Noise control method and device and readable storage medium

Abstract

The invention discloses a noise control method and device and a readable storage medium, and relates to the technical field of vehicle noise reduction, the method comprises the steps that a reference signal and a real error signal collected by a physical error microphone are acquired, and the reference signal is a noise source signal outside a vehicle; according to target virtual sensing parameters and the real error signals, virtual error signals corresponding to the virtual error points are determined, and the target virtual sensing parameters comprise secondary path transmission parameters corresponding to the virtual error points and primary path mapping parameters from the physical error microphone to the virtual error points; determining a target adaptive filter parameter according to the reference signal, the real error signal and the virtual error signal; processing the reference signal based on the target adaptive filter parameter to generate a target anti-noise signal; the target anti-noise signal is output through the anti-noise loudspeaker so as to perform noise reduction on the target area corresponding to the virtual error point, and the noise reduction effect of the area far away from the physical error microphone can be improved.

Description

Technical Field

[0001] This application relates to the technical field of vehicle noise reduction, and particularly to a noise control method, device, and readable storage medium. Background Art

[0002] Road noise refers to the structural noise and air noise generated by the contact excitation between the tire and the road surface during vehicle driving, which is transmitted to the vehicle interior through multiple paths to form broadband noise. Road noise will affect the driving state of the driver and also reduce the riding comfort of other occupants in the vehicle. Therefore, users' demand for in-vehicle noise reduction is also increasing.

[0003] Traditional passive noise reduction technology realizes in-vehicle noise reduction through damping, vibration damping materials, sound absorption, or sound insulation materials. However, passive noise reduction technology is only effective for road noise caused by specific frequency bands (such as medium and high frequencies), specific vehicle speeds, or specific regions, and has limited effects on low-frequency noise, making it difficult to meet users' noise reduction requirements for the entire frequency band.

[0004] In response, in related technologies, active noise reduction technology is adopted for noise reduction. This technology collects the excitation signal between the tire and the road surface through a vibration sensor, and uses an error microphone arranged in the vehicle interior to monitor the in-vehicle noise in real time. According to the excitation signal and the in-vehicle noise, an anti-noise signal with the same amplitude and opposite phase as the in-vehicle noise is generated, and the anti-noise signal is output through a speaker to cancel the in-vehicle noise and achieve noise reduction for the entire frequency band.

[0005] However, this method has a good noise reduction effect on the area near the error microphone, but has a poor noise reduction effect on the area far from the error microphone. Summary of the Invention

[0006] This application provides a noise control method, device, and readable storage medium, which can improve the noise reduction effect on the area far from the error microphone.

[0007] In a first aspect, some embodiments of this application provide a noise control method, including: obtaining a reference signal and a true error signal collected by a physical error microphone, where the reference signal is a noise source signal outside the vehicle; determining a virtual error signal corresponding to a virtual error point according to a target virtual sensing parameter and the true error signal, where the target virtual sensing parameter includes a secondary path transfer parameter corresponding to the virtual error point and a primary path mapping parameter from the physical error microphone to the virtual error point; determining a target adaptive filter parameter according to the reference signal, the true error signal, and the virtual error signal; processing the reference signal based on the target adaptive filter parameter to generate a target anti-noise signal; and outputting the target anti-noise signal through an anti-noise speaker to perform noise reduction on a target area, where the target area is a noise reduction area corresponding to the virtual error point.

[0008] In a second aspect, some embodiments of the present application further provide a noise control device, including: a signal acquisition module configured to acquire a reference signal and a true error signal collected by a physical error microphone, where the reference signal is a noise source signal outside the vehicle; a first determination module configured to determine a virtual error signal corresponding to a virtual error point according to a target virtual sensing parameter and the true error signal, where the target virtual sensing parameter includes a secondary path transfer parameter corresponding to the virtual error point and a primary path mapping parameter from the physical error microphone to the virtual error point; a second determination module configured to determine a target adaptive filter parameter according to the reference signal, the true error signal, and the virtual error signal; a signal generation module configured to process the reference signal based on the target adaptive filter parameter to generate a target anti-noise signal; and a noise reduction module configured to output the target anti-noise signal through an anti-noise speaker to reduce noise in a target area, where the target area is a noise reduction area corresponding to the virtual error point.

[0009] In a third aspect, some embodiments of the present application further provide a noise control system, including: a memory, a processor, and computer instructions stored on the memory and executable on the processor, where the computer instructions are configured to implement the steps of the noise control method described in the first aspect above.

[0010] In a fourth aspect, some embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the processor is caused to execute the noise control method described in the first aspect.

[0011] In a fifth aspect, some embodiments of the present application provide a computer program product, which includes: computer program code. When the computer program code runs on a display device, the display device is caused to execute the noise control method described in the first aspect.

[0012] The beneficial effects of the embodiments of the present application compared with the prior art are as follows: By using the physical error microphone arranged inside the vehicle to collect the true error signal at the physical error microphone, and using the secondary path transfer parameter corresponding to the virtual error point and the primary path mapping parameter from the physical error microphone to the virtual error point, the true error signal at the physical error microphone is mapped to the virtual error point, that is, the virtual error signal of the virtual error point is simulated. Then, based on the reference signal, the true error signal, and the virtual error signal, the target adaptive filter parameter is determined, and based on the target adaptive filter parameter, the reference signal is processed to generate the target anti-noise signal. In this way, the problem that the noise reduction area of the physical error microphone is limited can be avoided, the noise reduction effect of the virtual error point can be improved, the noise reduction area corresponding to the physical error microphone is extended to the virtual error point, so as to improve the noise reduction effect of the vehicle occupants away from the area of the physical error microphone and enhance the user experience. Description of the Drawings

[0013] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0014] Figure 1 It is a logic block diagram of a noise control method provided by an embodiment of the present application; Figure 2 It is a schematic diagram of the internal structure of a vehicle provided by an embodiment of the present application; Figure 3 It is one of the flow schematic diagrams of the noise control method provided by an embodiment of the present application; Figure 4 It is another flow schematic diagram of the noise control method provided by an embodiment of the present application; Figure 5 It is the third flow schematic diagram of the noise control method provided by an embodiment of the present application; Figure 6 It is the fourth flow schematic diagram of the noise control method provided by an embodiment of the present application; Figure 7 It is the fifth flow schematic diagram of the noise control method provided by an embodiment of the present application; Figure 8 It is the sixth flow schematic diagram of the noise control method provided by an embodiment of the present application; Figure 9 It is a hardware block diagram of a noise control device provided by an embodiment of the present application. Detailed implementation manners

[0015] The following will explain the embodiments in detail, and the examples are shown in the drawings. When the following description involves the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following embodiments do not represent all the implementation manners consistent with the present application. They are only examples of systems and methods consistent with some aspects of the present application.

[0016] It should be noted that the brief description of the terms in the present application is only for the convenience of understanding the following described implementation manners, rather than intending to limit the implementation manners of the present application. Unless otherwise specified, these terms should be understood in their ordinary and common meanings.

[0017] In this application, the terms "first", "second", "third", etc. in the specification are used to distinguish similar or like objects or entities, and do not necessarily mean to limit a specific order or sequence, unless otherwise noted. It should be understood that such terms can be interchanged under appropriate circumstances.

[0018] The terms "comprising" and "having" and any variations thereof are intended to cover but not exclude inclusion. For example, a product or device comprising a series of components does not necessarily have to be limited to all the components clearly listed, but may include other components not clearly listed or inherent to these products or devices.

[0019] The term "module" refers to any known or later developed hardware, software, firmware, artificial intelligence, fuzzy logic, or a combination of hardware or / and software code that can perform functions related to that element.

[0020] The technical solutions of the embodiments of this application will be described below.

[0021] Road noise refers to the structure noise and air noise caused by the excitation of the contact between the tire and the road surface during vehicle driving, which is transmitted to the vehicle interior through multiple paths to form broadband noise. Road noise will affect the driving state of the driver and also reduce the riding comfort of other passengers in the vehicle. Therefore, users' demand for noise reduction in the vehicle interior is also increasing.

[0022] Traditional passive noise reduction technologies achieve noise reduction in the vehicle interior through damping, vibration damping materials, sound absorption, or sound insulation materials. However, passive noise reduction technologies are only effective for road noise caused by specific frequency bands (such as medium and high frequencies), specific vehicle speeds, or specific regions, and have limited effects on low-frequency noise, making it difficult to meet users' noise reduction requirements for the full frequency band.

[0023] In response to this, in related technologies, active noise reduction technologies are used to control road noise in real time. For example, taking the active road noise cancellation (RNC) technology as an example, vibration sensors are arranged near the vehicle's tires, and the excitation signal between the tire and the road surface (i.e., the source excitation signal of road noise) is collected through the vibration sensors. The interior noise of the vehicle is monitored in real time through error microphones arranged in the vehicle interior. Based on the excitation signal and the interior noise, an anti-noise signal with the same amplitude and opposite phase as the interior noise is generated in real time, and the anti-noise signal is output through the speaker to cancel the interior noise and achieve noise reduction for the full frequency band.

[0024] However, this method can achieve effective noise reduction in the area near the error microphone (which can be called the noise reduction "sweet spot"), while the noise reduction effect drops significantly in the area far from the error microphone. For example, by arranging the error microphone near the car door, this will lead to a phenomenon that the noise reduction effect on the side of the occupant's head far from the error microphone decreases or even the noise increases, affecting the occupant's riding experience.

[0025] In some embodiments, increasing the number of error microphones arranged inside the vehicle can expand the noise reduction "sweet spot" and improve the noise reduction effect. However, this method will increase the cost.

[0026] To solve the above technical problems, the embodiments of the present application provide a noise control method. Using the physical error microphones arranged inside the vehicle, the real error signals at the physical error microphones are collected, and using the transfer parameters of the secondary path corresponding to the virtual error points and the mapping parameters of the primary path from the physical error microphones to the virtual error points, the real error signals at the physical error microphones are mapped to the virtual error points, that is, the virtual error signals at the virtual error points are simulated. Then, based on the reference signal, the real error signal, and the virtual error signal, the target adaptive filter parameters are determined. Based on the target adaptive filter parameters, the reference signal is processed to generate the target anti-noise signal. In this way, the problem that the noise reduction area of the physical error microphone is limited can be avoided, the noise reduction effect at the virtual error points can be improved, and the noise reduction area corresponding to the physical error microphone can be extended to the virtual error points to improve the noise reduction effect in the area where the vehicle occupants are far from the physical error microphones and improve the user experience.

[0027] The noise control method provided by the embodiments of the present application is applied to a noise control system. Before introducing the noise control method provided by the embodiments of the present application, a schematic description of the noise control system will be given first.

[0028] Figure 1 It is a schematic structural diagram of a noise control system provided by the embodiments of the present application. As Figure 1 shown, the noise control system may include a vibration sensor, a physical error microphone (which can also be called a real error microphone), an anti-noise speaker, and a controller (which can also be called an RNC controller).

