Noise testing method and device, electronic equipment and storage medium
By acquiring and processing noise data in the real car environment, generating control signals to excite speakers, simulating and measuring the noise propagation effect, the problem that simulation tests are difficult to accurately simulate real car noise, and accurate simulation and analysis of real car noise is achieved.
Patent Information
- Application Number
- CN202411522029.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-29
- Publication Date
- 2025-03-04
AI Technical Summary
Existing simulation tests are difficult to accurately simulate the noise environment of real vehicles, resulting in high complexity in the development and iteration of noise control algorithms.
By acquiring acceleration data and microphone data in a real-life environment, processing these data using a preset algorithm to generate control signals, excite speakers to make sounds, and pick up sounds through the microphone to simulate and measure the propagation effect of noise.
Accurate simulation of the noise situation of the real car is achieved, the limitations of the difficulty of simulation testing to fully cover the complex factors of the real car are overcome, and noise data and analysis are provided closer to the real use scenarios.
Smart Images
Figure CN119555394A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of noise testing, and in particular to a noise testing method, device, electronic equipment and storage medium. Background Art
[0002] In the automotive industry, cabin noise control is a key research area. Common methods include passive control based on NVH (NoiseVibrationHarshness) analysis and structural improvement, and active control through adaptive filtering and speaker sound playback. With the development of new energy vehicles, the increase in battery weight has led to the demand for lightweighting, making it easier for road tire vibrations to be transmitted into the vehicle. Therefore, the road noise control algorithm (RNC algorithm) in the active control scheme has become a research and development focus. However, due to the nonlinear and time-varying characteristics of its noise source and transmission path, conventional software simulation tests are difficult to accurately simulate the actual effect, resulting in the actual noise reduction effect can only be verified in real vehicle dynamic testing, which increases the complexity of development and iteration. Summary of the invention
[0003] The present application provides a noise testing method, device, electronic device and storage medium, aiming to solve the problem that existing simulation tests are difficult to accurately simulate the actual vehicle effect.
[0004] In a first aspect, the present application provides a noise testing method, the noise testing method comprising:
[0005] Acquire acceleration data and first microphone data;
[0006] The acceleration data and the first microphone data are processed by a preset algorithm to obtain a control signal, and the control signal is used to stimulate a speaker in a real vehicle environment to make a sound. After the speaker makes a sound, the sound is picked up by at least one first microphone and / or at least one second microphone configured in the real vehicle environment. The microphone data obtained by picking up the sound and the first microphone data are used as the microphone data after noise reduction.
[0007] In a second aspect, the present application further provides a noise testing device, the noise testing device comprising:
[0008] A data receiving module, used for acquiring acceleration data and first microphone data;
[0009] A data processing module is used to process the acceleration data and the first microphone data through a preset algorithm to obtain a control signal, wherein the control signal is used to stimulate a speaker in a real vehicle environment to make a sound, and after the speaker makes a sound, the sound is picked up by at least one first microphone and / or at least one second microphone configured in the real vehicle environment, and the microphone data obtained by picking up the sound and the first microphone data are used as the microphone data after noise reduction.
[0010] In a third aspect, the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the steps of the noise testing method as described in any one of the first aspects are implemented.
[0011] In a fourth aspect, the present application provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the noise testing method as described in any one of the first aspects.
[0012] The present application provides a noise testing method, device, electronic device and storage medium, which is intended to solve the problem that it is difficult for existing simulation tests to accurately simulate the effect of real vehicles. The method obtains acceleration data and first microphone data in a real vehicle environment, and uses a preset algorithm to process the acceleration data and the first microphone data to obtain a control signal, which is used to stimulate the speaker in the real vehicle environment to sound. Then, at least one first microphone and / or at least one second microphone configured in the real vehicle environment are used to pick up sound, and the microphone data obtained by picking up sound is superimposed with the first microphone data before noise reduction to obtain the microphone data after noise reduction. The speaker can simulate noise of a specific frequency and intensity, and then the microphone picks up sound, and the propagation effect of this simulated noise in the real vehicle environment and the effect after superposition with other actual noise sources (such as the noise source represented by the first microphone data) can be accurately measured. This actual operation and data processing method based on the real vehicle environment can more accurately simulate the noise situation in the real vehicle, and is not limited to the theoretical model like the simulation test, thereby solving the problem that the existing simulation test is difficult to accurately simulate the effect of the real vehicle.
