Multi-order noise processing method and device, electronic equipment and storage medium

Abstract

The invention discloses a multi-order noise processing method and device, electronic equipment and a storage medium. The method comprises the following steps: extracting a plurality of single-order noises from multi-order noises, determining bandwidths of the single-order noises, obtaining sound pressure level difference values between the single-order noises and background noises corresponding to the single-order noises according to the bandwidths of the single-order noises, predicting subjective evaluation of the single-order noises according to the sound pressure level difference values, and determining the target noise according to the subjective evaluation prediction values of the single-order noises. And calculating an overall subjective evaluation prediction value of the multi-order noise. According to the method, each single-order noise is analyzed in the complete bandwidth field, so that the problem of each order in the multi-order noise can be accurately evaluated; the subjective evaluation prediction value of the single-order noise is used as basic data, the overall subjective evaluation of the multi-order noise is predicted, the evaluation accuracy is improved, and rapid evaluation is achieved.

Description

Technical Field

[0001] The present application relates to the automotive field, and in particular to a multi-order noise processing method, device, electronic device and storage medium. Background Art

[0002] With the rapid development of the automobile industry, users' requirements for vehicle comfort and experience are constantly increasing. The multi-order noise generated by rotating machinery or rotating devices installed inside the vehicle, such as transmission howling noise, has also become an important factor affecting the vehicle sound quality and user experience.

[0003] Therefore, how to efficiently and accurately complete the evaluation and analysis of multi-order noise by processing multi-order noise, such as the multi-order noise generated by the vehicle's internal electronic control, motor, gear, transmission and other accessories, has become an urgent problem to be solved. Summary of the invention

[0004] Based on this, it is necessary to provide a multi-order noise processing method, device, electronic device and storage medium to address the above technical problems, so as to evaluate multi-order noise efficiently and accurately.

[0005] A multi-order noise processing method, comprising:

[0006] Acquire multi-order noise to be evaluated, and separate multiple single-order noises from the multi-order noise;

[0007] Acquire the bandwidth of the single-order noise, and determine the difference in sound pressure level of the single-order noise relative to the background noise of the single-order noise according to the bandwidth of the single-order noise;

[0008] According to the sound pressure level difference, the subjective evaluation of the single-order noise is predicted to obtain the predicted value of the subjective evaluation of the single-order noise;

[0009] According to the subjective evaluation prediction value of single-order noise, the overall subjective evaluation prediction value of multi-order noise is calculated.

[0010] A multi-order noise processing device, comprising:

[0011] An acquisition module, used for acquiring multi-order noise to be evaluated, and separating a plurality of single-order noises from the multi-order noise;

[0012] A determination module, configured to obtain a bandwidth of the single-order noise, and determine a difference in sound pressure level of the single-order noise relative to a background noise of the single-order noise according to the bandwidth of the single-order noise;

[0013] A prediction module, used for predicting the subjective evaluation of single-order noise according to the sound pressure level difference, and obtaining a predicted value of the subjective evaluation of single-order noise;

[0014] The calculation module is used to calculate the overall subjective evaluation prediction value of multi-order noise according to the subjective evaluation prediction value of single-order noise.

[0015] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the multi-order noise processing method are implemented.

[0016] A computer-readable storage medium stores a computer program, which implements the steps of the above-mentioned multi-order noise processing method when executed by a processor.

[0017] In summary, the present application proposes a multi-order noise processing method, device, electronic device and storage medium. The present application extracts multiple single-order noises from the multi-order noise, obtains the sound pressure level difference between the single-order noise and the corresponding background noise based on the bandwidth of the single-order noise, and predicts the subjective evaluation of the single-order noise according to the sound pressure level difference; the present application analyzes each single-order noise in its complete bandwidth field in order to accurately evaluate the problems of each order in the multi-order noise; uses the subjective evaluation prediction value of the single-order noise as the basic data to predict the overall subjective evaluation of the multi-order noise, improves the accuracy of the evaluation, and realizes rapid evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0019] Figure 1 is a flow chart of a multi-order noise processing method according to an exemplary embodiment of the present application;

[0020] Figure 2 is an exemplary multi-order noise collection scene diagram;

[0021] Figure 3 is a flow chart of a multi-order noise processing method according to another exemplary embodiment of the present application;

[0022] Figure 4 is a flow chart of a multi-order noise processing method according to another exemplary embodiment of the present application;

[0023] Figure 5 is an order slice diagram of an exemplary single-order noise and its background noise;

[0024] Figure 6is a correlation function diagram of a multi-order noise processing method according to an exemplary embodiment of the present application;

[0025] Figure 7 is a block diagram of a multi-order noise processing device according to an exemplary embodiment of the present application;

[0026] Figure 8 It is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. The embodiments described by reference to the drawings are exemplary and are intended to be used to explain the present application, and cannot be understood as limiting the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0028] Figure 1 is a flowchart of a multi-order noise processing method according to an exemplary embodiment of the present application. Figure 1 As shown, the multi-order noise processing method includes the following steps:

[0029] S101, obtaining multi-order noise to be evaluated, and separating a plurality of single-order noises from the multi-order noise.

