Vehicle howling level determination method and device, equipment and storage medium
By calculating the perceived weight coefficient and sharpness coefficient of noise using a preset perception weight function and spectral response function, and combining this with howling information, the problem of deviation between howling level determination and user perception is solved, achieving accurate howling level determination and effective acoustic control.
Patent Information
- Application Number
- CN202511141677.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-14
- Publication Date
- 2025-11-04
AI Technical Summary
Existing technologies classify the severity of howling by analyzing average sound pressure level values, without fully considering the impact of sound frequency distribution on human hearing. This results in a large discrepancy between the howling level and the user's actual experience, making it difficult to guide precise acoustic control strategies.
By employing a preset perception weighting function and a spectral response function, the perception weighting coefficient and sharpness coefficient of noise are calculated. Combined with whistling information, the whistling level of the vehicle is determined. Through the dynamic mapping between HPI value and whistling level and the target value of sound pressure level, accurate whistling level determination is achieved.
It significantly reduces the deviation between howling level determination and user subjective perception, provides accurate input for acoustic control strategies, and improves the efficiency of howling level reduction and auditory comfort.
Smart Images
Figure CN120895057A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of image forming technology, and more specifically to a method, apparatus, device, and storage medium for determining the whistling level of a vehicle. Background Technology
[0002] Noise, vibration, and harshness (NVH) performance is a core indicator for measuring ride quality. With the rapid development of vehicles, the increased operating frequency of powertrain systems has led to a growing problem of mid-to-high frequency whistling noises inside the vehicle. Compared to traditional low-frequency noise, whistling noises have a higher sharpness and prominence, easily causing strong feelings of annoyance to users.
[0003] In related technologies, the following scheme is mainly used to determine the severity of howling: a professional review team is formed to classify the severity based on subjective auditory perception in actual driving scenarios. However, this method is easily affected by individual differences in perception, resulting in insufficient stability. To address this issue, those skilled in the art have set up a method to measure the average sound pressure level of the target frequency band using acoustic instruments and determine the severity level based on a preset threshold range.
[0004] However, classifying the severity of feedback by analyzing the average sound pressure level does not fully consider the impact of sound frequency distribution on human hearing. This may lead to a significant discrepancy between the determined feedback level and the user's actual experience, making it difficult to guide precise acoustic control strategies to reduce the feedback level.
[0005] It should be noted that the information disclosed in the background section of this application is intended only to enhance the understanding of the general background of this application, and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention
[0006] In view of this, this application provides a method, apparatus, device and storage medium for determining the whistling level of a vehicle, in order to solve the problem that the severity of whistling is classified by analyzing the average sound pressure level value in the prior art, which does not fully consider the influence of sound frequency distribution on human ear perception, and may lead to a large deviation between the determined whistling level and the user's actual feeling, making it difficult to guide a precise acoustic control strategy to reduce the whistling level.
[0007] In a first aspect, embodiments of this application provide a method for determining the whistling level of a vehicle, including: Based on the collected vehicle noise, the whistling information corresponding to the vehicle noise is determined, wherein the whistling information is used to characterize the spectral characteristic parameters of the vehicle noise; Based on the spectral feature parameters corresponding to the whistling information, the perception weight coefficient corresponding to the vehicle noise is calculated through a preset perception weight function, wherein the preset perception weight function is used to characterize the mapping relationship between the noise signal and the subjective perception sensitivity of the human ear in a specific frequency band. Based on the spectral characteristic parameters corresponding to the whistling information, the sharpness coefficient corresponding to the vehicle noise is calculated through a preset spectral response function. The sharpness coefficient is used to characterize the degree to which the high-frequency energy concentration of the noise signal quantifies the piercing sensation in the human ear. The vehicle's whistling level is determined based on the whistling information, the perception weight coefficient, and the sharpness coefficient.