[0029] Among them, the vibration sensor is arranged on the chassis of the vehicle and near the vehicle's tire. The vibration sensor is used to collect the excitation signals generated by the contact between the vehicle's tire and the road surface and the vibration of the tire itself, that is, the reference signal generated by the noise source . Exemplarily, the vibration sensor can be an acceleration sensor.

[0030] Optionally, one or more vibration sensors may be provided. Exemplarily, a plurality of vibration sensors are provided on the chassis of the vehicle. Through the plurality of vibration sensors, a plurality of reference signals can be collected.

[0031] The physical error microphone is disposed inside the vehicle. The physical error microphone is used to collect the true error signal at the physical error microphone. , where the true error signal refers to the residual noise signal after the noise signal transmitted from the noise source outside the vehicle (i.e., at the vibration sensor) to the physical error microphone is superimposed on the anti-noise signal output by the anti-noise speaker inside the vehicle.

[0032] Of course, one or more physical error microphones may be provided. For example, as Figure 2 shown, a physical error microphone is arranged in the driver's area (such as on the driver's seat), a physical error microphone is arranged in the co-driver's area (such as on the co-driver's seat), a physical error microphone is arranged in the right rear area (such as on the right rear seat), and a physical error microphone is arranged in the left rear area (such as on the left rear seat).

[0033] For different areas inside the vehicle, virtual error points are selected. For example, as Figure 2 shown, two virtual error points are selected in the driver's area, two virtual error points are selected in the co-driver's area, two virtual error points are selected in the right rear area, and two virtual error points are selected in the left rear area.

[0034] The anti-noise speaker is used to output an anti-noise signal. It can be understood that the anti-noise speaker can reuse the speaker of the audio-visual entertainment system inside the vehicle. That is to say, the anti-noise speaker can also be used to output an audio signal. In addition, the anti-noise speaker is also used to output a preset calibration wave signal.

[0035] As Figure 2 shown, an anti-noise speaker is arranged in the driver's area (such as on the driver's seat), an anti-noise speaker is arranged in the co-driver's area (such as on the co-driver's seat), an anti-noise speaker is arranged in the right rear area (such as on the right rear seat), an anti-noise speaker is arranged in the left rear area (such as on the left rear seat), and an anti-noise speaker is arranged near the rear window.

[0036] As Figure 1 shown, the noise control system may further include an external environment information acquisition module M1 and an internal environment information acquisition module M2. The external environment information acquisition module M1 is used to acquire external environment information , such as road type information. The internal environment information acquisition module M2 is used to acquire internal environment information and vehicle state information (collectively referred to as ).

[0037] Among them, the external environment information acquisition module includes, but is not limited to, an external lidar, a millimeter-wave radar, a GPS, an external camera, a temperature sensor, a rain sensor, and other external working condition recognition sensor modules. The internal environment information acquisition module M2 includes, but is not limited to, a seat pressure sensor, an internal camera, a motor rotation encoder, a CAN signal, and other internal environment monitoring sensors.

[0038] Such as Figure 1 shown, the noise control system may further include a data update module M3, a quick response module M4, a noise reduction parameter module M5, a dynamic calibration module M6, and a fast dynamic virtual sensing module FD-VS. Among them, the data update module M3 is used to upload, store, and update data, such as target noise reduction parameters and target virtual sensing parameters. The quick response module M4 is used to match target noise reduction parameters and some target virtual sensing parameters (such as the mapping parameters from the physical error microphone to the virtual error point) according to the environmental information. The noise reduction parameter module M5 is used to update the adaptive filter parameters. The dynamic calibration module M6 is used to generate a calibration wave signal. The fast dynamic virtual sensing module FD-VS is used to determine the virtual error signal of the virtual error point according to the target virtual sensing parameters and the real error signal.

[0039] During the noise reduction process, the external environment information acquisition module M1 acquires external environment information . The internal environment information acquisition module M2 is used to acquire internal environment information and vehicle state information (collectively referred to as ). The data update module M3 is used to obtain external environment information , internal environment information, and vehicle state information (collectively referred to as ), and send the external environment information , internal environment information, and vehicle state information (collectively referred to as ) to the quick response module M4. The quick response module M4 determines corresponding target noise reduction parameters according to the external environment information , internal environment information, and vehicle state information (collectively referred to as ). Among them, the target noise reduction parameters include a convergence step size. The quick response module M4 sends the target noise reduction parameters to the noise reduction parameter module M5. The dynamic calibration module M6 is used to generate a calibration wave signal , and controls the anti-noise speaker to output a preset calibration wave signal . The fast dynamic virtual sensing module FD-VS determines the secondary sound field signal corresponding to the physical error microphone according to the initial anti-noise signal output by the anti-noise speaker and the secondary path transfer parameter of the physical error microphone , and determines the virtual error signal of the virtual error point according to the real error signal and the secondary sound field signal of the physical error microphone , according to the real error signal, the secondary sound field signal of the physical error microphone and mapping parameters to determine the primary sound field signal of the virtual error point and the secondary sound field signal of the virtual error point ; add the primary sound field signal of the virtual error point and the secondary sound field signal of the virtual error point to obtain the virtual error signal of the virtual error point . According to the secondary path transfer corresponding to the physical error microphone and the secondary path transfer parameter corresponding to the virtual error point , process the reference signal (i.e., filtered reference) to obtain the processed reference signal . Using the LMS algorithm, according to the real error signal , the virtual error signal , the processed reference signal and the convergence step size, update the adaptive filter parameters. The noise reduction parameter module M5 processes the processed reference signal according to the adaptive filter parameters to generate the target anti-noise signal. The noise reduction parameter module M5 controls the anti-noise speaker to output the target anti-noise signal through the power amplifier chip.

[0040] The following will combine the accompanying drawings to detail the noise control method provided by the embodiments of the present application.

[0041] Figure 3 is a schematic flowchart of a noise control method provided by the embodiments of the present application. As Figure 3 shown, the noise control method may include the following steps: S301. Obtain a reference signal and a real error signal collected by a physical error microphone, where the reference signal is a noise source signal outside the vehicle.

[0042] Among them, the reference signal refers to a signal related to external noise. In the embodiments of the present application, the reference signal (i.e., the noise source signal) refers to an excitation signal generated by the contact between the vehicle's tire and the road surface and the vibration of the tire itself collected by a vibration sensor.

[0043] Exemplarily, when the vehicle starts, the controller in the noise control system obtains the reference signal collected by the vibration sensor. Exemplarily, during the vehicle driving process, the controller obtains the reference signal collected by the vibration sensor. It can be understood that the obtained reference signal is the reference signal collected by the vibration sensor at the current moment. In addition, the reference signal refers to the reference signal in the time domain.

[0044] In some examples, as described above regarding the noise control system, the noise control system may include multiple vibration sensors, which are arranged on the chassis of the vehicle and near the tires. For example, one vibration sensor is arranged near each tire. When performing S301, for the case of setting multiple vibration sensors, multiple reference signals collected by the multiple vibration sensors at the current moment are obtained, and the multiple reference signals can be understood as multi-channel reference signals.

[0045] It should be noted that the number of channels of the reference signal is related to the number of arranged vibration sensors. In practical applications, the number of vibration sensors can be set according to actual needs. In this regard, in the embodiments of the present application, the number of channels of the reference signal is not specifically limited.

[0046] The true error signal refers to the residual noise signal after the in-vehicle noise signal at the physical error microphone and the secondary sound field signal at the physical error microphone are superimposed. Among them, the in-vehicle noise signal at the physical error microphone is the noise signal transmitted from the out-of-vehicle noise source signal (i.e., the excitation signal collected by the vibration sensor) to the physical error microphone. The secondary sound field signal of the physical error microphone is the sound field signal generated by the initial anti-noise signal output by the anti-noise speaker at the physical error microphone. The initial anti-noise signal can be understood as the anti-noise signal generated and output by the noise control system according to the initial adaptive filter parameters after the noise control system is started.

[0047] Exemplarily, when the vehicle starts, the controller in the noise control system obtains the true error signal collected by the physical error microphone. Exemplarily, during the driving process of the vehicle, the controller obtains the true error signal collected by the physical error microphone. It can be understood that the obtained true error signal is the true error signal collected by the physical error microphone at the current moment. In addition, the true error signal refers to the true error signal in the time domain.

[0048] In some examples, as described above regarding the noise control system, multiple physical error microphones can be arranged inside the vehicle. Exemplarily, physical error microphones are arranged for different areas inside the vehicle. For example, as Figure 2 shown, one physical error microphone is arranged in the driver's area (such as on the driver's seat), one physical error microphone is arranged in the co-driver's area (such as on the co-driver's seat), one physical error microphone is arranged in the right rear area (such as on the right rear seat), and one physical error microphone is arranged in the left rear area (such as on the left rear seat). When performing S301, for the case of setting multiple physical error microphones, multiple true error signals collected by the multiple physical error microphones at the current moment are obtained, and the multiple true error signals can be understood as multi-channel true error signals.

[0049] It should be noted that the number of channels of the true error signal is related to the number of physical error microphones arranged. In practical applications, the number of physical error microphones can be set according to actual requirements. In this regard, in the embodiments of the present application, the number of channels of the true error signal is not specifically limited.

[0050] In some examples, according to the in-vehicle environment information, the area to be noise-reduced is determined, and the physical error microphones among the multiple physical error microphones located in the area to be noise-reduced are determined as target physical error microphones, and the true error signals collected by the target physical error microphones are obtained.

[0051] S302. Determine the virtual error signal corresponding to the virtual error point according to the target virtual sensing parameter and the true error signal, where the target virtual sensing parameter includes the secondary path transfer parameter corresponding to the virtual error point and the primary path mapping parameter from the physical error microphone to the virtual error point.

[0052] Among them, the virtual error point refers to the measurement point of the error signal far from the position where the physical error microphone is located, that is, the virtual error point located outside the noise reduction area corresponding to the physical error microphone.

[0053] Optionally, one virtual error point can be set, or multiple virtual error points can be set. In practical applications, the virtual error points can be selected according to actual requirements. Exemplarily, for different areas in the vehicle, virtual error points are selected. For example, as Figure 2 shown, two virtual error points are selected in the driver's area, two virtual error points are selected in the co-driver's area, two virtual error points are selected in the right rear area, and two virtual error points are selected in the left rear area.

[0054] The virtual error signal refers to the error signal collected by the simulated virtual error point. That is to say, the virtual error signal refers to the residual noise signal after the in-vehicle noise signal at the virtual error point and the secondary sound field signal at the virtual error point are superimposed. Among them, the in-vehicle noise signal at the virtual error point is the noise signal transmitted from the external noise source signal (i.e., the excitation signal collected by the vibration sensor) to the virtual error point. The secondary sound field signal at the virtual error point is the sound field signal generated by the initial anti-noise signal output by the anti-noise speaker at the virtual error point.

[0055] The secondary path transfer parameter corresponding to the virtual error point refers to the transfer characteristic parameter from the anti-noise speaker to the virtual error point.

