Active noise reduction method and system, device, medium, and vehicle

Abstract

The present application provides an active noise reduction method and system, a device, a medium, and a vehicle. The method comprises: on the basis of an original noise signal acquired in real time by a noise sensor and an error signal acquired in real time by an error sensor, generating an anti-noise control signal using an improved FxLMS algorithm model, the anti-noise control signal being used for generating anti-noise superimposed on the original noise to suppress the original noise, wherein the improved FxLMS algorithm model comprises an adaptive filter, and the adaptive filter adaptively updates filter coefficients on the basis of environmental parameters generated in real time. In the method, the step size used for adaptively updating the filter coefficients is adjusted using the environmental parameters, thereby improving the iteration speed of the algorithm model, enabling rapid tracking of changes in non-stationary original noise, and achieving effective noise control.

Description

Active noise reduction method, system, device, medium and vehicle

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on February 29, 2024, with application number 202410234918.6 and invention name “Active Noise Reduction Method, System, Device, Medium and Vehicle”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The embodiments of the present application relate to, but are not limited to, the field of audio technology, and more specifically to an active noise reduction method, system, device, medium, and vehicle. Background Art

[0003] Noise affects the user experience of many products. For example, noise is an important factor affecting the driving experience of a car. Noise not only affects the listening environment and voice clarity in the car, but also makes the driver feel irritated, affecting driving safety.

[0004] Currently, active engine and road noise control typically utilizes the Filtered-x Least Mean Square (FxLMS) algorithm. While this algorithm requires minimal hardware computing power, it suffers from slow convergence and poor signal tracking capabilities. When the noise signal's frequency characteristics vary rapidly and the signal is non-stationary, such as wind noise generated by a car at high speed, the traditional FxLMS algorithm struggles to track noise variations in real-world situations, rendering it ineffective in achieving effective noise control. Therefore, achieving effective active control of non-stationary noise signals has become a pressing technical challenge. Summary of the Invention

[0005] The present application provides an active noise reduction method, system, device, medium and vehicle, which can effectively control non-stationary noise signals.

[0006] According to a first aspect of the present application, an active noise reduction method is provided, the active noise reduction method comprising:

[0007] Based on the original noise signal collected in real time by the noise sensor and the error signal collected in real time by the error sensor, an inverse noise control signal is generated through an improved FxLMS algorithm model. The inverse noise control signal is used to generate inverse noise superimposed on the original noise to suppress the original noise. The improved FxLMS algorithm model includes an adaptive filter, and the adaptive filter adaptively updates the filter coefficient based on the environmental parameters generated in real time.

[0008] In one embodiment of the present application, the real-time generation method of environmental parameters includes:

[0009] Based on the vehicle aerodynamic shape information and one or more of the vehicle speed information and wind speed information at the current moment, corresponding environmental parameters are obtained from a pre-established wind speed environmental parameter table.

[0010] In one embodiment of the present application, the adaptive updating of the filter coefficients based on the real-time generated environmental parameters includes:

[0011] Update the first-level adaptive parameters based on the real-time generated environmental parameters;

[0012] The filter coefficient is adaptively updated based on the first-level adaptive parameter at the current moment, the error signal at the previous moment, and the original noise signal at the previous moment.

[0013] In one embodiment of the present application, updating the first-level adaptive parameters based on the real-time generated environmental parameters further includes:

[0014] The first-level adaptive parameters at the previous moment are updated based on the environmental parameters at the current moment, the second-level memory parameters at the previous moment, the original noise signal at the previous moment, and the error signal at the previous moment to obtain the first-level adaptive parameters at the current moment.

[0015] In one embodiment of the present application, it further includes:

[0016] The secondary memory parameters at the current moment are generated based on the primary adaptive parameters at the current moment, the original noise signal at the previous moment, and the error signal at the previous moment.

[0017] In one embodiment of the present application, generating the secondary memory parameter at the current moment based on the primary adaptive parameter at the current moment, the original noise signal at the previous moment, and the error signal at the previous moment includes:

[0018] When the primary adaptive parameter at the current moment and the original noise signal at the previous moment meet a preset condition, iteratively updating the secondary memory parameter at the previous moment based on the primary adaptive parameter at the current moment, the original noise signal at the previous moment, and the error signal at the previous moment to obtain the secondary memory parameter at the current moment;

[0019] When the first-level adaptive parameter at the current moment and the original noise signal at the previous moment do not meet the preset conditions, the second-level memory parameter at the current moment is generated based on the first-level adaptive parameter at the current moment, the original noise signal at the previous moment and the error signal at the previous moment.

[0020] In one embodiment of the present application, the original noise signal is a noise signal generated by a moving vehicle, and the environmental parameter is an aerodynamic parameter.

[0021] In one embodiment of the present application, the active noise reduction method further includes:

[0022] The reverse noise control signal is sent to an actuator, and the actuator is configured to generate reverse noise under the driving of the reverse noise control signal.