[0030] The multi-order noise processing method of the embodiment of the present application can be applied to the scenario of evaluating the multi-order noise of rotating equipment such as motors and transmissions in vehicles, and can be executed by the multi-order noise processing device provided by the embodiment of the present application, and the multi-order noise processing device can be set in an electronic device.

[0031] In the embodiment of the present application, the multi-order noise inside the vehicle is collected by a data acquisition device, and the collected signal is a time domain noise signal, which is converted into the frequency domain. As a feasible implementation method, when performing time-frequency conversion, single-order noise is extracted by tracking the rotation speed to obtain multiple single-order noises contained in the multi-order noise.

[0032] For example, in actual application scenarios, a microphone sensor may be provided inside a vehicle, such as Figure 2 As shown, a microphone sensor is set on the seat near the human ear. Before collecting, the sound collection bandwidth and resolution can be preset to collect multi-order noise. In addition, according to the application scenario corresponding to this application, the multi-order noise collected by A-weighting processing can be preferentially selected.

[0033] S102, obtaining a bandwidth of the single-order noise, and determining a difference in sound pressure level of the single-order noise relative to background noise of the single-order noise according to the bandwidth of the single-order noise.

[0034] In the embodiment of the present application, for each single-order noise, the bandwidth of the single-order noise is obtained, and based on the complete bandwidth, the sound pressure level difference between the single-order noise and its background noise is obtained.

[0035] Optionally, the sound pressure level of the single-order noise and the sound pressure level of the background noise corresponding to the single-order noise are respectively acquired, and the sound pressure level difference is determined according to the sound pressure level of the single-order noise and the sound pressure level of the background noise.

[0036] Based on this, the noise problems of different orders appearing in a wider frequency domain can be accurately analyzed, and the single-order noise can be effectively evaluated.

[0037] S103, predicting a subjective evaluation of single-order noise according to the sound pressure level difference, and obtaining a predicted value of the subjective evaluation of single-order noise.

[0038] In the embodiment of the present application, the difference in sound pressure level between a single-order noise and its background noise can be determined as an objective test value for noise evaluation. Based on the objective test value, the subjective evaluation is predicted, and while the noise evaluation is carried out in accordance with the user's subjective feelings, the manpower, material resources and time costs consumed by the actual subjective evaluation are reduced.

[0039] Optionally, a correlation function between the actual subjective evaluation and the objective test value is pre-constructed. To ensure the correlation between the actual subjective evaluation and the objective test value, a reference threshold is set for the correlation coefficient R^2 of the correlation function when constructing the correlation function. The parameters of the correlation function are continuously adjusted to ensure that the correlation coefficient R^2 meets the reference threshold requirement. The reference threshold here can be set as needed and is not limited in this application.

[0040] Furthermore, to facilitate data transmission or organization, the sound pressure level difference can be further processed by segmentation, averaging, etc., and the processed value is used as the objective test value to construct a correlation function between the actual subjective evaluation and the objective test value. The specific processing method is not limited in this application.

[0041] For example, relevant professionals and non-professionals can be organized to jointly conduct a subjective evaluation of vehicle gear whine under specified working conditions at a test site. To ensure the validity of the actual subjective score, the actual subjective score can be preprocessed. In addition, the above-mentioned objective test values ​​corresponding to the vehicle gear whine can be tested under the same working conditions, and a correlation analysis is performed on the actual subjective score and the objective test value. Finally, a correlation function with a correlation coefficient R^2>0.8 is determined as the correlation function for subjective evaluation prediction.

[0042] S104, calculating the overall subjective evaluation prediction value of multi-order noise according to the subjective evaluation prediction value of single-order noise.

[0043] In the embodiment of the present application, the subjective evaluation prediction value of the single-order noise is used as the basic data for predicting the overall subjective evaluation of the multi-order noise. The overall quality of the multi-order noise is evaluated in combination with the quality of the single-order noise.

[0044] The present application first obtains the sound pressure level difference between the single-order noise and its corresponding background noise based on the bandwidth of the single-order noise, and makes a subjective evaluation prediction of the single-order noise according to the sound pressure level difference, so as to accurately analyze the noise problems of multiple orders appearing in a wider frequency domain and effectively evaluate the single-order noise; then, based on the subjective evaluation prediction value of the single-order noise, the overall subjective evaluation prediction value of the multi-order noise is determined, so as to improve the accuracy of the evaluation and achieve rapid evaluation.

[0045] Based on the above embodiments, Figure 3 As shown, in the embodiment of the present application, the above step S102, "determining the sound pressure level difference of the single-order noise relative to the background noise of the single-order noise based on the bandwidth of the single-order noise" includes the following steps:

[0046] S301, determining a first energy extraction bandwidth based on a first preset broadening coefficient and a bandwidth of the single-order noise, so as to obtain energy of the single-order noise according to the first energy extraction bandwidth.

