Pre-scanning test method and device

Abstract

The invention is suitable for the technical field of audio data processing, and provides a pre-scanning test method and device, and the method comprises the steps: obtaining standard audio data and to-be-recognized audio data outputted by a to-be-tested device under various electromagnetic interferences; extracting a preset identification audio data fragment in the to-be-identified audio data; performing alignment processing on the standard audio data and the to-be-identified audio data based on the preset identification audio data fragment; extracting an abnormal sampling point between the first audio data and the second audio data; and if the proportion of the abnormal sampling point in the total sampling point exceeds a preset proportion, determining that the test result of the electromagnetic interference test item corresponding to the to-be-identified audio is abnormal. According to the scheme, audio data comparison and analysis can be automatically carried out, and a traditional low-efficiency mode of manual recording and one-by-one audio signal analysis is avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of audio data processing, and in particular relates to a pre-scan test method and device. Background Art

[0002] With the widespread use of modern electronic devices, audio equipment plays an increasingly important role in our daily lives and work. Whether it's a mobile phone, computer, headphones, or sound system, these devices need to process and output high-quality audio signals. However, these devices are often affected by electromagnetic interference (EMI) during operation, resulting in a decrease in audio quality and, in turn, affecting the user experience. Therefore, electromagnetic compatibility (EMC) testing of audio equipment is particularly important.

[0003] The primary purpose of EMC testing is to ensure that audio equipment continues to function properly and produce high-quality audio signals despite various electromagnetic interference sources. This interference can come from the device's internal circuits, other electronic devices in the external environment, radio waves, and even power lines. To evaluate the performance of audio equipment in various electromagnetic environments, rigorous EMC testing is often required.

[0004] Traditional EMC testing methods typically involve subjecting a device to electromagnetic interference of varying types and intensities within a shielded room, followed by manual or automated recording and analysis of the device's output audio signals. While these methods can assess a device's anti-interference capabilities to a certain extent, they suffer from low efficiency, time consumption, and limited accuracy. Summary of the Invention

[0005] In view of this, an embodiment of the present invention provides a pre-scan test method and apparatus to solve the technical problems of low efficiency, long time consumption and low precision in recording and analyzing audio signals output by a device in a manual or automated manner.

[0006] A first aspect of an embodiment of the present invention provides a pre-scan test method, the pre-scan test method comprising:

[0007] Obtaining standard audio data and audio data to be identified output by the device under test under various electromagnetic interference conditions; wherein the standard audio data refers to audio data output by the device under test in a non-interference environment, and the standard audio data includes multiple preset identification audio data segments;

[0008] Extracting a preset identified audio data segment from the audio data to be identified;

[0009] Based on the preset identified audio data segment, the standard audio data and the audio data to be identified are aligned to obtain first audio data corresponding to the standard audio data and second audio data corresponding to the audio data to be identified;

[0010] extracting abnormal sampling points between the first audio data and the second audio data;

[0011] If the proportion of the abnormal sampling points in the total sampling points exceeds a preset proportion, it is confirmed that the test result of the electromagnetic interference test item corresponding to the audio to be identified is abnormal.

[0012] Furthermore, the step of extracting a preset identified audio data segment from the audio data to be identified includes:

[0013] Performing multi-scale segmentation processing on the audio data to be recognized to obtain multiple sub-audio segments;

[0014] Obtaining a first audio feature corresponding to the pre-stored preset identified audio data segment;

[0015] extracting a second audio feature corresponding to the sub-audio segment;

[0016] calculating similarities between the plurality of first audio features and the plurality of second audio features;

[0017] extracting a target sub-audio segment having a similarity greater than a first threshold;

[0018] The target sub-audio segment is used as a preset identified audio data segment in the audio data to be recognized.

[0019] Furthermore, the step of extracting the second audio feature corresponding to the sub-audio segment includes:

[0020] Performing frame processing on the sub-audio segment to obtain multiple audio frames;

[0021] Acquire multiple preset scales, perform wavelet transform on the audio frame based on the multiple preset scales, and obtain wavelet transform results corresponding to each of the multiple preset scales;

[0022] Calculating the spectrum energy corresponding to each of the wavelet transform results;

[0023] Normalizing multiple spectrum energies corresponding to each preset scale to obtain target spectrum energy;

[0024] The second audio feature is calculated based on the target spectral energy.

[0025] Furthermore, the step of calculating the second audio feature based on the target spectrum energy includes:

[0026] Acquire multiple preset audio feature ranges; wherein the preset audio feature ranges refer to numerical ranges set based on wavelet transform results corresponding to different notes, and different preset audio feature ranges map to different notes;

[0027] Mapping the plurality of wavelet transform results to a preset audio feature range based on the wavelet transform results corresponding to the plurality of preset scales;

[0028] Adding the target spectrum energies corresponding to the wavelet transform results mapped at the same preset scale within the preset audio feature range to obtain a feature value;

[0029] The characteristic values ​​corresponding to the plurality of preset scales in different preset audio feature ranges are used as the second audio feature.