[0013] Therefore, this application achieves accurate simulation of the noise conditions of a real vehicle by directly acquiring real noise data in a real vehicle environment and using preset algorithm processing and speakers to simulate noise propagation. It overcomes the limitation that simulation tests cannot fully cover the complex factors of a real vehicle, thereby providing noise data and analysis that are closer to real usage scenarios. BRIEF DESCRIPTION OF THE DRAWINGS
[0014] In order to more clearly illustrate the technical solutions in the present application or the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are 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 work.
[0015] Figure 1 is a flow chart of the noise testing method provided by the present application;
[0016] Figure 2is a schematic diagram of a noise testing device provided in Example 1 of the present application;
[0017] Figure 3 is a schematic diagram of a noise testing device provided in Embodiment 2 of the present application;
[0018] Figure 4 is a schematic diagram of a noise testing device provided in Embodiment 3 of the present application;
[0019] Figure 5 It is a flow chart of the first stage provided by the embodiment of the present application;
[0020] Figure 6 It is a flow chart of the second stage provided in the embodiment of the present application;
[0021] Figure 7 is a structural block diagram of the noise testing device provided by the present application;
[0022] Figure 8 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0023] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0024] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein.
[0025] In order to solve the problem that existing simulation tests are difficult to accurately simulate the effects of real vehicles, this application proposes a noise testing method, device, electronic device and storage medium. This method directly obtains real noise data (including acceleration data and first microphone data) in a real vehicle environment, processes these data using a preset algorithm to generate a control signal, and simulates the propagation and superposition of noise sources through a speaker, thereby achieving an accurate simulation of the noise conditions of the real vehicle, solving the limitation that simulation tests are difficult to fully cover the complex factors of the real vehicle. This noise testing method can be widely used in the automotive manufacturing industry (such as vehicle research and development and quality inspection), automotive parts suppliers (such as parts performance evaluation and new product development) and transportation research institutions (such as traffic noise research and noise reduction technology research and development). By accurately simulating the noise conditions of real vehicles, the vehicle noise control effect can be improved, parts and noise reduction technology can be optimized, and traffic noise research can be supported.
[0026] Combine the following Figure 1-Figure 8 The present application describes a noise testing method, device, electronic device and storage medium.
[0027] Please refer to Figure 1 , Figure 1 : is a flow chart of the noise testing method provided by the present application. A noise testing method comprises:
[0028] S110, acquiring acceleration data and first microphone data.
[0029] Specifically, the acceleration data and the first microphone data can be offline recorded data of real vehicle operation. Offline recording refers to the collection and recording of relevant data without relying on real-time online connection (for example, without relying on real-time data transmission network, etc.). This method can easily collect data during the operation of the real vehicle, and the recorded data can be used multiple times in subsequent analysis without the need to conduct real vehicle testing again to obtain data.
[0030] For example, multi-channel acceleration data (such as at least 12 channels, which means that acceleration data from 12 different positions or directions can be collected simultaneously) and multi-channel microphone data (such as at least 4 channels, which means that sound data can be collected from 4 different positions or directions simultaneously) of the actual vehicle under various working conditions and interference scenarios can be obtained. The recording process is collected synchronously under the same clock. Acceleration data can reflect the state of the actual vehicle during operation, such as the vibration of the vehicle. Acceleration data serves as an important reference in the noise reduction algorithm, because the vibration of the vehicle is related to the generation and propagation of noise. By analyzing the acceleration data, we can better understand the source and characteristics of the noise, thereby providing a reference for noise reduction.