[0056] It can be understood that the secondary path transfer parameter of the virtual error point is related to the environment in which the vehicle is located, that is, the secondary path transfer parameter of the virtual error point changes with the change of the environment in which the vehicle is located. Therefore, different secondary path transfer parameters of the virtual error point can be preset according to different environments in which the vehicle is located. During the noise reduction process, the environmental information of the environment in which the vehicle is located can be obtained. The environmental information includes at least one of the external environment information, the internal environment information, and the vehicle state information. According to the environmental information of the environment in which the vehicle is located, the secondary path transfer parameter of the virtual error point is determined.

[0057] Exemplarily, the secondary path transfer parameter of the virtual error point is determined by using the environmental information and a pre-trained fourth parameter prediction model.

[0058] Among them, for the process of determining the secondary path transfer parameter of the virtual error point according to the environmental information and the pre-trained fourth parameter prediction model, reference can be made to the process of determining the secondary path transfer parameter of the virtual error point in the following text, which will not be elaborated here.

[0059] The primary path mapping parameter refers to the transfer characteristic parameter of the noise source signal from the physical error microphone to the virtual error point.

[0060] It can be understood that the primary path mapping parameter is related to the environment in which the vehicle is located, that is, the primary path mapping parameter changes with the change of the environment in which the vehicle is located. Therefore, different primary path mapping parameters can be preset according to different environments in which the vehicle is located. During the noise reduction process, the environmental information of the environment in which the vehicle is located can be obtained, and the primary path mapping parameter is determined according to the environmental information of the environment in which the vehicle is located. In this way, during the driving of the vehicle, according to the environmental information of the environment in which the vehicle is located, the primary path mapping parameter is determined, and then the virtual error signal of the virtual error point is determined by using the secondary path transfer parameter corresponding to the virtual error point and the primary path mapping parameter from the physical error microphone to the virtual error point, which can improve the accuracy of determining the virtual error signal, and further improve the noise reduction effect of the target area corresponding to the virtual error point.

[0061] In a possible implementation manner, when determining the primary path mapping parameter, the environmental information of the environment in which the vehicle is located can be obtained. The environmental information includes at least one of the external environment information, the internal environment information, and the vehicle state information; the primary path mapping parameter is determined according to the environmental information and a pre-trained first parameter prediction model.

[0062] In the embodiment of the present application, the pre-trained first parameter prediction model can be used to determine the primary path mapping parameter, which can improve the data processing efficiency and obtain the mapping parameter from the physical error microphone to the virtual error point more quickly.

[0063] Among them, for the process of determining the primary path mapping parameter according to the environmental information and the pre-trained first parameter prediction model, reference can be made to the process of determining the mapping parameter from the physical error microphone to the virtual error point in the following text, which will not be elaborated here.

[0064] In some examples, to determine the virtual error signal corresponding to the virtual error point according to the target virtual sensing parameter and the real error signal, it may include: determining the secondary sound field signal of the virtual error point according to the initial anti-noise signal output by the anti-noise speaker and the secondary path transfer parameter of the virtual error point; determining the primary sound field signal of the virtual error point according to the real error signal, the primary path mapping parameter, and the secondary sound field signal of the physical error microphone; and determining the virtual error signal according to the secondary sound field signal and the primary sound field signal of the virtual error point.

[0065] Exemplarily, as Figure 4 shown, to determine the virtual error signal corresponding to the virtual error point according to the target virtual sensing parameter and the real error signal, it may include the following steps: S401. Determine the secondary sound field signal of the virtual error point according to the initial anti-noise signal output by the anti-noise speaker and the secondary path transfer parameter of the virtual error point.

[0066] Among them, the initial anti-noise signal may be the anti-noise signal output by the anti-noise speaker based on the initial noise reduction parameter and the reference signal. The secondary sound field signal of the virtual error point is the sound field signal generated by the initial anti-noise signal output by the anti-noise speaker at the virtual error point.

[0067] It can be understood that the secondary path transfer parameter of the virtual error point changes with the change of the environment where the vehicle is located.

[0068] Specifically, the secondary sound field signal of the virtual error point can be calculated according to the following formula (1): (1) Among them, represents the secondary sound field signal of the virtual error point at the current moment; represents the initial anti-noise signal output by the anti-noise speaker; represents the secondary path transfer parameter of the virtual error point; n represents the discrete time index, that is, the current moment, which is also the moment when the current reference signal and real error signal are collected.

[0069] S402. Determine the secondary sound field signal of the physical error microphone according to the initial anti-noise signal output by the anti-noise speaker and the secondary path transfer parameter of the physical error microphone.

[0070] The secondary sound field signal of the physical error microphone is the sound field signal generated at the physical error microphone by the initial anti-noise signal output by the anti-noise speaker.

[0071] The secondary path transfer parameter corresponding to the physical error microphone refers to the transfer characteristic parameter from the anti-noise speaker to the physical error microphone.

[0072] Among them, for the process of determining the secondary path transfer parameter of the physical error microphone according to the environmental information and the pre-trained fifth parameter prediction model, reference can be made to the determination process of the secondary path transfer parameter of the physical error microphone in the following text, which will not be elaborated here. Specifically, the secondary sound field signal of the physical error microphone can be calculated according to the following formula (2): (2) Among them, represents the secondary sound field signal of the physical error microphone at the current moment; represents the initial anti-noise signal output by the anti-noise speaker; represents the secondary path transfer parameter of the physical error microphone; n represents the discrete time index, that is, the current moment, which is also the moment when the current reference signal and the true error signal are collected.

[0073] It can be understood that the secondary path transfer parameter of the physical error microphone is related to the environment in which the vehicle is located, that is, the secondary path transfer parameter of the physical error microphone changes with the change of the environment in which the vehicle is located. Therefore, different secondary path transfer parameters of the physical error microphone can be preset according to different environments in which the vehicle is located. During the noise reduction process, the environmental information of the environment in which the vehicle is located can be obtained. The environmental information includes at least one of the external environment information, the internal environment information of the vehicle, and the vehicle state information. According to the environmental information of the environment in which the vehicle is located, the secondary path transfer parameter of the physical error microphone is determined.

[0074] Exemplarily, the secondary path transfer parameter of the physical error microphone is determined by using the environmental information and the pre-trained fifth parameter prediction model.

[0075] S403. Determine the primary sound field signal of the physical error microphone according to the true error signal and the secondary sound field signal of the physical error microphone.

[0076] Among them, the primary sound field signal of the physical error microphone is the noise signal transmitted from the noise source to the physical error microphone.

[0077] Specifically, the primary sound field signal of the physical error microphone can be calculated according to the following formula (3): (3) Among them, The primary sound field signal of the physical error microphone representing the current moment; The true error signal collected by the physical error microphone at the current moment; The secondary sound field signal of the physical error microphone representing the current moment; n represents the discrete time index, i.e., the current moment, which is also the moment when the current reference signal and true error signal are collected.

[0078] S404. Determine the primary sound field signal of the virtual error point according to the primary sound field signal of the physical error microphone and the primary path mapping parameter.

[0079] Among them, the primary sound field signal of the virtual error point is the noise signal transmitted from the noise source to the virtual error point.

[0080] It can be understood that the primary path mapping parameter changes with the change of the vehicle's environment.

[0081] Specifically, the primary sound field signal of the virtual error point can be calculated according to the following formula (4): (4) Among them, represents the primary sound field signal of the virtual error point at the current moment; represents the primary path mapping parameter from the physical error microphone to the virtual error point; represents the primary sound field signal of the physical error microphone at the current moment; n represents the discrete time index, i.e., the current moment, which is also the moment when the current reference signal and true error signal are collected.

[0082] Optionally, when determining the primary sound field signal of the virtual error point according to the primary sound field signal of the physical error microphone and the primary path mapping parameter, determine the primary sound field signal of the virtual error point according to the primary sound field signal of the physical error microphone, the primary path mapping parameter, and the time delay sampling points.

[0083] The primary sound field signal of the virtual error point can be calculated according to the following formula (5): (5) Among them, represents the primary sound field signal of the virtual error point at the current moment; represents the primary path mapping parameter from the physical error microphone to the virtual error point; represents the primary sound field signal of the physical error microphone at the current moment; represents the time delay sampling points; n represents the discrete time index, i.e., the current moment, which is also the moment when the current reference signal and true error signal are collected.

[0084] In this embodiment, considering that in the process of actual sound signal transmission, the noise source signal transmitted to the virtual error point may be earlier than the noise source signal transmitted to the physical error microphone. Therefore, when determining the primary sound field signal of the virtual error point, the primary sound field signal of the virtual error point can be determined by combining the primary sound field signal of the physical error microphone, the primary path mapping parameter, and the time delay sampling points, ensuring the accuracy of the determination of the secondary sound field signal of the virtual error point, and thus more accurately mapping the virtual error signal.

[0085] S405. Determine the virtual error signal of the virtual error point according to the secondary sound field signal and the primary sound field signal of the virtual error point.

[0086] Specifically, the virtual error signal of the virtual error point can be calculated according to the following formula (6): (6) where represents the virtual error signal of the virtual error point at the current moment; represents the secondary sound field signal of the virtual error point at the current moment; represents the primary sound field signal of the virtual error point at the current moment; n represents the discrete time index, that is, the current moment, which is also the moment when the current reference signal and the true error signal are collected.

[0087] S303. Determine the target adaptive filter parameters according to the reference signal, the true error signal, and the virtual error signal.

[0088] Among them, the adaptive filter coefficient refers to the weight coefficient of the adaptive filter. The adaptive filter parameters are used to track the characteristics of the reference signal and the error signal (the true error signal and the virtual error signal) in real time to generate an in-phase cancellation signal (i.e., an anti-noise signal). Correspondingly, the target adaptive filter parameters are the adaptive filter parameters dynamically adjusted based on the reference signal, the true error signal, and the virtual error signal at the current moment, that is, the adaptive filter parameters at the current moment.

[0089] Exemplarily, the adaptive filter coefficients can be dynamically adjusted through an adaptive filtering algorithm to obtain the target adaptive filter parameters, thereby minimizing the error between the noise signal and the reference signal.

[0090] Optionally, the adaptive filtering algorithm may be, for example, a Filtered-X Least Mean Squares (Fxlms) algorithm, a Filtered-X Normalized Least Mean Squares (Fxnlms), or a Filtered-X Recursive Least Squares (Fxrls) algorithm. The embodiments of the present application do not specifically limit the types of multi-channel adaptive filtering algorithms used.

[0091] In some examples, determining the target adaptive filter parameters based on the reference signal, the true error signal, and the virtual error signal may include: obtaining initial adaptive filter parameters and target noise reduction parameters, where the target noise reduction parameters include a convergence step size; and adjusting the initial adaptive filter parameters according to the reference signal, the true error signal, the virtual error signal, and the convergence step size to obtain the target adaptive filter parameters.

[0092] Among them, the initial adaptive filter parameters may be preset. The initial adaptive filter parameters may also be understood as the adaptive filter parameters at the previous moment.