[0030] Furthermore, after the step of extracting the preset identified audio data segment from the audio data to be identified, the method further includes:

[0031] If the preset identified audio data segment does not exist in the audio data to be identified, it is confirmed that the test result of the electromagnetic interference test item corresponding to the audio to be identified is abnormal.

[0032] Furthermore, the step of aligning the standard audio data and the audio data to be identified based on the preset identified audio data segment to obtain first audio data corresponding to the standard audio data and second audio data corresponding to the audio data to be identified includes:

[0033] Aligning the standard audio data and the audio data to be identified on the preset identified audio data segment to obtain third audio data corresponding to the standard audio data and fourth audio data corresponding to the audio data to be identified;

[0034] Obtain a first starting point and a first ending point of the third audio data, and obtain a second starting point and a second ending point of the fourth audio data;

[0035] If the first starting point is located to the left of the second starting point, the second starting point is used as the starting point of the third audio data;

[0036] If the first starting point is located to the right of the second starting point, the first starting point is used as the starting point of the fourth audio data;

[0037] If the first end point is located to the left of the second end point, the first end point is used as the starting point of the fourth audio data;

[0038] If the first end point is located to the right of the second end point, the second end point is used as the starting point of the third audio data;

[0039] The updated third audio data is used as the first audio data, and the updated fourth audio data is used as the second audio data.

[0040] Furthermore, the step of extracting abnormal sampling points between the first audio data and the second audio data includes:

[0041] extracting a plurality of first sampling points from the first audio data based on a preset sampling frequency, and extracting a plurality of second sampling points from the second audio data based on a preset sampling frequency;

[0042] Calculating the amplitude difference between a first sampling point and a second sampling point at the same position;

[0043] The sampling points whose amplitude differences are greater than the second threshold are regarded as the abnormal sampling points.

[0044] A second aspect of an embodiment of the present invention provides a pre-scan test apparatus, comprising:

[0045] an acquisition unit, configured to acquire standard audio data and audio data to be identified, respectively output by the device under test under various electromagnetic interference conditions; wherein the standard audio data refers to audio data output by the device under test in a non-interference environment, and the standard audio data includes a plurality of preset identified audio data segments;

[0046] A first extraction unit is used to extract a preset identified audio data segment from the audio data to be identified;

[0047] an alignment unit, configured to align the standard audio data and the audio data to be identified based on the preset identified audio data segment, to obtain first audio data corresponding to the standard audio data and second audio data corresponding to the audio data to be identified;

[0048] a second extraction unit, configured to extract abnormal sampling points between the first audio data and the second audio data;

[0049] A confirmation unit is configured to confirm that a test result of the electromagnetic interference test item corresponding to the audio to be identified is abnormal if a proportion of the abnormal sampling points in the total sampling points exceeds a preset proportion.

[0050] The third aspect of an embodiment of the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the pre-scan test method described in the first aspect when executing the computer program.

[0051] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the steps in the pre-scan test method described in the first aspect are implemented.

[0052] Compared to the prior art, the present invention offers the following advantages: by extracting pre-identified audio data segments from the audio data to be identified and aligning them with standard audio data, audio data comparison and analysis can be automated, avoiding the inefficient traditional method of manually recording and analyzing audio signals one by one. This method not only saves a significant amount of time but also significantly improves the level of test automation. By comparing and analyzing standard audio data with the audio data to be identified, abnormal sampling points are extracted, and the test results are judged based on the percentage of abnormal sampling points. This method effectively identifies and locates the impact of electromagnetic interference on the device's output audio signal, ensuring the accuracy of the test results. The comparison and alignment process minimizes errors or deviations. Through precise analysis of abnormal sampling points, this method enables in-depth identification and processing of every detail of the audio signal. If the percentage of abnormal sampling points in the audio data to be identified exceeds a preset value, the test result is quickly confirmed to be abnormal. This efficient and reliable judgment standard ensures high confidence in device performance testing under electromagnetic interference conditions. This method is not only applicable to EMC testing of different types of audio devices, but also effectively performs testing under different electromagnetic interference conditions, ensuring the stability and reliability of the device's audio output in various environments. At the same time, the method has strong adaptability to device compatibility and can be widely used in the fields of quality control, research and development, and testing of various audio devices. In summary, the EMC testing method of the present invention has significant advantages in improving the efficiency, accuracy, and reliability of audio device testing, providing a new, efficient, and accurate technical means for evaluating the performance of audio devices in electromagnetic interference environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 A schematic flow chart of a pre-scan test method provided by the present invention is shown;

[0055] Figure 2 A schematic diagram of a pre-scan test device provided by an embodiment of the present invention is shown;

[0056] Figure 3 A schematic diagram of a terminal device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0057] In the following description, specific details such as particular system structures and techniques are provided for purposes of illustration, not limitation, to facilitate a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0058] The embodiments of the present invention provide a pre-scanning test method and apparatus to solve the technical problem that existing visual monitoring methods are difficult to accurately and quickly detect potential abnormal situations in complex and dynamically changing environments.