[0031] In a real car environment, microphones can be placed in different locations inside the car (such as near the driver's seat, the back seat, near the engine compartment, etc.) or in specific locations outside the car to collect various sounds generated when the vehicle is running, including engine noise, tire-road friction, wind noise, etc. For example, sound data can be collected from 4 different locations or directions at the same time. Microphone data from different channels can reflect the noise conditions at different locations of the vehicle, which helps analyze the source of the noise, the propagation path, and the distribution in different areas of the car.
[0032] S120, the first microphone data is processed by a preset algorithm to obtain a control signal, where the control signal is used to stimulate a speaker in a real vehicle environment to make a sound. After the speaker makes a sound, the sound is picked up by at least one first microphone and / or at least one second microphone configured in the real vehicle environment. The microphone data obtained by picking up the sound and the first microphone data are used as the microphone data after noise reduction.
[0033] Specifically, the preset algorithm is used to deal with noise-related problems, such as RNC noise reduction algorithm, FxLMS (Filtered-x Least Mean Square), FeLMS (Filtered-e Least Mean Square), FxRLS (Filtered-x Recursive Least Squares), and other different types of adaptive filtering algorithms. The acceleration data and the first microphone data are processed by the preset algorithm in order to obtain useful information from this complex sound signal in order to generate a control signal, which is used to stimulate the speaker in the real car environment to sound. After receiving the control signal, the speaker will emit a sound of a specific frequency, intensity, and phase according to the instruction of the control signal. For example, if the control signal indicates that a sound with the same frequency but opposite phase as a certain noise is to be emitted, the speaker will sound according to this requirement in order to achieve noise cancellation or adjustment.
[0034] The above steps S110 and S120 are described in detail below through embodiments.
[0035] Please refer to Figure 2 , Figure 2 2 is a schematic diagram of a noise testing device provided in Embodiment 1 of the present application. A noise testing device includes a data management module 211 , a data conversion module 212 , a data processing module 213 , a data transmission module 214 and a real vehicle testing module 215 .
[0036] Exemplarily, the data management module 211 is used to analyze and process the first microphone data and the noise-reduced microphone data to obtain noise reduction result statistics.
[0037] Exemplarily, the data conversion module 212 is respectively connected to the data management module 211 and the data processing module 213, and is used to send the first microphone data to the data processing module 213, and forward the noise-reduced microphone data received from the data processing module 213 to the data management module 211.
[0038] Exemplarily, the data transmission module 214 includes a first circuit and a second circuit. The first circuit includes a DAC (Digital-to-Analog Converter) chip for digital-to-analog conversion and a PA (Power Amplifier) chip for power amplification. The second circuit includes a microphone preamplifier circuit, a bandpass processing circuit, and an ADC (Analog-to-Digital Converter) chip for analog-to-digital conversion.
[0039] Exemplarily, the real vehicle test module 215 is used to provide a real vehicle environment. For example, the pre-installed microphone and speaker configuration in the real vehicle can be externally connected and utilized. It is also possible to set up an additional microphone in a similar position or replace the control source to carry out the test. When performing static measurements, the real vehicle should be placed in a quiet garage or an open space without interference to fully avoid being affected by sudden noise in the surrounding environment.
[0040] Please refer to Figure 3 , Figure 3 Schematic diagram of the noise test device provided in the second embodiment of the present application. Exemplarily, the data management module 211 adopts a host computer, the data conversion module 212 adopts an A2B (Automotive Audio Bus, A2B) analyzer, and the data processing module 213 adopts a DSP (Digital Signal Processing) processing platform.
[0041] Specifically, the host computer is connected to the A2B analyzer device via a USB serial port, which supports the sending and receiving of audio data under the ASIO protocol, has data storage and signal processing capabilities, and can perform statistical analysis on the data and present the results. For example, the processed results are presented in a visual form (such as charts, numerical values, etc.) for easy viewing and analysis by users.
[0042] The A2B analyzer includes A2B bus related chip modules such as ADI AD24xx series, which are connected to the host computer via USB serial port and connected to the DSP processing platform via twisted pair. The analyzer supports communication with the host computer via ASIO (an audio stream input / output protocol) protocol, thereby realizing the sending and receiving of multi-channel digital audio streams; at the same time, it is connected to the DSP processing platform via A2B protocol, and its operating clock, channel mapping and node selection can be configured by the host computer software.