[0093] It can be understood that after obtaining the initial adaptive filter parameters, the initial adaptive filter parameters may be adjusted based on the reference signal, the true error signal, and the virtual error signal to obtain the target adaptive filter parameters. Among them, the reference signal, the true error signal, and the virtual error signal are related to the environment in which the vehicle is located. When the environment in which the vehicle is located changes, the collected reference signal, the collected true error signal, and the simulated virtual error signal will also change. Therefore, when the environment in which the vehicle is located changes, the target adaptive filter parameters at the previous moment may be adjusted based on the currently collected reference signal, the collected true error signal, and the simulated virtual error signal to obtain the target adaptive filter parameters at the current moment. That is to say, during the driving process of the vehicle, the target adaptive filter parameters will be continuously iterated to ensure that a better noise reduction effect can be provided as the environment in which the vehicle is located changes.

[0094] Exemplarily, the target adaptive filter parameters may be calculated according to the following formulas (7) and (8): (7) Among them, represents the error signal matrix formed by the conjugation of the true error signal and the virtual error signal; represents the true error signal collected by the physical error microphone at the current moment; represents the virtual error signal of the virtual error point at the current moment; The secondary sound field signal representing the virtual error point at the current moment; n represents the discrete time index, that is, the current moment, which is also the moment when the current reference signal and the true error signal are collected.

[0095] (8) Wherein, represents the target adaptive filter parameter (i.e., the adaptive filter parameter at the current moment); represents the initial adaptive filter parameter (i.e., the adaptive filter parameter at the previous moment before update); represents the reference signal matrix (i.e., the multi-channel reference signal); represents the convergence step size of the adaptive filter parameter; represents the error signal matrix formed by the conjugation of the true error signal and the virtual error signal.

[0096] In some examples, the target noise reduction parameter may further include a first weighting parameter (which may also be referred to as a first pre-weighting parameter). The parameter value of the first weighting parameter is related to the environment where the vehicle is located at the current moment when collecting the reference signal. Specifically, on the one hand, when the environment where the vehicle is located (such as the external environment of the vehicle) changes, the energy of the noise source signal (i.e., road noise) will change. For example, when the vehicle is driving on a bumpy road surface, the energy of the broadband impact noise will increase. Also, for example, under some harsh weather conditions (such as strong wind, heavy rain, etc.), the energy of the noise source signal will also increase significantly. On the other hand, when the environment where the vehicle is located (such as the external environment and / or the internal environment of the vehicle) changes, the acoustic propagation path (i.e., the transfer path) will also change, which will cause the signal (noise source signal or anti-noise signal) transmitted to the physical error microphone or the virtual error point to change. Therefore, before determining the target adaptive filter parameter, the first weighting parameter can be determined to perform a weighting process on the reference signal according to the first weighting parameter.

[0097] In this regard, in some examples, when determining the target adaptive filter parameter, according to the first weighting parameter, a weighting process is performed on the reference signal to obtain the weighted reference signal; according to the true error signal, the virtual error signal, and the weighted reference signal, the target adaptive filter parameter is determined.

[0098] Optionally, the first weighting parameter is related to the environmental information of the environment where the vehicle is located. Therefore, when determining the first weighting parameter, the first weighting parameter can also be determined according to the environmental information of the environment where the vehicle is located.

[0099] It can be understood that when the reference signal is a multi-channel reference signal, corresponding first weighting parameters can be determined for the reference signals of each channel, that is, a first weighting parameter matrix corresponding to the multi-channel reference signal is determined. The specific implementation manner of determining the first weighting parameter of the reference signal can refer to the relevant description of the determination process of the weighting parameter below and will not be elaborated here.

[0100] Optionally, before weighting the reference signal according to the first weighting parameter to obtain the weighted reference signal, the first weighting parameter is processed according to the primary path transfer parameter corresponding to the physical error microphone to obtain the processed first weighting parameter; the reference signal is weighted according to the processed first weighting parameter to obtain the weighted reference signal.

[0101] In this example, the target adaptive filter parameter can be calculated according to the following formula (9): (9) Wherein, represents the target adaptive filter parameter (i.e., the adaptive filter parameter at the current moment); represents the initial adaptive filter parameter (i.e., the adaptive filter parameter at the previous moment before update); represents the reference signal matrix (i.e., the multi-channel reference signal); represents the convergence step size of the adaptive filter parameter; represents the first weighting parameter matrix corresponding to the multi-channel reference signal; represents the error signal matrix formed by conjugating the true error signal and the virtual error signal.

[0102] Wherein, the primary path transfer parameter corresponding to the physical error microphone refers to the transfer characteristic parameter from the noise source to the physical error microphone.

[0103] In some examples, the primary path transfer parameter corresponding to the physical error microphone is related to the environment in which the vehicle is located, that is, the primary path transfer parameter corresponding to the physical error microphone changes with the change of the environment in which the vehicle is located. Therefore, different primary path transfer parameters corresponding to different physical error microphones can be preset according to the different environments in which the vehicle is located. During the noise reduction process, the environmental information of the environment in which the vehicle is located can be obtained, and the primary path transfer parameter corresponding to the physical error microphone can be determined according to the environmental information of the environment in which the vehicle is located. In this way, during the driving of the vehicle, the primary path transfer parameter corresponding to the physical error microphone is determined according to the environmental information of the environment in which the vehicle is located, and then the primary path transfer parameter corresponding to the physical error microphone and the first weighting parameter are used to perform weighted processing on the currently collected reference signal, and the adaptive filter parameters are updated according to the weighted reference signal, the true error signal, and the virtual error signal. In this way, when updating the adaptive filter parameters, the influence of various factors such as the actual road noise source excitation characteristics, the contribution degree of each channel, and the driving position is comprehensively considered, and the reference signals of each channel are amplified or attenuated, so as to improve the noise reduction effect and determine the virtual error signal of the virtual error point, which can improve the accuracy of determining the virtual error signal, and further improve the noise reduction effect of the target area corresponding to the virtual error point.

[0104] In a possible implementation manner, when obtaining the primary path transfer parameter corresponding to the physical error microphone, the environmental information of the environment in which the vehicle is located can be obtained, and the primary path transfer parameter corresponding to the physical error microphone can be determined according to the environmental information and a pre-trained third parameter prediction model.

[0105] In the embodiment of the present application, a pre-trained third parameter prediction model can be used to determine the primary path transfer parameter corresponding to the physical error microphone, which can improve the data processing efficiency and obtain the primary path transfer parameter corresponding to the physical error microphone more quickly.

[0106] In some examples, before performing weighted processing on the reference signal according to the first weighting parameter to obtain the weighted reference signal, the method further includes performing filtering processing on the reference signal to obtain the filtered reference signal; performing weighted processing on the filtered reference signal according to the first weighting parameter to obtain the weighted reference signal.

[0107] In some examples, the target noise reduction parameter may further include a second weighting parameter (which may also be referred to as a second pre-weighting parameter). In this regard, before determining the target adaptive filter parameter, the second weighting parameter can be determined to perform weighted processing on the true error signal according to the second weighting parameter to obtain the weighted true error signal.

[0108] In an embodiment of the present application, on the one hand, when the environment where the vehicle is located (such as the external environment of the vehicle) changes, the energy of the noise source signal (i.e., road noise) will change. For example, when the vehicle is driving on a bumpy road surface, the energy of the broadband impact noise will increase. Also, for example, under some adverse weather conditions (such as strong wind, heavy rain, etc.), the energy of the noise source signal will also increase significantly. On the other hand, when the environment where the vehicle is located (such as the external environment and / or the internal environment of the vehicle) changes, the acoustic propagation path (i.e., the transfer path) will also change, which will cause the signal transmitted to the physical error microphone (noise source signal or anti-noise signal) to change. On the other hand, the characteristics of the true error signal itself will also affect the noise reduction effect. For example, if the amplitude of the true error signal is too large, it will affect the accuracy of determining the adaptive filter parameters, and thus affect the noise reduction effect. That is to say, the second weighting parameter is related to the environment where the vehicle is located and also related to the signal characteristics of the true error signal itself. In this regard, the second weighting parameter can be set for the true error signal according to the environmental information of the environment where the vehicle is located and the signal characteristics of the true error signal.

[0109] It can be understood that when the true error signal is a multi-channel true error signal, the corresponding second weighting parameter can be determined for each channel of the true error signal, that is, a second weighting parameter matrix corresponding to the multi-channel true error signal is determined.

[0110] Optionally, before weighting the true error signal according to the second weighting parameter to obtain the weighted true error signal, the method further includes filtering the true error signal to obtain the filtered true error signal; weighting the filtered true error signal according to the second weighting parameter to obtain the weighted true error signal. This can filter out the signals in the true error signal that are not within the set range (such as the amplitude range of the speaker), improve the accuracy of determining the target adaptive filter parameters, and thus improve the noise reduction effect.

[0111] In some examples, when determining the virtual error signal corresponding to the virtual error point, the virtual error signal corresponding to the virtual error point can be determined according to the target virtual sensing parameter and the weighted true error signal. This can improve the accuracy of predicting the virtual error signal corresponding to the virtual error point, and thus improve the noise reduction effect of the virtual error point.

[0112] Optionally, after determining the virtual error signal corresponding to the virtual error point, the method further includes: filtering the virtual error signal to obtain the filtered virtual error signal. This can filter out the signals in the virtual error signal that are not within the set range (such as the amplitude range of the speaker), improve the accuracy of determining the target adaptive filter parameters, and thus improve the noise reduction effect.

[0113] That is to say, when determining the target adaptive filter parameters according to the reference signal, the true error signal, and the virtual error signal, the reference signal can be processed according to the first weighting parameter to obtain the weighted reference signal; the true error signal can be processed according to the second weighting parameter to obtain the weighted true error signal; the virtual error signal corresponding to the virtual error point can be determined according to the target virtual sensing parameter and the weighted true error signal; and the target adaptive filter parameters can be determined according to the weighted reference signal, the weighted true error signal, and the virtual error signal of the virtual error point.

[0114] In some examples, determining the target adaptive filter parameters according to the weighted reference signal, the weighted true error signal, and the virtual error signal includes: processing the weighted reference signal based on the primary path transfer parameter corresponding to the physical error microphone to obtain the target reference signal; and determining the target adaptive filter parameters according to the target reference signal, the weighted true error signal, and the virtual error signal.

[0115] It can be understood that according to the descriptions of the above convergence step size, the first weighting parameter, and the second weighting parameter, the convergence step size, the first weighting parameter, and the second weighting parameter can all be obtained from the target noise reduction parameters. That is to say, in some examples, the environmental information of the vehicle's location can be obtained, and according to the environmental information of the vehicle's location, the target noise reduction parameters can be determined. The target noise reduction parameters can include the convergence step size, the first weighting parameter, and the second weighting parameter.

[0116] Optionally, the target noise reduction parameters can further include a leakage factor, a signal gain ratio, and a phase compensation factor. Among them, the leakage factor is a coefficient attenuation factor introduced in the adaptive filtering process to prevent the filter coefficients from overflowing or diverging due to continuous updates. The signal gain ratio refers to the amplitude adjustment ratio of the noise control system for the target signal and the residual noise signal. The phase compensation factor is a correction parameter for the signal phase delay to ensure that the anti-noise signal of the noise control system is precisely aligned with the in-vehicle noise signal.