[0047] The present application broadens the bandwidth of single-order noise and its corresponding background noise by presetting a broadening coefficient, so as to extract relatively complete noise energy from the broadened bandwidth, and accurately obtains basic data for noise evaluation, thereby improving the accuracy of noise evaluation.

[0048] In some embodiments, T1 represents the first preset widening coefficient, the upper limit frequency corresponding to a single-order noise is F1, the lower limit frequency is F2, and the bandwidth of the single-order noise is B1=F1-F2. Then the first energy extraction bandwidth is the bandwidth obtained by widening the bandwidth of the single-order noise using the first preset widening coefficient: B2=F1+B1*T1 / 2-(F2-B1*T1 / 2).

[0049] Optionally, when evaluating the multi-order noise emitted by the motor, gear and other rotating machinery of the new energy vehicle, the first preset widening coefficient can be set to 3%. It should be noted that the setting of the first preset widening coefficient is intended to extract a relatively complete single-order noise energy, so the specific value setting of the first preset widening coefficient can be determined according to the actual noise signal to be processed, and this application does not limit it.

[0050] S302: Determine a second energy extraction bandwidth based on a second preset broadening coefficient and a bandwidth of single-order noise, so as to obtain energy of background noise according to the second energy extraction bandwidth.

[0051] The second energy extraction bandwidth is the bandwidth obtained by widening the bandwidth of the single-order noise using the second preset widening coefficient. The process of determining the second energy extraction bandwidth according to the second preset widening coefficient and the bandwidth of the single-order noise can refer to the calculation process of the first energy extraction bandwidth mentioned above, which will not be repeated here.

[0052] Optionally, when evaluating the multi-order noise emitted by the motor, gear and other rotating machinery of the new energy vehicle, the second preset widening coefficient can be set to 22%. It should be noted that the setting of the second preset widening coefficient is intended to extract a relatively complete background noise energy, so the specific value setting of the second preset widening coefficient can be determined according to the actual noise signal to be processed, and this application does not limit it.

[0053] S303, calculating the sound pressure level difference according to the energy of the background noise, the energy of the single-order noise and the preset superposition coefficient.

[0054] The present application extracts the signal energy in the first energy extraction bandwidth as the energy M of the single-order noise, and extracts the signal energy in the second energy extraction bandwidth as the energy E of the background noise of the single-order noise.

[0055] The preset superposition coefficient can be determined based on the principle of sound pressure level superposition. Considering the sound pressure level superposition effect of two related signals at the human ear, the present application proposes a preset superposition coefficient of 6. If for two unrelated signals, considering their sound pressure level superposition effect at the human ear, the preset superposition coefficient can be 3.

[0056] Specifically, the sound pressure level difference may be calculated based on the following formula: Δ=|EM|-6.

[0057] It should be noted that the specific value of the above-mentioned preset superposition coefficient can be set according to the actual processing signal, and this application does not impose any limitation.

[0058] In practice, the multi-order noises generated by the vehicle's internal electronic control, motors, gears and other accessories often have order problems. This application performs noise evaluation based on the full bandwidth of single-order noise, which can more accurately evaluate the order problems.

[0059] When calculating the difference in sound pressure levels between background noise and single-order noise, the preset superposition coefficient is subtracted so that the acquired data can be more consistent with the user's auditory perception and enhance the accuracy of noise evaluation.

[0060] Based on the above embodiments, Figure 4As shown, step S103 "predicting the subjective evaluation of single-order noise according to the sound pressure level difference to obtain the predicted value of the subjective evaluation of single-order noise" includes the following steps:

[0061] S401, obtaining the number of rotation speeds corresponding to multi-order noises.

[0062] In some embodiments, before collecting multi-order noise, a speed range may be preset for the rotating machinery corresponding to the multi-order noise. Therefore, the number of speeds may be the number of speeds within the speed range.

[0063] For example, when processing multi-order noises emitted by a motor, before noise collection, the speed range of the motor may be preset, such as 1000 rpm to 7000 rpm. Then the number of speeds may be the number of speeds within the range of 1000 rpm to 7000 rpm.

[0064] S402, taking the sound pressure level difference as the dividend and the number of rotation speeds as the divisor, and calculating to obtain an average value of the difference.

[0065] In the embodiment of the present application, the difference average value=sound pressure level difference Δ / number of rotation speeds.

[0066] Calculating the average value of the sound pressure level difference at the rotational speed can be understood as evenly distributing the sound pressure level difference to a plurality of rotational speed values ​​within the rotational speed range, so that the obtained average value of the difference is more convenient in transmission and is easy to count.

[0067] As a feasible implementation, a preset step size may be set to quantize the speed range corresponding to the multi-order noise to obtain multiple quantized speed sub-ranges, and the number of speed sub-ranges is determined to be the above-mentioned number of speeds.