[0043] The DSP processing platform includes, for example, core DSP chip modules such as the ADI Sharc series, which are connected to the A2B analyzer via twisted pair cables to transmit and receive multi-channel digital signals, and are connected to the data transmission module via chip pins to receive microphone signal input and speaker signal output.
[0044] For example, Figure 3 The noise testing device shown in the figure has a noise testing process including:
[0045] (1) Data recording stage:
[0046] When recording the original data of the real vehicle, it is necessary to obtain multi-channel accelerometer data (such as ≥12 channels) and multi-channel microphone data (such as ≥4 channels) with simultaneous clocks under multiple working conditions and multiple interference scenarios. During the recording process, attention should be paid to the channel sequence confirmation, value calibration, sensor front-end circuit preprocessing configuration, etc. of the real vehicle audio stream link. The data before noise reduction can use the real vehicle original data recorded offline.
[0047] (2) Data transmission and processing stage:
[0048] A2B analyzer data forwarding: The A2B analyzer sends the real vehicle operation accelerometer data (such as 12 channels) and microphone data (such as 4 channels) recorded offline to the DSP processing platform in real time; at the same time, it receives the noise-reduced microphone data (such as 4 channels) uploaded by the DSP processing platform in real time.
[0049] Data processing of DSP processing platform: The DSP processing platform mainly performs RNC noise reduction algorithm processing (such as presented by FxLMS algorithm) through a preset algorithm module. The reference signal input it receives is the acceleration data recorded offline, and the error signal input is the first microphone data recorded offline. The acceleration data and the first microphone data recorded offline can be pre-stored in the host computer, and sent by the host computer to the DSP processing platform through the A2B analyzer. The output first control signal is a multi-channel speaker digital signal excitation signal (such as 5 channels). The first control signal can be connected to the in-vehicle speakers (such as 4 door bass speakers and 1 low-frequency cannon) through the DAC and power amplifier chip PA in the data transmission module; the first microphone of the actual vehicle test module is connected to the DSP processing platform through the low-latency ADC and conditioning circuit configuration using the A2B protocol. Finally, the first microphone data is linearly superimposed with the second microphone data that cancels the sound field obtained in real time from the actual vehicle environment to obtain the microphone data after noise reduction.
[0050] Host computer data processing: The host computer obtains the noise reduction result statistics based on the input of the microphone data before noise reduction (i.e., the first microphone data) and the microphone data after noise reduction (i.e., the first microphone data and the second microphone data). Specifically, the host computer calculates the noise reduction result statistics by performing A-weighted filtering (a frequency weighting method used in sound measurement and analysis) and bandpass filtering on the data, and performs statistical analysis on the data according to the set algorithm, such as statistically analyzing the noise reduction curve of the crossover point, or the short-term average noise reduction amount that changes over time.
[0051] It is understandable that based on Figure 3 For example, in a static real car environment, the upper computer is used to send data based on offline recording of the real car under different working conditions and interference scenarios, and then obtain an inverted first control signal. The inverted first control signal can make the real speaker of the real car sound, and after the sound is transmitted through the real secondary path of the real car, it is picked up by the first microphone to obtain the second microphone data. The first microphone data and the second microphone data are then superimposed to obtain the microphone data after noise reduction for data analysis. In this way, it is easy to iterate and verify each algorithm version and compare the effects.
[0052] However, Figure 3 The static real car environment shown has only a secondary sound field. Since there is no real primary sound field, this means that the error microphone (i.e., the first microphone) can only receive the sound signal output by the secondary source (i.e., the first control signal). Therefore, in a static real car environment, it may not be possible to subjectively experience a better noise reduction effect, and it is not possible to set up a virtual microphone (i.e., the second microphone) to conduct an offline evaluation of the "virtual microphone technology" in the RNC algorithm. To further solve this problem, this application also provides Figure 4 Examples of displays.