[0023] According to a second aspect of the present application, an active noise reduction system is proposed, comprising a controller and a noise sensor, an error sensor, an environmental data acquisition sensor, and an actuator respectively connected to the controller;

[0024] The noise sensor is used to collect original noise and perform acoustic-to-electrical conversion on the original noise to generate an original noise signal;

[0025] The error sensor is used to collect the residual noise after the original noise and the reverse noise are superimposed, and perform acoustic-to-electrical conversion on the residual noise to generate an error signal;

[0026] The environmental data acquisition sensor is used to collect environmental information and generate environmental parameters;

[0027] The controller is configured to generate a reverse noise control signal using the active noise reduction method described above;

[0028] The actuator is configured to generate reverse noise based on the reverse noise control signal.

[0029] According to a third aspect of the present application, an electronic device is provided, comprising a memory and a processor, wherein the memory stores a computer program executed by the processor, and when the computer program is executed by the processor, the device equipped with the processor performs the above-mentioned active noise reduction method.

[0030] According to a fourth aspect of the present application, a storage medium is provided, on which a computer program is stored. The computer program runs on a computer, and when the computer program is run, the computer is caused to execute the above-mentioned active noise reduction method.

[0031] According to a fifth aspect of the present application, a vehicle is provided, comprising at least one of the above-mentioned active noise reduction system, the above-mentioned electronic device, and the above-mentioned storage medium.

[0032] The active noise reduction method of the present application can adjust the first-level adaptive parameters in real time according to the current environmental parameters, thereby adjusting the weight of the input signal when the adaptive filter weight coefficient is updated, assigning smaller parameters to input signals with weak correlation and larger parameters to input signals with strong correlation, thereby improving the iteration speed of the FxLMS algorithm model. In the process of generating reverse noise with a phase opposite to the original noise through the adaptive filter to reduce the non-stationary original noise, the changes in the original noise are tracked, thereby achieving effective noise control. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The above and other purposes, features, and advantages of the present application will become more apparent through a more detailed description of the embodiments of the present application in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0034] FIG1 is a schematic block diagram of a control device for an active noise reduction method according to an embodiment of the present application;

[0035] FIG2 is a schematic structural diagram of an active noise reduction system according to an embodiment of the present application;

[0036] FIG3 is a schematic flowchart of an active noise reduction method according to an embodiment of the present application;

[0037] FIG4 is an algorithm block diagram of an active noise reduction method according to an embodiment of the present application;

[0038] FIG5 is an algorithm block diagram of an active noise reduction method according to another embodiment of the present application;

[0039] FIG6 is a block diagram of an algorithm for generating secondary adaptive parameters according to an embodiment of the present application;

[0040] FIG7 is a schematic flow chart of an active noise reduction method according to another embodiment of the present application;

[0041] FIG8 is a block diagram of an algorithm for generating secondary adaptive parameters according to another embodiment of the present application;

[0042] FIG9 is a schematic structural block diagram of an active noise reduction system according to an embodiment of the present application;

[0043] FIG10 is a schematic structural diagram of a vehicle active noise reduction system according to an embodiment of the present application;

[0044] FIG11 is a schematic structural block diagram of a vehicle according to an embodiment of the present application;

[0045] FIG12 is a schematic structural block diagram of a vehicle according to another embodiment of the present application. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of the present application more apparent, the following is a detailed description of example embodiments of the present application with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application described in this application, all other embodiments obtained by those skilled in the art without creative work should fall within the scope of protection of this application.

[0047] To address the problem that existing active noise reduction methods cannot accurately distinguish different road surfaces based on the adhesion coefficient μ value, this application proposes an active noise reduction method, electronic equipment, medium and vehicle that can effectively track noise signals and thus achieve effective noise control. This method is described in detail below.

[0048] First, an example control device 100 for implementing an embodiment of the method of the present application is described with reference to FIG. 1 .

[0049] As shown in Figure 1, the control device 100 includes a processor 110, a memory 120, and a communication interface 130. The processor 110, the memory 120, and the communication interface 130 can be interconnected and communicated via a communication bus 140 and / or other connection mechanisms (not shown).

[0050] It should be noted that the components and structure of the control device 100 shown in FIG1 are merely exemplary and non-limiting. The control device may also have other components and structures as needed.

[0051] Optionally, the communication interface 130 may further include a transmitter and / or a receiver.

[0052] The processor 110 can be a microcontroller unit (MCU), a central processing unit (CPU), a digital signal processor (DSP), a single-chip microcomputer and an embedded device, or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and can control other components in the autonomous driving vehicle system to perform desired functions.

[0053] The memory 120 can be various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM), cache memory, synchronous dynamic random access memory (SDRAM), etc. Non-volatile memory may include, for example, read-only memory (ROM), a hard disk, flash memory, etc. One or more computer program instructions may also be stored on the computer-readable storage medium, and the memory 120 may execute the program instructions to implement the active noise reduction method in the embodiments of the present application described below.

[0054] First, the application scenario of the active noise reduction method in the embodiment of the present application is described.

[0055] Figure 2 is a schematic diagram of the active noise reduction system. As shown in Figure 2, x(n) is the reference signal generated by the reference sensor, y(n) is the reverse noise control signal generated by the controller, and e(n) is the error signal received by the error sensor, where n represents the nth moment. The overall workflow of the system is as follows: the original noise generated by the noise source passes through the primary path and is output as the primary noise signal d(n). The reference sensor receives the original noise and performs acoustic-to-electrical conversion to generate the reference signal x(n). The reference signal serves as the input to the controller. The controller calculates the reverse noise control signal y(n) according to the algorithm. The output is then driven by the power amplifier to drive the electro-acoustic conversion device to generate reverse noise, which is then transmitted to the error sensor via the secondary path. The error sensor simultaneously receives the original noise and reverse noise, superimposes them, and performs acoustic-to-electrical conversion to form the error signal e(n). The error signal is input to the controller, which continuously adjusts the controller weight coefficients to change the reverse noise control signal until the system reaches a stable state. The active noise reduction method provided in this application can be applied to the controller shown in Figure 2. The following description uses the controller shown in Figure 2 as an example.

