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

Abstract

The invention provides an active noise reduction method, system and device, a medium and a vehicle. The method comprises the steps that based on an original noise signal collected by a noise sensor in real time and an error signal collected by an error sensor in real time, through an improved FxLMS algorithm model, a reverse noise control signal is generated, and the reverse noise control signal is used for generating reverse noise superposed with the original noise to suppress the original noise; wherein the improved FxLMS algorithm model comprises a self-adaptive filter, and the self-adaptive filter is used for carrying out self-adaptive updating of a filter coefficient on the basis of environment parameters generated in real time. According to the method, the step length during adaptive updating of the filter coefficient is adjusted through environmental parameters, so that the iteration speed of an algorithm model is improved, the change of non-stationary original noise is quickly tracked, and effective noise control is realized.

Description

Technical Field

[0001] The present application relates to the field of audio technology, and more specifically to an active noise reduction method, system, device, medium and vehicle. Background Art

[0002] Noise affects the user experience during the use 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.

[0003] At present, active control of engine noise and road noise usually uses the Filtered-x Least Mean Square (FxLMS) algorithm for noise control. Although the algorithm has low requirements for hardware computing performance, it has a slow convergence speed and poor signal tracking ability. When the frequency characteristics of the noise signal change quickly and the signal is non-stationary, such as the wind noise generated by a car driving at high speed, the traditional FxLMS algorithm is difficult to track the changes in noise in actual situations and cannot achieve effective noise control. Therefore, achieving effective active control of non-stationary noise signals has become a technical problem that urgently needs to be solved. Summary of the invention

[0004] The present application is proposed in view of the above problems. The present application provides an active noise reduction method, system, device, medium and vehicle, which can achieve effective control of non-stationary noise signals.

[0005] According to a first aspect of the present application, an active noise reduction method is provided, the active noise reduction method comprising:

[0006] 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, and 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, and the adaptive filter adaptively updates the filter coefficient based on the environmental parameters generated in real time.

[0007] In one embodiment of the present application, the real-time generation method of environmental parameters includes:

[0008] 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.

[0009] In one embodiment of the present application, the adaptive updating of the filter coefficient based on the real-time generated environmental parameters includes:

[0010] Update the first-level adaptive parameters based on the environmental parameters generated in real time;

[0011] 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.

[0012] In one embodiment of the present application, the updating of the primary adaptive parameters based on the environmental parameters generated in real time further includes:

[0013] Based on the environmental parameters at the current 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 primary adaptive parameters at the previous moment are updated to obtain the primary adaptive parameters at the current moment.

[0014] In one embodiment of the present application, it also includes:

[0015] 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.

[0016] In one embodiment of the present application, the generating of 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:

[0017] When the primary adaptive parameter at the current moment and the original noise signal at the previous moment meet a preset condition, the secondary memory parameter at the previous moment is iteratively updated 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;

[0018] When the first-level adaptive parameters at the current moment and the original noise signal at the previous moment do not meet the preset conditions, the second-level memory parameters at the current moment are generated based on the first-level adaptive parameters at the current moment, the original noise signal at the previous moment and the error signal at the previous moment.

[0019] In one embodiment of the present application, the original noise signal is a noise signal of a vehicle in motion, and the environmental parameter is an aerodynamic parameter.

[0020] In one embodiment of the present application, the active noise reduction method further includes:

[0021] The reverse noise control signal is sent to an actuator, and the actuator is used to generate reverse noise under the driving of the reverse noise control signal.

[0022] 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;

[0023] The noise sensor is used to collect original noise and perform acoustic-to-electric conversion on the original noise to generate an original noise signal;

[0024] The error sensor is used to collect the residual noise after the original noise and the reverse noise are superimposed, and perform acoustic-to-electric conversion on the residual noise to generate an error signal;

[0025] The environmental data acquisition sensor is used to collect environmental information and generate environmental parameters;

[0026] The controller is used to generate a reverse noise control signal through the above-mentioned active noise reduction method;

[0027] The actuator is used to generate reverse noise based on the reverse noise control signal.

[0028] 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, a device equipped with the processor executes the above-mentioned active noise reduction method.

[0029] 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 running, the computer executes the above-mentioned active noise reduction method.

[0030] 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.

