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, and the method comprises the steps: obtaining a reference signal and a real error signal collected by a physical error microphone; determining a secondary path transmission parameter corresponding to the physical error microphone by using a preset calibration wave signal and a target calibration wave signal collected by the physical error microphone; determining a virtual error signal corresponding to the virtual error point according to the real error signal, a secondary path transmission parameter corresponding to the physical error microphone and a mapping parameter from the physical error microphone to the virtual error point; determining a target adaptive filter parameter according to the reference signal, the real error signal and the virtual error signal; and processing the reference signal based on the target adaptive filter parameter, generating a target anti-noise signal and outputting the target anti-noise signal through an anti-noise loudspeaker so as to carry out noise reduction on a target area corresponding to the virtual error point, thereby improving the noise reduction effect of an area far away from the physical error microphone.

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 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 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 to this, 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 of 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 real error signal collected by a physical error microphone, where the reference signal is a noise source signal outside the vehicle; determining the secondary path transfer parameter corresponding to the physical error microphone by using a preset calibration wave signal and a target calibration wave signal collected by the physical error microphone; determining the virtual error signal corresponding to the virtual error point according to the real error signal, the secondary path transfer parameter corresponding to the physical error microphone, and the mapping parameter from the physical error microphone to the virtual error point; determining the 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; 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 the 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 secondary path transfer parameter corresponding to the physical error microphone by using a preset calibration wave signal and a target calibration wave signal collected by the physical error microphone; a second determination module, configured to determine a virtual error signal corresponding to a virtual error point according to the true error signal, the secondary path transfer parameter corresponding to the physical error microphone, and a mapping parameter from the physical error microphone to the virtual error point; a third 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; and 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.

[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, including: 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: During the driving process of the vehicle, the secondary path transfer parameter of the physical error microphone can be measured in real time by using the preset calibration wave signal and the target calibration wave signal collected by the physical error microphone arranged inside the vehicle. The obtained secondary path transfer parameter corresponding to the physical error microphone can reflect the environment where the vehicle is currently located. Furthermore, when noise reduction is performed, the real error signal at the physical error microphone is collected by using the physical error microphone arranged inside the vehicle. According to the secondary path transfer parameter of the physical error microphone measured in real time and the mapping parameter from the physical error microphone to the virtual error point, the real error signal at the physical error microphone is mapped to the virtual error point, and the virtual error signal at the virtual error point can be more accurately simulated. Furthermore, based on the reference signal, the real 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 of limited noise reduction area of the physical error microphone can be avoided, the noise reduction effect at 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 for the vehicle occupants far away from the physical error microphone area and enhance the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order 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 use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can 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 diagrams of the noise control method provided by an embodiment of the present application; Figure 4 It is another flow diagram of the noise control method provided by an embodiment of the present application; Figure 5 It is a third flow diagram of the noise control method provided by an embodiment of the present application; Figure 6 It is a fourth flow diagram of the noise control method provided by an embodiment of the present application; Figure 7 It is a fifth flow diagram of the noise control method provided by an embodiment of the present application; Figure 8It is the sixth flowchart diagram of the noise control method provided by the embodiment of the present application; Figure 9 It is the hardware block diagram of a noise control device provided by the embodiment of the present application. Detailed implementation manners

[0015] The embodiments will be described in detail below, and the examples are shown in the accompanying drawings. When the following description refers to the accompanying 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 facilitating the understanding of 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 general meanings.

[0017] The terms "first", "second", "third", etc. in the specification and claims of the present application 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 "include" and "have" and any variations thereof are intended to cover but not exclude inclusion. For example, a product or device including a series of components does not necessarily have to be limited to all the clearly listed components, 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 the element.

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

[0021] Road noise refers to the structural noise and air noise caused by the contact excitation between the tire and the road surface during vehicle driving, and the broadband noise formed in the vehicle through multiple paths. 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 in-vehicle noise reduction is also increasing.