[0056] It should be noted that in the following description, the reference sensor is referred to as a noise sensor. Since the original noise signal serves as the reference signal, the reference signal is also referred to as the original noise signal.

[0057] The present application provides an active noise reduction method, which includes: generating an inverse noise control signal based on an original noise signal and an error signal collected in real time by a noise sensor and an error sensor, and using an improved FxLMS algorithm model. The inverse noise control signal is used to generate inverse noise superimposed on the original noise to suppress the original noise. The improved FxLMS algorithm model includes an adaptive filter, and the adaptive filter adaptively updates the filter coefficient based on environmental parameters generated in real time.

[0058] Next, the active noise reduction method according to the embodiment of the present application is described in detail with reference to FIG3 and FIG4 .

[0059] As shown in FIG3 , the present application provides an active noise reduction method, including the following steps S310 to S320 .

[0060] In step S310 , an original noise signal collected in real time by a noise sensor and an error signal collected in real time by an error sensor are acquired.

[0061] Here, the original noise signal is the electrical signal formed by the noise sensor picking up the original noise generated by the noise source and performing acoustic-to-electrical conversion on the original noise, and the error signal is the electrical signal formed by the acoustic-to-electrical conversion after the superposition of the original noise and the reverse noise.

[0062] Specifically, the noise sensor and the error sensor may be microphones.

[0063] In step S320, an inverse noise control signal is generated based on the original noise signal through an improved FxLMS algorithm model; wherein the improved FxLMS algorithm model includes an adaptive filter, and the adaptive filter adaptively updates the filter coefficient based on the environmental parameters generated in real time.

[0064] In this application, an adaptive filter receives an original noise signal and, based on the original noise signal and the adaptive filter weight coefficients, generates an output signal, namely a reverse noise control signal. The reverse noise control signal is used to drive an acoustic-electric device, which serves as a secondary sound source, to generate reverse noise. The reverse noise has a phase opposite to that of the original noise. In this way, the reverse noise can attenuate the original noise in the user's environment, thereby reducing the impact of the original noise on the user.

[0065] Active noise reduction methods use an adaptive filter to generate a signal with a phase opposite to the original noise to cancel it out. The adaptive filter coefficients are iteratively updated based on the system's input signal. In this application, an improved FxLMS algorithm model is built on the FxLMS algorithm. The original noise is collected by an error sensor and superimposed with the reverse noise, which is then converted into an electrical signal through acoustic-to-electrical conversion. During iterative updates, the filter coefficients are adaptively updated based on real-time environmental parameters.

[0066] The environmental parameters in this application are determined based on the application scenario of the active noise reduction method of this application. For example, when the active noise reduction method of this application is applied to a vehicle, the environmental parameters can be determined based on one or more of the current wind speed, vehicle speed, and aerodynamic shape information. For another example, the active noise reduction method of this application can also be applied to a range hood device, in which case the environmental parameters can be determined based on the exhaust fan speed information and / or information related to cooking on the stove.

[0067] The active noise reduction method of the present application can adjust the filter coefficient in real time according to the current environmental parameters, thereby adjusting the weight of the input signal when the adaptive filter weight coefficient is updated, assigning smaller parameters to input signals with weak correlation and larger parameters to input signals with strong correlation, thereby improving the iteration speed of the FxLMS algorithm model. In the process of generating reverse noise with a phase opposite to the original noise through the adaptive filter to reduce the original noise, the changes in the original noise are tracked, thereby achieving effective noise control.

[0068] According to one embodiment of the present application, the filter coefficient is adaptively updated based on the environmental parameters generated in real time, including: updating the first-level adaptive parameters based on the environmental parameters generated in real time; adaptively updating the filter coefficient based on the first-level adaptive parameters at the current moment, the error signal at the previous moment, and the original noise signal at the previous moment.

[0069] In this embodiment, the improved FxLMS algorithm model iteratively updates the coefficients of the adaptive filter, and the iteration speed is determined by the step size factor. The noise reduction method of this embodiment adopts a dual adjustment mechanism, using real-time environmental parameters to update the step size factor. Therefore, in this application, the step size factor that directly determines the iteration speed is defined as a primary adaptive parameter.

[0070] In this embodiment, the filter coefficient w of the adaptive filter is updated according to the following equation (1): w(n)=w(n-1)+μ(n)x'(n-1)e(n-1) (1)

[0071] Among them, w(n) is the coefficient of the adaptive filter at the current moment, that is, the n-th moment, w(n-1) is the coefficient of the adaptive filter at the n-1-th moment, x'(n-1) is the filtering reference signal at the n-1-th moment, e(n-1) is the error signal at the n-1-th moment, and μ(n) is the first-level adaptive parameter at the n-th moment.

