A wind noise evaluation method and device based on a dummy model, a medium and equipment

Abstract

The application provides a dummy model-based wind noise evaluation method and device, medium and equipment. Different testers subjectively evaluate wind noise data of different vehicles under the same test condition to obtain subjective evaluation indexes. Objective evaluation indexes are calculated based on the wind noise data. The prominence coefficient of a target vehicle is calculated based on the frequency spectrum data of the target vehicle. The wind noise evaluation result of the target vehicle is comprehensively obtained based on the subjective evaluation indexes, the objective evaluation indexes and the prominence coefficient. The wind noise data is evaluated in terms of subjective comparison and evaluation, objective data evaluation and frequency spectrum data characteristics, and the wind noise evaluation result of the vehicle is comprehensively obtained based on the evaluation indexes in the three aspects. The subjective feeling of the user and the objective data of the wind noise are considered, and the prominent effect of the wind noise frequency spectrum data is also considered, so that a more accurate wind noise evaluation result is obtained.

Description

Technical Field

[0001] This application relates to the field of automotive evaluation technology, specifically to a method, apparatus, medium, and equipment for wind noise evaluation based on a dummy model. Background Technology

[0002] Wind noise has become one of the most common issues in automotive quality complaints, directly impacting user comfort and satisfaction. As consumers increasingly demand quieter vehicles, wind noise performance is gradually becoming a crucial indicator of vehicle quality and a core selling point in market competition. Therefore, automakers are placing greater emphasis on wind noise performance during product development.

[0003] Currently, domestic automobile manufacturers have not yet established a unified and standardized testing method for automotive wind noise. Existing wind noise testing standards are inconsistent and fragmented, leading to significant differences in test results. The consistency and comparability of these results are poor, hindering the sharing and unified analysis of wind noise measurement data, and severely restricting the improvement of wind noise design and control technologies. At the same time, the existing wind noise performance evaluation system is also incomplete. Different evaluation methods vary and generally suffer from single evaluation dimensions and narrow scope. Existing methods cannot effectively combine subjective evaluation with objective test data, making it difficult to comprehensively and accurately assess the overall vehicle wind noise level. This, to some extent, hinders the exchange and progress of automotive wind noise technology and indirectly increases the development costs of automotive products.

[0004] Therefore, it is urgent to establish a complete and accurate test method for wind noise performance and a wind noise evaluation system to solve the above-mentioned outstanding problems. Summary of the Invention

[0005] To address the aforementioned technical problems, this application is proposed. Embodiments of this application provide a method, apparatus, medium, and device for wind noise evaluation based on a dummy model.

[0006] According to one aspect of this application, a wind noise evaluation method based on a dummy model is provided, comprising: acquiring wind noise data of a target vehicle; subjectively evaluating the wind noise data of different vehicles under the same test conditions using different testers to obtain a sample comparison matrix; wherein the sample comparison matrix characterizes the relative superiority or inferiority of the wind noise data of different vehicles under the same test conditions; calculating a subjective evaluation index of the target vehicle based on the sample comparison matrix; performing a Fourier transform on the wind noise data to obtain the spectral data of the target vehicle; calculating the sound pressure level, speech intelligibility index, and loudness of the target vehicle based on the spectral data of the target vehicle; calculating an objective evaluation index of the target vehicle based on the sound pressure level, speech intelligibility index, and loudness of the target vehicle; calculating a prominence coefficient of the target vehicle based on the spectral data of the target vehicle; wherein the prominence coefficient characterizes the difference between the amplitude of a single frequency and the amplitude of adjacent frequencies in the spectral data; and comprehensively obtaining a wind noise evaluation result of the target vehicle based on the subjective evaluation index, the objective evaluation index, and the prominence coefficient.

[0007] In one embodiment, the step of using different testers to subjectively evaluate the wind noise data of different vehicles under the same test conditions and obtaining a sample comparison matrix includes: using different testers to subjectively evaluate the wind noise data of different vehicles under the same test conditions and obtaining a corresponding vehicle sample matrix; wherein, the vehicle sample matrix represents the relative superiority or inferiority of the wind noise data of different vehicles under the same test conditions as evaluated by the tester; and the sample comparison matrix is ​​obtained by combining multiple vehicle sample matrices.