[0053] Please refer to Figure 4 , Figure 4 is a schematic diagram of a noise testing device provided in Embodiment 3 of the present application. For example, Figure 4 The demonstrated example mainly includes the primary noise field reconstruction step in the first stage and the noise reduction effect evaluation step based on the virtual microphone in the second stage, as follows:
[0054] Exemplarily, in the first stage, the acquired acceleration data and first microphone data are used to reconstruct the primary noise field through a preset algorithm (such as an adaptive filtering algorithm) to obtain a second control signal. The second control signal is used to stimulate the speaker in the real vehicle environment to sound (i.e., the primary source), so that at least one first microphone picks up sound and at least one second microphone picks up sound, and obtains second microphone data corresponding to the sound picked up by the first microphone and third microphone data corresponding to the sound picked up by the second microphone.
[0055] Specifically, please refer to Figure 5 , Figure 5 This is a flow chart of the first stage provided by the embodiment of the present application. The steps of primary noise field reconstruction based on the first stage include:
[0056] S501, modeling the primary path transfer function based on an adaptive filtering algorithm, and in the modeling process, iteratively updating the first adaptive filter parameters using a minimum mean square error criterion.
[0057] S502, when the iterative process converges, the obtained first adaptive filter parameters can characterize the primary path transfer function under a stable working condition to complete the reconstruction of the primary noise field.
[0058] Specifically, based on steps S501 and S502, the modeling of the primary path transfer function is implemented based on the adaptive filtering algorithm, and the primary path transfer function is defined as:
[0059] w1(n+1)=w1(n)+μe(n)x(n); (1)
[0060] Wherein, formula (1) describes the updating method of the first adaptive filter parameter w1; μ is the learning step size, which can be taken as 0.01 to avoid divergence of the learning process; x(n) represents the acceleration data sequence; e(n) represents the sound field reconstruction residual at the corresponding moment.
[0061] Formula (1) indicates that at each discrete moment n, the first adaptive filter parameter w1(n+1) at the next moment is obtained by adding a correction term to the first adaptive filter parameter w1(n) at the current moment. The correction term is the product of the learning step size μ, the sound field reconstruction residual e(n) at the current moment, and the input acceleration data sequence x(n).
[0062] e(n)=d(n)-S P (x)*[w1(n) T x(n)]; (2)
[0063] Where d(n) represents the value of the first microphone data, S P (x) represents the function related to the secondary path, w1(n) T x(n) represents the second control signal. P (x)*[w1(n) T x(n) represents the second control signal w1(n) T x(n) passes through the primary path S P (x) Get the value of the second microphone data.
[0064] Formula (2) indicates that the sound field reconstruction residual e(n) is the difference between the first microphone data and the second microphone data obtained after the second control signal output passes through the primary path. That is, in reconstructing the primary noise field, the second microphone data is used to adjust the first adaptive filter parameter w1 of the preset algorithm module. After reconstructing the primary noise field, the second microphone data can be used to adjust the second adaptive filter parameter w2 of the preset algorithm module.
[0065] Furthermore, under unstable conditions, the primary path transfer function has time-varying characteristics. In order to avoid affecting the convergence of the first adaptive filter parameter w1, a characteristic statistic η(n) with a value of 0-1 is added to adjust the learning step size μ(n) in real time to avoid system divergence, which is defined as follows:
[0066] μ(n)=η(n)*μ0; (3);
[0067] Wherein, μ0 is a fixed step size preset for updating the first adaptive filter parameter w1. The value of the characteristic statistic η(n) depends on the short-time average energy E x and the short-time average coherence Track the time-varying characteristics of the system transfer path, which is defined as follows:
[0068]
[0069] Among them, k1 in the conditional judgment is the proportionality coefficient, which can be 0.8. k2 in the conditional judgment is the proportionality coefficient, which can be 0.9. E0 is the average energy corresponding to the steady-state working condition.
[0070] Among them, the short-time average coherence is defined as follows:
[0071]
[0072] Among them, R xd represents the cross-correlation function between the acceleration reference and the reconstruction noise error, R xx and R dd They represent the autocorrelation functions of the acceleration reference and the reconstruction noise error, respectively.