[0022] Traditional passive noise reduction technologies achieve in-vehicle noise reduction 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 this regard, in the related art, active noise cancellation technology is adopted to control road noise in real time. For example, taking the road noise cancellation (RNC) technology as an example, vibration sensors are arranged near the tires of the vehicle, and the excitation signals between the tires and the road surface (i.e., the source excitation signals of road noise) are collected through the vibration sensors. The interior noise of the vehicle is monitored in real time through the error microphones arranged inside the vehicle. Based on the excitation signals and the interior noise, an anti-noise signal with the same amplitude and opposite phase to the interior noise amplitude is generated in real time, and the anti-noise signal is output through the speakers to cancel the interior noise and achieve noise reduction in the full frequency band.

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

[0025] In some embodiments, the number of error microphones arranged inside the vehicle is increased, which 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. During the driving of the vehicle, the secondary path transfer parameter of the physical error microphone can be measured in real time by using a preset calibration wave signal and the target calibration wave signal collected by the physical error microphones arranged inside the vehicle. The obtained secondary path transfer parameter corresponding to the physical error microphone can reflect the environment where the vehicle is currently located. Furthermore, during noise reduction, the real error signal at the physical error microphone is collected by using the physical error microphones arranged inside the vehicle. According to the secondary path transfer parameter of the physical error microphone measured in real time and the mapping parameter from the physical error microphone to the virtual error point, the real error signal at the physical error microphone is mapped to the virtual error point, and the virtual error signal at the virtual error point can be more accurately simulated. Furthermore, based on the reference signal, the real 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 a target anti-noise signal. In this way, the problem of limited noise reduction area of the physical error microphone can be avoided, the noise reduction effect at 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.

[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 may also be referred to as a true error microphone), an anti-noise speaker, and a controller (which may also be referred to as an RNC controller).

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

[0030] Optionally, one or more vibration sensors may be provided. Exemplarily, multiple vibration sensors are disposed on the chassis of the vehicle. Through multiple vibration sensors, multiple 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 , and 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, 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 passenger's area (such as on the passenger'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).

[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 passenger'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 speakers of the audio-visual entertainment system in 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] 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 environment 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 the external vehicle environment information , the internal vehicle environment information, and the vehicle status information (collectively referred to as ), and send the external vehicle environment information , the internal vehicle environment information, and the vehicle status information (collectively referred to as ) to the quick response module M4. The quick response module M4 determines the corresponding target noise reduction parameters according to the external vehicle environment information , the internal vehicle environment information, and the vehicle status information (collectively referred to as ). Among them, the target noise reduction parameters include the 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 control 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 , determines the primary sound field signal and the secondary sound field signal of the virtual error point according to the real error signal, the secondary sound field signal of the physical error microphone and the mapping parameter; superimposes 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, processes the reference signal (i.e., filtered reference) to obtain the processed reference signal . Using the LMS algorithm, updates the adaptive filter parameters according to the real error signal , the virtual error signal , the processed reference signal and the convergence step size. The noise reduction parameter module M5 processes the processed reference signal according to the adaptive filter parameters to generate a 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 describe in detail the noise control method provided by the embodiments of the present application with reference to the accompanying drawings.

[0041] Figure 3The flowchart of a noise control method provided by an embodiment of this application is shown as follows. As Figure 3 shown, the noise control method may include the following steps: S301. 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.

[0042] Among them, the reference signal refers to a signal related to external noise. In the embodiment of this application, the reference signal (i.e., the noise source signal) refers to an excitation signal generated by the contact between the vehicle's tires and the road surface and the vibration of the tires themselves 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 about the noise control system, the noise control system may include multiple vibration sensors, which are arranged on the vehicle chassis and near the tires. For example, a vibration sensor is arranged near each tire. When executing S301, for the case where multiple vibration sensors are set, obtain multiple reference signals collected by the multiple vibration sensors at the current moment, and these 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 embodiment of this 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 noise source signal outside the vehicle (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 acquires the real error signal collected by the physical error microphone. Exemplarily, during the vehicle driving process, the controller acquires the real error signal collected by the physical error microphone. It can be understood that the acquired real error signal is the real error signal collected by the physical error microphone at the current moment. Additionally, the real error signal refers to the real 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 executing S301, for the case of setting multiple physical error microphones, multiple real error signals collected by the multiple physical error microphones at the current moment are acquired, and these multiple real error signals can be understood as multi-channel real error signals.