[0031] 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, and tracking the changes of the original noise 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, thereby achieving effective noise control. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The above and other purposes, features and advantages of the present invention will become more apparent by describing the embodiments of the present invention in more detail in conjunction with the accompanying drawings. The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0033] Figure 1 is a schematic block diagram of a control device for an active noise reduction method according to an embodiment of the present invention;

[0034] Figure 2 is a schematic structural diagram of an active noise reduction system according to an embodiment of the present application;

[0035] Figure 3 is a schematic flow chart of an active noise reduction method according to an embodiment of the present application;

[0036] Figure 4 is an algorithm block diagram of an active noise reduction method according to an embodiment of the present application;

[0037] Figure 5 is an algorithm block diagram of an active noise reduction method according to another embodiment of the present application;

[0038] Figure 6 It is a block diagram of an algorithm for generating secondary adaptive parameters according to an embodiment of the present application;

[0039] Figure 7 is a schematic flow chart of an active noise reduction method according to another embodiment of the present application;

[0040] Figure 8 is a block diagram of an algorithm for generating secondary adaptive parameters according to another embodiment of the present application;

[0041] Fig. 9 is a schematic structural block diagram of an active noise reduction system according to an embodiment of the present application;

[0042] Fig.10 is a schematic structural diagram of a vehicle active noise reduction system according to an embodiment of the present application;

[0043] Fig.11 is a schematic structural block diagram of a vehicle according to an embodiment of the present application;

[0044] Fig.12 It is a schematic structural block diagram of a vehicle according to another embodiment of the present application. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical scheme and advantages of the present invention more obvious, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described herein. Based on the embodiments of the present invention described in the present invention, all other embodiments obtained by those skilled in the art without creative work should fall within the protection scope of the present invention.

[0046] In view of the problem that existing active noise reduction methods are difficult to accurately distinguish different road surfaces based on the adhesion coefficient μ value, the present application proposes an active noise reduction method, electronic equipment, medium and vehicle, which can effectively track noise signals and thus achieve effective noise control, which is described in detail below.

[0047] First, refer to Figure 1 An exemplary control device 100 for implementing an embodiment of the method of the present invention is described below.

[0048] like Figure 1 As shown, 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 forms of connection mechanisms (not shown).

[0049] It should be noted that Figure 1 The components and structures of the control device 100 shown are merely exemplary and non-limiting. The control device may also have other components and structures as required.

[0050] Optionally, the communication interface 130 may also include a transmitter and / or a receiver.

[0051] 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.

[0052] The memory 120 may be a computer-readable storage medium in various forms, such as a volatile memory and / or a non-volatile memory. Volatile memory may include, for example, a random access memory (RAM), a cache memory (cache), a synchronous dynamic random access memory (SDRAM), etc. Non-volatile memory may include, for example, a read-only memory (ROM), a hard disk, a flash memory (Flash), etc. One or more computer program instructions may also be stored on the computer-readable storage medium, and the memory 120 may run the program instructions to implement the active noise reduction method in the embodiment of the present invention described below.

[0053] First, the application scenario of the active noise reduction method in the embodiment of the present application is described.

[0054] Figure 2 is a schematic structural diagram of the active noise reduction system, such as Figure 2 As shown, x(n) is the reference signal generated by the reference sensor, y(n) is the reverse noise control signal generated by the controller, e(n) is the error signal received by the error sensor, and n represents the nth moment. The entire workflow of the system is as follows: the original noise generated by the noise source is output as the primary noise signal d(n) after passing through the primary path, and the reference sensor receives the original noise and performs acoustic-to-electric conversion to generate a reference signal x(n), which is used as the input of the controller. The controller calculates the reverse noise control signal y(n) according to the algorithm rules, and after output, it drives the electroacoustic conversion device through the power amplifier to generate reverse noise, which is transmitted to the error sensor through the secondary path. The error sensor simultaneously receives the original noise and the reverse noise, and the two are superimposed and acoustic-to-electrically converted to form an error signal e(n). The error signal is input into the controller, and the controller continuously adjusts the controller weight coefficient to change the reverse noise control signal until the system reaches a stable state. An active noise reduction method provided in the present application can be applied to Figure 2 In the controller shown, the following Figure 2 The controller shown is used as an example for explanation.

[0055] It should be noted that in the following description, the reference sensor is referred to as a noise sensor, and since the original noise signal is used as the reference signal, the reference signal is also referred to as the original noise signal.