[0008] In one embodiment, obtaining the sample comparison matrix based on multiple vehicle sample matrices includes: removing abnormal sample matrices from the vehicle sample matrices; and obtaining the sample comparison matrix based on the vehicle sample matrices after removing the abnormal sample matrices.

[0009] In one embodiment, the step of removing abnormal sample matrices from the vehicle sample matrix includes: calculating the cyclic misjudgment coefficient of the vehicle sample matrix; wherein the cyclic misjudgment coefficient represents the degree of cyclic misjudgment in the corresponding vehicle sample matrix; and removing vehicle sample matrices whose cyclic misjudgment coefficient is less than a preset misjudgment threshold.

[0010] In one embodiment, removing abnormal sample matrices from the vehicle sample matrix includes: calculating the correlation coefficient between each of the vehicle sample matrices; wherein the correlation coefficient represents the correlation between two corresponding vehicle sample matrices; and removing vehicle sample matrices whose correlation coefficient is less than a preset correlation threshold.

[0011] In one embodiment, calculating the objective evaluation index of the target vehicle based on the sound pressure level, speech intelligibility index, and loudness of the target vehicle includes: weighting the sound pressure level, speech intelligibility index, and loudness of the target vehicle to calculate the objective evaluation index of the target vehicle.

[0012] In one embodiment, calculating the prominence coefficient of the target vehicle based on the spectral data of the target vehicle includes: calculating the difference between the amplitude of a single frequency in the spectral data and the average amplitude of multiple adjacent frequencies of the single frequency, to obtain the prominence coefficient of the target vehicle.

[0013] According to another aspect of this application, a wind noise evaluation device based on a dummy model is provided, comprising: a wind noise data acquisition module for acquiring wind noise data of a target vehicle; a sample matrix acquisition module for subjectively evaluating wind noise data of different vehicles under the same test conditions using different testers to obtain a sample comparison matrix; wherein the sample comparison matrix characterizes the relative superiority or inferiority of wind noise data of different vehicles under the same test conditions; a subjective index calculation module for calculating a subjective evaluation index of the target vehicle based on the sample comparison matrix; a spectrum data calculation module for performing a Fourier transform on the wind noise data to obtain spectrum data of the target vehicle; and an objective data calculation module. The system includes a calculation module for calculating the sound pressure level, speech intelligibility index, and loudness of the target vehicle based on its spectral data; an objective index calculation module for calculating objective evaluation indicators of the target vehicle based on its sound pressure level, speech intelligibility index, and loudness; a prominence coefficient calculation module for calculating the prominence coefficient of the target vehicle based on its spectral data, wherein the prominence coefficient characterizes the difference between the amplitude of a single frequency and the amplitude of adjacent frequencies in the spectral data; and an evaluation result determination module for comprehensively obtaining the wind noise evaluation result of the target vehicle based on the subjective evaluation indicators, the objective evaluation indicators, and the prominence coefficient.

[0014] According to another aspect of this application, a computer-readable storage medium is provided, the storage medium storing a computer program for performing any of the methods described above.

[0015] According to another aspect of this application, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; the processor being configured to perform any of the methods described above.

[0016] This application provides a wind noise evaluation method, apparatus, medium, and equipment based on a dummy model. The method involves acquiring wind noise data of a target vehicle; employing different testers to subjectively evaluate the wind noise data of different vehicles under the same test conditions, resulting in a sample comparison matrix; where the sample comparison matrix represents the relative superiority or inferiority of wind noise data between different vehicles under the same test conditions; calculating the subjective evaluation index of the target vehicle based on the sample comparison matrix; performing a Fourier transform on the wind noise data to obtain the spectral data of the target vehicle; calculating the sound pressure level, speech intelligibility index, and loudness of the target vehicle based on the spectral data of the target vehicle; and calculating the target vehicle's sound pressure level, speech intelligibility index, and loudness based on the sound pressure level, speech intelligibility index, and loudness. Objective evaluation indicators for the target vehicle; based on the target vehicle's spectral data, the prominence coefficient of the target vehicle is calculated; the prominence coefficient characterizes the difference between the amplitude of a single frequency and the amplitude of adjacent frequencies in the spectral data; based on subjective evaluation indicators, objective evaluation indicators, and prominence coefficient, the wind noise evaluation result of the target vehicle is obtained comprehensively; the evaluation indicators for wind noise data are calculated from three aspects: subjective comparison evaluation, objective data evaluation, and spectral data characteristics, and the wind noise evaluation result of the vehicle is obtained by comprehensively combining the evaluation indicators from the three aspects. This not only takes into account the user's subjective feelings and objective wind noise data, but also considers the prominence effect of wind noise spectral data, thus obtaining a more accurate wind noise evaluation result. Attached Figure Description

[0017] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0018] Figure 1 This is a flowchart illustrating a wind noise evaluation method based on a dummy model provided in an exemplary embodiment of this application.