[0068] In practice, the quantization step size can be set based on the speed of the rotating machinery. For example, if the speed of the rotating machinery inside the new energy vehicle increases quickly, the step size can be set to 50 rpm, and if the speed increases slowly, the step size can be set to 100 rpm. The specific value is not limited in this application.

[0069] Thus, a speed sub-interval, for example [1000 rpm, 1050 rpm), can be mapped to a speed.

[0070] In this way, the differences are averaged and organized into data that is easy to output and count.

[0071] Furthermore, the above step S104 "calculating the overall subjective evaluation prediction value of multi-order noise according to the subjective evaluation prediction value of single-order noise" can be implemented by the following process:

[0072] Based on the preset mapping relationship, the subjective evaluation prediction values ​​of all single-order noises corresponding to the multi-order noises are weightedly calculated, and the weighted calculation result is used as the overall subjective evaluation prediction value of the multi-order noise.

[0073] The preset mapping relationship is used to characterize the mapping between the subjective evaluation prediction value of single-order noise and its corresponding weighting coefficient. Optionally, by analyzing a large number of multi-order noises in advance, the weights of different subjective evaluation prediction values ​​of single-order noise in the overall subjective evaluation prediction value of the multi-order noise are determined, and the weighting coefficients corresponding to different subjective evaluation prediction values ​​of single-order noise are determined according to the weights. Exemplarily, the mapping relationship between the subjective evaluation prediction value of single-order noise and its corresponding weighting coefficient can be shown in Table 1 below.

[0074]

[0075] Table 1

[0076] In some embodiments of the present application, when performing an overall subjective evaluation prediction on multi-order noise, based on the above-mentioned preset mapping relationship, a weighted calculation is performed on the subjective evaluation prediction values ​​of all single-order noises corresponding to the multi-order noise, and the weighted calculation result is used as the overall subjective evaluation prediction value of the multi-order noise.

[0077] Furthermore, based on the preset mapping relationship, the weighting coefficient corresponding to the subjective evaluation prediction value of the single-order noise can be determined, and the subjective evaluation prediction value of the single-order noise can be weighted calculated as follows:

[0078]

[0079] Among them, s1, s2, ...sn are the subjective evaluation prediction values ​​of different single-order noises respectively; N1, N2, ...Nn are the weighting coefficients corresponding to s1, s2, ...sn respectively, n is the number of single-order noises, and S is the weighted calculation result.

[0080] For example, if s1 is 5, then according to the above preset mapping relationship, the weighting coefficient N1 corresponding to s1 is 0.45, and if s2 is 5.7, then according to the above preset mapping relationship, the weighting coefficient N2 corresponding to s2 is 0.15. Thus, the weighting coefficient corresponding to the subjective evaluation prediction value of each single-order noise can be determined through the preset mapping relationship, and the above weighted calculation is performed in combination with the corresponding weighting coefficient.

[0081] Therefore, based on the predicted value of the subjective evaluation of single-order noise, the embodiment of the present application combines the characteristics of multi-order noise sources and adopts a mathematical weighted method to predict the overall subjective evaluation of multi-order noise, analyze the overall level of multi-order noise, improve the accuracy and precision of multi-order noise processing, and make it more convenient to use mathematical models for subjective evaluation prediction, thereby efficiently completing the evaluation of multi-order noise.

[0082] The embodiment of the present application processes multi-order noise to obtain an overall subjective evaluation prediction value of the multi-order noise. As a feasible implementation method, a judgment threshold can be set for the multi-order noise emitted by the rotating machinery according to product requirements or project requirements, and the quality of the multi-order noise can be judged based on the judgment threshold and the obtained overall subjective evaluation prediction value of the multi-order noise, so as to determine whether the corresponding rotating machinery or even components and products need to be optimized.

[0083] For example, if the judgment threshold is set to 6.5, and the overall subjective evaluation prediction value of the multi-order noise obtained by the multi-order noise processing method of the present application is 6.7, it can be considered that the multi-order noise has good quality and is easily accepted by users.

[0084] In practice, multi-order noise may correspond to multiple noise sources. For example, single-order noises m1, m2, m3, and m4 are extracted from multi-order noise, where m1, m2, and m3 correspond to the same noise source A, and m4 corresponds to noise source D. When the overall subjective evaluation prediction value of multi-order noise does not meet the requirements, the quality of each noise source can be further analyzed to screen the noise source to be optimized.

[0085] On the basis of the above embodiments, the multi-order noise processing method of the present application may also include a process of subjective evaluation prediction of a single noise source:

[0086] According to the subjective evaluation prediction value of single-order noise, the subjective evaluation prediction value corresponding to a single noise source is calculated; according to the subjective evaluation prediction value corresponding to the noise source, the noise source to be optimized is screened from multiple noise sources.

