, production and after-sales.

[0035] 5. The spectrum similarity and single-frequency energy ratio algorithms used in this invention have a strong ability to suppress background noise interference, ensuring that the device audio performance status can still be accurately determined in the presence of general environmental noise, thereby improving the stability of actual applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a flow chart of an automatic audio device self-test method based on an embedded phone device according to the present invention;

[0037] Figure 2 The present invention is a schematic diagram of an automatic audio device self-test method based on an embedded telephone device. DETAILED DESCRIPTION

[0038] The following is a complete and detailed description of the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are also within the scope of protection of the present invention.

[0039] This paper proposes an automatic audio component self-test method for embedded phone devices. This method is used to automatically detect anomalies in audio components such as speakers and microphones within embedded devices. It is suitable for audio quality testing and fault diagnosis in consumer electronics, communications equipment, and industrial electronics. Leveraging the embedded platform's audio input and output interfaces and processor computing power, this method enables automated testing of audio acquisition and playback paths without relying on external auxiliary equipment.

[0040] This method can be widely applied to production testing and troubleshooting scenarios of devices including but not limited to VoIP terminals, smart access control, intercom equipment, video conferencing terminals, and industrial embedded systems. By combining spectrum analysis, RMS detection, and audio device characteristics, it realizes multi-dimensional hardware and software loop detection of audio device status, and can effectively determine whether the hardware devices and software basic settings are working properly.

[0041] Based on the audio acquisition and playback paths of the device locally, the working states of audio components such as speakers and microphones and the voice paths of software are automatically analyzed to determine whether there are abnormal situations. The overall detection process is divided into four stages: frequency similarity detection, single-frequency detection, loudness anomaly detection, and component anomaly detection; the test contents of the four stages are combined into a test audio file, and the test audio file is played through an embedded device. After being played by the speaker of the embedded device and passing through the hardware of air or other media, the audio will be deformed to a certain extent. Due to the consistency and stability of frequency, without the application of external conditions such as oscillators and Doppler effects, the deformed audio still has a certain similarity with the original audio in terms of frequency response characteristics. At the same time, through some supplementary detection means, it is possible to more comprehensively detect whether the functions of audio components are normal.

[0042] As Figure 1 shown, the specific steps are as follows:

[0043] Step 1: Input the set IP address, trigger the play command through the web page, play the test audio, and perform real-time sampling and analysis;

[0044] Relying on the interface of the specific hardware platform, play a test audio (usually a swept-frequency signal or a composite wave containing multiple frequency points) for real-time sampling and analysis. The process is triggered by a web page command. When the set IP is input, the self-check algorithm starts, and the self-check test audio is played.

[0045] The hardware processing path includes power amplifiers and audio interface IOs, and the software processing for recording includes the sound card layer and the VDSP module, etc. Therefore, the analysis results have strong software and hardware dependencies.

[0046] Step 2: The microphone collects the playback signal of the test audio in the first 3S and performs frequency similarity detection;

[0047] First, extract the spectral features F1 of the signal collected by the microphone; perform standard parsing on the test audio to obtain the spectral features F2;

[0048] Then, divide the spectral features F1 and F2 into N frequency points respectively, use the Goertzel algorithm to calculate the relative amplitude values of each frequency point, calculate the difference between the two amplitude values at the same frequency point, and take the Euclidean distance of the amplitude differences at all frequency points as the spectral similarity S. Compare the spectral shapes of the audio before and after the device plays the test audio to evaluate whether the two audio segments have consistent frequency response characteristics. When S is compared with the set threshold TH1, if S < TH1, it means that the frequency response has not undergone obvious distortion, and the frequency similarity detection is qualified; otherwise, the self-check is unqualified.

[0049] The formula for calculating the Euclidean distance between the features F1 and F2 at the i-th frequency point is:

[0050]

[0051] is the amplitude value of the i-th frequency point of feature F1, is the amplitude value of the i-th frequency point of feature F2; 、 are vectors composed of all frequency amplitudes of features F1 and F2 respectively;

[0052] Step 3: The microphone continues to collect the 4th to 5th second playback signal of the test audio and performs single frequency detection;

[0053] First, the spectral features of the signal collected by the microphone are extracted and divided into N frequency points. The amplitude value of each frequency point is analyzed using the Goertzel algorithm, and the average amplitude value of the N frequency points is calculated.

[0054] Then, for the single frequency point to be measured, calculate the ratio of the amplitude value of the single frequency point (such as 500Hz, 1kHz, etc., the default is 500Hz) to the average amplitude value to obtain the amplitude ratio R;

[0055] The amplitude ratio is calculated as follows:

[0056]

[0057] in is the amplitude value corresponding to the single frequency point to be measured, is the amplitude value corresponding to the i-th frequency point.