[0072] FIG4 is an algorithm block diagram of an active noise reduction method according to an embodiment of the present application. As shown in FIG4 , the improved FxLMS algorithm model uses the original noise signal x(n) as a reference signal and processes the reference signal using secondary path estimation to obtain a filtered reference signal x'(n). Specifically, the original noise signal x(n) is convolved with the secondary path estimation to generate the filtered reference signal x'(n). The error signal e(n) is generated by the acoustic-to-electrical conversion of the original noise collected by the error sensor and the reverse noise. The first-level adaptive parameter μ(n+1) is used as the step size factor. The filtered reference signal x'(n), the error signal, and the determined step size factor are multiplied together. The adaptive filter weight coefficients are iteratively updated using the LMS algorithm to adjust the strength of the reverse noise until the error signal reaches a preset threshold. The original noise signal x(n) is then filtered by the adaptive filter to generate the reverse noise control signal y(n).

[0073] The active noise reduction method of the present application can adjust the first-level adaptive parameters in real time according to the current environmental parameters, thereby adjusting the weight of the input signal when the adaptive filter weight coefficient is updated, assigning smaller parameters to input signals with weak correlation and larger parameters to input signals with strong correlation, thereby improving the iteration speed of the FxLMS algorithm model. In the process of generating reverse noise with a phase opposite to the original noise through the adaptive filter to reduce the original noise, the changes in the original noise are tracked, thereby achieving effective noise control.

[0074] According to one embodiment of the present application, a method for generating environmental parameters in real time includes:

[0075] Based on the vehicle aerodynamic shape information and one or more of the vehicle speed information and wind speed information at the current moment, corresponding environmental parameters are obtained from a pre-established wind speed environmental parameter table.

[0076] In a specific implementation, the corresponding environmental parameters are obtained from a pre-established wind speed environmental parameter table based on the vehicle's aerodynamic shape information and the current vehicle speed information and wind speed information. The specific steps include:

[0077] Obtain the current vehicle speed information, wind speed information and vehicle aerodynamic shape information;

[0078] determining a relative wind speed based on the vehicle speed information and the wind speed information;

[0079] Based on the relative wind speed, the corresponding environmental parameter values ​​are obtained from a pre-established wind speed environmental parameter table; the wind speed environmental parameter table is calibrated for the vehicle aerodynamic shape information of different vehicles, and the environmental parameter values ​​are the environmental parameter values ​​at which the improved FxLMS algorithm model can achieve the maximum noise reduction in the shortest time under the relative wind speed.

[0080] The vehicle speed information includes vehicle speed data, the wind speed information includes wind speed data, and the vehicle aerodynamic shape information includes vehicle aerodynamic shape data.

[0081] Specifically, the method for generating current environmental parameters based on vehicle speed, wind speed, and vehicle aerodynamic shape information involves first determining the relative wind speed based on the vehicle speed and wind speed information. Then, based on the relative wind speed, the corresponding environmental parameter value is obtained by looking up the table from a pre-established wind speed environmental parameter table. When the vehicle speed and wind speed are in the same direction, the relative wind speed is calculated as their sum; when they are in opposite directions, the relative wind speed is calculated as their difference.

[0082] The wind speed environmental parameter table is generated by calibrating the aerodynamic profiles of different vehicles. During the calibration process, the improved FxLMS algorithm model records the environmental parameter values ​​that achieve the maximum noise reduction in the shortest time while driving at different speeds and wind speeds for each vehicle with different aerodynamic profiles. This generates a wind speed environmental parameter table for each vehicle.

[0083] According to one embodiment of the present application, updating the first-level adaptive parameters based on the real-time generated environmental parameters further includes:

[0084] The first-level adaptive parameters at the previous moment are updated based on the current environmental parameters, the second-level memory parameters at the previous moment, the original noise signal at the previous moment, and the error signal at the previous moment to obtain the first-level adaptive parameters at the current moment.

[0085] Next, a method for updating the first-level adaptive parameters in the active noise reduction method according to an embodiment of the present application will be described with reference to FIG5 and FIG6 .

[0086] Please refer to Figure 5. In this embodiment, the adaptive filter coefficient adopts a two-stage adjustment mechanism. The original noise signal x(n) and the error signal e(n) are both used as input signals of the two-stage adjustment module. The original noise signal x(n) is filtered using an adaptive filter to generate an inverse noise control signal y(n). The first-stage adjustment module controls the update of the adaptive filter coefficient, and the second-stage adjustment module controls the update of the first-stage adaptive parameters.

[0087] The following describes a method for the secondary adjustment module to control the updating of the primary adaptive parameters with reference to FIG6 .

[0088] First, the environmental parameters at the previous moment, the secondary memory parameters at the previous moment, the original noise signal at the previous moment, and the error signal at the previous moment are obtained.

[0089] Based on the environmental parameters at the previous moment, the secondary memory parameters at the previous moment, the original noise signal at the previous moment, and the error signal at the previous moment, the Least Mean Square (LMS) algorithm is used to update the secondary adaptive parameters at the previous moment using the following formula (2): α(n)=ln(μ(n-1))+δx(n-1)e(n-1)β(n-1) (2)

[0090] Where x(n-1) is the original noise signal collected by the noise sensor at the n-1th moment, β(n-1) is the secondary memory parameter at the n-1th moment, e(n-1) is the error signal collected by the error sensor at the n-1th moment, μ(n-1) is the primary adaptive parameter at the n-1th moment, α(n) is the secondary adaptive parameter at the nth moment, that is, the secondary adaptive parameter at the current moment, and δ is the environmental parameter.