[0008] In this embodiment, firstly, based on the collected vehicle noise, the whistling information corresponding to the vehicle noise is determined; then, based on the spectral characteristic parameters corresponding to the whistling information, the perceptual weighting coefficient corresponding to the vehicle noise is calculated using a preset perceptual weighting function; next, based on the spectral characteristic parameters corresponding to the whistling information, the sharpness coefficient corresponding to the vehicle noise is calculated using a preset spectral response function; finally, based on the whistling information, the perceptual weighting coefficient, and the sharpness coefficient, the whistling level of the vehicle is determined. It can be understood that since the preset perceptual weighting function quantifies the mapping relationship between noise and human ear perception sensitivity in a specific frequency band, and the sharpness coefficient characterizes the degree to which the high-frequency energy concentration of noise quantifies the piercing sensation in the human ear, a comprehensive evaluation based on the whistling information, the perceptual weighting coefficient, and the sharpness coefficient can accurately determine the whistling level that matches the user's actual auditory experience. This method significantly reduces the deviation between the whistling level determination and the user's subjective perception, providing accurate input for acoustic control strategies, thereby effectively reducing the whistling level.
[0009] In one possible implementation, the howling information includes: the center frequency, bandwidth, and signal-to-noise ratio corresponding to the vehicle noise.
[0010] In this embodiment, the howling information includes the center frequency, bandwidth, and signal-to-noise ratio corresponding to the vehicle noise. This helps to accurately determine the perception weight coefficient and sharpness coefficient, thereby accurately determining the howling level that matches the user's actual auditory experience. To a certain extent, this reduces the deviation between the howling level determination and the user's subjective feeling, providing accurate input for acoustic control strategies.
[0011] One possible implementation includes: The time-domain signal of the vehicle noise was analyzed using short-time fast Fourier transform to determine the center frequency of the howling component.
[0012] In this embodiment, a short-time fast Fourier transform (SFT) is used to analyze the time-domain signal of vehicle noise to determine the center frequency of the whistling component. It can be understood that by dynamically tracking the time-varying whistling component using SFT, accurate positioning of the center frequency under transient conditions such as acceleration / deceleration can be achieved, which is beneficial for determining a more precise center frequency.
[0013] In one possible implementation, the step of calculating the sharpness coefficient of the vehicle noise based on the spectral feature parameters corresponding to the whistling information using a preset spectral response function includes: According to the formula: Determine the sharpness coefficient corresponding to the vehicle noise; Among them, the The center frequency is B, the bandwidth is B, and the center frequency is B. For the preset spectral response function, the This is the sharpness coefficient corresponding to the vehicle noise.
[0014] In this embodiment, by using the logarithmic weighted integral of the amplitude ratio of the frequency response function, the sensitivity of the human ear to the center frequency shift is accurately quantified within the howling frequency band, reducing the calculation of the sharpness coefficient and improving the calculation efficiency.
[0015] In one possible implementation, determining the vehicle's whistling level based on the whistling information, the perception weight coefficient, and the sharpness coefficient includes: The HPI value is calculated based on the howling information, the perception weight coefficient, and the sharpness coefficient. Based on the preset HPI value-howling level mapping relationship, the corresponding howling level is output.
[0016] In this embodiment, the HPI value is first calculated based on the howling information, the perception weight coefficient, and the sharpness coefficient; then, the corresponding howling level is output according to the preset HPI value-howling level mapping relationship. It can be understood that by utilizing the preset HPI value-howling level mapping relationship, the howling level that matches the user's actual auditory experience can be determined more quickly and accurately, reducing the deviation between howling level determination and the user's subjective feeling to a certain extent, and providing accurate input for acoustic control strategies.
[0017] In one possible implementation, calculating the HPI value based on the howling information, the perception weight coefficient, and the sharpness coefficient includes: According to the formula: Calculate the HPI value; Among them, the The center frequency is B, the bandwidth is B, and the center frequency is B. The HPI value, the The signal-to-noise ratio corresponding to the vehicle noise, the The preset reference signal-to-noise ratio corresponding to the vehicle noise, the The term refers to the perception weighting coefficient corresponding to the vehicle noise. This is the sharpness coefficient corresponding to the vehicle noise.