[0087] The subjective evaluation prediction value of single-order noise is used as the basic data for the subjective evaluation prediction of a single noise source to enhance the accuracy of the subjective evaluation prediction of a single noise source. The contribution of a single noise source to multi-order noise is evaluated by subjective evaluation prediction of the single noise source, and the subjective evaluation prediction value of each noise source is separated from the overall subjective evaluation prediction value of multi-order noise, which can effectively screen the noise source to be optimized.

[0088] Furthermore, based on the above-mentioned preset mapping relationship, a subjective evaluation prediction can be performed on a single noise source: first, the single-order noise corresponding to the noise source is determined, and the weighting coefficient corresponding to the subjective evaluation prediction value of the single-order noise is determined through the above-mentioned preset mapping relationship, and the subjective evaluation prediction values ​​of all single-order noises corresponding to the noise source are weightedly calculated, and the weighted calculation result is used as the subjective evaluation prediction value corresponding to the noise source.

[0089] Furthermore, the weighted calculation process can be referred to in the above embodiment and will not be described in detail here.

[0090] For example, single-order noises m1, m2, m3, and m4 are extracted from multi-order noises, where m1, m2, and m3 correspond to the same noise source A, and m4 corresponds to the noise source D.

[0091] Calculate the subjective evaluation prediction value of noise source A:

[0092]

[0093] Among them, s1, s2, s3 are the subjective evaluation prediction values ​​of single-order noise m1, m2, m3 respectively; N1, N2, N3 are the weighting coefficients corresponding to s1, s2, s3 respectively.

[0094] Subjective evaluation prediction value of noise source D:

[0095]

[0096] Among them, s4 is the subjective evaluation prediction value of the single-order noise m4, and N4 is the weighting coefficient corresponding to s4.

[0097] As a feasible implementation method, it is necessary to set a corresponding judgment threshold for the noise source to determine whether the noise source meets the sound quality requirements in combination with the subjective evaluation prediction value of the noise source, and optimize the noise source that does not meet the requirements.

[0098] As another feasible implementation, when there are multiple noise sources to be optimized, the noise sources may be sorted according to their subjective evaluation prediction values ​​to determine the optimization order of the noise sources.

[0099] The embodiment of the present application determines the weights of different subjective evaluation prediction values ​​in the overall noise evaluation of the noise source based on a pre-generated mapping relationship, and based on this, performs mathematical weighted calculation on the subjective evaluation prediction values ​​of multiple single-order noises corresponding to the noise source to obtain the subjective evaluation prediction value of the noise source. The prediction value can be regarded as a quality score for the noise source.

[0100] As a feasible implementation method, it is possible to judge whether the noise source meets the quality requirements based on the subjective evaluation prediction value of the noise source, thereby screening out the noise source to be optimized. The present application constructs the above mathematical model to accurately calculate the score of each noise source (i.e., the subjective evaluation prediction value), evaluate the noise source, and screen out the noise source to be optimized, which is more efficient.

[0101] To describe the multi-order noise processing method proposed in the present application in detail, an overall description is now given through the following examples.

[0102] The multi-order noise of the motor in the vehicle (the multi-order noise corresponds to two noise sources 1 and 2) is collected, wherein the sound collection bandwidth is set to 10280Hz, the resolution is 4Hz, and the signal is A-weighted. When the collected multi-order noise is converted into time-frequency, multiple single-order noises m1, m2, and m3 in the multi-order noise are extracted by tracking the speed, wherein m1 and m3 are emitted by noise source 1, and m2 is emitted by noise source 2; for each single-order noise, the corresponding order slice diagram can be obtained (for examples, see Figure 5 , where the horizontal axis is the rotation speed and the vertical axis is the noise amplitude. Figure 5 The two continuous broken lines in the figure represent a single-order noise and the background noise corresponding to the single-order noise respectively); set the first preset widening coefficient to 3%, the second preset widening coefficient to 22%, widen the bandwidth of the single-order noise by 3% of the original bandwidth on the basis of the original bandwidth as the first energy extraction bandwidth of the single-order noise, and widen the bandwidth of the background noise by 22% of the original bandwidth on the basis of the single-order original bandwidth as the second energy extraction bandwidth of the background noise; extract the energy in the first energy extraction bandwidth as the energy M of the single-order noise, and extract the energy in the second energy extraction bandwidth as the energy E of the above-mentioned background noise; obtain the sound pressure level difference between the single-order noise and its background noise based on the formula Δ=|EM|-6dB; calculate the difference average value=sound pressure level difference / number of rotation speeds; take the difference average value as Figure 6The x in the correlation function shown is used as the actual subjective score y corresponding to the average value of the difference based on the pre-generated correlation function y=0.575x+5.4191, and the actual subjective score is used as the subjective evaluation prediction value of the single-order noise; the subjective evaluation prediction value s1=6.075 points corresponding to the single-order noise m1, the subjective evaluation prediction value s2=6.552 points corresponding to the single-order noise m2, and the subjective evaluation prediction value s3=6.655 points corresponding to the single-order noise m3 are obtained; based on the preset mapping relationship in the above embodiment, the obtained subjective evaluation prediction values ​​of the single-order noise are After weighted calculation, the overall subjective evaluation prediction value of multi-order noise is 6.34 points, the subjective evaluation prediction value of noise source 1 is 6.27 points, and the subjective evaluation prediction value of noise source 2 is 6.56 points; if according to the project development goal, the subjective evaluation prediction value of the multi-order noise needs to be greater than 6.5 points, then combined with the above calculation results, the noise source 1 that needs to be optimized can be determined; optionally, after replacing the component corresponding to the noise source 1, the multi-order noise after replacement is processed again by the above-mentioned multi-order noise processing method, and the overall subjective evaluation prediction value of the multi-order noise after replacement is 6.6 points, which meets the requirements.