[0019] Figure 2 This is a schematic diagram of the structure of a wind noise evaluation device based on a dummy model provided in an exemplary embodiment of this application.

[0020] Figure 3 This is a structural diagram of an electronic device provided in an exemplary embodiment of this application. Detailed Implementation

[0021] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0022] Figure 1 This is a flowchart illustrating an exemplary embodiment of the wind noise assessment method based on a dummy model provided in this application. Figure 1 As shown, the wind noise evaluation method based on the dummy model includes the following steps: Step 110: Obtain wind noise data for the target vehicle.

[0023] During aeroacoustic wind tunnel testing, the airflow fluctuations vary depending on the vehicle's position within the wind tunnel, thus affecting the wind noise test results. This application sets the target vehicle's center to coincide with the center of the wind tunnel balance, and the target vehicle is parallel to the longitudinal centerline of the wind tunnel, with a deviation of ≤1mm. The longitudinal centerline should be marked on the front and rear of the target vehicle.

[0024] This application primarily employs two standard operating conditions: real vehicle and fully sealed conditions. The aeroacoustic wind tunnel measurements are taken at wind speeds of 100-160 km / h and yaw angles of ±20°. During testing, the central moving belt and boundary layer suction system are closed, and the wheels are stationary. Specific standard operating conditions are shown in the table below. The test conditions in this application encompass various wind speeds, yaw angles, sealing conditions, and external sound sources of concern in actual vehicle development, thus obtaining all the data required for complete vehicle wind noise development. Furthermore, it specifically obtains information on external sound sources under different operating conditions. The overall operating condition design includes information on the source, path, and response aspects affecting wind noise performance, thereby providing a more systematic guide for wind noise design.

[0025] Table 1 Standard Operating Condition Statistics

[0026] This application utilizes acoustic artificial head dummies placed in four positions—driver's seat, front passenger seat, left rear seat, and right rear seat—to conduct wind noise performance tests. Each dummy has an acoustic microphone installed at the corresponding ear position to collect sound data; thus, the dummy has two measurement points: the outer ear and the inner ear. In wind tunnel tests, the wind noise performance at the driver's outer ear measurement point represents the vehicle's true wind noise level. Therefore, the spatial location of the driver's seat dummy's outer ear measurement point is crucial for the accuracy and standardization of the wind noise results. To accurately determine the location of the external ear spatial measurement point in the driver's seat, this application uses statistical data on human anatomy to determine the driver's average height and adjusts the seat's fore-aft, heel-and-groove position and backrest angle to find the most comfortable driving posture. The most comfortable driving posture is defined as follows: the steering wheel is adjusted to its highest and foremost position, the seat's heel-and-groove position is adjusted to the middle position, and the seat's front end (leg rest) is adjusted to its lowest position. The driver sits in the driver's seat with their right arm extended, palm and forearm at a right angle, resting on the 12 o'clock position on the steering wheel. With this position, the seat's fore-aft position is adjusted, and the driver grips the steering wheel at the 9 o'clock and 15 o'clock positions. This is the most comfortable driving posture. The location of the driver's external ear spatial measurement point is then determined based on the distance from the 9 o'clock position on the steering wheel to the driver's external ear.

[0027] This application selects multiple target test subjects, has them sit in a car, adjusts them to the most comfortable driving posture, and measures the distance (F value) from the 9 o'clock position of the steering wheel to the driver's outer ear. The results are shown in the table below.

[0028] Table 2. Statistics on F-value Measurement

[0029] As shown in Table 2, the critical dimension F value for determining the external ear measurement point of the acoustic artificial head dummy model in the driver's seat during wind noise testing was selected from the median of 14 testers, specifically 565 mm.