[0018] In this embodiment, a composite calculation model that integrates bandwidth normalization, dynamic signal-to-noise ratio compensation, and perception weight coefficient and sharpness coefficient is used to achieve robust quantification of the howling perception hazard index across operating conditions, thereby reducing the deviation between howling level determination and user subjective experience and improving calculation efficiency.
[0019] In one possible implementation, after determining the vehicle's whistling level based on the whistling information, the perception weight coefficient, and the sharpness coefficient, the method further includes: Based on the preset HPI value-reference sound pressure level mapping relationship, determine the reference sound pressure level target value corresponding to the HPI value; The final target sound pressure level is determined based on the target reference sound pressure level, the HPI value, the reference HPI value, and the target background sound pressure level.
[0020] In this embodiment, a target reference sound pressure level (SPL) value corresponding to the HPI value is determined based on a preset HPI value-reference sound pressure level mapping relationship. The final target SPL value is then determined based on the target SPL value, the reference SPL target value, the HPI value, the reference HPI value, and the background SPL target value. It can be understood that by dynamically mapping the HPI value to the target SPL and through multi-parameter collaborative correction, the final target SPL value for howling control is generated, improving the efficiency of noise reduction measures while ensuring auditory comfort under different operating conditions.
[0021] Secondly, embodiments of this application provide a vehicle whistling level determination device, comprising: The whistling information determination module is used to determine the whistling information corresponding to the vehicle noise based on the collected vehicle noise, wherein the whistling information is used to characterize the spectral characteristic parameters of the vehicle noise; The perception weight coefficient calculation module is used to calculate the perception weight coefficient corresponding to the vehicle noise based on the spectral feature parameters corresponding to the howling information and through a preset perception weight function. The preset perception weight function is used to characterize the mapping relationship between the noise signal and the subjective perception sensitivity of the human ear in a specific frequency band. The sharpness coefficient calculation module is used to calculate the sharpness coefficient of the vehicle noise based on the spectral feature parameters corresponding to the howling information and through a preset spectral response function. The sharpness coefficient is used to characterize the degree to which the high-frequency energy concentration of the noise signal quantifies the piercing sensation in the human ear. The whistling level determination module is used to determine the whistling level of a vehicle based on the whistling information, the perception weight coefficient, and the sharpness coefficient.
[0022] Thirdly, embodiments of this application provide an electronic device, including: processor; Memory; And a computer program, wherein the computer program is stored in the memory, the computer program including instructions that, when executed by the processor, cause the electronic device to perform the method described in any one of the first aspects.
[0023] Fourthly, embodiments of this application provide a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform the method described in any one of the first aspects.
[0024] Understandably, the vehicle whistling level determination device provided in the second aspect, the electronic device provided in the third aspect, and the computer-readable storage medium provided in the fourth aspect are all used to perform some or all of the methods provided in this application. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here. Attached Figure Description
[0025] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0026] Figure 1 This is a schematic diagram of an application scenario provided by an embodiment of this application.
[0027] Figure 2 This is a flowchart illustrating a method for determining the whistling level of a vehicle, as provided in an embodiment of this application.
[0028] Figure 3 This is a schematic diagram of a vehicle whistling level determination device provided in an embodiment of this application.
[0029] Figure 4This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation
[0030] To better understand the technical solution of this application, the embodiments of this application will be described in detail below with reference to the accompanying drawings.
[0031] It should be understood that the described embodiments are merely some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.
[0032] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.
[0033] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.
[0034] Vehicle noise, vibration, and acoustic roughness performance are core indicators for measuring ride quality. With the accelerated development of vehicles, the increased operating frequency of powertrain systems has led to a growing problem of mid-to-high frequency whistling noises inside the vehicle. Compared to traditional low-frequency noise, whistling sounds have higher sharpness and prominence, easily causing strong annoyance to users. To facilitate understanding, specific application scenarios will be illustrated below.
[0035] See Figure 1 This is a schematic diagram illustrating an application scenario provided by an embodiment of this application. For example... Figure 1 As shown, this application scenario includes vehicle 101. Specifically, an increased operating frequency of the powertrain system inside vehicle 101 may cause high-frequency whistling within the vehicle, leading to intense annoyance for passengers. Therefore, noise reduction measures are needed to address the high-frequency whistling. Before implementing noise reduction, it is necessary to determine the degree of impact of the high-frequency whistling on the user, i.e., the whistling level, in order to accurately utilize acoustic control strategies to reduce the whistling level.