[0058] Finally, the amplitude ratio R is compared with the set threshold TH2. If R>TH2, the single frequency point detection is qualified. This means that the frequency point is clearly visible in the playback signal, verifying that there is no filtering or attenuation abnormality in the frequency transmission link. If the frequency point signal is significant enough, the test passes. Otherwise, the self-test fails.

[0059] At the same time, this detection method can detect the actual sampling rate of the voice loop, preventing the output audio from lacking frequency due to the lack of frequency response of some hardware or incorrect processing of the sampling rate and effects in the software loop.

[0060] Step 4: Calculate the root mean square (RMS) value of the first 3 seconds of playback signal collected by the microphone and convert it into loudness. Perform loudness anomaly detection to measure whether the playback volume is within a reasonable range.

[0061] The conversion formula is:

[0062]

[0063] Indicates the signal energy of the nth frequency point; calculates the signal energy value for each frequency point based on the pre-set FFT value; Indicates loudness in decibels.

[0064] Judging decibels If the loudness is lower than the set loudness threshold TH3, it indicates that the speaker output power is insufficient or there is gain loss in the signal path, and the loudness abnormality detection fails; otherwise, the loudness abnormality detection passes, indicating that the loudness of the device's sound path is normal.

[0065] Step 5: The microphone continues to collect the playback signal of the 6th to 7th second of the test audio for sampling and analysis, calculates its root mean square (RMS) value and converts it into loudness L2, and determines whether the loudness value L2 is lower than the set loudness threshold TH4. If so, it indicates that there is an abnormality in the MIC device and the self-test fails; otherwise, the self-test passes.

[0066] 3-4s and 5-6s are two blank intervals left to avoid confusion in audio detection.

[0067] The system controls the playback of a completely silent signal. Theoretically, ambient noise is inherent in the factory or office environment being tested. A microphone with the required sensitivity should capture a certain amount of ambient noise energy. However, for devices with abnormal sound reception, the microphone may capture empty data or hardware-induced circuit noise. The loudness of this noise is easily distinguishable from the ambient noise. Therefore, we sample and analyze the device's silent output. If the calculated loudness value for this signal is less than the set threshold, we suspect a device malfunction, preventing the normal capture of ambient noise.

[0068] Step 6: Through frequency similarity detection, single frequency detection, loudness anomaly detection and device anomaly detection, the results of the four rounds of detection are reported in binary form. It supports analyzing the cause of device sound anomaly based on the four reported results.

[0069] The specific analysis of the multi-round detection combination judgment mechanism is as follows: if the frequency similarity and single-frequency detection fail, but the loudness anomaly detection and device anomaly detection succeed, it indicates that the hardware can receive and play sound but the polarity may be reversed; if the loudness anomaly detection and MIC device anomaly detection fail, it indicates that the microphone may not be connected or is damaged. Other combination situations can also be further classified and processed through the internal state machine.

[0070] The present invention supports customization of the detection frequency point list to adapt to the frequency response characteristics of different devices; supports remote parameter configuration and detection result reporting, which is convenient for testing and subsequent maintenance.

[0071] Example:

[0072] like Figure 2 As shown, after the self-check process of this embodiment starts, the legality of the input parameters is verified.

[0073] This step primarily involves checking the validity of the audio capture data set, the sampling rate, the number of samples, and the correctness of the system's built-in parameter structure. Two audio data arrays, representing the original playback audio data and the microphone capture return data, originate from the playback channel and capture channel, respectively. Verification criteria include, but are not limited to, ensuring the data is non-null, whether the length meets the minimum analysis window, whether the data segmentation is abnormal, and whether the sampling rate meets the requirements for audio signal analysis. If any parameter does not meet the specifications, the subsequent analysis process is terminated and an error code is returned. This ensures that the system does not further process invalid or malformed data, thus avoiding computational errors and wasted resources.

[0074] Sampling rate is an important prerequisite for audio data analysis, and frequency domain characteristics will vary significantly at different sampling rates.

[0075] This embodiment supports sampling rates of 8000 Hz, 16000 Hz, 32000 Hz, 44100 Hz, and 48000 Hz. These support typical application scenarios such as voice communication, multimedia playback, and high-definition video, and meet the frequency resolution and bandwidth requirements of the self-check algorithm. If the input sampling rate is not in the allowed list, it indicates that the audio signal may have an illegal sampling rate. The system will output an illegal prompt and terminate the detection process to avoid misjudgment.