[0030] After the data acquisition is completed, the post-processing and analysis of the acquired data will have a certain impact on the final test results of wind noise. Therefore, this application processes and analyzes the data. The noise analysis frequency range is 20-10000Hz. Fourier transform is performed on the time domain signal of each working condition, the self-power spectrum of the time domain data is calculated, and the one-third octave band spectrum is output. All spectrum analysis is performed using A-weighting.

[0031] Step 120: Different testers subjectively evaluate the wind noise data of different vehicles under the same test conditions to obtain a sample comparison matrix.

[0032] The sample comparison matrix represents the relative merits of wind noise data for different vehicles under the same test conditions. After obtaining accurate wind noise data, this application employs different testers to subjectively evaluate the wind noise data of different vehicles under the same test conditions, resulting in a sample comparison matrix. The relative merits are then compared to obtain the matrix, thus reducing the subjective error of directly providing evaluation results.

[0033] Step 130: Calculate the subjective evaluation index of the target vehicle based on the sample comparison matrix.

[0034] Based on the relative advantages and disadvantages of wind noise data of different vehicles under the same test conditions, this application calculates the subjective evaluation index of the target vehicle to obtain a more objective evaluation index that conforms to the real human perception.

[0035] Step 140: Perform a Fourier transform on the wind noise data to obtain the spectrum data of the target vehicle.

[0036] This application performs a Fourier transform on the time-domain wind noise data to obtain the frequency-domain wind noise data of the target vehicle, i.e., the spectrum data.

[0037] Step 150: Based on the target vehicle's spectral data, calculate the target vehicle's sound pressure level, speech intelligibility index, and loudness.

[0038] Based on wind noise data, this application calculates multiple sound indicators for evaluating wind noise, such as sound pressure level, speech intelligibility index, and loudness of the target vehicle.

[0039] Step 160: Calculate the objective evaluation index of the target vehicle based on its sound pressure level, speech intelligibility index, and loudness.

[0040] This application calculates objective evaluation indicators for the target vehicle based on its sound pressure level, speech intelligibility index, and loudness, in order to obtain objective wind noise data evaluation indicators.

[0041] Step 170: Calculate the prominence coefficient of the target vehicle based on its spectral data.

[0042] The prominence coefficient characterizes the difference between the amplitude of a single frequency and the amplitude of adjacent frequencies in the spectral data. This application calculates the prominence coefficient of the target vehicle based on its spectral data to determine the difference between the amplitude of a single frequency and the amplitude of adjacent frequencies in the spectral data.

[0043] Step 180: Based on subjective evaluation indicators, objective evaluation indicators, and prominence coefficient, the wind noise evaluation result of the target vehicle is obtained by combining the results.

[0044] This application combines subjective evaluation indicators, objective evaluation indicators, and prominence coefficient to obtain a comprehensive wind noise evaluation result for the target vehicle.

[0045] This application provides a wind noise evaluation method based on a dummy model, which acquires wind noise data of a target vehicle; employs different testers to subjectively evaluate the wind noise data of different vehicles under the same test conditions, obtaining a sample comparison matrix; wherein, the sample comparison matrix represents the relative superiority or inferiority of wind noise data of different vehicles under the same test conditions; based on the sample comparison matrix, calculates the subjective evaluation index of the target vehicle; performs Fourier transform on the wind noise data to obtain the spectral data of the target vehicle; based on the spectral data of the target vehicle, calculates the sound pressure level, speech intelligibility index, and loudness of the target vehicle; based on the sound pressure level, speech intelligibility index, and loudness of the target vehicle, calculates the target vehicle's... Objective evaluation indicators; based on the target vehicle's spectral data, the prominence coefficient of the target vehicle is calculated; whereby the prominence coefficient characterizes the difference between the amplitude of a single frequency and the amplitude of adjacent frequencies in the spectral data; based on subjective evaluation indicators, objective evaluation indicators, and prominence coefficient, the wind noise evaluation result of the target vehicle is obtained comprehensively; the wind noise data is evaluated from three aspects: subjective comparison evaluation, objective data evaluation, and spectral data characteristics, and the wind noise evaluation result of the vehicle is obtained by combining the evaluation indicators from the three aspects. This not only takes into account the user's subjective feelings and objective wind noise data, but also considers the prominence effect of wind noise spectral data, thus obtaining a more accurate wind noise evaluation result.