[0073] Among them, the short-time average energy E x is defined as follows:
[0074] E x =x(n) T x(n); (6)
[0075] therefore, Figure 4 and Figure 5The example shows the steps of the first stage of primary noise field reconstruction. The adaptive filtering algorithm is used to iteratively update the first adaptive filter parameters. The first adaptive filter parameters converged with the minimum mean square error criterion to characterize the primary path transfer function under stable conditions. And by setting the learning step size for acceleration data, the sound field reconstruction residual is calculated, and the learning step size is adjusted by characteristic statistics under unstable conditions to cope with time-varying characteristics to ensure that the algorithm works effectively.
[0076] The above formula (1) provides a transfer function between the first microphone (i.e., physical microphone) and the second microphone (i.e., virtual microphone), which is used to characterize the virtual microphone technology used in the algorithm processing. The sound signal is collected by remotely configuring the second microphone and transmitted to the data processing module. In addition, the configured second microphone (i.e., virtual microphone) is a simulation device that characterizes the sound received by the passenger's ears when riding. For example, an artificial head model is used to simulate the position of the passenger's head and ears to collect sound data. The second microphone is arranged on the left and right sides of the headrest of each seat to simulate the sound received by the passenger's ears. Each seat position contains a total of 2 channels for the left and right ears, and a total of 8 channels at four seat positions. The first microphone is a physical microphone actually configured at the roof position of the vehicle body, which is used to actually collect sound signals. The ceiling at each seat position has a total of 1 channel, and the four seat positions have a total of 4 channels.
[0077] Please refer to Figure 6 , Figure 6 : is a flowchart of the second stage provided by the embodiment of the present application. The noise reduction effect evaluation steps of the microphone based on the second stage include:
[0078] S601 , based on an adaptive filtering algorithm, using the second microphone data obtained after reconstructing the primary noise field, updating the second adaptive filter parameters to obtain a third control signal.
[0079] S602, the third control signal stimulates the speaker in the real vehicle environment to make sound (ie, the secondary source), so that at least one second microphone picks up sound, and the third microphone data and the first microphone data obtained by picking up the sound are used as the microphone data after noise reduction.
[0080] It should be noted that the first stage is to complete the reconstruction of the steady-state primary noise field for a given working condition. The steady-state primary noise field here can be understood as the basic state of noise in the system when no noise reduction processing is performed. The first stage can be marked as the T1 noise reduction OFF scene, and the second stage can be marked as the T2 noise reduction ON scene. The second stage will receive the second microphone data and the third microphone data. Among them, the second microphone data is used to update the second adaptive filter parameters, which helps to continuously optimize the noise reduction algorithm to adapt to the actual noise situation. The third microphone data is used to analyze the noise reduction effect before and after noise reduction. By comparing the changes in microphone data before and after noise reduction, the effect of noise reduction can be intuitively evaluated.
[0081] The noise testing device provided in the present application is described below. The noise testing device described below and the noise testing method described above can be referenced to each other.
[0082] Please refer to Figure 7 , Figure 7 A noise testing device 700 includes a data receiving module 701 and a data processing module 702 .
[0083] Exemplarily, the data receiving module 701 is used to obtain acceleration data and first microphone data.
[0084] Exemplarily, the data processing module 702 is used to process the acceleration data and the first microphone data through a preset algorithm to obtain a control signal, and the control signal is used to stimulate a speaker in a real vehicle environment to make a sound. After the speaker makes a sound, the sound is picked up by at least one first microphone and / or at least one second microphone configured in the real vehicle environment, and the microphone data obtained by picking up the sound and the first microphone data are used as the microphone data after noise reduction.
[0085] Exemplarily, the data processing module 702 is used to process the acceleration data and the first microphone data through a preset algorithm to obtain a control signal, and the control signal is used to stimulate a speaker in a real vehicle environment to make a sound. After the speaker makes a sound, the sound is picked up by at least one first microphone and / or at least one second microphone configured in the real vehicle environment, and the microphone data obtained by picking up the sound and the first microphone data are used as the microphone data after noise reduction.