[0091] Based on the updated secondary adaptive parameters, the primary adaptive parameters are updated by the following formula (3) to obtain the primary adaptive parameters at the current moment: μ(n) = exp(α(n)) (3)

[0092] Among them, μ(n) is the first-level adaptive parameter at the nth moment.

[0093] In this embodiment, the secondary memory parameter is used to represent the memory of the accumulated changes in the filter coefficients during the most recent updates.

[0094] This embodiment uses a secondary adjustment module, so that the adaptive parameters can be automatically adjusted in real time according to the current environmental parameters.

[0095] According to one embodiment of the present application, the active noise reduction method further includes:

[0096] Obtain the original noise signal at the previous moment and the error signal collected by the error sensor at the previous moment; obtain the first-level adaptive parameters at the current moment;

[0097] Based on the first-level adaptive parameters at the current moment, the original noise signal at the previous moment, and the error signal collected by the error sensor at the previous moment, the second-level memory parameters at the current moment are generated by the following formula (4): β(n)=H(μ,x)β(n-1)+μ(n)x(n-1)e(n-1) (4)

[0098] Among them, β(n) is the secondary memory parameter at the nth moment, and H(μ,x) is the relationship parameter between the primary adaptive parameter and the original noise signal.

[0099] Specifically, the preset condition is expressed as the expression shown in the following formula (5): 1-μ(n)|x(n-1)|2 >0 (5)

[0100] Then, when the first-level adaptive parameter at the current moment and the original noise signal at the previous moment meet the preset conditions, H(μ,x) takes the value of 1-μ(n)|x(n-1)| 2 , thus iteratively updating the secondary memory parameters at the previous moment based on the current primary adaptive parameters, the original noise signal at the previous moment, and the error signal collected by the error sensor at the previous moment, and obtaining the secondary memory parameters at the current moment through the following formula (6): β(n)=(1-μ(n)|x(n-1)| 2 )β(n-1)+μ(n)x(n-1)e(n-1) (6)

[0101] When the first-level adaptive parameters at the current moment and the noise signal at the previous moment do not meet the preset conditions, that is, 1-μ(n)|x(n-1)| 2 When ≤0, H(μ,x) takes the value of 0, thus generating the current second-level memory parameter through the following formula (7) based on the current first-level adaptive parameter, the original noise signal at the previous moment, and the error signal collected by the error sensor at the previous moment. β(n)=μ(n)x(n-1)e(n-1) (7)

[0102] According to one embodiment of the present application, the original noise signal is a noise signal generated by a moving vehicle. Specifically, the noise signal can be the vehicle's wind noise signal. When a vehicle is traveling at high speed, the air in front of the vehicle forms a high-speed airflow. This airflow creates a large amount of turbulence when encountering structures such as the A-pillars and rearview mirrors. Simultaneously, air pressure pulsations act on the front windshield and side windows, generating wind noise. When a vehicle is idling or traveling at low speeds, the primary noise inside the vehicle comes from the engine and other power-assisted systems (such as the intake and exhaust systems). Wind noise begins to appear when the vehicle speed reaches 100 km / h. When the vehicle speed reaches 120 km / h, wind noise becomes the primary sound source. As the vehicle speed increases further, wind noise can completely mask engine noise and road-tire noise. The impact of wind noise on interior noise varies with vehicle speed, external environment (such as wind speed, weather, etc.), and road conditions. Therefore, applying active noise reduction methods to control wind noise signals can achieve model convergence at a high speed, effectively track changes in wind noise, and effectively improve wind noise reduction effectiveness.

[0103] It should be noted that the vehicle speed mentioned here is only a range, and the specific value varies depending on factors such as external environment, road conditions and vehicle model.

[0104] Next, referring to Figures 7 and 8 , we will describe an active noise reduction method according to one embodiment of the present application. This method is applied to a vehicle, where the original noise signal is a wind noise signal and the environmental parameters are aerodynamic parameters. Thus, this method is specifically used to reduce the impact of wind noise on the user. The specific implementation process of the active noise reduction method may include the following steps.

[0105] S1. Obtain the error signal e(n). The error signal is generally obtained by the error microphone in the car. It is used to evaluate the noise control effect and drive the update iteration of the adaptive algorithm to make the algorithm converge in the direction of reducing the error signal.

[0106] S2. Obtain the original noise signal x(n), or wind noise signal, as a reference signal. The wind noise reference signal can be obtained from a combination of signals such as an external wind noise microphone, an anemometer, a glass vibration sensor, or an internal wind noise microphone. Alternatively, a wind noise reference signal can be constructed using sensors based on information such as the current vehicle speed and external air velocity. The wind noise reference signal serves as the input signal to the adaptive filter. After filtering by the adaptive filter, the wind noise control signal is generated. The wind noise reference signal also drives the iteration of the adaptive algorithm.

[0107] S3. Obtain the aerodynamic parameter γ, which is determined by the vehicle's aerodynamic shape. The aerodynamic parameter γ can be considered the algorithm's meta-step size and is pre-determined through cross-validation using vehicles with different aerodynamic shapes and at different speeds. The aerodynamic parameter controls the update rate of the secondary control module, optimizing the algorithm's performance across different vehicle models and operating conditions.