[0036] It should be noted that the vehicle 101 shown in the figure is only an exemplary illustration, and this application does not impose specific restrictions on the specific model of each vehicle.
[0037] In related technologies, the following scheme is mainly used to determine the severity of howling: a professional review team is formed to classify the severity based on subjective auditory perception in actual driving scenarios. However, this method is easily affected by individual differences in perception, resulting in insufficient stability. To address this issue, those skilled in the art have set up a method to measure the average sound pressure level of the target frequency band using acoustic instruments and determine the severity level based on a preset threshold range.
[0038] However, classifying the severity of feedback by analyzing the average sound pressure level does not fully consider the impact of sound frequency distribution on human hearing. This may lead to a significant discrepancy between the determined feedback level and the user's actual experience, making it difficult to guide precise acoustic control strategies to reduce the feedback level.
[0039] To address the aforementioned issues, this embodiment first determines the howling information corresponding to the vehicle noise based on the collected vehicle noise. Then, based on the spectral characteristic parameters corresponding to the howling information, a preset perception weighting function is used to calculate the perception weighting coefficient corresponding to the vehicle noise. Next, based on the spectral characteristic parameters corresponding to the howling information, a preset spectral response function is used to calculate the sharpness coefficient corresponding to the vehicle noise. Finally, the howling level of the vehicle is determined based on the howling information, the perception weighting coefficient, and the sharpness coefficient. It is understood that since the preset perception weighting function quantifies the mapping relationship between noise and human ear perception sensitivity in a specific frequency band, and the sharpness coefficient characterizes the quantification degree of the high-frequency energy concentration of noise on the ear's piercing sensation, a comprehensive evaluation based on the howling information, perception weighting coefficient, and sharpness coefficient can accurately determine the howling level that matches the user's actual auditory experience. This method significantly reduces the deviation between howling level determination and the user's subjective perception, providing accurate input for acoustic control strategies, thereby effectively reducing howling levels. Specifically, a detailed description is provided below in conjunction with the accompanying drawings and specific embodiments.
[0040] See Figure 2 This is a flowchart illustrating a method for determining the whistling level of a vehicle according to an embodiment of this application. This method can be applied to... Figure 1 In the application scenarios shown, such as Figure 2 As shown, it mainly includes the following steps.
[0041] Step S201: Based on the collected vehicle noise, determine the whistling information corresponding to the vehicle noise.
[0042] In practical applications, vehicle noise is first collected. Specifically, the vehicle is equipped with a microphone array for collecting vehicle noise. In a specific implementation, this microphone array may include four channels.
[0043] In this embodiment, after the vehicle noise is collected, the corresponding howling information is determined based on the collected vehicle noise. It can be understood that the howling information is used to characterize the spectral characteristics of the vehicle noise.
[0044] Specifically, in one possible implementation, the howling information includes the center frequency, bandwidth, and signal-to-noise ratio (SNR) corresponding to the vehicle noise. It is understood that including the center frequency, bandwidth, and SNR of the vehicle noise information helps to accurately determine the perception weight coefficient and sharpness coefficient, thereby accurately determining the howling level that matches the user's actual auditory experience. This reduces the deviation between the howling level determination and the user's subjective perception to a certain extent, providing precise input for acoustic control strategies.
[0045] Of course, those skilled in the art can also set the howling information, including the order energy ratio, modulation depth, and prominence ratio, according to actual needs, and this application does not impose specific restrictions on this.
[0046] Understandably, the center frequency is used to characterize the frequency corresponding to the peak sound pressure level. In one possible implementation, a short-time fast Fourier transform is used to analyze the time-domain signal of the vehicle noise to determine the center frequency of the howling component.
[0047] Specifically, according to the formula: Determine the center frequency .in, For the first Frame signal at frequency The sound pressure amplitude at that location, The number of frames.