[0049] It should be noted that the number of channels of the real error signal is related to the number of arranged physical error microphones. In practical applications, the number of physical error microphones can be set according to actual needs. In this regard, in the embodiments of the present application, the number of channels of the real 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 located in the area to be noise-reduced among the multiple physical error microphones are determined as target physical error microphones, and the real error signal collected by the target physical error microphones is acquired.

[0051] S302: Use the preset calibration wave signal and the target calibration wave signal collected by the physical error microphone to determine the secondary path transfer parameter corresponding to the physical error microphone.

[0052] Among them, the preset calibration wave signal is a sound signal used to measure the physical error microphone. Optionally, the preset calibration wave signal can include a sound signal with preset frequency band (low frequency) information.

[0053] The target calibration wave signal refers to the sound signal currently collected by the physical error microphone when the anti-noise speaker outputs a preset calibration wave signal. In the embodiments of the present application, the secondary path transfer parameter corresponding to the physical error microphone is related to the environment where the vehicle is located, that is, the target virtual sensing parameter changes with the change of the environment where 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 the secondary path transfer parameter corresponding to the physical error microphone is determined according to the adaptively processed target calibration wave signal and the preset calibration wave signal. 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 the secondary path transfer parameter corresponding to the physical error microphone in real time. The obtained secondary path transfer parameter corresponding to the physical error microphone can reflect the environment where 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.

[0054] In a possible implementation manner, when the anti-noise speaker outputs a first audio signal (that is, 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; the secondary path transfer parameter corresponding to the physical error microphone is determined according to the preset calibration wave signal and the target calibration wave signal.

[0055] It can be understood that the speaker of the vehicle's internal audio and video entertainment system can be used as the anti-noise speaker, that is, the internal speaker of 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.

[0056] In another possible implementation manner, 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; the secondary path transfer parameter corresponding to the physical error microphone is determined according to the preset calibration wave signal and the target calibration wave signal.

[0057] In yet another possible implementation, when the anti-noise speaker does not output the first audio signal (i.e., when the vehicle's audio and video 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; 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. 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.

[0058] In this embodiment, when using the preset calibration wave signal to measure in real time the secondary path transfer parameter corresponding to the physical error microphone, the first audio signal currently output by the anti-noise speaker can be obtained. When the first audio signal includes preset frequency band information, based on 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, which will not affect the audio and video entertainment experience of the vehicle occupants.

[0059] 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, improving the accuracy of determining the secondary path transfer parameter corresponding to the physical error microphone. At the same time, it will not affect the audio and video entertainment experience of the vehicle occupants.

[0060] S303. Determine the virtual error signal corresponding to the virtual error point according to the true error signal, the secondary path transfer parameter corresponding to the physical error microphone, and the mapping parameter from the physical error microphone to the virtual error point.

[0061] Specifically, obtain the target virtual sensing parameter, where the target virtual sensing parameter includes the mapping parameter from the physical error microphone to the virtual error point; according to the true error signal, the secondary path transfer parameter corresponding to the physical error microphone, and the mapping parameter from the physical error microphone to the virtual error point, determine the virtual error signal corresponding to the virtual error point.

[0062] 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.

[0063] Optionally, one or more virtual error points can be set. In practical applications, the virtual error points can be selected according to actual requirements. Exemplarily, 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 passenger'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.

[0064] The virtual error signal refers to the error signal collected from the simulated virtual error points. That is to say, the virtual error signal refers to the residual noise signal after the superposition of the in-vehicle noise signal of the virtual error point and the secondary sound field signal of the virtual error point. Among them, the in-vehicle noise signal of 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 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.