[0108] S4. Based on the input aerodynamic environment information, including the wind noise reference signal, the error signal e(n) and the aerodynamic parameter γ, the adaptive parameter α in the secondary regulation mechanism is updated according to the above formula (1).

[0109] The secondary memory parameter β is multiplied with the aerodynamic parameter γ, the original noise signal x(n), and the error signal e(n), and the secondary adaptive parameter α is updated according to the LMS algorithm; according to the secondary adaptive parameter α.

[0110] S5. Based on the secondary adaptive parameter α(n+1) obtained in the previous step, calculate the new primary adaptive parameter μ(n+1) according to the above formula (2).

[0111] S6. Update the adaptive filter coefficient w.

[0112] The original noise signal x(n) is filtered using secondary path estimation to obtain the filtered reference signal x'(n). The goal of secondary path estimation is to accurately estimate the changes in the control signal as it travels from filter generation, through the speaker, and finally to the error sensor.

[0113] According to the first-level adaptive parameter μ(n+1) updated by the second-level adjustment module, the filter reference signal x'(n) and the error signal e(n), the coefficient w of the adaptive filter is updated in the first-level adjustment module using the above formula (1) using the LMS algorithm.

[0114] S7. Update the memory parameter β in the secondary adjustment module for the next update of the filter coefficient. The memory parameter carries the accumulated change memory of the filter coefficient w in the last few updates.

[0115] Specifically, the original noise signal x(n), the error signal e(n) and the first-level adaptive parameter μ(n+1) are multiplied together to calculate the second-level memory parameter β(n+1) for the next update of the filter coefficient.

[0116] It should be noted that the second-level memory parameter β(n) for updating the filter coefficient is obtained by multiplying the original noise signal x(n-1), the error signal e(n-1) and the first-level adaptive parameter μ(n).

[0117] S8. The wind noise reference signal is filtered by an adaptive filter and an inverse noise control signal y(n) is output, i.e., the wind noise control signal. The inverse noise and the original wind noise cancel each other out in the control area, thus achieving a noise control effect.

[0118] In order to enable the adaptive parameters of active noise control to keep up with real-time changes in wind noise, this embodiment provides an active noise control algorithm that automatically adjusts the adaptive parameters according to the aerodynamic environment. The first-level adjustment module adjusts the coefficients of the adaptive filter according to the current adaptive parameters, and the second-level adjustment module adjusts the adaptive parameters according to the current aerodynamic environment. This solves the problem in the existing technology that active noise control has difficulty tracking wind noise changes.

[0119] According to one embodiment of the present application, the active noise reduction method further includes: sending a reverse noise control signal to an actuator, where the actuator is configured to generate reverse noise under the drive of the reverse noise control signal.

[0120] The actuator here can be an electroacoustic conversion device, such as a loudspeaker. Optionally, the loudspeaker is connected to a power amplifier module. The power amplifier module is used to amplify the power of the reverse noise control signal and then drive the loudspeaker to convert the electrical signal into an acoustic signal, thereby generating reverse noise that is superimposed on the original noise to suppress the original noise.

[0121] In a second aspect, an embodiment of the present application further provides an active noise reduction system. FIG9 is a schematic structural block diagram of an active noise reduction system according to an embodiment of the present application. As shown in FIG9 , the active noise reduction system 900 includes a controller 910 and a noise sensor 920, an error sensor 930, an environmental data acquisition sensor 940, and an actuator 950, respectively connected to the controller.

[0122] Noise sensor 920, used to collect original noise and perform acoustic-to-electrical conversion on the original noise to generate an original noise signal;

[0123] The error sensor 930 is used to collect the residual noise after the original noise and the reverse noise are superimposed, and perform acoustic-to-electrical conversion on the residual noise to generate an error signal;

[0124] Environmental data acquisition sensor 940, used to collect environmental information and generate environmental parameters;

[0125] The controller 910 is configured to generate a reverse noise control signal using the active noise reduction method described above;

[0126] The actuator 950 is configured to generate reverse noise based on the reverse noise control signal.

[0127] Specifically, the noise sensor 920 may include but is not limited to a microphone, a vibration sensor, etc., the error sensor 930 may be a microphone, and the actuator 950 may include an electroacoustic conversion device, such as a speaker, etc.

[0128] Since the active noise reduction system of this embodiment adopts the above-mentioned active noise reduction method, it has the same technical effects and functional principles as the above-mentioned method, which will not be further described here.

[0129] The following describes an active noise reduction system used in a vehicle to reduce wind noise with reference to FIG10 .

[0130] The reference sensor provides the controller with a reference signal related to wind noise. The noise sensor may include but is not limited to a microphone, a vibration sensor, an anemometer, etc.

[0131] Error sensors in vehicles monitor interior noise in real time and provide input to the controller. These sensors are typically sound pressure sensors. To strike a balance between noise reduction performance and cost, active noise control systems can use three or four error sensors. For example, three microphones could be used as error sensors: two located on the front roof handles and one in the center of the rear roof.

[0132] The active noise controller receives signals from the reference sensor and the error sensor, and automatically generates a driving signal according to the control target and control algorithm to drive the speakers in the car.