[0056] The present application provides an active noise reduction method, which includes: based on an original noise signal collected in real time by a noise sensor and an error signal collected in real time by an error sensor, and through an improved FxLMS algorithm model, generating a reverse noise control signal, 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, and the adaptive filter adaptively updates the filter coefficient based on the environmental parameters generated in real time.

[0057] Next, refer to Figure 3 and Figure 4 The active noise reduction method according to the embodiment of the present application is described in detail.

[0058] like Figure 3 As shown, an active noise reduction method provided by the present application includes the following steps S310 to S320.

[0059] 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 obtained.

[0060] Here, the original noise signal is an electrical signal formed after the noise sensor picks up the original noise generated by the noise source and performs acoustic-to-electric conversion on the original noise, and the error signal is an electrical signal formed after the original noise and the reverse noise are superimposed and then the acoustic-to-electric conversion is performed.

[0061] Specifically, the noise sensor and the error sensor may be microphones.

[0062] In step S320, based on the original noise signal, a reverse noise control signal is generated 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.

[0063] In the present application, the adaptive filter receives the original noise signal and obtains an output signal, i.e., a reverse noise control signal, according to the original noise signal and the weight coefficient of the adaptive filter. The reverse noise control signal is used to drive the acoustic and electrical device as a secondary sound source to generate reverse noise, and the reverse noise is opposite in phase to the original noise, so that the reverse noise can weaken the original noise in the user environment, thereby reducing the impact of the original noise on the user.

[0064] The active noise reduction method is based on an adaptive filter that generates a signal with a phase opposite to the original noise to cancel the noise. It iteratively updates the coefficients of the adaptive filter according to the input signal of the system. In this application, the improved FxLMS algorithm model is established based on the FxLMS algorithm. At the same time, the original noise is collected by the error sensor and the reverse noise is superimposed and then converted into an electrical signal by acoustic-electric conversion. During the iterative update, the filter coefficient is adaptively updated based on the environmental parameters generated in real time.

[0065] The environmental parameters in this application are determined according to 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 speed information of the exhaust fan and / or cooking-related information on the stove.

[0066] 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, and tracking the changes of the original noise in the process of generating reverse noise with a phase opposite to the original noise through the adaptive filter to reduce the original noise, thereby achieving effective noise control.

[0067] According to one embodiment of the present application, the filter coefficients are 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 coefficients 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.

[0068] In this embodiment, when the improved FxLMS algorithm model iteratively updates the coefficients of the adaptive filter, the iteration speed is determined by the step size factor. The noise reduction method of this embodiment adopts a dual adjustment mechanism, and updates the step size factor with real-time environmental parameters. Therefore, in this application, the step size factor that directly determines the iteration speed is defined as a primary adaptive parameter.

[0069] In this embodiment, the filter coefficient w of the adaptive filter is updated according to the following formula (1):

[0070] 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 nth moment, w(n-1) is the coefficient of the adaptive filter at the n-1th moment, x'(n-1) is the filtering reference signal at the n-1th moment, e(n-1) is the error signal at the n-1th moment, and μ(n) is the first-level adaptive parameter at the nth moment.

[0072] Figure 4 is an algorithm block diagram of an active noise reduction method according to an embodiment of the present application, such as Figure 4 As shown in the figure, the improved FxLMS algorithm model takes the original noise signal x(n) as the reference signal, processes the reference signal using the secondary path estimation, and obtains the 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 signal formed by the acoustic-to-electric conversion after the original noise collected by the error sensor and the reverse noise are superimposed is used as the error signal e(n), the first-level adaptive parameter μ(n+1) is used as the step factor, the filtered reference signal x'(n), the error signal and the determined step factor are multiplied, and the adaptive filter weight coefficient is iteratively updated through the LMS algorithm to adjust the strength of the reverse noise until the error signal reaches the preset threshold. After that, the original noise signal x(n) is 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, so as to adjust the weight of the input signal when the adaptive filter weight coefficient is updated, assign smaller parameters to input signals with weak correlation, and assign larger parameters to input signals with strong correlation, so as to improve the iteration speed of the FxLMS algorithm model, and track the changes of the original noise in the process of generating reverse noise with opposite phase to the original noise through the adaptive filter to achieve 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 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 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 the current environmental parameters based on the vehicle speed information, wind speed information and vehicle aerodynamic shape information is as follows: first, the relative wind speed is determined based on the vehicle speed information 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. Here, when the vehicle speed and wind speed are in the same direction, the relative wind speed value is the sum of the two, and when the two are in opposite directions, the relative wind speed value is the difference between the two.