[0117] In some examples, when determining the target noise reduction parameters according to the environmental information of the vehicle's location, the target noise reduction parameters can be determined according to the pre-trained second parameter prediction model and the environmental information.

[0118] Among them, for the specific implementation manner of determining the target noise reduction parameters, reference can be made to the determination process of the target noise reduction parameters below, which will not be elaborated here.

[0119] S304. Process the reference signal based on the target adaptive filter parameters to generate the target anti-noise signal.

[0120] S305. Output the target anti-noise signal through the anti-noise speaker to reduce the noise in the target area, where the target area is the noise reduction area corresponding to the virtual error point.

[0121] For example, the target area can be Figure 2 the light gray area shown.

[0122] In the embodiments of the present application, a physical error microphone arranged inside the vehicle is used to collect the real error signal at the physical error microphone, and the secondary path transfer parameter corresponding to the virtual error point and the primary path mapping parameter from the physical error microphone to the virtual error point are used to map the real error signal at the physical error microphone to the virtual error point, that is, the virtual error signal of the virtual error point is simulated. Then, based on the reference signal, the real error signal, and the virtual error signal, the target adaptive filter parameters are determined. Based on the target adaptive filter parameters, the reference signal is processed to generate the target anti-noise signal. In this way, the problem that the noise reduction area of the physical error microphone is limited can be avoided, the noise reduction effect of the virtual error point can be improved, the noise reduction area corresponding to the physical error microphone can be extended to the virtual error point, so as to improve the noise reduction effect in the area where the vehicle occupants are far from the physical error microphone and improve the user experience.

[0123] In some embodiments, the target virtual sensing parameters include the secondary path transfer parameter corresponding to the physical error microphone and the secondary path mapping parameter from the physical error microphone to the virtual error point. In this regard, determining the virtual error signal corresponding to the virtual error point according to the target virtual sensing parameter and the real error signal may include: determining the secondary sound field signal of the physical error microphone according to the initial anti-noise signal output by the anti-noise speaker and the secondary path transfer parameter of the physical error microphone; determining the secondary sound field signal of the virtual error point according to the secondary sound field signal of the physical error microphone and the secondary path mapping parameter; determining the primary sound field signal of the virtual error point according to the real error signal, the primary path mapping parameter, and the secondary sound field signal of the physical error microphone; determining the virtual error signal according to the secondary sound field signal of the virtual error point and the primary sound field signal of the virtual error point. Among them, the specific implementation manner of determining the virtual error signal can refer to the specific implementation process of the determination process (Method 1) of the virtual error signal in the following text, which will not be elaborated here.

[0124] Among them, the secondary path transfer parameter corresponding to the physical error microphone refers to the transfer characteristic parameter from the anti-noise speaker to the physical error microphone.

[0125] Exemplarily, as Figure 5 shown, determining the virtual error signal corresponding to the virtual error point according to the target virtual sensing parameter and the real error signal may include the following steps: S501. Determine the secondary sound field signal of the physical error microphone based on the initial anti-noise signal output by the anti-noise speaker and the secondary path transfer parameter of the physical error microphone.

[0126] Among them, the initial anti-noise signal can be the anti-noise signal output by the anti-noise speaker based on the initial noise reduction parameter and the reference signal.

[0127] The secondary sound field signal of the physical error microphone is the sound field signal generated by the initial anti-noise signal output by the anti-noise speaker at the physical error microphone.

[0128] It can be understood that the secondary path transfer parameter of the physical error microphone changes with the change of the environment where the vehicle is located.

[0129] Specifically, the secondary sound field signal of the physical error microphone can be calculated according to the following formula (10): (10) Among them, represents the secondary sound field signal of the physical error microphone at the current moment; represents the initial anti-noise signal output by the anti-noise speaker; represents the secondary path transfer parameter of the physical error microphone; n represents the discrete time index, that is, the current moment, which is also the moment when the current reference signal and the true error signal are collected.

[0130] S502. Determine the secondary sound field signal of the virtual error point based on the secondary sound field signal of the physical error microphone and the secondary path mapping parameter.

[0131] Among them, the secondary sound field signal of the virtual error point is the sound field signal generated by the initial anti-noise signal output by the anti-noise speaker at the virtual error point.

[0132] It can be understood that the secondary path mapping parameter changes with the change of the environment where the vehicle is located.

[0133] Specifically, the secondary sound field signal of the virtual error point can be calculated according to the following formula (11): (11) Among them, represents the secondary sound field signal of the virtual error point at the current moment; represents the secondary path mapping parameter from the physical error microphone to the virtual error point; represents the secondary sound field signal of the physical error microphone at the current moment; n represents the discrete time index, that is, the current moment, which is also the moment when the current reference signal and the true error signal are collected.

[0134] Optionally, when determining the secondary sound field signal of the virtual error point based on the secondary sound field signal of the physical error microphone and the secondary path mapping parameter, determine the secondary sound field signal of the virtual error point according to the secondary sound field signal of the physical error microphone, the secondary path mapping parameter, and the time delay sampling points.

[0135] Specifically, the secondary sound field signal of the virtual error point can be calculated according to the following formula (12): (12) Wherein, represents the secondary sound field signal of the virtual error point at the current moment; represents the secondary path mapping parameter from the physical error microphone to the virtual error point; represents the secondary sound field signal of the physical error microphone at the current moment; represents the time delay sampling points; n represents the discrete time index, that is, the current moment, which is also the moment when the current reference signal and the true error signal are collected.

[0136] In this embodiment, considering that in the process of actual sound signal transmission, the anti-noise signal transmitted to the virtual error point may be earlier than the anti-noise signal transmitted to the physical error microphone, therefore, when determining the secondary sound field signal of the virtual error point, the secondary sound field signal of the virtual error point can be determined by combining the secondary sound field signal of the physical error microphone, the secondary path mapping parameter, and the time delay sampling points, ensuring the accuracy of the determination of the secondary sound field signal of the virtual error point, and thus more accurately mapping the virtual error signal.

[0137] S503. Determine the primary sound field signal of the physical error microphone according to the true error signal and the secondary sound field signal of the physical error microphone.

[0138] S504. Determine the primary sound field signal of the virtual error point according to the primary sound field signal of the physical error microphone and the primary path mapping parameter.

[0139] S505. Determine the virtual error signal of the virtual error point according to the secondary sound field signal of the virtual error point and the primary sound field signal of the virtual error point.

[0140] Among them, for the specific implementation manners of S503 to S505, reference can be made to the above S403 to S405, which will not be elaborated here.

[0141] In the embodiments of the present application, the secondary path transfer parameter corresponding to the physical error microphone is related to the environment in which the vehicle is located, that is, the target virtual sensing parameter changes with the change of the environment in which the vehicle is located. Therefore, when determining the secondary path transfer parameter corresponding to the physical error microphone, the anti-noise speaker can be controlled to output a preset calibration wave signal, and the target calibration wave signal currently collected by the physical error microphone can be obtained; the target calibration wave signal currently collected by the physical error microphone is adaptively processed, and then, based on the adaptively processed target calibration wave signal and the preset calibration wave signal, the secondary path transfer parameter corresponding to the physical error microphone is determined. In this way, when determining the virtual error signal of the virtual error point, the preset calibration wave signal output by the anti-noise speaker and the target calibration wave signal currently collected by the physical error microphone can be used to measure in real time the secondary path transfer parameter corresponding to the physical error microphone. The obtained secondary path transfer parameter corresponding to the physical error microphone can reflect the environment in which the vehicle is currently located (such as the external environment of the vehicle, the internal environment of the vehicle, and the vehicle state). Furthermore, based on the secondary path transfer parameter corresponding to the physical error microphone measured in real time, the virtual error signal of the virtual error point can be simulated more accurately.

[0142] Among them, the preset calibration wave signal is a sound signal used to measure the physical error microphone. Optionally, the preset calibration wave signal may include a sound signal with preset frequency band (low frequency) information.

[0143] The target calibration wave signal refers to the sound signal currently collected by the physical error microphone when the anti-noise speaker outputs the preset calibration wave signal.

[0144] In a possible implementation manner, when the anti-noise speaker outputs a first audio signal (i.e., when the vehicle's audio and video entertainment system is working), the first audio signal output by the vehicle's anti-noise speaker is obtained; when the first audio signal includes preset frequency band information, the first audio signal is used as the preset calibration wave signal, and the target calibration wave signal currently collected by the physical error microphone is obtained; based on the preset calibration wave signal and the target calibration wave signal, the secondary path transfer parameter corresponding to the physical error microphone is determined.

[0145] It can be understood that the speaker of the vehicle's in-vehicle audio and video entertainment system can be used as the anti-noise speaker, that is, the speaker inside the vehicle is used to output the audio signal of the audio and video entertainment system and output the anti-noise signal (including the initial anti-noise signal). Exemplarily, the first audio signal refers to the audio signal currently output by the anti-noise speaker. For example, when the audio and video entertainment system plays a media resource, the audio signal output by the anti-noise speaker. Among them, the media resource can be, for example, a music media resource, a news media resource, navigation voice information, etc.

[0146] In another possible implementation, when the first audio signal does not include preset frequency band information, the anti-noise speaker is controlled to output a preset calibration wave signal, and the target calibration wave signal currently collected by the physical error microphone is obtained; according to the preset calibration wave signal and the target calibration wave signal, the secondary path transfer parameter corresponding to the physical error microphone is determined.

[0147] In yet another possible implementation, when the anti-noise speaker does not output the first audio signal (i.e., when the vehicle's audio-visual entertainment system is not working), the anti-noise speaker is controlled to output a preset calibration wave signal, and the target calibration wave signal currently collected by the physical error microphone is obtained; according to the preset calibration wave signal and the target calibration wave signal, the secondary path transfer parameter corresponding to the physical error microphone is determined. It can be understood that when controlling the anti-noise speaker to output the preset calibration wave signal, the preset calibration wave signal is superimposed on the first audio signal and output.

[0148] In this embodiment, when using the preset calibration wave signal to measure the secondary path transfer parameter corresponding to the physical error microphone in real time, the first audio signal currently output by the anti-noise speaker can be obtained. When the first audio signal includes preset frequency band information, according to the first audio signal and the target calibration wave signal currently collected by the physical error microphone, the secondary path transfer parameter corresponding to the physical error microphone is determined. In this way, the secondary path transfer parameter matching the current vehicle environment can be measured in real time, and at the same time, there is no need to control the anti-noise speaker to output other calibration wave signals, and the audio-visual entertainment experience of the vehicle occupants will not be affected.

[0149] In addition, when the first audio signal does not include the information of the preset frequency band, or when the anti-noise speaker does not output the first audio signal, the anti-noise speaker can be controlled to superimpose the preset calibration wave signal on the first audio signal. In this way, the secondary path transfer parameter corresponding to the physical error microphone can be determined by using the preset calibration wave signal and the target calibration wave signal currently collected by the physical error microphone, and the accuracy of determining the secondary path transfer parameter corresponding to the physical error microphone can be improved. At the same time, the audio-visual entertainment experience of the vehicle occupants will not be affected.