[0046] In one embodiment, the specific implementation of step 120 above may be as follows: different testers subjectively evaluate the wind noise data of different vehicles under the same test conditions to obtain the corresponding vehicle sample matrix; wherein, the vehicle sample matrix represents the relative superiority or inferiority of the wind noise data of different vehicles under the same test conditions as evaluated by the tester; and a sample comparison matrix is ​​obtained by combining multiple vehicle sample matrices.

[0047] This application employs different testers to subjectively evaluate the wind noise data of different vehicles under the same test conditions, resulting in a corresponding vehicle sample matrix. The specific structure of the vehicle sample matrix is ​​shown in the table below (where 1-13 are vehicle numbers): Table 3 Vehicle Sample Matrix

[0048] Because repeated listening to different sounds can influence each other—for example, after adapting to a noisy environment, other sounds may seem quieter—this application evaluates only two sounds at a time and records their relative quality, ultimately compiling a vehicle sample matrix. Based on subjective evaluations from multiple testers, this matrix is ​​then mathematically calculated to determine the vehicle's position within the overall sample, effectively avoiding the problem of mutual influence among a series of subjective evaluations.

[0049] In one embodiment, step 120 can be implemented by: removing abnormal sample matrices from the vehicle sample matrix; and obtaining a sample comparison matrix based on the vehicle sample matrix after removing the abnormal sample matrices.

[0050] This application further removes abnormal sample matrices from the vehicle sample matrix, and based on the vehicle sample matrix after removing abnormal sample matrices, a comprehensive sample comparison matrix is ​​obtained to remove inaccurate vehicle sample matrices, thereby improving the accuracy of the final sample comparison matrix.

[0051] In one embodiment, step 120 can be implemented by: calculating the cyclic misjudgment coefficient of the vehicle sample matrix; wherein the cyclic misjudgment coefficient represents the degree of cyclic misjudgment in the corresponding vehicle sample matrix; and removing vehicle sample matrices whose cyclic misjudgment coefficient is less than a preset misjudgment threshold.

[0052] Specifically, during the evaluation process, testers may encounter unstable evaluation results, resulting in cyclical misjudgments where vehicle A is superior to vehicle B, vehicle B is superior to vehicle C, and vehicle C is superior to vehicle A. This application improves the accuracy of subjective evaluation by calculating the cyclic misjudgment coefficient of the vehicle sample matrix and using a preset misjudgment threshold to eliminate erroneous results from the vehicle sample matrix. The formula for calculating the cyclic misjudgment coefficient is as follows: When n is odd ; When n is even ; in, For the cyclic misjudgment coefficient, The number of ternary cycles, The number of samples. When the value is 1, there is no false positive for loops. The lower the value, the more serious the loop misjudgment.

[0053] In one embodiment, step 120 can be implemented by: calculating the correlation coefficient between each vehicle sample matrix; wherein the correlation coefficient represents the correlation between two corresponding vehicle sample matrices; and removing vehicle sample matrices whose correlation coefficient is less than a preset correlation threshold.

[0054] Specifically, during the evaluation process, testers may produce evaluation results that significantly differ from those of most other testers. Including such a tester's evaluation result can bias the overall result. Therefore, this application improves the accuracy of subjective evaluations by calculating the correlation coefficient between various vehicle sample matrices and eliminating vehicle sample matrices with correlation coefficients lower than a preset correlation threshold. The formula for calculating the correlation coefficient is as follows: ; in, The correlation coefficient is... Let i be the rank of the i-th vehicle in the column of the vehicle sample matrix. Let i be the rank of the i-th vehicle in the row of the vehicle sample matrix. The column-level mean of the vehicle sample matrix. This represents the row-level mean of the vehicle sample matrix.

[0055] In one embodiment, step 160 can be implemented by weighting the sound pressure level, speech intelligibility index and loudness of the target vehicle to calculate the objective evaluation index of the target vehicle.

[0056] This application uses a weighted summation method to calculate the objective evaluation index of the target vehicle. Specifically, the calculation formula for the objective evaluation index of the target vehicle is: P=a*dB(A)+b*AI%+c*Sone, where P is the objective evaluation index, dB(A) is the sound pressure level, AI% is the speech intelligibility index, Sone is the loudness, and a, b, and c are the weighting coefficients for sound pressure level, speech intelligibility index, and loudness, respectively. For example, a=0.3, b=0.5, and c=0.2.