[0082] The wind speed environment parameter table is obtained by calibrating the vehicle aerodynamic shape information of different vehicles. During the calibration process, for vehicles with different vehicle aerodynamic shape parameters, when the vehicle is driving at different speeds and wind speeds, the values ​​of the environmental parameters that the improved FxLMS algorithm model can achieve the maximum noise reduction in the shortest time are recorded, thereby generating a wind speed environment parameter table for each vehicle.

[0083] According to an embodiment of the present application, updating the first-level adaptive parameters based on the environmental parameters generated in real time also 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, see Figure 5 and Figure 6 A method for updating the first-level adaptive parameters in an active noise reduction method according to an embodiment of the present application is described.

[0086] See also 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 by an adaptive filter to generate a reverse 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 combination Figure 6 The method of controlling the updating of the primary adaptive parameters by the secondary adjustment module is described.

[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 through the following formula (2):

[0090] α(n)=ln(μ(n-1))+δx(n-1)e(n-1)β(n-1) (2)

[0091] Among them, 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.

[0092] 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:

[0093] μ(n)=exp(α(n)) (3)

[0094] Among them, μ(n) is the first-level adaptive parameter at the nth moment.

[0095] In this embodiment, the secondary memory parameter is used to represent the memory of the accumulated changes of the filter coefficients in the most recent updates.

[0096] 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.

[0097] According to one embodiment of the present application, the active noise reduction method further includes:

[0098] 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;

[0099] Based on the primary 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 secondary memory parameters at the current moment are generated by the following formula (4):

[0100] β(n)=H(μ,x)β(n-1)+μ(n)x(n-1)e(n-1) (4)

[0101] 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.

[0102] Specifically, the preset condition is expressed as the following expression (5):

[0103] 1-μ(n)|x(n-1)| 2 >0 (5)

[0104] Then, when the first-level adaptive parameters 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 , so that the secondary memory parameters at the previous moment are iteratively updated 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 the secondary memory parameters at the current moment are obtained by the following formula (6):

[0105] β(n)=(1-μ(n)|x(n-1)| 2 )β(n-1)+μ(n)x(n-1)e(n-1) (6)

[0106] 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, so that the current second-level memory parameter is generated by 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.

[0107] β(n)=μ(n)x(n-1)e(n-1) (7)

[0108] According to one embodiment of the present application, the original noise signal is a noise signal during vehicle driving. Specifically, the noise signal during vehicle driving can be a wind noise signal of the vehicle. When the vehicle is driving at high speed, the air in front of the vehicle body will form a high-speed airflow. When this airflow encounters structures such as A-pillars and rearview mirrors, a large amount of turbulence will be formed. At the same time, the air pressure pulsation acts on the front windshield and side windows, thereby forming wind noise. When the car is idling and driving at low speed, the main noise in the car comes from the engine and other power auxiliary systems (such as the intake system and the exhaust system). When the vehicle speed reaches 100km / h, wind noise begins to appear. When the vehicle speed reaches 120km / h, wind noise becomes the main sound source of the car. When the vehicle speed is further increased, the wind noise may completely cover the engine noise and the road-tire noise. The influence of wind noise on the noise inside the car changes with the vehicle speed, external environment (such as wind speed, weather, etc.) and road conditions. Therefore, applying the active noise reduction method to the control of wind noise signals can achieve model convergence at a higher speed, effectively track the changes in wind noise, and effectively improve the noise reduction effect of wind noise.

[0109] 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.

[0110] Next, see Figure 7 and Figure 8 The active noise reduction method according to an embodiment of the present application is described below. The method of this embodiment is applied to a vehicle, the original noise signal is a wind noise signal, and the environmental parameter is an aerodynamic parameter, so that the method of this embodiment 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.

[0111] S1. Obtain an error signal e(n). The error signal is generally a signal obtained by an error microphone in the vehicle. It is used to evaluate the noise control effect and drive the update iteration of the adaptive algorithm so that the algorithm converges in the direction of reducing the error signal.