[0103] In summary, the multi-order noise processing method of the present application extracts the single-order noise in the multi-order noise for separate noise analysis, and widens the bandwidth on the basis of the bandwidth of the single-order noise to extract the complete single-order noise energy and background noise energy, thereby enhancing the accuracy of noise evaluation; on this basis, the sound pressure level difference is calculated considering the principle of superposition of the sound pressure level of the sound in the human ear, so that the noise evaluation result is more in line with the human ear's auditory perception; the sound pressure level difference is then averaged using the number of rotation speeds so that the data can be organized into data that is convenient for transmission and statistics; the subjective evaluation of the single-order noise is then predicted based on the average value of the difference, and the overall subjective evaluation prediction value of the multi-order noise and the subjective evaluation prediction value of a single noise source are obtained by weighted calculation of the subjective evaluation prediction value of the single-order noise. The present application analyzes each single-order noise in its complete bandwidth field, and can accurately evaluate the problems of each order in the multi-order noise; using the subjective evaluation prediction value of the single-order noise as the basic data, the overall subjective evaluation of the multi-order noise is predicted through a mathematical model, thereby improving the accuracy of the evaluation and achieving rapid evaluation. In addition, the evaluation of each noise source can be quickly predicted based on the subjective evaluation prediction value of single-order noise. According to the evaluation prediction results, the noise source to be optimized can be screened out more efficiently to improve the sound quality of the product.

[0104] Figure 7 is a block diagram of a multi-order noise processing device according to an exemplary embodiment of the present application. Figure 7 As shown, the device 700 includes: an acquisition module 701, a determination module 702, a prediction module 703 and a calculation module 704.

[0105] An acquisition module 701 is used to acquire multi-order noise to be evaluated and separate multiple single-order noises from the multi-order noise;

[0106] A determination module 702 is used to obtain a bandwidth of the single-order noise, and determine a difference in sound pressure level of the single-order noise relative to a background noise of the single-order noise according to the bandwidth of the single-order noise;

[0107] A prediction module 703 is used to predict the subjective evaluation of the single-order noise according to the sound pressure level difference, and obtain a predicted value of the subjective evaluation of the single-order noise;

[0108] The calculation module 704 is used to calculate the overall subjective evaluation prediction value of the multi-order noise according to the subjective evaluation prediction value of the single-order noise.

[0109] In the embodiment of the present application, multi-order noise corresponds to multiple noise sources, and the calculation module 804 is further used to: calculate the subjective evaluation prediction value corresponding to a single noise source according to the subjective evaluation prediction value of the single-order noise, so as to determine the noise source to be optimized; and screen the noise source to be optimized from multiple noise sources according to the subjective evaluation prediction value corresponding to the noise source.

[0110] In an embodiment of the present application, the determination module 702 is further used to: determine a first energy extraction bandwidth based on a first preset widening coefficient and the bandwidth of a single-order noise, so as to obtain the energy of the single-order noise according to the first energy extraction bandwidth; determine a second energy extraction bandwidth based on a second preset widening coefficient and the bandwidth of the single-order noise, so as to obtain the energy of the background noise according to the second energy extraction bandwidth; calculate the sound pressure level difference according to the energy of the background noise, the energy of the single-order noise and a preset superposition coefficient, and the preset superposition coefficient is determined based on the sound pressure level superposition principle.

[0111] In an embodiment of the present application, the prediction module 703 is further used to: obtain the number of rotational speeds corresponding to multi-order noise; use the sound pressure level difference as the dividend and the number of rotational speeds as the divisor to calculate the difference average value; predict the subjective evaluation prediction value of the single-order noise based on the difference average value and the corresponding preset correlation function, and the preset correlation function is obtained by correlation analysis of the difference average value and the actual subjective score.

[0112] In an embodiment of the present application, the prediction module 703 is further used to: obtain a speed range corresponding to multi-order noise; quantize the speed range according to a preset step size to obtain a quantized speed sub-range, and determine the number of speed sub-ranges as the number of speeds.

[0113] In an embodiment of the present application, the calculation module 704 is further used to: based on a preset mapping relationship, perform weighted calculation on the subjective evaluation prediction values ​​of all single-order noises corresponding to the multi-order noise, and use the weighted calculation result as the overall subjective evaluation prediction value of the multi-order noise. The preset mapping relationship is used to characterize the mapping between the subjective evaluation prediction value of the single-order noise and the weighting coefficient corresponding to the subjective evaluation prediction value.