[0059] First, the present invention provides a method for pre-scanning test. Figure 1 , Figure 1 FIG. 1 shows a schematic flow chart of a pre-scan test method provided by the present invention. Figure 1 As shown, the pre-scan test method may include the following steps:

[0060] Step 101: Acquire standard audio data and audio data to be identified output by the device under test under various electromagnetic interference conditions; wherein the standard audio data refers to audio data output by the device under test in a non-interference environment, and the standard audio data includes a plurality of preset identified audio data segments;

[0061] First, it's necessary to obtain "standard audio data," which is the audio data produced by the device under test without any electromagnetic interference (EMI). This standard audio data serves as a reference and contains the expected output of the device under normal operating conditions. The standard audio data is pre-designed and contains multiple "marker audio data segments," specifically marked portions of the audio signal for comparison and analysis during subsequent processing.

[0062] Secondly, the "audio data to be identified" is obtained, that is, the audio data output by the device under the influence of various electromagnetic interferences. This data represents the performance of the device in the electromagnetic interference environment.

[0063] Step 102: extracting a preset identified audio data segment from the audio data to be identified;

[0064] The device's output audio data is extracted to identify the audio data segments with the same identifiers as the standard audio data. These segments are pre-defined signature signals that can be used for subsequent alignment processing to determine whether the device is performing normally in an interference environment.

[0065] Specifically, step 102 includes steps 1021 to 1026:

[0066] Step 1021: performing multi-scale segmentation processing on the audio data to be recognized to obtain multiple sub-audio segments;

[0067] The audio data to be identified is segmented into different time window lengths (multi-scale) to generate multiple sub-audio segments. Multi-scale segmentation can improve coverage, thereby finding potential matching preset identification segments within different length ranges.

[0068] Step 1022: Obtain a first audio feature corresponding to the pre-stored audio data segment with the preset identifier;

[0069] The features of the identified segments in the standard audio data are extracted, which are called "first audio features". These features serve as "templates" for subsequent matching to compare whether there are similar segments in the target audio.

[0070] Step 1023: extracting a second audio feature corresponding to the sub-audio segment;

[0071] The audio features of the multiple sub-audio segments obtained by the previous segmentation are also extracted one by one, which are called "second audio features" to prepare for similarity calculation.

[0072] Specifically, step 1023 includes steps A1 to A5:

[0073] Step A1: dividing the sub-audio segment into frames to obtain multiple audio frames;

[0074] An audio signal is a continuous time series that needs to be broken down into segments, each called an "audio frame." This step converts the audio signal into a form more suitable for processing. Framing uses a fixed-length frame window, ranging from 20ms to 40ms. Frames overlap (e.g., 50%) to preserve temporal continuity.

[0075] Step A2: obtaining multiple preset scales, performing wavelet transform on the audio frame based on the multiple preset scales, and obtaining wavelet transform results corresponding to each of the multiple preset scales;

[0076] The wavelet transform is a time-frequency analysis method that decomposes signals at different scales (frequencies). The "preset scales" here refer to the multiple scales (or frequency bands) selected during the wavelet transform, each representing a different frequency range. The wavelet transform can be used to analyze audio frames at different scales to determine their time-frequency characteristics. The wavelet transform results at each scale reflect the characteristics of the audio signal in different frequency ranges.

[0077] The wavelet transform is implemented using a continuous wavelet transform function. This application improves upon the traditional continuous wavelet transform function by enhancing two aspects of the standard wavelet transform: frequency weighting and time weighting. This improvement allows for more flexible analysis of the different frequency and time components of an audio signal, adapting to the unique characteristics of audio data, particularly the localized frequency and time variations commonly found in audio signals. The improved continuous wavelet transform function is as follows:

[0078]

[0079] Among them, W mod (a, b, f) represents the wavelet transform result, γ(f) represents the frequency weighting function, x(t) represents the audio frame, ψ * (t) represents the mother wavelet function, Indicates that the parameter is The mother wavelet function, θ(t) represents the time weighting function, Indicates that the parameter is The time weighting function of , a represents the scale parameter, b represents the translation parameter, f represents the frequency parameter, f0 represents the center frequency, and α represents the adjustment parameter.

[0080] It is worth noting that the improved continuous wavelet transform introduces two new elements: the frequency weighting function γ(f) and the time weighting function θ(t).

[0081] Frequency weighting function γ(f): Audio signals typically contain multiple frequency components. Signals of different frequencies may require different processing in certain analyses, such as detailed analysis of high frequencies or smoothing of low frequencies. By introducing the frequency weighting function γ(f), certain frequency components in the audio signal can be given greater weight while suppressing less important frequencies. By adjusting the frequency response using γ(f), the wavelet transform can be more focused or dispersed in the frequency domain, enhancing the resolution of specific frequency bands.

[0082] Audio signals often have varying importance in different time periods. For example, at certain moments, the signal may fluctuate significantly, while at others, it may be relatively stable. By introducing a time-weighted function, θ(t), we can enhance signal analysis within certain time periods. For example, using a time window function to emphasize specific portions of the signal. By weighting the analysis of different time periods, the wavelet transform can better reflect the local characteristics of the signal in the time domain.