[0086] Exemplarily, the noise testing device further includes:
[0087] A data management module, used for analyzing and processing the first microphone data and the noise-reduced microphone data to obtain noise reduction result statistics;
[0088] The data forwarding module is connected to the data management module and the data processing module respectively, and is used to send the first microphone data to the data processing module, and forward the noise-reduced microphone data received from the data processing module to the data management module.
[0089] Exemplarily, the data processing module 702 is further used for:
[0090] The acceleration data and the first microphone data are processed by a preset algorithm to obtain a first control signal, wherein the first control signal is used to stimulate a speaker in a real vehicle environment to make a sound. After the speaker makes a sound, the sound is picked up by at least one first microphone configured in the real vehicle environment. The second microphone data obtained by picking up the sound is superimposed with the first microphone data to obtain the microphone data after noise reduction.
[0091] Exemplarily, the data processing module 702 is further used for:
[0092] In the first stage, a primary noise field is reconstructed from the acceleration data and the first microphone data by a preset algorithm to obtain a second control signal, wherein the second control signal is used to stimulate a speaker in the real vehicle environment to make sound, so that at least one first microphone picks up sound and at least one second microphone picks up sound, and second microphone data corresponding to the sound picked up by the first microphone and third microphone data corresponding to the sound picked up by the second microphone are obtained respectively;
[0093] In the second stage, based on the adaptive filtering algorithm, the second microphone data obtained after reconstructing the primary noise field is used to update the second adaptive filter parameters to obtain a third control signal. The third control signal is used to stimulate the speaker in the real vehicle environment to make sound, so that at least one second microphone picks up sound. The third microphone data obtained by picking up sound is superimposed with the first microphone data to obtain the microphone data after noise reduction.
[0094] It should be noted here that the above-mentioned noise testing device provided in the embodiment of the present application can implement all the method steps implemented in the above-mentioned method embodiment, and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as those in the method embodiment will not be described in detail here.
[0095] Figure 8 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application, such as Figure 8As shown, the electronic device may include: a processor (Processor) 810, a communication interface (Communications Interface) 820, a memory (Memory) 830 and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other through the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute the noise testing method.
[0096] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art, and the computer software product is stored in a storage medium, including several instructions to ensure that a computer device (which can be a personal computer, a server, or a network device, etc.) executes all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk.
[0097] On the other hand, the present application also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the noise testing method provided by the above methods.
[0098] On the other hand, the present application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the computer program is executed by a processor to perform the noise testing methods provided above.
[0099] The embodiments of the present application provide an electronic device, a computer program product, and a processor-readable storage medium, on which the computer program stored enables the processor to implement all the method steps implemented in the above-mentioned method embodiments and to achieve the same technical effects. The parts and beneficial effects of this embodiment that are the same as those in the method embodiments will not be described in detail herein.
[0100] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.
[0101] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions to ensure that a computer device (which can be a personal computer, a server, or a network device, etc.) executes the methods described in each embodiment or some parts of the embodiments.
[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims
1. A noise testing method, characterized in that: The noise testing method comprises: Acquire acceleration data and first microphone data; The acceleration data and the first microphone data are processed by a preset algorithm to obtain a control signal, and the control signal is used to stimulate a speaker in a real vehicle environment to make a sound. After the speaker makes a sound, the sound is picked up by at least one first microphone and / or at least one second microphone configured in the real vehicle environment. The microphone data obtained by picking up the sound and the first microphone data are used as the microphone data after noise reduction.
2. The noise testing method according to claim 1, characterized in that: The control signal includes a first control signal, and the first microphone data is processed by a preset algorithm to obtain a control signal including: The acceleration data and the first microphone data are processed by a preset algorithm to obtain a first control signal, wherein the first control signal is used to stimulate a speaker in a real vehicle environment to make a sound. After the speaker makes a sound, the sound is picked up by at least one first microphone configured in the real vehicle environment. The second microphone data obtained by picking up the sound is superimposed with the first microphone data to obtain the microphone data after noise reduction.