[0048] Understandably, by dynamically tracking time-varying whistling components through short-time fast Fourier transform, the center frequency can be accurately located under transient conditions such as acceleration / deceleration, which is beneficial for determining a more precise center frequency.
[0049] It should also be noted that the bandwidth mentioned above refers to the significant perceived width of the howling energy distribution in the frequency domain. Specifically, the sound pressure level reduction is determined using the full width at half maximum (FWHM) method. The frequency range is the bandwidth. The signal-to-noise ratio mentioned above is the energy ratio of the sound pressure level of the howling component to the sound pressure level of the background noise within the same center frequency bandwidth.
[0050] Step S202: Calculate the perception weight coefficient corresponding to vehicle noise based on the spectral feature parameters corresponding to the whistling information using a preset perception weight function.
[0051] In this embodiment, the perceptual weighting coefficient corresponding to the vehicle noise is calculated using a preset perceptual weighting function based on the spectral characteristic parameters corresponding to the howling information. The preset perceptual weighting function characterizes the mapping relationship between the noise signal and the subjective perceptual sensitivity of the human ear in a specific frequency band.
[0052] Understandably, the human auditory system is not a simple physical microphone; its perception of sound is non-linear and selective. In real life, sounds of different frequencies, even with different physical sound pressure levels, may sound equally loud; conversely, sounds of different frequencies at the same sound pressure level may sound vastly different in loudness.
[0053] Based on the above findings, a pre-defined perceptual weighting function can be used to characterize the mapping relationship between noise signals and the subjective perceptual sensitivity of the human ear in a specific frequency band.
[0054] For example, the preset perception weight function is when the center frequency is... When the center frequency is within the range of [1000Hz, 3000Hz), the perception weighting coefficient is 0.8; when the center frequency is within the range of [1000Hz, 3000Hz], the perception weighting coefficient is 0.8. When the center frequency is ∈ [3000Hz, 4000Hz), the perception weight coefficient is 1; when the center frequency is ∈ [3000Hz, 4000Hz), the perception weight coefficient is 1. When the center frequency is ∈ [4000Hz, 5000Hz], the perception weight coefficient is 1.2. At this time, when the determined center frequency is 3566Hz, since 3566Hz ∈ [3000Hz, 4000Hz), the perception weight coefficient is determined to be 1; similarly, this can be deduced.
[0055] Step S203: Based on the spectral characteristic parameters corresponding to the howling information, calculate the sharpness coefficient corresponding to the vehicle noise through a preset spectral response function.
[0056] In this embodiment, based on the spectral characteristic parameters corresponding to the howling information, the sharpness coefficient corresponding to the vehicle noise is calculated using a preset spectral response function. The sharpness coefficient characterizes the degree to which the high-frequency energy concentration of the noise signal quantifies the piercing sensation in the human ear.
[0057] To facilitate understanding, let's first explain the masking effect. The masking effect refers to the phenomenon where the presence of one sound (the masking sound) raises, weakens, or even eliminates the human ear's perception threshold for another sound (the masked sound). In other words, one sound covers up another. This can be understood as a strong sound signal masking weaker signals at adjacent frequencies. The more concentrated the energy distribution, the more pronounced the masking effect, and the higher the sharpness of the sound perceived by the human ear.
[0058] Of course, if the energy distribution within the frequency band is dispersed, the sharpness is lower; if the energy is concentrated near the center frequency, the sharpness is higher.
[0059] In one possible implementation, according to the formula: Determine the sharpness coefficient corresponding to the vehicle noise. Among them, B is the center frequency, and B is the bandwidth. For the preset spectral response function, This represents the sharpness coefficient corresponding to vehicle noise. This is understandable. It covers the entire howling frequency band.
[0060] In the specific implementation, firstly, the frequency point f relative to the center frequency is calculated. Energy ratio: .
[0061] It is understandable that if E(f) > 1, it means that the energy of this frequency is higher than the energy of the center frequency; if E(f) > 1, it means that the energy of this frequency is higher than the energy of the center frequency.
[0062] Then, quantify the degree to which the energy deviates from the center: .