[0083] Different weights can be assigned to different frequency components through γ(f), which can selectively enhance or suppress certain parts of the signal in the frequency space.

[0084] Different weights can be assigned to different time periods through θ(t). This allows the wavelet transform to focus not only on specific frequency bands, but also on the characteristics of the signal in certain time periods. Similar to the standard wavelet transform, scale a and position b are still used for localized analysis, but the introduction of the weighting function makes the transform more flexible in different frequencies and times. This improved wavelet transform is particularly suitable for audio signal analysis because audio signals are often non-stationary, that is, they have different characteristics in different time periods and frequencies.

[0085] Step A3: Calculating the spectrum energy corresponding to each wavelet transform result;

[0086] Wavelet transform results are essentially representations in the time-frequency domain. Calculating "spectral energy" involves analyzing the energy of each frequency component within each wavelet transform result. By squared and summing the transform results, we obtain the energy at that scale. Spectral energy reflects the energy distribution of an audio signal across various frequency bands. Frequency bands with higher energy often correlate with characteristics or important information in the audio signal. Therefore, spectral energy is a key characteristic in audio analysis.

[0087] Step A4: performing normalization processing on multiple spectrum energies corresponding to each preset scale to obtain target spectrum energy;

[0088] The normalized target spectrum energy removes the differences between scales, so that the energies at different scales can be directly compared and processed in the same dimension.

[0089] Step A5: Calculate the second audio feature based on the target spectrum energy.

[0090] A second audio feature is extracted based on the normalized spectrum energy.

[0091] In the embodiments corresponding to steps A1 to A5, a series of steps, including framing, wavelet transform, spectral energy calculation, and normalization, are used to precisely extract the spectral features of the audio signal at different scales, thereby forming a second audio feature. This method not only effectively captures information about the audio signal in the time and frequency domains, but also enhances the algorithm's robustness to different scales and frequency bands. This multi-scale wavelet analysis enables the extraction of richer and more accurate features across different frequency bands of the audio signal, improving the accuracy of audio recognition, matching, and analysis.

[0092] Specifically, step A5 includes steps A51 to A54:

[0093] Step A51: Acquire multiple preset audio feature ranges; wherein the preset audio feature ranges are value ranges set based on wavelet transform results corresponding to different notes, and different preset audio feature ranges map to different notes;

[0094] A set of predefined "preset audio feature ranges" is defined, corresponding to different notes or pitches. Notes are important features in audio and are typically frequency-related. Each note corresponds to a specific frequency range, and the relationship between the size of this range and the frequency correspondence is defined by the results of a wavelet transform. By setting predefined feature ranges based on the note frequency range, the frequency components of the audio signal can be effectively mapped to specific notes. This facilitates note recognition and extraction.

[0095] Step A52: mapping the plurality of wavelet transform results to a preset audio feature range based on the wavelet transform results corresponding to the plurality of preset scales;

[0096] In the previous steps, wavelet transforms were used to obtain audio signal information at different scales. Next, these wavelet transform results are mapped to corresponding note ranges. This involves comparing and matching the wavelet transform results at different scales with the corresponding notes within a pre-set audio feature range. Each wavelet transform result is compared to the frequency range of the corresponding note based on its frequency range, and then mapped to the frequency range of a specific note. This step ensures that the frequency components of the audio signal can be associated with specific notes and pitches.

[0097] Step A53: Adding target spectrum energies corresponding to the wavelet transform results mapped at the same preset scale within the preset audio feature range to obtain a feature value;

[0098] The spectral energy of the wavelet transform results within the same note range, at the same preset scale, is summed to produce a single "eigenvalue." By summing the spectral energy within the same note range, we can integrate the energy information of the frequency components corresponding to that note. This helps capture the overall energy characteristics of the note, thereby extracting its presence and intensity within the audio signal.

[0099] Step A54: using characteristic values ​​corresponding to the plurality of preset scales in different preset audio characteristic ranges as the second audio feature.

[0100] The feature values ​​obtained under different preset scales and in different note ranges are integrated to form the final "second audio feature".

[0101] In the embodiment corresponding to steps A51 to A54, by combining the wavelet transform results with the frequency range of the notes, the energy distribution of the audio signal across different notes can be meticulously analyzed. The spectral energy of each note is mapped, summed, and summarized, ultimately yielding a second audio feature that integrates multi-scale, note-specific, and spectral energy information. This approach efficiently captures the note characteristics of the audio signal while preserving the signal's time-frequency characteristics at different scales, enhancing the analysis accuracy and recognition effectiveness of the audio signal.

[0102] Step 1024: Calculate similarities between the plurality of first audio features and the plurality of second audio features;

[0103] The similarity between the features of the standard segment (first feature) and the features of all sub-segments (second feature) is calculated pairwise. Similarity can be calculated using methods such as cosine similarity, DTW (Dynamic Time Warping), Euclidean distance, and correlation.