[0076] Frequency similarity detection is an important means to determine the consistency between the device audio playback path and the acquisition path, and is also the core detection and processing step of the present invention.

[0077] First, the system preprocesses the original playback data and the retrieved data separately, including removing DC offset and weighting the window function, to reduce the impact of boundary effects on frequency domain calculations.

[0078] Subsequently, the Goertzel algorithm is used to convert the two signals from the time domain to the frequency domain, and the spectrum amplitude is normalized so that their energy is under the same reference standard.

[0079] The system uses a vector similarity metric, namely, calculating the Euclidean distance, to calculate the difference between two normalized spectra and quantify the degree of difference in their spectral structures. The smaller the distance value, the more similar the spectral distribution of the two signals is. The system sets a similarity threshold, which can be flexibly configured based on product type, environmental background, and tolerance level. If the distance value is below the threshold, the audio signal is judged to be consistent in the frequency domain, and the playback and acquisition links are considered to have good frequency response consistency. If the similarity is insufficient, a prompt message will be output, indicating that distortion, frequency distortion, frequency shift, or poor device wiring may exist.

[0080] Single-frequency detection is used to verify whether the system can correctly identify the existence of a specific frequency point in the collected signal to determine the response capability of the microphone or speaker at that frequency point.

[0081] This step first selects multiple target frequencies from the acquired signal. These frequencies are typically key frequencies in the FFT frequency partition, such as 500 Hz, which are representative of audio testing. The system then applies the Goertzel algorithm to extract the energy values ​​corresponding to the target frequencies and calculates the average energy of all the frequencies, which serves as a reference for the background spectrum energy.

[0082] The system then analyzes the target frequency again and calculates the ratio of its energy to the background energy. If this ratio exceeds the set threshold, the frequency is significantly identified in the acquired signal, and the detection result is successful. If the target frequency energy is close to the background or not significantly prominent, it indicates that the playback / capture device may be experiencing distortion, loss, or insensitive response in this frequency band, and the system will issue an abnormality warning.

[0083] Loudness detection aims to evaluate whether the overall intensity of the currently captured audio reaches a normal and recognizable level.

[0084] The system uses root mean square (RMS) energy combined with a decibel calculation formula to convert the collected audio signal into a loudness indicator expressed in dB. This value can be considered an objective approximation of the human ear's subjective perception of loudness. During testing, if the loudness value exceeds the set lower threshold, the signal strength is considered within the acceptable range and the loudness test passes. If it falls below the threshold, it indicates that the microphone may not be collecting valid sound signals or there is severe loss in the audio path, and the system will output a loudness abnormality alert.

[0085] Device anomaly detection is used to identify whether there are abnormal background noise or physical device damage inside the device.

[0086] After playback ends, the system mutes the device's audio output and continuously collects data from several seconds of silence. The system calculates the RMS loudness of this data to determine whether there is still audio energy above the normal background noise level. If the loudness of the silent segment is still above the threshold, it may indicate a hardware issue within the device, such as excessive noise floor, circuit interference, an active microphone, or speaker leakage. In such cases, the system will output a device abnormality diagnosis result and recommend further repair or component replacement.

[0087] To facilitate system integration, the present invention provides a standard interface for parameter configuration and test result transmission.

[0088] The parameter interface includes sampling rate setting, detection threshold adjustment, frequency configuration, and detection duration setting, and can be issued through the embedded operating system, command line tools, or web configuration interface. The detection result interface includes the status of each detection item, loudness value, spectrum similarity value, single-frequency energy ratio, and silent noise value, and can be uploaded to the upper management system (such as the MMA platform or VDSP commissioning platform) via serial port, network, or memory sharing.

[0089] In specific applications, the system structure of embedded devices can be divided into playback module, acquisition module, self-test module and communication module.

[0090] The playback module is responsible for playing standard test audio according to instructions; the acquisition module controls the microphone recording and caches data; the self-test module completes various analyses and judgments according to the process of the present invention; and the communication module is used to exchange parameters and results with the host computer.

[0091] The overall self-test process is as follows: 1. The user sends a self-test start command through the web interface or a commissioning command; 2. The device plays a preset test audio (containing multiple frequency components); 3. The microphone return audio is simultaneously collected and cached; 4. The system performs frequency similarity detection, single-frequency identification, loudness analysis, and silent segment anomaly judgment on the collected data; 5. Based on multiple indicators, the system outputs a self-test report and uploads it to the management platform through the interface.