[0057] In one embodiment, step 170 can be implemented by calculating the difference between the amplitude of a single frequency in the spectrum data and the average amplitude of multiple adjacent frequencies of the single frequency, to obtain the prominence coefficient of the target vehicle.

[0058] This application calculates the difference between the amplitude of a single frequency in the spectral data and the average amplitude of multiple adjacent frequencies to obtain the prominence coefficient of the target vehicle. Based on the prominence coefficient, it determines whether the noise at that frequency is significantly more prominent than the overall noise, thereby determining whether the noise at that frequency is easily identifiable subjectively. The formula for calculating the prominence coefficient is as follows: ; in, For prominence coefficient, For frequency f n The sound pressure level at 1 / 3 octave.

[0059] In one embodiment, the specific implementation method of step 180 above may be: summing the subjective evaluation score and the objective evaluation score to obtain the final score.

[0060] The calculated normalized subjective evaluation score is multiplied by 40%, and the calculated objective score P is divided by the prominence coefficient multiplied by 60%. The final score of the vehicle is obtained by summing the results. That is, the final score = subjective evaluation index × 40% + (objective evaluation index / prominence coefficient) × 60%.

[0061] Figure 2 This is a schematic diagram of the structure of a wind noise evaluation device based on a dummy model provided in an exemplary embodiment of this application. Figure 2 As shown, the wind noise evaluation device 20 based on a dummy model includes: a wind noise data acquisition module 21 for acquiring wind noise data of the target vehicle; a sample matrix acquisition module 22 for subjectively evaluating the wind noise data of different vehicles under the same test conditions using different testers to obtain a sample comparison matrix; wherein, the sample comparison matrix represents the relative superiority or inferiority of wind noise data of different vehicles under the same test conditions; a subjective index calculation module 23 for calculating the subjective evaluation index of the target vehicle based on the sample comparison matrix; a spectrum data calculation module 24 for performing Fourier transform on the wind noise data to obtain the spectrum data of the target vehicle; and objective data. The calculation module 25 is used to calculate the sound pressure level, speech intelligibility index, and loudness of the target vehicle based on its spectral data; the objective index calculation module 26 is used to calculate the objective evaluation index of the target vehicle based on its sound pressure level, speech intelligibility index, and loudness; the prominence coefficient calculation module 27 is used to calculate the prominence coefficient of the target vehicle based on its spectral data; wherein, the prominence coefficient characterizes the difference between the amplitude of a single frequency and the amplitude of adjacent frequencies in the spectral data; and the evaluation result determination module 28 is used to comprehensively obtain the wind noise evaluation result of the target vehicle based on the subjective evaluation index, the objective evaluation index, and the prominence coefficient.

[0062] This application provides a wind noise evaluation device based on a dummy model. A wind noise data acquisition module 21 acquires wind noise data of the target vehicle; a sample matrix acquisition module 22 uses different testers to subjectively evaluate the wind noise data of different vehicles under the same test conditions, obtaining a sample comparison matrix; wherein, the sample comparison matrix represents the relative superiority or inferiority of wind noise data between different vehicles under the same test conditions; a subjective index calculation module 23 calculates the subjective evaluation index of the target vehicle based on the sample comparison matrix; a spectrum data calculation module 24 performs a Fourier transform on the wind noise data to obtain the spectrum data of the target vehicle; an objective data calculation module 25 calculates the sound pressure level, speech intelligibility index, and loudness of the target vehicle based on the spectrum data of the target vehicle; and an objective index calculation module 26 calculates the sound pressure level of the target vehicle based on the sound pressure level of the target vehicle. The system calculates the objective evaluation indicators of the target vehicle using the speech intelligibility index and loudness. The prominence coefficient calculation module 27 calculates the prominence coefficient of the target vehicle based on its spectral data; the prominence coefficient represents the difference between the amplitude of a single frequency and the amplitude of adjacent frequencies in the spectral data. The evaluation result determination module 28 comprehensively obtains the wind noise evaluation result of the target vehicle based on the subjective evaluation indicators, objective evaluation indicators, and prominence coefficient. The system calculates evaluation indicators for wind noise data from three aspects: subjective comparison evaluation, objective data evaluation, and spectral data characteristics. It then comprehensively obtains the wind noise evaluation result of the vehicle by integrating these three evaluation indicators. This approach not only considers the user's subjective experience and objective wind noise data but also takes into account the prominence effect of the wind noise spectral data, thus obtaining a more accurate wind noise evaluation result.