[0104] Step 1025: extracting target sub-audio segments with similarity greater than a first threshold;

[0105] Filter out the fragments whose similarity with the standard features exceeds a certain threshold, namely the "target sub-fragments".

[0106] Step 1026: Use the target sub-audio segment as a preset identified audio data segment in the audio data to be identified.

[0107] Ultimately, the highly similar sub-segments identified are identified as "marker segments" in the audio to be identified. These extracted segments serve as reference points in subsequent alignment and outlier detection, comparing the similarities and differences between the standard audio and the interference audio.

[0108] In the embodiments corresponding to steps 1021 through 1026, the use of audio feature matching combined with multi-scale segmentation effectively improves the accuracy, robustness, and automation of identifying pre-identified audio segments in complex interference environments. Compared to traditional methods that rely solely on time or fixed-length comparisons, this approach is more resilient to issues such as noise, temporal drift, and inconsistent segment lengths.

[0109] As an optional embodiment of the present application, after step 102, the method further includes: if there is no preset identified audio data segment in the audio data to be identified, confirming that the test result of the electromagnetic interference test item corresponding to the audio to be identified is determined to be abnormal.

[0110] Step 103: Based on the preset identified audio data segment, align the standard audio data and the audio data to be identified to obtain first audio data corresponding to the standard audio data and second audio data corresponding to the audio data to be identified;

[0111] Align the extracted identified audio data segments, aligning the standard audio data with the corresponding segments in the audio data to be identified for further comparative analysis. The aligned data is then converted into "first audio data" and "second audio data," representing the device's output in a standard environment and interference environment, respectively.

[0112] Specifically, step 103 includes steps 1031 to 1037:

[0113] Step 1031: aligning the standard audio data and the audio data to be recognized on the preset identified audio data segment to obtain third audio data corresponding to the standard audio data and fourth audio data corresponding to the audio data to be recognized;

[0114] The purpose of this step is to align the reference audio data and the audio data to be recognized based on the preset identifying audio data segments. This alignment process aligns the corresponding segments of the reference audio and the audio data to be recognized, making them more consistent in time. This facilitates comparing their similarities and differences or extracting more useful features.

[0115] Step 1032: Obtain a first starting point and a first ending point of the third audio data, and obtain a second starting point and a second ending point of the fourth audio data;

[0116] This step is to determine the actual starting and ending positions of the standard audio data and the audio data to be recognized on the timeline. Next, based on this information, their time intervals will be further compared and adjusted.

[0117] Step 1033: If the first starting point is located to the left of the second starting point, use the second starting point as the starting point of the third audio data;

[0118] Step 1034: If the first starting point is to the right of the second starting point, use the first starting point as the starting point of the fourth audio data;

[0119] By comparing the starting points of the standard audio and the audio to be recognized, it is determined which audio segment has a starting point earlier, thereby deciding how to align them. If the starting point (first starting point) of the standard audio data is before the starting point (second starting point) of the audio data to be recognized, the starting point of the audio to be recognized is used as the starting point of the standard audio to ensure that they are aligned in time. If the starting point of the standard audio data is after the starting point of the audio data to be recognized, then conversely, the starting point of the standard audio is used as the starting point of the audio to be recognized. The purpose of adjusting the starting point is to ensure that the two audio segments are aligned from the same time point so that subsequent audio analysis or feature matching can be performed more accurately.

[0120] Step 1035: If the first end point is located to the left of the second end point, use the first end point as the start point of the fourth audio data;

[0121] Step 1036: If the first end point is to the right of the second end point, use the second end point as the start point of the third audio data;

[0122] This step involves adjusting the end point of the audio clip. By comparing the end points of the standard audio and the audio to be recognized, decide how to adjust the end time point of the audio. If the end point of the standard audio (first end point) is before the end point of the audio to be recognized (second end point), the end point of the standard audio needs to be used as the end point of the audio to be recognized to ensure that they are aligned at the end. If the end point of the standard audio is after the end point of the audio to be recognized, the end point of the audio to be recognized is used as the end point of the standard audio to ensure that their end points are aligned. By adjusting the end points, ensure that the duration of the two audio clips is consistent so that their positions on the timeline match exactly.

[0123] Step 1037: Use the updated third audio data as the first audio data, and use the updated fourth audio data as the second audio data.

[0124] Based on the adjusted start and end points, the standard audio data and the audio data to be recognized are updated to the new versions. The updated third audio data is used as the standard audio data (first audio data), and the updated fourth audio data is used as the audio data to be recognized (second audio data). In this way, the time alignment of the two audio clips is ensured, enabling effective comparison and matching.

[0125] In the embodiment corresponding to steps 1031 to 1037, the start and end points of the audio data segments are dynamically adjusted to precisely align the standard audio data and the audio data to be recognized on the time axis. This alignment ensures that the two audio segments are compared, analyzed, or feature extracted within the same time interval, thereby improving the accuracy of subsequent audio recognition or analysis.