[0112] S2. Obtain the original noise signal x(n), i.e., the wind noise signal, as a reference signal. The wind noise reference signal can be a variety of signals obtained by a wind noise microphone outside the car, an anemometer, a glass vibration sensor, or a wind noise microphone inside the car. The wind noise reference signal can also be constructed by combining sensors based on the current vehicle speed, the air flow rate outside the car, and other information. The wind noise reference signal is the input signal of the adaptive filter. After being filtered by the adaptive filter, a wind noise control signal is generated. At the same time, the wind noise reference signal also drives the update and iteration of the adaptive algorithm.

[0113] S3. Obtain the aerodynamic parameter γ, which is determined by the aerodynamic shape of the vehicle. The aerodynamic parameter γ can be regarded as the meta-step length of this algorithm, which is obtained in advance by cross-validation using vehicles with different aerodynamic shapes under different speed conditions. The aerodynamic parameter can control the update rate of the secondary regulation module and optimize the performance of the algorithm under different vehicle models and different working conditions.

[0114] 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).

[0115] 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.

[0116] S5. Based on the secondary adaptive parameter α(n+1) obtained in the previous step, calculate a new primary adaptive parameter μ(n+1) according to the above formula (2).

[0117] S6. Update the adaptive filter coefficient w.

[0118] The original noise signal x(n) is filtered through the secondary path estimation to obtain the filtered reference signal x'(n). The purpose of the secondary path estimation is to correctly estimate the changes in the control signal when it starts from the filter generation, passes through the speaker, and finally reaches the error sensor.

[0119] 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 by the above formula (1) using the LMS algorithm.

[0120] 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.

[0121] Specifically, the original noise signal x(n), the error signal e(n) and the primary adaptive parameter μ(n+1) are multiplied together to calculate the secondary memory parameter β(n+1) for the next update of the filter coefficient.

[0122] 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).

[0123] S8. The wind noise reference signal is filtered by an adaptive filter, and a reverse noise control signal y(n) is output, i.e., the wind noise control signal. The reverse noise and the original wind noise cancel each other out in the control area, thereby achieving a noise control effect.

[0124] In order to enable the adaptive parameters of active noise control to keep up with the real-time changes of wind noise, the present 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, thereby solving the problem in the prior art that active noise control is difficult to track changes in wind noise.

[0125] According to an embodiment of the present application, the active noise reduction method further includes: sending a reverse noise control signal to an actuator, wherein the actuator is configured to generate reverse noise under the drive of the reverse noise control signal.

[0126] The actuator here can be an electroacoustic conversion device, such as a speaker. Preferably, the speaker is connected to a power amplifier module, which is used to amplify the power of the reverse noise control signal and then drive the speaker 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.

[0127] In a second aspect, the present application also provides an active noise reduction system. Fig. 9 is a schematic structural block diagram of an active noise reduction system according to an embodiment of the present application, such as Fig. 9 As shown, 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;

[0128] The noise sensor 920 is used to collect the original noise and perform acoustic-electric conversion on the original noise to generate an original noise signal;

[0129] 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-electric conversion on the residual noise to generate an error signal;

[0130] Environmental data collection sensor 940, used to collect environmental information and generate environmental parameters;

[0131] A controller 910, configured to generate a reverse noise control signal by using the above-mentioned active noise reduction method;

[0132] The actuator 950 is configured to generate reverse noise based on the reverse noise control signal.

[0133] 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.

[0134] 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 described in detail here.

[0135] The following combination Fig.10 An active noise reduction system used in a vehicle to reduce wind noise is described.

[0136] 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.

[0137] The error sensor in the vehicle is used to monitor the noise inside the vehicle in real time and provide input to the controller. It is usually a sound pressure sensor. In order to strike a balance between noise reduction performance and cost, the active noise control system can use 3 or 4 error sensors. For example, three microphones are used as error sensors, two of which are located at the front roof handles and one is located in the center of the rear roof.

[0138] 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 the control algorithm to drive the speakers in the car to work.

[0139] The speaker emits reverse sound waves to "neutralize" the noise inside the car, achieving noise elimination. The improved FxLMS algorithm model performs forward filtering through the secondary path estimation function, and adaptively updates the filter coefficients with the minimum root mean square value of the error signal as the cost function, achieving the effect of noise elimination.

[0140] Active noise reduction control can be applied to the cockpit and passenger compartment to improve the in-car sound quality and driving experience. In the cockpit, wind noise and flutter during driving can be suppressed by installing vibration sensors and control units on the vehicle's windshield. In the passenger compartment, secondary speakers and microphones can be installed inside the doors and roof panels, and the control unit controls the emission and reception of sound, thereby achieving noise reduction of the doors and roof panels.