[0114] In an embodiment of the present application, the above-mentioned device 704 is further used to: determine the single-order noise corresponding to the noise source; based on a preset mapping relationship, perform weighted calculation on the subjective evaluation prediction values ​​of all single-order noises corresponding to the noise source, and use the weighted calculation result as the subjective evaluation prediction value corresponding to the noise source. The preset mapping relationship is used to characterize the mapping between the subjective evaluation prediction value of the single-order noise and the weighting coefficient corresponding to the subjective evaluation prediction value.

[0115] In the embodiment of the present application, the calculation module 704 is further used to: determine the weighting coefficient corresponding to the subjective evaluation prediction value of the single-order noise based on a preset mapping relationship;

[0116] The subjective evaluation prediction value of single-order noise is weighted as follows;

[0117]

[0118] Among them, s1, s2, ...sn are the subjective evaluation prediction values ​​of different single-order noises respectively; N1, N2, ...Nn are the weighting coefficients corresponding to s1, s2, ...sn respectively, n is the number of single-order noises, and S is the weighted calculation result.

[0119] It should be noted that the above explanation of the embodiment of the multi-order noise processing method is also applicable to the multi-order noise processing device of the vehicle subsystem in the embodiment of the present application, and the specific process will not be repeated here.

[0120] Each module in the above-mentioned multi-order noise processing device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0121] In summary, the multi-order noise processing device of the present application extracts the single-order noise in the multi-order noise for separate noise analysis, and widens the bandwidth on the basis of the bandwidth of the single-order noise to extract the complete single-order noise energy and background noise energy to enhance the accuracy of noise evaluation; on this basis, the sound pressure level difference is calculated considering the principle of superposition of the sound pressure level of the sound in the human ear, so that the noise evaluation result is more in line with the human ear's auditory perception; the sound pressure level difference is then averaged using the number of rotation speeds to organize the data into data that is convenient for transmission and statistics; the subjective evaluation of the single-order noise is then predicted based on the average value of the difference, and the overall subjective evaluation prediction value of the multi-order noise and the subjective evaluation prediction value of a single noise source are obtained by weighted calculation of the subjective evaluation prediction value of the single-order noise. The present application analyzes each single-order noise in its complete bandwidth field, and can accurately evaluate the problems of each order in the multi-order noise; using the subjective evaluation prediction value of the single-order noise as the basic data, the overall subjective evaluation of the multi-order noise is predicted through a mathematical model, thereby improving the accuracy of the evaluation and achieving rapid evaluation.

[0122] In addition, based on the subjective evaluation prediction value of single-order noise, the evaluation of each noise source can be quickly predicted. According to the evaluation prediction result, the noise source to be optimized can be screened out more efficiently to improve the sound quality of the product. In order to implement the above embodiment, the embodiment of the present application also proposes an electronic device 800, such as Figure 8 As shown, the electronic device 800 may specifically include: a memory 801, a processor 802, and a computer program stored in the memory 801 and executable on the processor 802. When the processor 802 executes the program, the steps of the multi-order noise processing method shown in the above embodiment are implemented.

[0123] In summary, the present application proposes an electronic device, which extracts the single-order noise from the multi-order noise for separate noise analysis, and widens the bandwidth on the basis of the bandwidth of the single-order noise to extract the complete single-order noise energy and background noise energy, so as to enhance the accuracy of noise evaluation; on this basis, the sound pressure level difference is calculated considering the principle of superposition of the sound pressure level of the sound in the human ear, so that the noise evaluation result is more in line with the human ear's auditory perception; the sound pressure level difference is then averaged using the number of rotation speeds, so that the data can be organized into data that is convenient for transmission and statistics; the subjective evaluation of the single-order noise is then predicted based on the average value of the difference, and the overall subjective evaluation prediction value of the multi-order noise and the subjective evaluation prediction value of a single noise source are obtained by weighted calculation of the subjective evaluation prediction value of the single-order noise. The present application analyzes each single-order noise in its complete bandwidth field, and can accurately evaluate the problems of each order in the multi-order noise; the subjective evaluation prediction value of the single-order noise is used as the basic data, and the overall subjective evaluation of the multi-order noise is predicted through a mathematical model, so as to improve the accuracy of the evaluation and achieve rapid evaluation. In addition, the evaluation of each noise source can be quickly predicted based on the subjective evaluation prediction value of single-order noise. According to the evaluation prediction results, the noise source to be optimized can be screened out more efficiently to improve the sound quality of the product.

[0124] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0125] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. 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.