[0092] To improve the accuracy of detection, the present invention has designed a judgment logic based on cross-validation of multiple test results. Typical rules include but are not limited to: 1. If the frequency similarity test fails and the single-frequency test fails, but the loudness test and device test are both successful, it is speculated that the audio device may be wired incorrectly or installed in the wrong direction; 2. If the loudness test fails and the device test fails, it may be that the microphone is faulty or the speaker is not responding; 3. If the frequency similarity is normal but the device is abnormal, it may be that the audio circuit is leaking; 4. Other combinations can be further classified and the possibility of fault can be determined based on historical samples and models.

[0093] Through the above-mentioned multi-step, full-process self-test mechanism, the present invention realizes rapid, accurate and automated testing of the audio system of embedded devices, effectively improving the efficiency of product factory testing and on-site troubleshooting capabilities.

Claims

1. An automatic audio device self-test method based on an embedded telephone device, characterized in that: The specific steps are as follows: Step 1: Input the set IP address, trigger the playback instruction through the web page, play the test audio, and perform real-time sampling and analysis; Step 2: The microphone collects the playback signal of the first 3S of the test audio and performs frequency similarity detection; First, extract the spectral feature F1 of the signal collected by the microphone; perform standard parsing on the test audio to obtain the spectral feature F2; Then, divide the spectral features F1 and F2 into N frequency points respectively, use the Goertzel algorithm to calculate the relative amplitude value of each frequency point, calculate the difference between the two amplitude values at the same frequency point, and take the Euclidean distance of the amplitude differences at all frequency points as the spectral similarity S. When S is compared with the set threshold TH1, if S < TH1, the frequency similarity detection is qualified; Otherwise, the self-check is unqualified; Step 3: The microphone continues to collect the playback signal of the test audio from 4s to 5s and performs single-frequency point detection; First, extract the spectral feature of the signal collected by the microphone and divide it into N frequency points, use the Goertzel algorithm to analyze the amplitude value of each frequency point, and calculate the average amplitude value of the N frequency points; Then, for the single frequency point to be measured, calculate the ratio of the amplitude value of this single frequency point to the average amplitude value to obtain the amplitude ratio R; Finally, judge the amplitude ratio R and the set threshold TH2: If R > TH2, the single-frequency point detection is qualified, and it is confirmed that this frequency point is clearly visible in the playback signal, verifying that there is no abnormal filtering or attenuation in the frequency transmission link; otherwise, the self-check is unqualified; Step 4: For the first 3S playback signal collected by the microphone, calculate its root mean square (RMS) value and convert it into loudness, and perform loudness abnormality detection; The conversion formula is: Indicates the signal energy of the nth frequency point; decibels to indicate loudness; Judging decibels If the loudness is lower than the set loudness threshold TH3, it indicates that the speaker output power is insufficient or there is gain loss in the signal path, and the loudness abnormality detection fails; otherwise, the loudness abnormality detection passes; Step 5: The microphone continues to collect the playback signal of the test audio from 6s to 7s for sampling analysis, calculate its root mean square (RMS) value and convert it into loudness, and perform MIC device abnormality detection; Step 6: Through frequency similarity detection, single-frequency detection, loudness abnormality detection and device abnormality detection, report the results of the four rounds of detection in binary form, and analyze the reasons for abnormal device sound.

2. The method according to claim 1, wherein In the second step, the Euclidean distance calculation formula between the features F1 and F2 at the i-th frequency point is: is the amplitude value of the i-th frequency point of feature F1, is the amplitude value of the i-th frequency point of feature F2; 、 are vectors consisting of all frequency amplitudes of features F1 and F2 respectively.

3. The method according to claim 1, wherein In the third step, the amplitude ratio is calculated as follows: in is the amplitude value corresponding to the single frequency point to be measured, is the amplitude value corresponding to the i-th frequency point.

4. The method according to claim 1, wherein In the fifth step, the playback signal from 6s to 7s is an audio signal containing ambient noise in a silent state, calculate its loudness and make a judgment; judge whether the loudness value is lower than the set loudness threshold TH4, if so, it indicates that there is an abnormality in the MIC device, and the self-check is unqualified; otherwise, the self-check is qualified.

5. The method according to claim 1, wherein The specific analysis of the sixth step is: If the frequency similarity and single-frequency detection fail, but the loudness abnormality detection and device abnormality detection are successful, it indicates that the hardware can play and record sound but may have the wrong polarity connection; if the loudness abnormality detection and MIC device abnormality detection fail, it indicates that the microphone may not be connected or is damaged; other combination situations are further classified and processed through the internal state machine.

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