[0063] It is understandable that logarithmic operations can enhance the weight of small signals and highlight subtle differences in energy distribution.
[0064] Finally, the normalized energy and the logarithmic difference are weighted and summed: It's understandable that higher energy and greater deviation from the center frequency contribute more to the integral. This is also true when divided by the bandwidth. This helps to eliminate the direct impact of bandwidth on the results.
[0065] In this embodiment, by using the logarithmic weighted integral of the amplitude ratio of the frequency response function, the sensitivity of the human ear to the center frequency shift is accurately quantified within the howling frequency band, reducing the computational load of the sharpness coefficient and improving computational efficiency.
[0066] Step S204: Determine the vehicle's whistling level based on the whistling information, perception weight coefficient, and sharpness coefficient.
[0067] In this embodiment of the application, the whistling level of the vehicle is determined based on whistling information, perception weight coefficient, and sharpness coefficient.
[0068] Specifically, in one possible implementation, the HPI value is first calculated based on the howling information, the perception weight coefficient, and the sharpness coefficient; then, the corresponding howling level is output according to the preset HPI value-howling level mapping relationship.
[0069] Understandably, by using the preset HPI value-feedback level mapping relationship, the feedback level that matches the user's actual auditory experience can be determined more quickly and accurately. This reduces the deviation between feedback level judgment and user subjective feeling to a certain extent, and provides accurate input for acoustic control strategies.
[0070] Furthermore, in one possible implementation, according to the formula: Calculate the HPI value.
[0071] in, B is the center frequency, and B is the bandwidth. HPI value, The signal-to-noise ratio corresponding to vehicle noise, The preset reference signal-to-noise ratio corresponding to vehicle noise, The above refers to the perception weighting coefficient corresponding to vehicle noise. This is the sharpness coefficient corresponding to vehicle noise.
[0072] In this embodiment, a composite calculation model that integrates bandwidth normalization, dynamic signal-to-noise ratio compensation, and perception weight coefficient and sharpness coefficient is used to achieve robust quantification of the howling perception hazard index across operating conditions, thereby reducing the deviation between howling level determination and user subjective experience and improving calculation efficiency.
[0073] The preset HPI value-howling level mapping relationship mentioned above can be obtained experimentally. Specifically, a preset number of subjects are selected, and their feedback under noise environments corresponding to different HPI values is obtained, thereby determining the HPI value-howling level mapping relationship.
[0074] For ease of understanding, please refer to Table 1, which provides a mapping relationship between HPI value and howling level, and the corresponding user experience.
[0075] Table 1: It is understandable that when the HPI value is 0.9, according to the HPI value-howling level mapping relationship shown in Table 1, the corresponding howling level is level four; when the HPI value is 0.3, according to the HPI value-howling level mapping relationship shown in Table 1, the corresponding howling level is level one; and so on.
[0076] It should be noted that those skilled in the art may divide the howling into more or fewer levels according to specific needs, and this application does not impose any specific restrictions on this.
[0077] In practical use, after determining the vehicle's whistling level, it is also necessary to determine the final target value of the sound pressure level to control the whistling noise.
[0078] Specifically, in one possible implementation, a target value for the reference sound pressure level corresponding to the HPI value is determined based on a preset HPI value-reference sound pressure level mapping relationship; and a final target value for the sound pressure level is determined based on the target value for the reference sound pressure level, the target value for the reference sound pressure level, the HPI value, the reference HPI value, and the target value for the background sound pressure level.
[0079] In practice, the final target sound pressure level can be determined using the following formula.
[0080] , in, For the final target sound pressure level value, The target value of the reference sound pressure level HPI value, For reference HPI values, This is the target value for the background sound pressure level.
[0081] Understandably, by dynamically mapping the HPI value to the sound pressure level target and coordinating the correction of multiple parameters, the final sound pressure level target value for howling control is generated, which improves the efficiency of noise reduction measures and ensures auditory comfort under different working conditions.