[0133] The speakers emit reverse sound waves to neutralize the noise inside the vehicle, achieving noise cancellation. The improved FxLMS algorithm model uses a secondary path estimation function for forward filtering and adaptively updates the filter coefficients using the minimum root mean square value of the error signal as a cost function, achieving the desired noise cancellation effect.

[0134] Active noise reduction can be applied to both the cockpit and passenger compartment, improving in-vehicle sound quality and the driving experience. In the cockpit, a vibration sensor and control unit can be installed on the windshield to suppress wind noise and chatter during driving. In the passenger compartment, secondary speakers and microphones can be installed inside the doors and roof panels, with the control unit controlling sound transmission and reception to achieve noise reduction in the doors and roof panels.

[0135] An embodiment of the present application further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program executed by the processor, and when the computer program is executed by the processor, the device equipped with the processor executes the active noise reduction method of any of the above embodiments.

[0136] An embodiment of the present application further provides a storage medium, on which a computer program is stored. The computer program runs on a computer, and when the computer program runs, it causes the computer to execute the active noise reduction method of any of the above embodiments.

[0137] An embodiment of the present application further provides a vehicle comprising at least one of the aforementioned active noise reduction system, the aforementioned electronic device, and the aforementioned storage medium. FIG11 exemplarily illustrates a vehicle according to one embodiment of the present application, the vehicle comprising the electronic device. FIG12 exemplarily illustrates a vehicle according to another embodiment of the present application, the vehicle comprising the storage medium.

[0138] Although example embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above example embodiments are merely illustrative and are not intended to limit the scope of the present application. Various changes and modifications may be made therein by those skilled in the art without departing from the scope and spirit of the present application. All such changes and modifications are intended to be included within the scope of the present application as required by the appended claims.

[0139] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel 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.

[0140] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units described is merely a logical function division. In actual implementation, other division methods may be used, such as combining or integrating multiple units or components into another device, or ignoring or not performing some features.

[0141] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0142] Similarly, it should be understood that in order to streamline the present application and aid in understanding one or more of the various inventive aspects, in the description of the exemplary embodiments of the present application, the various features of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, this approach of the present application should not be interpreted as reflecting the intention that the application claimed for protection requires more features than those explicitly recited in each claim. More precisely, as reflected in the corresponding claims, the inventive point is that the corresponding technical problem can be solved with features that are less than all the features of a single disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into the detailed description, with each claim itself serving as a separate embodiment of the present application.

[0143] It will be understood by those skilled in the art that, except where mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or apparatus disclosed herein may be combined in any combination. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature providing the same, equivalent, or similar purpose.

[0144] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of this application and to form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.

[0145] The various component embodiments of the present application can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all of the functions of some modules in the article analysis device according to the embodiment of the present application. The application can also be implemented as a device program (e.g., computer program and computer program product) for executing a part or all of the methods described herein. Such a program implementing the present application can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0146] It should be noted that the above embodiments illustrate rather than limit the present application, and that a person skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbols placed between brackets should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present application may be implemented by means of hardware comprising several different elements and by means of appropriately programmed computers. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names.

[0147] The above description is merely a specific embodiment or illustration of a specific embodiment of the present application, and the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. The scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. An active noise reduction method, comprising: Based on the original noise signal collected in real time by the noise sensor and the error signal collected in real time by the error sensor, an inverse noise control signal is generated through an improved FxLMS algorithm model. The inverse noise control signal is used to generate inverse noise superimposed on the original noise to suppress the original noise. The improved FxLMS algorithm model includes an adaptive filter, and the adaptive filter adaptively updates the filter coefficient based on the environmental parameters generated in real time.

2. The active noise reduction method according to claim 1, wherein: The real-time generation method of the environmental parameters includes: Based on the vehicle aerodynamic shape information and one or more of the vehicle speed information and wind speed information at the current moment, corresponding environmental parameters are obtained from a pre-established wind speed environmental parameter table.

3. The active noise reduction method according to claim 1, wherein: The adaptive updating of the filter coefficient based on the real-time generated environmental parameters includes: Update the first-level adaptive parameters based on the real-time generated environmental parameters; The filter coefficient is adaptively updated based on the first-level adaptive parameter at the current moment, the error signal at the previous moment, and the original noise signal at the previous moment.

4. The active noise reduction method according to claim 3, wherein: The updating of the first-level adaptive parameters based on the real-time generated environmental parameters further includes: The first-level adaptive parameters at the previous moment are updated based on the environmental parameters at the current moment, the second-level memory parameters at the previous moment, the original noise signal at the previous moment, and the error signal at the previous moment to obtain the first-level adaptive parameters at the current moment.

5. The active noise reduction method according to claim 4, wherein: Also includes: The secondary memory parameters at the current moment are generated based on the primary adaptive parameters at the current moment, the original noise signal at the previous moment, and the error signal at the previous moment.

6. The active noise reduction method according to claim 5, wherein: The generating of the secondary memory parameters at the current moment based on the primary adaptive parameters at the current moment, the original noise signal at the previous moment, and the error signal at the previous moment includes: When the primary adaptive parameter at the current moment and the original noise signal at the previous moment meet a preset condition, iteratively updating the secondary memory parameter at the previous moment based on the primary adaptive parameter at the current moment, the original noise signal at the previous moment, and the error signal at the previous moment to obtain the secondary memory parameter at the current moment; When the first-level adaptive parameter at the current moment and the original noise signal at the previous moment do not meet the preset conditions, the second-level memory parameter at the current moment is generated based on the first-level adaptive parameter at the current moment, the original noise signal at the previous moment and the error signal at the previous moment.