3. The noise testing method according to claim 1, characterized in that: The control signal includes a second control signal, and the control signal obtained by processing the first microphone data through a preset algorithm includes: The primary noise field is reconstructed for the acceleration data and the first microphone data through a preset algorithm to obtain a second control signal, wherein the second control signal is used to stimulate the speaker in the real vehicle environment to make sound, so that at least one first microphone and at least one second microphone pick up sound, and second microphone data corresponding to the sound picked up by the first microphone and third microphone data corresponding to the sound picked up by the second microphone are respectively obtained.
4. The noise testing method according to claim 3, characterized in that: The reconstructing the primary noise field by using a preset algorithm for the acceleration data and the first microphone data comprises: Modeling the primary path transfer function based on an adaptive filtering algorithm, and in the modeling process, iteratively updating the first adaptive filter parameters using a minimum mean square error criterion; When the iterative process converges, the obtained first adaptive filter parameters can characterize the primary path transfer function under stable conditions to complete the reconstruction of the primary noise field.
5. The noise testing method according to claim 4, characterized in that: The control signal further includes a third control signal, and the control signal obtained by processing the first microphone data through a preset algorithm includes: Based on the adaptive filtering algorithm, the second microphone data obtained after reconstructing the primary noise field is used to update the second adaptive filter parameters to obtain a third control signal. The third control signal is used to stimulate the speaker in the real vehicle environment to make sound, so that at least one second microphone picks up sound. The third microphone data and the first microphone data obtained by picking up sound are used as the microphone data after noise reduction.
6. The noise testing method according to claim 1, characterized in that: The noise testing method further comprises: The first microphone data and the noise-reduced microphone data are analyzed and processed to obtain noise reduction result statistics.
7. A noise testing device, characterized in that: The noise testing device comprises: A data receiving module, used for acquiring acceleration data and first microphone data; A data processing module is used to process the acceleration data and the first microphone data through a preset algorithm to obtain a control signal, wherein the control signal is used to stimulate a speaker in a real vehicle environment to make a sound, and after the speaker makes a sound, the sound is picked up by at least one first microphone and / or at least one second microphone configured in the real vehicle environment, and the microphone data obtained by picking up the sound and the first microphone data are used as the microphone data after noise reduction.
8. The noise testing device according to claim 7, characterized in that: The noise testing device also includes: A data management module, used for analyzing and processing the first microphone data and the noise-reduced microphone data to obtain noise reduction result statistics; The data forwarding module is connected to the data management module and the data processing module respectively, and is used to send the first microphone data to the data processing module, and forward the noise-reduced microphone data received from the data processing module to the data management module.
9. The noise testing device according to claim 7, characterized in that: The data processing module is also used for: The acceleration data and the first microphone data are processed by a preset algorithm to obtain a first control signal, wherein the first control signal is used to stimulate a speaker in a real vehicle environment to make a sound. After the speaker makes a sound, the sound is picked up by at least one first microphone configured in the real vehicle environment. The second microphone data obtained by picking up the sound is superimposed with the first microphone data to obtain the microphone data after noise reduction.
10. The noise testing device according to claim 7, characterized in that: The data processing module is also used for: In the first stage, a primary noise field is reconstructed from the acceleration data and the first microphone data by a preset algorithm to obtain a second control signal, wherein the second control signal is used to stimulate a speaker in the real vehicle environment to make sound, so that at least one first microphone picks up sound and at least one second microphone picks up sound, and second microphone data corresponding to the sound picked up by the first microphone and third microphone data corresponding to the sound picked up by the second microphone are obtained respectively; In the second stage, based on the adaptive filtering algorithm, the second microphone data obtained after reconstructing the primary noise field is used to update the second adaptive filter parameters to obtain a third control signal. The third control signal is used to stimulate the speaker in the real vehicle environment to make sound, so that at least one second microphone picks up sound. The third microphone data obtained by picking up sound is superimposed with the first microphone data to obtain the microphone data after noise reduction.
11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the noise testing method according to any one of claims 1 to 6 are implemented.
12. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the noise testing method according to any one of claims 1 to 6 are implemented.