[0126] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A multi-order noise processing method, characterized in that: include: Acquire multi-order noise to be evaluated, and separate multiple single-order noises from the multi-order noise; Acquire the bandwidth of the single-order noise, and determine the difference in sound pressure level of the single-order noise relative to the background noise of the single-order noise according to the bandwidth of the single-order noise; Predicting a subjective evaluation of the single-order noise according to the sound pressure level difference to obtain a predicted value of the subjective evaluation of the single-order noise; The overall subjective evaluation prediction value of the multi-order noise is calculated according to the subjective evaluation prediction value of the single-order noise.

2. The method according to claim 1, characterized in that: The multi-order noise corresponds to multiple noise sources. After obtaining the subjective evaluation prediction value of the single-order noise, the method further includes: Calculate the subjective evaluation prediction value corresponding to a single noise source according to the subjective evaluation prediction value of the single-order noise; A noise source to be optimized is screened from the plurality of noise sources according to the subjective evaluation prediction value corresponding to the noise source.

3. The method according to claim 1, characterized in that The step of determining the difference in sound pressure level of the single-order noise relative to the background noise of the single-order noise based on the bandwidth of the single-order noise comprises: Determining a first energy extraction bandwidth based on a first preset broadening coefficient and the bandwidth of the single-order noise, so as to obtain the energy of the single-order noise according to the first energy extraction bandwidth; Determine a second energy extraction bandwidth based on a second preset broadening coefficient and the bandwidth of the single-order noise, so as to obtain the energy of the background noise according to the second energy extraction bandwidth; The sound pressure level difference is calculated according to the energy of the background noise, the energy of the single-order noise and a preset superposition coefficient.

4. The method according to claim 1, characterized in that: The step of predicting the subjective evaluation of the single-order noise according to the sound pressure level difference to obtain a predicted value of the subjective evaluation of the single-order noise includes: Obtaining the number of rotation speeds corresponding to the multi-order noise; The sound pressure level difference is used as the dividend and the number of rotation speeds is used as the divisor to calculate and obtain the difference average value; The subjective evaluation prediction value of the single-order noise is predicted according to the difference average value and a corresponding preset correlation function, wherein the preset correlation function is obtained by analyzing the correlation between the difference average value and the actual subjective score of the noise.

5. The method according to claim 4, characterized in that The obtaining the number of rotation speeds corresponding to the multi-order noise includes: Obtaining a speed range corresponding to the multi-order noise; The speed interval is quantized according to a preset step length to obtain quantized speed sub-intervals, and the number of the speed sub-intervals is determined as the number of speeds.

6. The method according to claim 1, characterized in that The step of calculating the overall subjective evaluation prediction value of the multi-order noise according to the subjective evaluation prediction value of the single-order noise comprises: Based on a preset mapping relationship, a weighted calculation is performed on the subjective evaluation prediction values ​​of all single-order noises corresponding to the multi-order noise, and the weighted calculation result is used as the overall subjective evaluation prediction value of the multi-order noise. The preset mapping relationship is used to characterize the mapping between the subjective evaluation prediction value of the single-order noise and the weighting coefficient corresponding to the subjective evaluation prediction value.

7. The method according to claim 2, characterized in that: The step of calculating the subjective evaluation prediction value corresponding to a single noise source according to the subjective evaluation prediction value of the single-order noise includes: Determine the single-order noise corresponding to the noise source; Based on a preset mapping relationship, a weighted calculation is performed on the subjective evaluation prediction values ​​of all single-order noises corresponding to the noise source, and the weighted calculation result is used as the subjective evaluation prediction value corresponding to the noise source. The preset mapping relationship is used to characterize the mapping between the subjective evaluation prediction value of the single-order noise and the weighting coefficient corresponding to the subjective evaluation prediction value.

8. The method according to claim 6 or 7, characterized in that: Based on the preset mapping relationship, a weighted calculation is performed on the subjective evaluation prediction value of the single-order noise, including: Based on the preset mapping relationship, determining a weighting coefficient corresponding to the subjective evaluation prediction value of the single-order noise; The subjective evaluation prediction value of the single-order noise is weighted as follows: Among them, s1, s2, ...sn are respectively the subjective evaluation prediction values ​​of different single-order noises; N1, N2, ...Nn are respectively the weighting coefficients corresponding to s1, s2, ...sn, n is the number of the single-order noises, and S is the weighted calculation result.

9. A multi-order noise processing device, characterized in that: include: An acquisition module, used for acquiring multi-order noise to be evaluated, and separating a plurality of single-order noises from the multi-order noise; A determination module, configured to obtain a bandwidth of the single-order noise, and determine a difference in sound pressure level of the single-order noise relative to a background noise of the single-order noise according to the bandwidth of the single-order noise; A prediction module, used for predicting the subjective evaluation of the single-order noise according to the sound pressure level difference, and obtaining a predicted value of the subjective evaluation of the single-order noise; A calculation module is used to calculate the overall subjective evaluation prediction value of the multi-order noise according to the subjective evaluation prediction value of the single-order noise.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the multi-order noise processing method according to any one of claims 1 to 8 are implemented.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the multi-order noise processing method as described in any one of claims 1 to 8 are implemented.

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