[0150] The secondary path mapping parameter refers to the transfer characteristic parameter of the anti-noise signal emitted by the anti-noise speaker from the physical error microphone to the virtual error point. It can be understood that the secondary path mapping parameter and the primary path mapping parameter in the above text are collectively referred to as the mapping parameter from the physical error microphone to the virtual error point.

[0151] In the embodiments of the present application, different from the secondary path transfer parameters corresponding to the physical error microphone, when determining the mapping parameters from the physical error microphone to the virtual error point, an error microphone needs to be arranged at the virtual error point. Then, based on the true error signal collected by the physical error microphone and the error signal collected by the error microphone arranged at the virtual error point, the mapping parameters from the physical error microphone to the virtual error point are determined. However, in the actual noise reduction process, an error microphone is not arranged at the virtual error point. That is to say, the mapping parameters from the physical error microphone to the virtual error point cannot be measured in real time. Therefore, in the embodiments of the present application, the mapping parameters from the physical error microphone to the virtual error point can be obtained in advance.

[0152] In some examples, the mapping parameters from the physical error microphone to the virtual error point are related to the environment in which the vehicle is located, that is, the mapping parameters from the physical error microphone to the virtual error point change with the change of the environment in which the vehicle is located. Therefore, different mapping parameters can be set in advance according to different environments in which the vehicle is located. During the noise reduction process, the environmental information of the environment in which the vehicle is located can be obtained, and the mapping parameters from the physical error microphone to the virtual error point can be determined according to the environmental information of the environment in which the vehicle is located. In this way, during the driving of the vehicle, according to the environmental information of the environment in which the vehicle is located, the mapping parameters from the physical error microphone to the virtual error point are determined, and then the virtual error signal at the virtual error point is determined by using the secondary path transfer parameters corresponding to the physical error microphone and the mapping parameters from the physical error microphone to the virtual error point, which can improve the accuracy of determining the virtual error signal, and further improve the noise reduction effect of the target area corresponding to the virtual error point.

[0153] In a possible implementation manner, when determining the mapping parameters from the physical error microphone to the virtual error point, the environmental information of the environment in which the vehicle is located can be obtained, and the mapping parameters from the physical error microphone to the virtual error point can be determined according to the environmental information and a pre-trained first parameter prediction model.

[0154] In the embodiments of the present application, a pre-trained first parameter prediction model can be used to determine the mapping parameters from the physical error microphone to the virtual error point, which can improve the data processing efficiency and obtain the mapping parameters from the physical error microphone to the virtual error point more quickly.

[0155] Among them, for the process of determining the mapping parameters from the physical error microphone to the virtual error point according to the environmental information and the pre-trained first parameter prediction model, reference can be made to the process of determining the mapping parameters from the physical error microphone to the virtual error point in the following text, which will not be elaborated here.

[0156] The above is a schematic description of the overall solution of the noise control method provided by the embodiments of this application. The following will separately elaborate on the determination process of the mapping parameters from the physical error microphone to the virtual error point, the determination process of the primary path transfer parameters corresponding to the physical error microphone, the determination process of the secondary path transfer parameters corresponding to the physical error microphone, the secondary path transfer parameters corresponding to the virtual error point, and the determination process of the target noise reduction parameters.

[0157] 1. Determination process of the mapping parameters from the physical error microphone to the virtual error point Among them, the mapping parameters from the physical error microphone to the virtual error point include secondary path mapping parameters and primary path mapping parameters. The secondary path mapping parameters refer to the transfer characteristic parameters of the anti-noise signal emitted by the anti-noise speaker from the physical error microphone to the virtual error point. The primary path mapping parameters refer to the transfer characteristic parameters of the noise source signal from the physical error microphone to the virtual error point.

[0158] As Figure 6 shown, in this noise control method, the process of obtaining the mapping parameters from the physical error microphone to the virtual error point may include the following steps: S601. Obtain the environmental information of the vehicle's environment.

[0159] Among them, the environmental information includes external vehicle environment information, internal vehicle environment information, and vehicle status information. The external vehicle environment information includes road type information (which can also be called road condition information), weather information, temperature information, air pressure information, wind speed information, etc. Among them, the road type information is used to indicate the road surface type of the road on which the vehicle is traveling. The road type can include, for example, smooth asphalt road type, rough asphalt road type, speed bump road type, and pothole road type (such as severe potholes). The weather information is used to indicate the weather type of the vehicle's environment. The weather type can be, for example, sunny, rainy (such as light rain, moderate rain, heavy rain), snowy (such as light snow, moderate snow, heavy snow). The temperature information refers to the environmental temperature outside the vehicle. For example, the environmental temperature is 20°C. The air pressure information is the information indicating the air pressure environment type. The air pressure environment type can be, for example, plateau, plain, etc. The wind speed information is the wind speed type information. The wind speed type can be, for example, light wind, strong wind, etc.

[0160] In specific implementation, the external vehicle environment information is obtained through the sensors set on the vehicle. Among them, the sensors can include external vehicle lidar, millimeter-wave radar, GPS, external vehicle camera, temperature sensor, air pressure sensor, rain sensor, wind speed sensor, etc.

[0161] The in-vehicle environment information includes the information on the occupants in the vehicle (such as the number of occupants, sitting postures, positions), seat states (seat displacements and tilting angles), other sound source information (such as air conditioners, entertainment systems, and the speaking volumes of occupants), in-vehicle temperature information, in-vehicle humidity information, and microphone occlusion information. Among them, the information on the occupants in the vehicle can include the number of occupants and the sitting posture information of each occupant. The seat states can include the seat positions and seat angles of each seat. The other sound source information can include the air conditioner settings, the volume information of the audio-visual entertainment system, and the occupant volume information. Among them, the air conditioner settings can be, for example, high, medium, and low gears. The volume information of the audio-visual entertainment system can be, for example, low volume, medium volume, and high volume. The occupant volume information can be, for example, low volume, medium volume, and high volume. The in-vehicle temperature information refers to the temperature inside the vehicle. For example, the in-vehicle temperature is 20°C. The in-vehicle humidity information refers to the humidity inside the vehicle. For example, the in-vehicle humidity is 50%. The microphone occlusion information refers to the information indicating the occlusion situation of the microphone, such as slight occlusion.

[0162] In specific implementation, the in-vehicle environment information is obtained through the sensors installed inside the vehicle. Among them, the sensors can include seat pressure sensors, physical error microphones, temperature sensors, humidity sensors, in-vehicle cameras, and motor rotary encoders.

[0163] The vehicle state information includes vehicle attribute information, such as tire pressure information, vehicle speed information, in-vehicle wear information, and engine state. Among them, the in-vehicle wear information refers to the information indicating the degree of in-vehicle wear. For example, the in-vehicle wear information is moderate wear. The engine state can be, for example, low speed, medium speed, or high speed. The vehicle state information can also include opening and closing information, such as the window opening and closing state information, the sunroof opening and closing state information, and the opening and closing state information of the vehicle's closure parts. Among them, the window opening and closing state information is, for example, 30% open. The opening and closing state information of the vehicle's closure parts is, for example, 30% open.

[0164] In specific implementation, the vehicle state information is obtained through the sensors installed on the vehicle. Among them, the sensors can include pressure gauges, in-vehicle cameras, vibration sensors, physical error microphones, motor rotary encoders, and CAN signals.

[0165] S602. Determine the mapping parameters from the physical error microphone to the virtual error point according to the environment information.

[0166] In some examples, S602 can include: determining the mapping parameters from the physical error microphone to the virtual error point according to the environment information and a pre-trained first parameter prediction model.

[0167] Exemplarily, the first parameter prediction model is obtained by training a first initial model based on multiple sets of first sample data. Each set of first sample data includes sample environmental information 1, sample sound signal 1, sample sound signal 2, sample sound signal 3, and sample sound signal 4 of the sample environment where the vehicle is located.

[0168] When training the first initial model, when the vehicle is in the sample environment (i.e., the reference signal collected by the vibration sensor is the sample reference signal), obtain sample sound signal 1 collected by the physical error microphone and sample sound signal 2 collected by the virtual error microphone at the virtual error point. Among them, sample sound signal 1 is the sound signal transmitted from the noise source (i.e., the sample reference signal) to the physical error microphone. Sample sound signal 2 is the sound signal transmitted from the noise source (i.e., the sample reference signal) to the virtual error microphone. Determine the actual primary path transfer parameter according to sample sound signal 1 and sample sound signal 2.

[0169] Optionally, based on the preset time delay sampling points, perform time delay processing on sample sound signal 2 to obtain the time delay processed sample sound signal 2.

[0170] When the vehicle is in the sample environment, obtain sample sound signal 3 collected by the physical error microphone and sample sound signal 4 collected by the virtual error microphone at the virtual error point. Among them, sample sound signal 3 is the sound signal transmitted from the signal output by the speaker to the physical error microphone. Sample sound signal 4 is the sound signal transmitted from the signal output by the speaker to the virtual error microphone. Determine the actual secondary path transfer parameter according to sample sound signal 3 and sample sound signal 4.

[0171] Optionally, based on the preset time delay sampling points, perform time delay processing on sample sound signal 4 to obtain the time delay processed sample sound signal 4.

[0172] After that, input sample environmental information 1 into the first initial model, and output the predicted primary path transfer parameter and the predicted secondary path transfer parameter. Take the actual primary path transfer parameter as the supervision information to perform iterative training on the first initial model, and take the actual secondary path transfer parameter as the supervision information to perform iterative training on the first initial model, to obtain the trained model, that is, the first parameter prediction module, and complete the training of the first initial model.

[0173] 2. Determination process of the primary path transfer parameter corresponding to the physical error microphone In this noise control method, the determination process of the primary path transfer parameter corresponding to the physical error microphone may include: obtaining the environmental information of the environment where the vehicle is located; determining the primary path transfer parameter corresponding to the physical error microphone according to the environmental information.

[0174] In some examples, determining the primary path transfer parameter corresponding to the physical error microphone according to the environmental information may include: determining the primary path transfer parameter corresponding to the physical error microphone according to the environmental information and a pre-trained third parameter prediction model.

[0175] Among them, when training the third initial model, when the vehicle is in a sample environment, obtain the sample reference signal collected by the vibration sensor and the sample sound signal 5 collected by the physical error microphone. According to the sample reference signal and the sample sound signal 5, determine the actual primary path transfer parameter corresponding to the physical error microphone. Specifically, when the vehicle is in a sample environment, obtain the sample environmental information 2, input the sample environmental information 2 into the third initial model, and output the predicted primary path transfer parameter corresponding to the physical error microphone. Using the actual primary path transfer parameter as the supervision information, perform iterative training on the third initial model, that is, the third parameter prediction module.

[0176] 3. Determination process of the primary path transfer parameter corresponding to the physical error microphone In this noise control method, the determination process of the secondary path transfer parameter corresponding to the physical error microphone may include: obtaining the environmental information of the vehicle environment; determining the secondary path transfer parameter corresponding to the physical error microphone according to the environmental information.