[0065] The mapping parameters from the physical error microphone to the virtual error point can 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.

[0066] In the embodiment 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, it is necessary to arrange an error microphone at the virtual error point, and then based on the real error signal collected by the physical error microphone and the error signal collected by the error microphone arranged at the virtual error point, determine the mapping parameters from the physical error microphone to the virtual error point. And during the actual noise reduction process, an error microphone will not be 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 embodiment of the present application, the mapping parameters from the physical error microphone to the virtual error point can be obtained in advance.

[0067] 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 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 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 can be determined. 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 of 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.

[0068] 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 according to the environmental information and a pre-trained first parameter prediction model, the mapping parameters from the physical error microphone to the virtual error point can be determined.

[0069] 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.

[0070] 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.

[0071] In some examples, determining the virtual error signal corresponding to the virtual error point according to the true error signal, 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 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 parameters 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 parameters; determining the primary sound field signal of the virtual error point according to the true error signal, the primary path mapping parameters, and the secondary sound field signal of the physical error microphone; and 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.

[0072] Among them, for the specific implementation of determining the virtual error signal, reference can be made to the specific implementation process of the virtual error signal determination process (Method 1) in the following text, which will not be elaborated here.

[0073] In some other examples, 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.

[0074] That is to say, the secondary sound field signal of the virtual error point can be determined according to the initial anti-noise signal output by the anti-noise speaker and the secondary path transfer parameter of the virtual error point; the primary sound field signal of the virtual error point can be determined according to the real error signal, the primary path mapping parameter, and the secondary sound field signal of the physical error microphone; and the virtual error signal can be determined according to the secondary sound field signal and the primary sound field signal of the virtual error point.

[0075] Among them, for the specific implementation of determining the virtual error signal, reference can be made to the specific implementation process of the virtual error signal determination process (Method 2) in the following text, which will not be elaborated here.

[0076] Among them, 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.

[0077] 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 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 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.

[0078] 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, and the primary path mapping parameter can be determined according to the environmental information and a pre-trained first parameter prediction model.

[0079] In the embodiments of the present application, a 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.

[0080] 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.

[0081] The secondary path transfer parameter of the virtual error point refers to the transfer characteristic parameter from the anti-noise speaker to the virtual error point. It can be understood that the secondary path transfer parameter of the virtual error point is related to the environment where the vehicle is located, that is, the secondary path transfer parameter of the virtual error point changes with the change of the environment where the vehicle is located. Therefore, different secondary path transfer parameters of the virtual error point can be preset according to different environments where the vehicle is located. During the noise reduction process, the environmental information of the environment where the vehicle is located can be obtained, and the secondary path transfer parameter of the virtual error point can be determined according to the environmental information of the environment where the vehicle is located.

[0082] Exemplarily, the secondary path transfer parameter of the virtual error point is determined by using the environmental information and the pre-trained fourth parameter prediction model.

[0083] 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.

[0084] S304. Determine the target adaptive filter parameter according to the reference signal, the true error signal, and the virtual error signal.

[0085] Among them, the adaptive filter coefficient refers to the weight coefficient of the adaptive filter. The adaptive filter parameter is 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., the anti-noise signal). Correspondingly, the target adaptive filter parameter is the adaptive filter parameter 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 parameter at the current moment.

[0086] Exemplarily, the adaptive filter coefficient can be dynamically adjusted through an adaptive filtering algorithm to obtain the target adaptive filter parameter, thereby minimizing the error between the noise signal and the reference signal.

[0087] Optionally, the adaptive filtering algorithm can be, for example, the Filtered-X Least Mean Squares (Fxlms) algorithm, the Filtered-X Normalized Least Mean Squares (Fxnlms), or the 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.

[0088] In some examples, determining the target adaptive filter parameters based on the reference signal, the real error signal, and the virtual error signal may include: obtaining the initial adaptive filter parameters and the target noise reduction parameters, where the target noise reduction parameters include the convergence step size; and adjusting the initial adaptive filter parameters according to the reference signal, the real error signal, the virtual error signal, and the convergence step size to obtain the target adaptive filter parameters.