[0141] An embodiment of the present application further provides an electronic device, the electronic device comprising a memory and a processor, the memory storing 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.

[0142] 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 is running, the computer executes the active noise reduction method of any of the above embodiments.

[0143] An embodiment of the present application further provides a vehicle, comprising at least one of the above-mentioned active noise reduction system, the above-mentioned electronic device, and the above-mentioned storage medium. Fig.11 A vehicle according to an embodiment of the present application is exemplarily shown, and the vehicle includes an electronic device. Fig.12 A vehicle according to another embodiment of the present application is exemplarily shown, the vehicle including a storage medium.

[0144] Although example embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above example embodiments are merely exemplary and are not intended to limit the scope of the present invention thereto. Various changes and modifications may be made therein by one of ordinary skill in the art without departing from the scope and spirit of the present invention. All such changes and modifications are intended to be included within the scope of the present invention as required by the appended claims.

[0145] 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 to be beyond the scope of the present invention.

[0146] 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 only schematic, for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed.

[0147] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention 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.

[0148] Similarly, it should be understood that in order to streamline the present invention and help understand one or more of the various inventive aspects, in the description of the exemplary embodiments of the present invention, the various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, the method of the present invention should not be interpreted as reflecting the following intention: the claimed invention requires more features than the features explicitly stated in each claim. More specifically, as reflected in the corresponding claims, the inventive point is that the corresponding technical problem can be solved with less than all the features of a single disclosed embodiment. Therefore, the claims following the specific embodiment are hereby expressly incorporated into the specific embodiment, wherein each claim itself serves as a separate embodiment of the present invention.

[0149] It will be understood by those skilled in the art that, except for mutually exclusive features, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this specification may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature that provides the same, equivalent or similar purpose.

[0150] In addition, those skilled in the art will appreciate that, although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, in the claims, any one of the claimed embodiments may be used in any combination.

[0151] The various component embodiments of the present invention may 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) may be used in practice to implement some or all of the functions of some modules in the article analysis device according to an embodiment of the present invention. The present invention may also be implemented as a device program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing the present invention may be stored on a computer-readable medium, or may be in the form of one or more signals. Such a signal may be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0152] It should be noted that the above embodiments illustrate the present invention rather than limit it, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be construed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising a number of different elements and by means of a suitably programmed computer. In a unit claim enumerating a number of devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc., does not indicate any order. These words may be interpreted as names.

[0153] The above is only a specific embodiment of the present invention or an explanation of a specific embodiment. The protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. An active noise reduction method, characterized in that: The active noise reduction method comprises: 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, and 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, and the adaptive filter adaptively updates the filter coefficient based on the environmental parameters generated in real time.

2. The active noise reduction method as claimed in claim 1, characterized in that: 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 as claimed in claim 1, characterized in that: 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 environmental parameters generated in real time; 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 as claimed in claim 3, characterized in that: The updating of the first-level adaptive parameters based on the environmental parameters generated in real time also includes: Based on the environmental parameters at the current 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 primary adaptive parameters at the previous moment are updated to obtain the primary adaptive parameters at the current moment.

5. The active noise reduction method as claimed in claim 4, characterized in that: 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 as claimed in claim 5, characterized in that: 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 comprises: When the primary adaptive parameter at the current moment and the original noise signal at the previous moment meet a preset condition, the secondary memory parameter at the previous moment is iteratively updated 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 parameters at the current moment and the original noise signal at the previous moment do not meet the preset conditions, the second-level memory parameters at the current moment are generated based on the first-level adaptive parameters 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, characterized in that: 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, characterized in that: The active noise reduction method further includes: The reverse noise control signal is sent to an actuator, and the actuator is used to generate reverse noise under the driving of the reverse noise control signal.

9. An active noise reduction system, characterized in that: It includes 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-electric 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-electric 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 used 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 used to generate reverse noise based on the reverse noise control signal.

10. An electronic device, characterized in that: The electronic device includes 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 according to any one of claims 1 to 8.

11. A storage medium, characterized in that: The storage medium stores a computer program, which runs on a computer. When the computer program is running, the computer executes the active noise reduction method according to any one of claims 1 to 8.

12. A vehicle, characterized in that: The invention comprises at least one of the active noise reduction system according to claim 10, the electronic device according to claim 10, and the storage medium according to claim 12.

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