[0177] In some examples, determining the secondary path transfer parameter corresponding to the physical error microphone according to the environmental information may include: determining the secondary path transfer parameter corresponding to the physical error microphone according to the environmental information and a pre-trained fifth parameter prediction model.

[0178] Among them, when training the fifth initial model, when the vehicle is in a sample environment, control the anti-noise speaker to output a sample calibration signal, and obtain the currently collected sound signal (i.e., the sample sound signal 6) through the physical error microphone. According to the sample calibration signal and the sample sound signal 6, determine the actual secondary path transfer parameter corresponding to the physical error microphone. Specifically, when the vehicle is in a sample environment, obtain the sample environmental information 4, input the sample environmental information 4 into the fifth initial model, and output the predicted secondary path transfer parameter corresponding to the physical error microphone. Using the actual secondary path transfer parameter corresponding to the physical error microphone as the supervision information, perform iterative training on the fifth initial model, that is, the fifth parameter prediction module.

[0179] 4. Determination process of the secondary path transfer parameter corresponding to the virtual error point In this noise control method, the determination process of the secondary path transfer parameter corresponding to the virtual error point may include: obtaining the environmental information of the vehicle environment; determining the secondary path transfer parameter corresponding to the virtual error point according to the environmental information.

[0180] In some examples, determining the secondary path transfer parameter corresponding to the virtual error point according to the environmental information may include: determining the secondary path transfer parameter corresponding to the virtual error point according to the environmental information and a pre-trained fourth parameter prediction model.

[0181] Among them, when training the fourth initial model, when the vehicle is in a sample environment, controlling the anti-noise speaker to output a sample calibration signal, and acquiring the sound signal currently collected by a virtual error microphone arranged at the virtual error point (i.e., sample sound signal 6). According to the sample calibration signal and sample sound signal 6, determine the actual secondary path transfer parameter corresponding to the virtual error point. When the vehicle is in a sample environment, acquire sample environment information 5, input the sample environment information 5 into the fourth initial model, and output the predicted secondary path transfer parameter corresponding to the virtual error point. Using the actual secondary path transfer parameter corresponding to the virtual error point as supervision information, perform iterative training on the fourth initial model, that is, the fourth parameter prediction module.

[0182] 5. Determination process of the target noise reduction parameter As Figure 7 shown, in this noise control method, the determination process of the target noise reduction parameter may include the following steps: S701. Acquire the environmental information of the environment where the vehicle is located.

[0183] Among them, for the specific implementation process of S701, reference may be made to the specific implementation process of S601 described above, which will not be elaborated here.

[0184] S702. Determine the target noise reduction parameter according to the environmental information. Among them, the target noise reduction parameter includes a convergence step size, a first weighting parameter, a second weighting parameter, a leakage factor, a signal gain ratio, and a phase compensation factor.

[0185] In some examples, S702 may include: determining the target noise reduction parameter according to the environmental information and a pre-trained second parameter prediction model.

[0186] Exemplarily, the second parameter prediction model is obtained by training a second initial model based on multiple groups of second sample data. Among them, each group of second sample data includes the sample environmental information 3 of the sample environment where the vehicle is located and a set of reference noise reduction parameters corresponding to the sample environmental information 3. When training the second initial model, input the sample environmental information 3 into the second initial model, and output a set of predicted noise reduction parameters. Using the reference noise reduction parameter as supervision information, perform iterative training on the second initial model, that is, the second parameter prediction module.

[0187] Among them, the reference noise reduction parameter may include a reference first weighting parameter.

[0188] Specifically, taking the reference signal with multiple channels as an example, the process of determining the first weighted parameter of the reference may include: S801. Perform a Fourier transform on the reference signal to obtain a frequency-domain reference signal.

[0189] S802. Determine the power spectrum information of the frequency-domain reference signal.

[0190] Specifically, the power spectrum information of the frequency-domain reference signal can be calculated according to the following formula (13): (13) where represents the power spectrum information of the frequency-domain reference signal; represents the frequency-domain reference signal.

[0191] S803. Determine the first weighted parameter of the reference for the reference signal according to the preset peak frequency, the preset power spectrum information, and the power spectrum information of the frequency-domain reference signal.

[0192] where the preset peak frequency refers to the peak frequency point of the road noise signal (i.e., the reference reference signal).

[0193] Specifically, the first weighted parameter of the reference for the reference signal can be calculated according to the following formula (14): (14) where represents the first weighted parameter of the reference corresponding to the reference signal of the i-th channel in the reference signal with multiple channels; represents the power spectrum signal of the reference signal of the i-th channel in the reference signal with multiple channels; represents the power spectrum information of the reference reference signal; represents the preset peak frequency. Wherein, the reference reference signal can be a reference signal measured in a sample environment in advance.

[0194] It should be noted that the above first parameter prediction model and second parameter prediction model can be two sub-models of the parameter prediction model, that is, the parameter prediction model obtained through training can output target noise reduction parameters (such as convergence step size, first weighted parameter, second weighted parameter) and target virtual sensing parameters (primary path mapping parameter, secondary path transfer parameter corresponding to the virtual error point, and secondary path transfer parameter of the physical error microphone).

[0195] The following Table 1 gives a schematic description of the first weighted parameter of the reference signal corresponding to the vibration sensor, the second weighted parameter of the real error signal collected by the physical error microphone, and the observation filter parameter when the vehicle is in different environments.

[0196] Table 1 shows the target noise reduction parameters and target virtual sensing parameters when the vehicle is in different environments

[0197] Next, a specific example is used to schematically illustrate the noise control method provided in the embodiments of the present application.

[0198] Exemplarily, as Figure 8 shown, the noise control method may include the following steps: S901. After the vehicle starts, obtain reference signal 1 collected by a vibration sensor.

[0199] S902. Start the RNC controller, and process reference signal 1 according to the initial adaptive filter parameters to generate anti-noise signal 1.

[0200] S903. Output anti-noise signal 1 through an anti-noise speaker.

[0201] S904. During the vehicle driving process, obtain reference signal 2 collected by a vibration sensor and real error signal 1 collected by a physical error microphone.

[0202] S905. Obtain environment information 1 of the vehicle environment, and the environment information 1 may include in-vehicle environment information, out-of-vehicle environment information, and vehicle state information.

[0203] S906. According to environment information 1 and the second parameter prediction model, determine noise reduction parameter 1, and the noise reduction parameter 1 includes convergence step 1.

[0204] S907. According to environment information 1 and the first parameter prediction model, determine target virtual sensing parameter 1, and the target virtual sensing parameter 1 includes the secondary path transfer parameter corresponding to the virtual error point and the primary path mapping parameter from the physical error microphone to the virtual error point.

[0205] S908. According to real error signal 1, the primary path mapping parameter, and the secondary path transfer parameter corresponding to the virtual error point, determine the virtual error signal of the virtual error point.

[0206] S909. According to reference signal 2, real error signal 1, virtual error signal 1, and convergence step 1, determine adaptive filter parameter 1.

[0207] S910. Process the reference signal 2 based on the adaptive filter parameter 1 to generate an anti-noise signal 2.

[0208] S911. Output the anti-noise signal 2 through the anti-noise speaker to reduce noise in the target area, where the target area is the noise reduction area corresponding to the virtual error point.

[0209] S912. During vehicle driving, obtain the reference signal 3 collected by the vibration sensor and the real error signal 2 collected by the physical error microphone.

[0210] S913. Obtain the environmental information 2 of the vehicle's environment, and the environmental information 2 may include in-vehicle environmental information, out-of-vehicle environmental information, and vehicle status information.

[0211] S914. Determine whether the environmental information 2 is the same as the environmental information 1. If so, execute S915; otherwise, execute S917.

[0212] S915. Process the reference signal 3 based on the adaptive filter parameter 1 to generate an anti-noise signal 3.

[0213] S916. Output the anti-noise signal 3 through the anti-noise speaker to reduce noise in the target area, where the target area is the noise reduction area corresponding to the virtual error point.

[0214] S917. Determine the noise reduction parameter 2 according to the environmental information 2 and the second parameter prediction model, and the noise reduction parameter 2 includes the convergence step size 2.

[0215] S918. Determine the target virtual sensing parameter 2 according to the environmental information 2 and the first parameter prediction model, and the target virtual sensing parameter 2 includes the secondary path transfer parameter 2 corresponding to the virtual error point and the primary path mapping parameter 2 from the physical error microphone to the virtual error point.

[0216] S919. Determine the virtual error signal 2 of the virtual error point according to the real error signal 2, the primary path mapping parameter 2, and the secondary path transfer parameter 2 corresponding to the virtual error point.

[0217] S920. Determine the adaptive filter parameter 2 according to the reference signal 3, the real error signal 2, the virtual error signal 2, and the convergence step size 2.

[0218] S921. Process the reference signal 3 based on the adaptive filter parameter 2 to generate an anti-noise signal 4.

[0219] S922. Output the anti-noise signal 4 through the anti-noise speaker to reduce noise in the target area, where the target area is the noise reduction area corresponding to the virtual error point.

[0220] In some embodiments, the embodiments of the present application further provide a noise control device. Exemplarily, as Figure 9 shown, the noise control device 900 may include a signal acquisition module 901, a first determination module 902, a second determination module 903, a signal generation module 904, and a noise reduction module 905. Among them, the signal acquisition module 901 is configured to acquire a reference signal and a true error signal collected by a physical error microphone, and the reference signal is a noise source signal outside the vehicle. The first determination module 902 is configured to determine a virtual error signal corresponding to a virtual error point according to a target virtual sensing parameter and the true error signal, where the target virtual sensing parameter includes a secondary path transfer parameter corresponding to the virtual error point and a primary path mapping parameter from the physical error microphone to the virtual error point. The second determination module 903 is configured to determine a target adaptive filter parameter according to the reference signal, the true error signal, and the virtual error signal. The signal generation module 904 is configured to process the reference signal based on the target adaptive filter parameter to generate a target anti-noise signal. The noise reduction module 905 is configured to output the target anti-noise signal through an anti-noise speaker to perform noise reduction on a target area, and the target area is a noise reduction area corresponding to the virtual error point.

[0221] Optionally, the first determination module 902 is specifically configured to: determine a secondary sound field signal of the virtual error point according to an initial anti-noise signal output by the anti-noise speaker and a secondary path transfer parameter of the virtual error point; determine a primary sound field signal of the virtual error point according to the true error signal, the primary path mapping parameter, and the secondary sound field signal of the physical error microphone; and determine the virtual error signal according to the secondary sound field signal and the primary sound field signal of the virtual error point.

[0222] Exemplarily, the target virtual sensing parameter further includes a secondary path transfer parameter of the physical error microphone; the first determination module 902 is specifically configured to: determine a secondary sound field signal of the physical error microphone according to an initial anti-noise signal output by the anti-noise speaker and the secondary path transfer parameter of the physical error microphone; determine a primary sound field signal of the physical error microphone according to the true error signal and the secondary sound field signal of the physical error microphone; and determine a primary sound field signal of the virtual error point according to the primary sound field signal of the physical error microphone and the primary path mapping parameter.