[0126] Step 104: extracting abnormal sampling points between the first audio data and the second audio data;

[0127] A comparison is performed between the first audio data and the second audio data to identify "abnormal sampling points." These abnormal sampling points indicate that the output signal of the device in the interference environment has a significant deviation from the standard signal.

[0128] Specifically, step 104 includes steps 1041 to 1043:

[0129] Step 1041: extracting a plurality of first sampling points from the first audio data based on a preset sampling frequency, and extracting a plurality of second sampling points from the second audio data based on a preset sampling frequency;

[0130] The purpose of this step is to extract multiple sampling points from the standard audio data (first audio data) and the audio data to be identified (second audio data) according to a preset sampling frequency. The sampling frequency refers to the number of samples collected per unit time, usually expressed as the number of samples per second. Through the sampling frequency, sample points at specific time intervals can be obtained from the audio data. For the first audio data and the second audio data, based on the same preset sampling frequency, the corresponding multiple sampling points are extracted respectively. This step ensures that the extracted sampling point intervals are the same in the two audio data, which provides a basis for subsequent comparison of their similarities.

[0131] Step 1042: Calculate the amplitude difference between the first sampling point and the second sampling point at the same position;

[0132] Step 1043: The sampling points whose amplitude differences are greater than the second threshold are regarded as the abnormal sampling points.

[0133] The second threshold is a preset threshold used to determine whether the deviation between sampling points is large enough to be considered an anomaly. This threshold can be used to filter out sampling points with excessive deviations on the timeline, which are considered anomalies. Extracting these anomalous sampling points facilitates further analysis of the differences in the audio data and helps correct potential alignment issues or recognition errors.

[0134] In the embodiment corresponding to steps 1041 to 1043, by comparing the sampling points at the same time position of the standard audio data and the audio data to be identified, abnormal sampling points whose amplitude deviation exceeds a set threshold are identified. By extracting these abnormal sampling points, the differences between the two audio data can be effectively identified, and alignment errors or other abnormalities in the audio signals can be further analyzed, thereby supporting accurate audio alignment, matching, or identification.

[0135] Step 105: If the proportion of the abnormal sampling points in the total sampling points exceeds a preset proportion, it is determined that the test result of the electromagnetic interference test item corresponding to the audio to be identified is abnormal.

[0136] If the proportion of abnormal sampling points among all sampling points exceeds the preset threshold, the test item is considered "abnormal," meaning the device's performance under electromagnetic interference does not meet expectations. This indicates that the device's performance under electromagnetic interference has been affected and requires further investigation or improvement.

[0137] In the embodiment corresponding to steps 101 to 104, by extracting pre-identified audio data segments from the audio data to be identified and aligning them with the standard audio data, audio data comparison and analysis can be automated, avoiding the inefficient traditional method of manually recording and analyzing audio signals one by one. This method not only saves a considerable amount of time but also significantly improves the level of test automation. By comparing and analyzing the standard audio data with the audio data to be identified, abnormal sampling points are extracted, and the test results are judged based on the percentage of abnormal sampling points. This method effectively identifies and locates the impact of electromagnetic interference on the device's output audio signal, ensuring the accuracy of the test results. The comparison and alignment process minimizes errors or deviations. Through precise analysis of abnormal sampling points, this method enables in-depth identification and processing of every detail of the audio signal. If the percentage of abnormal sampling points in the audio data to be identified exceeds a preset value, the test result is quickly confirmed to be abnormal. This efficient and reliable judgment standard ensures high confidence in device performance testing under electromagnetic interference conditions. This method is not only applicable to EMC testing of different types of audio devices, but also effectively performs testing under different electromagnetic interference conditions, ensuring the stability and reliability of the device's audio output in various environments. At the same time, the method has strong adaptability to device compatibility and can be widely used in the fields of quality control, research and development, and testing of various audio devices. In summary, the EMC testing method of the present invention has significant advantages in improving the efficiency, accuracy, and reliability of audio device testing, providing a new, efficient, and accurate technical means for evaluating the performance of audio devices in electromagnetic interference environments.

[0138] like Figure 2 The present invention provides a pre-scan test device, see Figure 2 , Figure 2 A schematic diagram of a pre-scan test device provided by the present invention is shown in FIG. Figure 2 The apparatus for a pre-scan test includes:

[0139] An acquisition unit 21 is configured to acquire standard audio data and audio data to be identified, respectively output by the device under test under various electromagnetic interference conditions; wherein the standard audio data refers to audio data output by the device under test in a non-interference environment, and the standard audio data includes a plurality of preset identified audio data segments;

[0140] A first extraction unit 22 is configured to extract a preset identified audio data segment from the audio data to be identified;

[0141] an alignment unit 23, configured to align the standard audio data and the audio data to be identified based on the preset identified audio data segment, to obtain first audio data corresponding to the standard audio data and second audio data corresponding to the audio data to be identified;

[0142] A second extraction unit 24 is configured to extract abnormal sampling points between the first audio data and the second audio data;

[0143] The confirmation unit 25 is configured to confirm that the test result of the electromagnetic interference test item corresponding to the audio to be identified is abnormal if the proportion of the abnormal sampling points in the total sampling points exceeds a preset proportion.