[0082] In this embodiment, firstly, based on the collected vehicle noise, the whistling information corresponding to the vehicle noise is determined; then, based on the spectral characteristic parameters corresponding to the whistling information, the perceptual weighting coefficient corresponding to the vehicle noise is calculated using a preset perceptual weighting function; next, based on the spectral characteristic parameters corresponding to the whistling information, the sharpness coefficient corresponding to the vehicle noise is calculated using a preset spectral response function; finally, based on the whistling information, the perceptual weighting coefficient, and the sharpness coefficient, the whistling level of the vehicle is determined. It can be understood that since the preset perceptual weighting function quantifies the mapping relationship between noise and human ear perception sensitivity in a specific frequency band, and the sharpness coefficient characterizes the degree to which the high-frequency energy concentration of noise quantifies the piercing sensation in the human ear, a comprehensive evaluation based on the whistling information, the perceptual weighting coefficient, and the sharpness coefficient can accurately determine the whistling level that matches the user's actual auditory experience. This method significantly reduces the deviation between the whistling level determination and the user's subjective perception, providing accurate input for acoustic control strategies, thereby effectively reducing the whistling level.
[0083] Corresponding to the above embodiments, this application also provides a device for determining the whistling level of a vehicle. Specifically, see... Figure 3 This figure shows a schematic diagram of a vehicle whistling level determination device according to an embodiment of this application. The whistling level determination device 300 includes: a whistling information determination module 301, a perception weight coefficient calculation module 302, a sharpness coefficient calculation module 303, and a whistling level determination module. Specifically, the whistling information determination module 301 determines the whistling information corresponding to the vehicle noise based on the collected vehicle noise; the perception weight coefficient calculation module 302 calculates the perception weight coefficient corresponding to the vehicle noise based on the spectral feature parameters corresponding to the whistling information using a preset perception weight function; the sharpness coefficient calculation module 303 calculates the sharpness coefficient corresponding to the vehicle noise based on the spectral feature parameters corresponding to the whistling information using a preset spectral response function; and the whistling level determination module 304 determines the vehicle whistling level based on the whistling information, the perception weight coefficient, and the sharpness coefficient.
[0084] Specifically, the embodiments described above can be referred to. For the sake of brevity, this application does not impose any specific limitations.
[0085] Corresponding to the above embodiments, this application also provides a schematic diagram of the structure of an electronic device. See also Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device 400 may include a processor 401, a memory 402, and a communication unit 403. These components communicate through one or more buses. Those skilled in the art will understand that the structure of the electronic device shown in the figure does not constitute a limitation on the embodiments of the present invention. It may be a bus-shaped structure or a star-shaped structure, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.
[0086] The communication unit 403 is used to establish a communication channel, enabling the electronic device to communicate with other devices. It receives user data from other devices or sends user data to other devices.
[0087] The processor 401 serves as the control center of the electronic device, connecting various parts of the device via various interfaces and lines. It executes software programs, instructions, and / or modules stored in the memory 402, and calls data stored in the memory to perform various functions and / or process data. The processor may be composed of integrated circuits (ICs), such as a single packaged IC or multiple packaged ICs with the same or different functions connected together. For example, the processor 401 may consist only of a central processing unit (CPU). In this embodiment, the CPU may have a single processing core or include multiple processing cores.
[0088] The memory 402 is used to store the execution instructions of the processor 401. The memory 402 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.
[0089] When the execution instructions in memory 402 are executed by processor 401, the electronic device 400 is able to perform operations. Figure 2 Some or all of the steps in the illustrated embodiments.
[0090] In a specific implementation, this application also provides a computer storage medium, wherein the computer storage medium may store a program, and when the program is executed, it may include some or all of the steps in the various embodiments of the simulation scene generation method provided by this invention. The storage medium may be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.
[0091] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, or the existence of B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects have an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, and c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.
[0092] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0093] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.
[0094] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0095] The same or similar parts between the various embodiments in this specification can be referred to mutually. In particular, the device embodiments and terminal embodiments are basically similar to the method embodiments, so the description is relatively simple, and the relevant parts can be referred to the description in the method embodiments.