[0223] Exemplarily, the first determination module 902 is specifically configured to: perform a time delay process on the primary path mapping parameter according to the number of time delay sampling points to obtain a time delay processed primary path mapping parameter; and determine a primary sound field signal of the virtual error point according to the primary sound field signal of the physical error microphone and the time delay processed primary path mapping parameter.

[0224] In some examples, the noise control device may further include a first parameter acquisition module, and the first parameter acquisition module is configured to acquire target noise reduction parameters, where the target noise reduction parameters include a first weighting parameter. The second determination module 903 is specifically configured to: perform weighting processing on the reference signal according to the first weighting parameter to obtain a weighted reference signal; determine target adaptive filter parameters according to the weighted reference signal, the true error signal, and the virtual error signal.

[0225] In some examples, the target virtual sensing parameter further includes a primary path transfer parameter corresponding to the physical error microphone; the second determination module 903 is specifically configured to: process the first weighting parameter according to the primary path transfer parameter corresponding to the physical error microphone to obtain a processed first weighting parameter; perform weighting processing on the reference signal according to the processed first weighting parameter to obtain a weighted reference signal.

[0226] In some examples, the target noise reduction parameters further include a second weighting parameter, and the first determination module 902 is specifically configured to: perform weighting processing on the true error signal according to the second weighting parameter to obtain a weighted true error signal; determine the virtual error signal corresponding to the virtual error point according to the target virtual sensing parameter and the weighted true error signal. The second determination module 903 is specifically configured to: determine target adaptive filter parameters according to the weighted reference signal, the weighted true error signal, and the virtual error signal.

[0227] In some examples, the second determination module 903 is specifically configured to: process the weighted reference signal based on the primary path transfer parameter corresponding to the physical error microphone to obtain a target reference signal; determine target adaptive filter parameters according to the target reference signal, the weighted true error signal, and the virtual error signal.

[0228] In some examples, the second determination module 903 is specifically configured to: acquire initial adaptive filter parameters and target noise reduction parameters, where the target noise reduction parameters include a convergence step size; adjust the initial adaptive filter parameters according to the reference signal, the true error signal, the virtual error signal, and the convergence step size to obtain target adaptive filter parameters.

[0229] In some examples, the noise control device may further include: an environment information acquisition module and a second parameter acquisition module, where the environment information acquisition module is configured to acquire environment information of the vehicle's location, and the environment information includes at least one of external vehicle environment information, internal vehicle environment information, and vehicle status information. The second parameter acquisition module is configured to determine a primary path mapping parameter from the physical error microphone to the virtual error point according to the environment information.

[0230] In some examples, the second parameter acquisition module is specifically configured to determine the primary path mapping parameter from the physical error microphone to the virtual error point according to the environment information and the first parameter prediction model.

[0231] In some examples, the third parameter acquisition module is specifically configured to determine the secondary path transfer parameter corresponding to the virtual error point according to the environment information.

[0232] In some examples, the third parameter acquisition module is specifically configured to determine the secondary path transfer parameter corresponding to the virtual error point according to the environment information and the fourth parameter prediction model.

[0233] Embodiments of the present disclosure provide a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements each process executed by the above Bluetooth connection method and can achieve the same technical effects. To avoid repetition, it will not be described in detail here. Among them, the computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc, etc.

[0234] The present disclosure provides a computer program product, which includes a computer program. When the computer program runs on a computer, the computer is enabled to implement the above noise control method.

[0235] For the sake of convenience of explanation, the above description has been made in conjunction with specific embodiments. However, the above discussion in some embodiments is not intended to be exhaustive or to limit the embodiments to the specific forms disclosed above. According to the above teachings, various modifications and variations can be obtained. The selection and description of the above embodiments are for better explaining the principles and actual applications, so that those skilled in the art can better use the embodiments and various different modified embodiments suitable for specific use considerations.

[0236] In this application, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following items" or its similar expression refers to any combination of these items, including any combination of single item or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, c can be single or multiple.

[0237] It should be understood that in various embodiments of the present application, the magnitudes of the serial numbers of the above processes do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0238] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0239] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0240] In 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 units is only a logical function division, and there can be other division methods in actual implementation; for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces, and the indirect coupling or communication connection of devices or units can be in an electrical, mechanical or other form.

[0241] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0242] In addition, the functional units in each embodiment of this application can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0243] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application.

Claims

1. A noise control method, characterized in that, Including: Obtain a reference signal and a true error signal collected by a physical error microphone, where the reference signal is a noise source signal outside the vehicle; According to the target virtual sensing parameters and the true error signal, determine a virtual error signal corresponding to a virtual error point, where the target virtual sensing parameters include a secondary path transfer parameter corresponding to the virtual error point and a primary path mapping parameter from the physical error microphone to the virtual error point; According to the reference signal, the true error signal, and the virtual error signal, determine target adaptive filter parameters; Based on the target adaptive filter parameters, process the reference signal to generate a target anti-noise signal; Output the target anti-noise signal through an anti-noise speaker to perform noise reduction on a target area, where the target area is a noise reduction area corresponding to the virtual error point.

2. The method according to claim 1, wherein The step of determining a virtual error signal corresponding to a virtual error point according to the target virtual sensing parameters and the true error signal includes: According to the initial anti-noise signal output by the anti-noise speaker and the secondary path transfer parameter of the virtual error point, determine the secondary sound field signal of the virtual error point; According to the true error signal, the primary path mapping parameter, and the secondary sound field signal of the physical error microphone, determine the primary sound field signal of the virtual error point; According to the secondary sound field signal of the virtual error point and the primary sound field signal of the virtual error point, determine the virtual error signal.

3. The method according to claim 2, wherein The target virtual sensing parameters further include the secondary path transfer parameter of the physical error microphone. The step of determining the primary sound field signal of the virtual error point according to the true error signal, the primary path mapping parameter, and the secondary sound field signal of the physical error microphone includes: According to the initial anti-noise signal output by the anti-noise speaker and the secondary path transfer parameter of the physical error microphone, determine the secondary sound field signal of the physical error microphone; According to the true error signal and the secondary sound field signal of the physical error microphone, determine the primary sound field signal of the physical error microphone; According to the primary sound field signal of the physical error microphone and the primary path mapping parameter, determine the primary sound field signal of the virtual error point.

4. The method according to claim 3, wherein The step of determining the primary sound field signal of the virtual error point according to the primary sound field signal of the physical error microphone and the primary path mapping parameter includes: Perform time delay processing on the primary path mapping parameter according to the number of time delay sampling points to obtain a time delay processed primary path mapping parameter; According to the primary sound field signal of the physical error microphone and the time delay processed primary path mapping parameter, determine the primary sound field signal of the virtual error point.

5. The method according to claim 1, characterized in that Before determining the target adaptive filter parameters according to the reference signal, the true error signal, and the virtual error signal, the method further includes: Obtain target noise reduction parameters, where the target noise reduction parameters include a first weighting parameter; The step of determining the target adaptive filter parameters according to the reference signal, the true error signal, and the virtual error signal includes: Weight the reference signal according to the first weighting parameter to obtain a weighted reference signal; Determine the target adaptive filter parameters according to the weighted reference signal, the true error signal and the virtual error signal; 6. The method according to claim 5, wherein The target virtual sensing parameter further includes a primary path transfer parameter corresponding to the physical error microphone; the weighting the reference signal according to the first weighting parameter to obtain a weighted reference signal includes: Process the first weighting parameter according to the primary path transfer parameter corresponding to the physical error microphone to obtain a processed first weighting parameter; Weight the reference signal according to the processed first weighting parameter to obtain the weighted reference signal; 7. The method according to claim 6, wherein The target noise reduction parameter further includes a second weighting parameter; the determining the virtual error signal corresponding to the virtual error point according to the target virtual sensing parameter and the true error signal includes: Weight the true error signal according to the second weighting parameter to obtain a weighted true error signal; Determine the virtual error signal corresponding to the virtual error point according to the target virtual sensing parameter and the weighted true error signal; The determining the target adaptive filter parameters according to the weighted reference signal, the true error signal and the virtual error signal includes: Determine the target adaptive filter parameters according to the weighted reference signal, the weighted true error signal and the virtual error signal; 8. The method according to claim 7, wherein The determining the target adaptive filter parameters according to the weighted reference signal, the weighted true error signal and the virtual error signal includes: Process the weighted reference signal based on the primary path transfer parameter corresponding to the physical error microphone to obtain a target reference signal; Determine the target adaptive filter parameters according to the target reference signal, the weighted true error signal and the virtual error signal; 9. The method according to any one of claims 1-8, characterized in that The determining the target adaptive filter parameters according to the reference signal, the true error signal and the virtual error signal includes: Obtain initial adaptive filter parameters and target noise reduction parameters, where the target noise reduction parameters include a convergence step size; Adjust the initial adaptive filter parameters according to the reference signal, the true error signal, the virtual error signal and the convergence step size to obtain the target adaptive filter parameters; 10. The method according to any one of claims 1-8, characterized in that The method further includes: Obtain environment information of the vehicle, where the environment information includes at least one of external vehicle environment information, internal vehicle environment information and vehicle state information; Determine a primary path mapping parameter from the physical error microphone to the virtual error point according to the environment information; 11. The method according to claim 10, characterized in that, The determining the primary path mapping parameter from the physical error microphone to the virtual error point according to the environment information includes: Determine the primary path mapping parameters from the physical error microphone to the virtual error point according to the environmental information and the first parameter prediction model.

12. The method according to any one of claims 1-8, characterized in that, The method further includes: Obtain environmental information of the vehicle, where the environmental information includes at least one of external vehicle environmental information, internal vehicle environmental information, and vehicle state information; Determine the secondary path transfer parameters corresponding to the virtual error point according to the environmental information.

13. The method according to claim 12, characterized in that, The determining the secondary path transfer parameters corresponding to the virtual error point according to the environmental information includes: Determine the secondary path transfer parameters corresponding to the virtual error point according to the environmental information and the fourth parameter prediction model.

14. A noise control device, characterized in that, Includes: A signal acquisition module, configured to acquire a reference signal and a true error signal collected by a physical error microphone, where the reference signal is a noise source signal outside the vehicle; A first determination module, configured to determine a virtual error signal corresponding to a virtual error point according to a target virtual sensing parameter and the true error signal, where the target virtual sensing parameter includes secondary path transfer parameters corresponding to the virtual error point and primary path mapping parameters from the physical error microphone to the virtual error point; A second determination module, configured to determine target adaptive filter parameters according to the reference signal, the true error signal, and the virtual error signal; A signal generation module, configured to process the reference signal based on the target adaptive filter parameters to generate a target anti-noise signal; A noise reduction module, configured to output the target anti-noise signal through an anti-noise speaker to perform noise reduction on a target area, where the target area is a noise reduction area corresponding to the virtual error point.

15. A readable storage medium, characterized in that, The readable storage medium stores a computer program, and when the computer program is executed, the method according to any one of claims 1 to 13 is implemented.

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