7. The active noise reduction method according to any one of claims 1 to 6, wherein: The original noise signal is a noise signal of a vehicle in motion, and the environmental parameter is an aerodynamic parameter.

8. The active noise reduction method according to any one of claims 1 to 6, wherein: The active noise reduction method further includes: The reverse noise control signal is sent to an actuator, and the actuator is configured to generate reverse noise under the driving of the reverse noise control signal.

9. An active noise reduction system, comprising a controller and a noise sensor, an error sensor, an environmental data acquisition sensor, and an actuator respectively connected to the controller; The noise sensor is used to collect original noise and perform acoustic-to-electrical conversion on the original noise to generate an original noise signal; The error sensor is used to collect the residual noise after the original noise and the reverse noise are superimposed, and perform acoustic-to-electrical conversion on the residual noise to generate an error signal; The environmental data acquisition sensor is used to collect environmental information and generate environmental parameters; The controller is configured to generate a reverse noise control signal by the active noise reduction method according to any one of claims 1 to 8; The actuator is configured to generate reverse noise based on the reverse noise control signal.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program executed by the processor, and when the computer program is executed by the processor, the device equipped with the processor performs the following active noise reduction method: Based on the original noise signal collected in real time by the noise sensor and the error signal collected in real time by the error sensor, and through the improved FxLMS algorithm model, a reverse noise control signal is generated, wherein the reverse noise control signal is used to generate reverse noise superimposed on the original noise to suppress the original noise; wherein, The improved FxLMS algorithm model includes an adaptive filter, which adaptively updates the filter coefficients based on environmental parameters generated in real time.

11. The electronic device according to claim 10, wherein: When the computer program is executed by the processor, the device equipped with the processor performs the following steps: Based on the vehicle aerodynamic shape information and one or more of the vehicle speed information and wind speed information at the current moment, corresponding environmental parameters are obtained from a pre-established wind speed environmental parameter table.

12. The electronic device as claimed in claim 10, wherein: When the computer program is executed by the processor, the device equipped with the processor performs the following steps: Update the first-level adaptive parameters based on the real-time generated environmental parameters; The filter coefficient is adaptively updated based on the first-level adaptive parameter at the current moment, the error signal at the previous moment, and the original noise signal at the previous moment.

13. The electronic device as claimed in claim 12, wherein: When the computer program is executed by the processor, the device equipped with the processor performs the following steps: The first-level adaptive parameters at the previous moment are updated based on the environmental parameters at the current moment, the second-level memory parameters at the previous moment, the original noise signal at the previous moment, and the error signal at the previous moment to obtain the first-level adaptive parameters at the current moment.

14. The electronic device as claimed in claim 13, wherein: When the computer program is executed by the processor, the device equipped with the processor performs the following steps: The secondary memory parameters at the current moment are generated based on the primary adaptive parameters at the current moment, the original noise signal at the previous moment, and the error signal at the previous moment.

15. The electronic device as claimed in claim 14, wherein: When the computer program is executed by the processor, the device equipped with the processor performs the following steps: When the primary adaptive parameter at the current moment and the original noise signal at the previous moment meet a preset condition, iteratively updating the secondary memory parameter at the previous moment based on the primary adaptive parameter at the current moment, the original noise signal at the previous moment, and the error signal at the previous moment to obtain the secondary memory parameter at the current moment; When the first-level adaptive parameter at the current moment and the original noise signal at the previous moment do not meet the preset conditions, the second-level memory parameter at the current moment is generated based on the first-level adaptive parameter at the current moment, the original noise signal at the previous moment and the error signal at the previous moment.

16. The electronic device according to any one of claims 10 to 15, wherein: When the computer program is executed by the processor, the device equipped with the processor performs the following steps: The original noise signal is a noise signal of a vehicle in motion, and the environmental parameter is an aerodynamic parameter.

17. The electronic device according to any one of claims 10 to 15, wherein: When the computer program is executed by the processor, the device equipped with the processor performs the following steps: The reverse noise control signal is sent to an actuator, and the actuator is configured to generate reverse noise under the driving of the reverse noise control signal.

18. A storage medium having a computer program stored thereon, the computer program running on a computer, the computer program causing the computer to perform the following active noise reduction method when running: Based on the original noise signal collected in real time by the noise sensor and the error signal collected in real time by the error sensor, and through the improved FxLMS algorithm model, a reverse noise control signal is generated, wherein the reverse noise control signal is used to generate reverse noise superimposed on the original noise to suppress the original noise; wherein, The improved FxLMS algorithm model includes an adaptive filter, which adaptively updates the filter coefficients based on environmental parameters generated in real time.

19. The storage medium as claimed in claim 18, wherein: When the computer program is executed, the computer is enabled to perform the active noise reduction method according to any one of claims 2 to 8.

20. A vehicle comprising at least one of the active noise reduction system according to claim 9, the electronic device according to any one of claims 10 to 17, and the storage medium according to any one of claims 18 to 19.

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