[0063] In one embodiment, the sample matrix acquisition module 22 can be further configured to: use different testers to subjectively evaluate the wind noise data of different vehicles under the same test conditions to obtain the corresponding vehicle sample matrix; wherein, the vehicle sample matrix represents the relative superiority or inferiority of the wind noise data of different vehicles under the same test conditions as evaluated by the tester; and based on multiple vehicle sample matrices, a sample comparison matrix is ​​obtained.

[0064] In one embodiment, the sample matrix acquisition module 22 can be further configured to: remove abnormal sample matrices from the vehicle sample matrix; and obtain a sample comparison matrix based on the vehicle sample matrix after removing the abnormal sample matrices.

[0065] In one embodiment, the sample matrix acquisition module 22 can be further configured to: calculate the cyclic misjudgment coefficient of the vehicle sample matrix; wherein the cyclic misjudgment coefficient represents the degree of cyclic misjudgment in the corresponding vehicle sample matrix; and remove vehicle sample matrices whose cyclic misjudgment coefficient is less than a preset misjudgment threshold.

[0066] In one embodiment, the sample matrix acquisition module 22 can be further configured to: calculate the correlation coefficient between each vehicle sample matrix; wherein the correlation coefficient represents the correlation between two corresponding vehicle sample matrices; and remove vehicle sample matrices whose correlation coefficient is less than a preset correlation threshold.

[0067] In one embodiment, the objective index calculation module 26 can be further configured to: weight the sound pressure level, speech intelligibility index and loudness of the target vehicle to calculate the objective evaluation index of the target vehicle.

[0068] In one embodiment, the prominence coefficient calculation module 27 can be further configured to: calculate the difference between the amplitude of a single frequency in the spectrum data and the average amplitude of multiple adjacent frequencies of the single frequency, to obtain the prominence coefficient of the target vehicle.

[0069] Below, for reference Figure 3 This application describes an electronic device according to embodiments thereof. The electronic device may be either or both of a first device and a second device, or a standalone device independent of them, which may communicate with the first device and the second device to receive acquired input signals from them.

[0070] Figure 3 A block diagram of an electronic device according to an embodiment of this application is illustrated.

[0071] like Figure 3 As shown, the electronic device 10 includes one or more processors 11 and memory 12.

[0072] The processor 11 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.

[0073] The memory 12 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 11 may execute the program instructions to implement the methods of the various embodiments of this application described above and / or other desired functions. Various contents such as input signals, signal components, and noise components may also be stored in the computer-readable storage medium.

[0074] In one example, the electronic device 10 may also include an input device 13 and an output device 14, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0075] When the electronic device is a standalone device, the input device 13 can be a communication network connector for receiving the collected input signals from the first device and the second device.

[0076] In addition, the input device 13 may also include, for example, a keyboard, a mouse, etc.

[0077] The output device 14 can output various information to the outside, including determined distance information, direction information, etc. The output device 14 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0078] Of course, for the sake of simplicity, Figure 3 Only some of the components of the electronic device 10 relevant to this application are shown in this illustration; components such as buses, input / output interfaces, etc., are omitted. In addition, the electronic device 10 may include any other suitable components depending on the specific application.

[0079] In addition to the methods and apparatus described above, embodiments of this application may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of this application described in the "Exemplary Methods" section above.

[0080] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0081] Furthermore, embodiments of this application may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of this application described in the "Exemplary Methods" section above.

[0082] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0083] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0084] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0085] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0086] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0087] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A wind noise evaluation method based on a dummy model, characterized in that, include: Obtain wind noise data for the target vehicle; Different testers subjectively evaluate the wind noise data of different vehicles under the same test conditions to obtain a sample comparison matrix; wherein, the sample comparison matrix represents the relative superiority or inferiority of the wind noise data of different vehicles under the same test conditions. Based on the sample comparison matrix, the subjective evaluation index of the target vehicle is calculated; The wind noise data is subjected to Fourier transform to obtain the spectrum data of the target vehicle; Based on the spectral data of the target vehicle, the sound pressure level, speech intelligibility index, and loudness of the target vehicle are calculated. Based on the sound pressure level, speech intelligibility index, and loudness of the target vehicle, calculate the objective evaluation index of the target vehicle. Based on the spectral data of the target vehicle, the prominence coefficient of the target vehicle is calculated; wherein the prominence coefficient characterizes the difference between the amplitude of a single frequency and the amplitude of adjacent frequencies in the spectral data. Based on the subjective evaluation index, the objective evaluation index, and the prominence coefficient, the wind noise evaluation result of the target vehicle is obtained comprehensively.