[0144] The present invention provides a pre-scan test device that automatically compares and analyzes audio data by extracting pre-identified audio data segments from the audio data to be identified and aligning them with standard audio data. This method avoids the inefficient traditional method of manually recording and analyzing audio signals one by one. This method not only saves a significant amount of time but also significantly improves the automation level of testing. By comparing and analyzing the standard audio data with the audio data to be identified, abnormal sampling points are extracted. The test results are then judged based on the percentage of abnormal sampling points. This method effectively identifies and locates the impact of electromagnetic interference on the device's output audio signal, ensuring the accuracy of the test results. The comparison and alignment process minimizes errors or deviations. Through precise analysis of abnormal sampling points, this method enables in-depth identification and processing of every detail of the audio signal. If the percentage of abnormal sampling points in the audio to be identified exceeds a preset value, the test result is quickly confirmed to be abnormal. This efficient and reliable judgment criterion ensures high confidence in device performance testing under electromagnetic interference conditions. This method is not only applicable to EMC testing of different types of audio devices, but also effectively performs testing under different electromagnetic interference conditions, ensuring the stability and reliability of the device's audio output in various environments. At the same time, the method has strong adaptability to device compatibility and can be widely used in the fields of quality control, research and development, and testing of various audio devices. In summary, the EMC testing method of the present invention has significant advantages in improving the efficiency, accuracy, and reliability of audio device testing, providing a new, efficient, and accurate technical means for evaluating the performance of audio devices in electromagnetic interference environments.

[0145] Figure 3 FIG. 1 is a schematic diagram of a terminal device provided by an embodiment of the present invention. Figure 3 As shown, a terminal device 3 of this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30, such as a pre-scan test program. When the processor 30 executes the computer program 32, the steps of each of the above-mentioned pre-scan test method embodiments are implemented, such as Figure 1Alternatively, when the processor 30 executes the computer program 32, the functions of each unit in the above-mentioned device embodiments are realized, for example, Figure 2 Function of the unit shown.

[0146] Exemplarily, the computer program 32 may be divided into one or more units, which are stored in the memory 31 and executed by the processor 30 to implement the present invention. The one or more units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 32 in the terminal device 3. For example, the specific functions of each unit of the computer program 32 are as follows:

[0147] an acquisition unit, configured to acquire standard audio data and audio data to be identified, respectively output by the device under test under various electromagnetic interference conditions; wherein the standard audio data refers to audio data output by the device under test in a non-interference environment, and the standard audio data includes a plurality of preset identified audio data segments;

[0148] A first extraction unit is used to extract a preset identified audio data segment from the audio data to be identified;

[0149] an alignment unit, configured to align the standard audio data and the audio data to be identified based on the preset identified audio data segment, to obtain first audio data corresponding to the standard audio data and second audio data corresponding to the audio data to be identified;

[0150] a second extraction unit, configured to extract abnormal sampling points between the first audio data and the second audio data;

[0151] A confirmation unit is configured to confirm that a test result of the electromagnetic interference test item corresponding to the audio to be identified is abnormal if a proportion of the abnormal sampling points in the total sampling points exceeds a preset proportion.

[0152] The terminal device includes but is not limited to a processor 30 and a memory 31. Those skilled in the art will understand that Figure 3 It is only an example of a terminal device 3 and does not constitute a limitation on a terminal device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device may also include input and output devices, network access devices, buses, etc.

[0153] The processor 30 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0154] The memory 31 may be an internal storage unit of the terminal device 3, such as a hard disk or memory of the terminal device 3. The memory 31 may also be an external storage device of the terminal device 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device 3. Furthermore, the memory 31 may include both an internal storage unit of the terminal device 3 and an external storage device. The memory 31 is used to store the computer program and other programs and data required by the roaming control device. The memory 31 may also be used to temporarily store data that has been output or is to be output.

[0155] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0156] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0157] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0158] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0159] An embodiment of the present invention provides a computer program product. When the computer program product is run on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0160] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk.

[0161] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0162] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0163] In the embodiments provided by the present invention, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0164] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units.

[0165] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0166] It will also be understood that the term "and / or" used in the present description and appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0167] As used in the present specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to monitoring," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is monitored" may be interpreted as meaning "upon determination" or "in response to determining" or "upon monitoring [described condition or event]" or "in response to monitoring [described condition or event]," depending on the context.

[0168] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0169] References to "one embodiment" or "some embodiments" in the present specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present invention. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in yet other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0170] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A pre-scan test method, characterized in that: The pre-scan test method includes: Obtaining standard audio data and audio data to be identified output by the device under test under various electromagnetic interference conditions; wherein the standard audio data refers to audio data output by the device under test in a non-interference environment, and the standard audio data includes multiple preset identification audio data segments; Extracting a preset identified audio data segment from the audio data to be identified; Based on the preset identified audio data segment, the standard audio data and the audio data to be identified are aligned to obtain first audio data corresponding to the standard audio data and second audio data corresponding to the audio data to be identified; extracting abnormal sampling points between the first audio data and the second audio data; If the proportion of the abnormal sampling points in the total sampling points exceeds a preset proportion, it is confirmed that the test result of the electromagnetic interference test item corresponding to the audio to be identified is abnormal.