[0089] Among them, the initial adaptive filter parameters can be preset. The initial adaptive filter parameters can also be understood as the adaptive filter parameters at the previous moment.

[0090] It can be understood that after obtaining the initial adaptive filter parameters, the initial adaptive filter parameters can be adjusted based on the reference signal, the real error signal, and the virtual error signal to obtain the target adaptive filter parameters. Among them, the reference signal, the real 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 real 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 can be adjusted based on the currently collected reference signal, the collected real 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 good noise reduction effect can be provided as the environment in which the vehicle is located changes.

[0091] Exemplarily, the target adaptive filter parameters can be calculated according to the following formulas (1) and (2): (1) Among them, represents the error signal matrix formed by the conjugation of the real error signal and the virtual error signal; represents the real 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.

[0092] (2) Among them, 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 signals); 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.

[0093] 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, the energy of the broadband impact noise will increase. Also, for example, under some bad 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.

[0094] 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.

[0095] 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.

[0096] It can be understood that when the reference signal is a multi-channel reference signal, the corresponding first weighting parameter can be determined for the reference signal of each channel, that is, the first weighting parameter matrix corresponding to the multi-channel reference signal is determined. Among them, for the specific implementation method of determining the first weighting parameter of the reference signal, reference can be made to the relevant description of the determination process of the weighting parameter below, which will not be elaborated here.

[0097] Optionally, before performing weighted processing on 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.

[0098] In this example, the target adaptive filter parameter can be calculated according to the following formula (3): (3) where 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.

[0099] Among them, 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.

[0100] 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 influences of various factors such as the actual road noise source excitation characteristics, the contribution degree of each channel, and the driving position are comprehensively considered, and the reference signals of each channel are amplified or attenuated, so as to improve the noise reduction effect. The virtual error signal for determining the virtual error point 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.

[0101] 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.

[0102] In the embodiments 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.

[0103] 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; and performing weighted processing on the filtered reference signal according to the first weighting parameter to obtain the weighted reference signal.

[0104] 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.

[0105] 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 travels on a bumpy road, 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.

[0106] 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, the second weighting parameter matrix corresponding to the multi-channel true error signal is determined.

[0107] 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.

[0108] 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 weighted true error signal, the secondary path transfer parameter corresponding to the physical error microphone, and the mapping parameter from the physical error microphone to the virtual error point. This can improve the accuracy of predicting the virtual error signal corresponding to the virtual error point, and thus improve the noise reduction effect at the virtual error point.

[0109] 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.

[0110] 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; according to the weighted true error signal, the secondary path transfer parameter corresponding to the physical error microphone, and the mapping parameter from the physical error microphone to the virtual error point, the virtual error signal corresponding to the virtual error point can be determined; according to the weighted reference signal, the weighted true error signal, and the virtual error signal of the virtual error point, the target adaptive filter parameters can be determined.

[0111] 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 according to the primary path transfer parameter corresponding to the physical error microphone to obtain the target reference signal; determining the target adaptive filter parameters according to the target reference signal, the weighted true error signal, and the target virtual error signal.

[0112] 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 environment can be obtained, and according to the environmental information of the vehicle's environment, 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.

[0113] 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.

[0114] In some examples, when determining the target noise reduction parameters according to the environmental information of the vehicle's environment, the target noise reduction parameters can be determined according to a pre-trained second parameter prediction model and the environmental information.

[0115] Among them, for the specific implementation method 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.

[0116] S305. Process the reference signal based on the target adaptive filter parameters to generate a target anti-noise signal.

[0117] S306. Output the target anti-noise signal 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.

[0118] For example, the target area can be Figure 2 the light gray area shown.