2. The wind noise evaluation method based on a dummy model according to claim 1, characterized in that, The method involves different testers subjectively evaluating the wind noise data of different vehicles under the same test conditions, resulting in a sample comparison matrix including: Different testers subjectively evaluate the wind noise data of different vehicles under the same test conditions, resulting in a corresponding vehicle sample matrix; wherein, the vehicle sample matrix represents the relative superiority or inferiority of the wind noise data of different vehicles under the same test conditions as evaluated by the tester. The sample comparison matrix is ​​obtained by combining multiple vehicle sample matrices.

3. The wind noise evaluation method based on a dummy model according to claim 2, characterized in that, The sample comparison matrix obtained by combining multiple vehicle sample matrices includes: Remove the abnormal sample matrix from the vehicle sample matrix; The sample comparison matrix is ​​obtained by combining the vehicle sample matrix after removing the abnormal sample matrix.

4. The wind noise evaluation method based on a dummy model according to claim 3, characterized in that, The abnormal sample matrix to be removed from the vehicle sample matrix includes: Calculate the cyclic misclassification coefficient of the vehicle sample matrix; wherein the cyclic misclassification coefficient represents the degree of cyclic misclassification in the corresponding vehicle sample matrix; Remove vehicle sample matrices whose cyclic misjudgment coefficient is less than a preset misjudgment threshold.

5. The wind noise evaluation method based on a dummy model according to claim 3, characterized in that, The abnormal sample matrix to be removed from the vehicle sample matrix includes: Calculate the correlation coefficient between each of the vehicle sample matrices; wherein the correlation coefficient represents the correlation between two corresponding vehicle sample matrices; Vehicle sample matrices with correlation coefficients less than a preset correlation threshold are removed.

6. The wind noise evaluation method based on a dummy model according to claim 1, characterized in that, The objective evaluation indicators for the target vehicle, based on its sound pressure level, speech intelligibility index, and loudness, include: The objective evaluation index of the target vehicle is calculated by weighting the sound pressure level, speech intelligibility index, and loudness.

7. The wind noise evaluation method based on a dummy model according to claim 1, characterized in that, The calculation of the prominence coefficient of the target vehicle based on its spectral data includes: The difference between the amplitude of a single frequency in the spectrum data and the average amplitude of multiple adjacent frequencies of the single frequency is calculated to obtain the prominence coefficient of the target vehicle.

8. A wind noise assessment device based on a dummy model, characterized in that, include: The wind noise data acquisition module is used to acquire wind noise data of the target vehicle. The sample matrix acquisition module is used to subjectively evaluate the wind noise data of different vehicles under the same test conditions by different testers to obtain a sample comparison matrix; wherein, the sample comparison matrix represents the relative superiority or inferiority of the wind noise data of different vehicles under the same test conditions. The subjective index calculation module is used to calculate the subjective evaluation index of the target vehicle based on the sample comparison matrix. The spectrum data calculation module is used to perform Fourier transform on the wind noise data to obtain the spectrum data of the target vehicle; An objective data calculation module is used to calculate the sound pressure level, speech intelligibility index, and loudness of the target vehicle based on its spectral data. The objective index calculation module is used to calculate the objective evaluation index of the target vehicle based on the sound pressure level, speech intelligibility index and loudness of the target vehicle. The prominence coefficient calculation module is used to calculate the prominence coefficient of the target vehicle based on the spectrum data of the target vehicle; wherein the prominence coefficient characterizes the difference between the amplitude of a single frequency and the amplitude of adjacent frequencies in the spectrum data; The evaluation result determination module is used to comprehensively obtain the wind noise evaluation result of the target vehicle based on the subjective evaluation index, the objective evaluation index, and the prominence coefficient.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program for performing the method described in any one of claims 1-7.

10. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is used to execute the method described in any one of claims 1-7.

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