2. The pre-scan test method according to claim 1, wherein: The step of extracting a preset identified audio data segment from the audio data to be identified comprises: Performing multi-scale segmentation processing on the audio data to be recognized to obtain multiple sub-audio segments; Obtaining a first audio feature corresponding to the pre-stored preset identified audio data segment; extracting a second audio feature corresponding to the sub-audio segment; calculating similarities between the plurality of first audio features and the plurality of second audio features; extracting a target sub-audio segment having a similarity greater than a first threshold; The target sub-audio segment is used as a preset identified audio data segment in the audio data to be recognized.

3. The pre-scan test method according to claim 2, wherein: The step of extracting the second audio feature corresponding to the sub-audio segment includes: Performing frame processing on the sub-audio segment to obtain multiple audio frames; Acquire multiple preset scales, perform wavelet transform on the audio frame based on the multiple preset scales, and obtain wavelet transform results corresponding to each of the multiple preset scales; Calculating the spectrum energy corresponding to each of the wavelet transform results; Normalizing multiple spectrum energies corresponding to each preset scale to obtain target spectrum energy; The second audio feature is calculated based on the target spectral energy.

4. The pre-scan test method according to claim 3, wherein: The step of calculating the second audio feature based on the target spectrum energy includes: Acquire multiple preset audio feature ranges; wherein the preset audio feature ranges refer to numerical ranges set based on wavelet transform results corresponding to different notes, and different preset audio feature ranges map to different notes; Mapping the plurality of wavelet transform results to a preset audio feature range based on the wavelet transform results corresponding to the plurality of preset scales; Adding the target spectrum energies corresponding to the wavelet transform results mapped at the same preset scale within the preset audio feature range to obtain a feature value; The characteristic values ​​corresponding to the plurality of preset scales in different preset audio feature ranges are used as the second audio feature.

5. The pre-scan test method according to claim 1, wherein: After the step of extracting the preset identified audio data segment from the audio data to be identified, the method further includes: If the preset identified audio data segment does not exist in the audio data to be identified, it is confirmed that the test result of the electromagnetic interference test item corresponding to the audio to be identified is abnormal.

6. The pre-scan test method according to claim 1, wherein: The step of aligning the standard audio data and the audio data to be identified based on the preset identified audio data segment to obtain first audio data corresponding to the standard audio data and second audio data corresponding to the audio data to be identified includes: Aligning the standard audio data and the audio data to be identified on the preset identified audio data segment to obtain third audio data corresponding to the standard audio data and fourth audio data corresponding to the audio data to be identified; Obtain a first starting point and a first ending point of the third audio data, and obtain a second starting point and a second ending point of the fourth audio data; If the first starting point is located to the left of the second starting point, the second starting point is used as the starting point of the third audio data; If the first starting point is located to the right of the second starting point, the first starting point is used as the starting point of the fourth audio data; If the first end point is located to the left of the second end point, the first end point is used as the starting point of the fourth audio data; If the first end point is located to the right of the second end point, the second end point is used as the starting point of the third audio data; The updated third audio data is used as the first audio data, and the updated fourth audio data is used as the second audio data.

7. The pre-scan test method according to claim 1, wherein: The step of extracting abnormal sampling points between the first audio data and the second audio data comprises: extracting a plurality of first sampling points from the first audio data based on a preset sampling frequency, and extracting a plurality of second sampling points from the second audio data based on a preset sampling frequency; Calculating the amplitude difference between a first sampling point and a second sampling point at the same position; The sampling points whose amplitude differences are greater than the second threshold are regarded as the abnormal sampling points.

8. A pre-scan test device, characterized in that: The device for the pre-scan test comprises: an acquisition unit, configured to acquire standard audio data and audio data to be identified output by the device under test under various electromagnetic interference conditions; wherein the standard audio data refers to audio data output by the device under test in a non-interference environment, and the standard audio data includes a plurality of preset identified audio data segments; A first extraction unit is used to extract a preset identified audio data segment from the audio data to be identified; an alignment unit, configured to align the standard audio data and the audio data to be identified based on the preset identified audio data segment, to obtain first audio data corresponding to the standard audio data and second audio data corresponding to the audio data to be identified; a second extraction unit, configured to extract abnormal sampling points between the first audio data and the second audio data; A confirmation unit is configured to confirm that a test result of the electromagnetic interference test item corresponding to the audio to be identified is abnormal if a proportion of the abnormal sampling points in the total sampling points exceeds a preset proportion.

9. A terminal device, characterized in that: The terminal device includes: a memory, a processor, and a pre-scan test program stored in the memory and executable on the processor, wherein the pre-scan test program is configured to implement the steps in the pre-scan test method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the pre-scan test method according to any one of claims 1 to 7 are implemented.

Images