Claims
1. A method for determining the whistling level of a vehicle, characterized in that, include: Based on the collected vehicle noise, the whistling information corresponding to the vehicle noise is determined, wherein the whistling information is used to characterize the spectral characteristic parameters of the vehicle noise; Based on the spectral feature parameters corresponding to the whistling information, the perception weight coefficient corresponding to the vehicle noise is calculated through a preset perception weight function, wherein the preset perception weight function is used to characterize the mapping relationship between the noise signal and the subjective perception sensitivity of the human ear in a specific frequency band. Based on the spectral characteristic parameters corresponding to the whistling information, the sharpness coefficient corresponding to the vehicle noise is calculated through a preset spectral response function. The sharpness coefficient is used to characterize the degree to which the high-frequency energy concentration of the noise signal quantifies the piercing sensation in the human ear. The vehicle's whistling level is determined based on the whistling information, the perception weight coefficient, and the sharpness coefficient.
2. The method according to claim 1, characterized in that, The whistling information includes: the center frequency, bandwidth, and signal-to-noise ratio corresponding to the vehicle noise.
3. The method according to claim 2, characterized in that, include: The time-domain signal of the vehicle noise was analyzed using short-time fast Fourier transform to determine the center frequency of the howling component.
4. The method according to claim 2, characterized in that, The step of calculating the sharpness coefficient of the vehicle noise based on the spectral feature parameters corresponding to the whistling information using a preset spectral response function includes: According to the formula: Determine the sharpness coefficient corresponding to the vehicle noise; Among them, the The center frequency is B, the bandwidth is B, and the center frequency is B. For the preset spectral response function, the This is the sharpness coefficient corresponding to the vehicle noise.
5. The method according to claim 2, characterized in that, Determining the vehicle's whistling level based on the whistling information, the perception weighting coefficient, and the sharpness coefficient includes: The HPI value is calculated based on the howling information, the perception weight coefficient, and the sharpness coefficient. Based on the preset HPI value-howling level mapping relationship, the corresponding howling level is output.
6. The method according to claim 5, characterized in that, The step of calculating the HPI value based on the howling information, the perception weight coefficient, and the sharpness coefficient includes: According to the formula: Calculate the HPI value; Among them, the The center frequency is B, the bandwidth is B, and the center frequency is B. The HPI value, the The signal-to-noise ratio corresponding to the vehicle noise, the The preset reference signal-to-noise ratio corresponding to the vehicle noise, the The term refers to the perception weighting coefficient corresponding to the vehicle noise. This is the sharpness coefficient corresponding to the vehicle noise.
7. The method according to claim 5, characterized in that, After determining the vehicle's whistling level based on the whistling information, the perception weighting coefficient, and the sharpness coefficient, the method further includes: Based on the preset HPI value-reference sound pressure level mapping relationship, determine the reference sound pressure level target value corresponding to the HPI value; The final target sound pressure level is determined based on the target reference sound pressure level, the HPI value, the reference HPI value, and the target background sound pressure level.
8. A device for determining the whistling level of a vehicle, characterized in that, include: The whistling information determination module is used to determine the whistling information corresponding to the vehicle noise based on the collected vehicle noise, wherein the whistling information is used to characterize the spectral characteristic parameters of the vehicle noise; The perception weight coefficient calculation module is used to calculate the perception weight coefficient corresponding to the vehicle noise based on the spectral feature parameters corresponding to the howling information and through a preset perception weight function. The preset perception weight function is used to characterize the mapping relationship between the noise signal and the subjective perception sensitivity of the human ear in a specific frequency band. The sharpness coefficient calculation module is used to calculate the sharpness coefficient of the vehicle noise based on the spectral feature parameters corresponding to the howling information and through a preset spectral response function. The sharpness coefficient is used to characterize the degree to which the high-frequency energy concentration of the noise signal quantifies the piercing sensation in the human ear. The whistling level determination module is used to determine the whistling level of a vehicle based on the whistling information, the perception weight coefficient, and the sharpness coefficient.
9. An electronic device, characterized in that, include: processor; Memory; And a computer program, wherein the computer program is stored in the memory, the computer program including instructions that, when executed by the processor, cause the electronic device to perform the method of any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1 to 7.