[0119] In the embodiment 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. Using the preset calibration wave signal and the target calibration wave signal collected by the physical error microphone, the secondary path transfer parameter of the physical error microphone is determined. Based on the secondary path transfer parameter of the physical error microphone and the mapping parameter from the physical error microphone to the virtual error point, the real 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. Furthermore, 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 a target anti-noise signal. This can avoid the problem of limited noise reduction area of the physical error microphone, improve the noise reduction effect of the virtual error point, expand the noise reduction area corresponding to the physical error microphone 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 area and enhance the user experience.

[0120] The above is a schematic description of the overall solution of the noise control method provided by the embodiment of the present application. The determination process of the virtual error signal, the determination process of the mapping parameter from the physical error microphone to the virtual error point, the determination process of the primary path transfer parameter corresponding to the physical error microphone, the secondary path transfer parameter corresponding to the virtual error point, and the determination process of the target noise reduction parameter will be described in detail below.

[0121] 1. Determination process of the virtual error signal Method 1: The target virtual sensing parameters include the secondary path transfer parameter corresponding to the physical error microphone and the mapping parameter from the physical error microphone to the virtual error point. Determining the virtual error signal corresponding to the virtual error point according to the target virtual sensing parameter and the true error signal (i.e., S303 above) 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 true 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.

[0122] Exemplarily, as Figure 4 shown, determining the virtual error signal corresponding to the virtual error point according to the target virtual sensing parameter and the true error signal may include the following steps: S401. 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.

[0123] 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.

[0124] 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.

[0125] It can be understood that the secondary path transfer parameter of the physical error microphone changes with the change of the vehicle's environment.

[0126] Specifically, the secondary sound field signal of the physical error microphone can be calculated according to the following formula (4): (4) 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 true error signal are collected.

[0127] S402. Determine 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.

[0128] 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.

[0129] It can be understood that the secondary path mapping parameter varies with the change of the environment where the vehicle is located.

[0130] Specifically, the secondary sound field signal of the virtual error point can be calculated according to the following formula (5): (5) 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.

[0131] Optionally, when 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, the secondary sound field signal of the virtual error point is determined according to the secondary sound field signal of the physical error microphone, the secondary path mapping parameter and the time delay sampling points.

[0132] Specifically, the secondary sound field signal of the virtual error point can be calculated according to the following formula (6): (6) 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; 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.

[0133] In this embodiment, considering that in the process of actual sound signal transmission, the anti-noise signal transmitted to the virtual error point can 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.

[0134] S403. Determine the primary sound field signal of the physical error microphone based on the true error signal and the secondary sound field signal of the physical error microphone.

[0135] Among them, the primary sound field signal of the physical error microphone is the noise signal transmitted by the noise source to the physical error microphone.

[0136] Specifically, the primary sound field signal of the physical error microphone can be calculated according to the following formula (7): (7) Among them, represents the primary sound field signal of the physical error microphone at the current moment; represents the true error signal collected by the physical error microphone at the current moment; 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 true error signal are collected.

[0137] S404. Determine the primary sound field signal of the virtual error point based on the primary sound field signal of the physical error microphone and the primary path mapping parameter.

[0138] Among them, the primary sound field signal of the virtual error point is the noise signal transmitted by the noise source to the virtual error point.

[0139] It can be understood that the primary path mapping parameter changes with the change of the vehicle's environment.

[0140] Specifically, the primary sound field signal of the virtual error point can be calculated according to the following formula (8): (8) 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, that is, the current moment, which is also the moment when the current reference signal and true error signal are collected.

[0141] Optionally, when determining the primary sound field signal of the virtual error point based on 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 number of delay sampling points.

[0142] The primary sound field signal of the virtual error point can be calculated according to the following formula (9): (9) 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 number of 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.

[0143] In this embodiment, considering that in the process of actual sound signal transmission, the noise source signal transmitted to the virtual error point can 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, it is possible to combine the primary sound field signal of the physical error microphone, the primary path mapping parameter, and the number of time delay sampling points to determine the primary sound field signal of the virtual error point, 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.

[0144] S405. 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.

[0145] Specifically, the virtual error signal of the virtual error point can be calculated according to the following formula (10): (10) Among them, 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.

[0146] Method 2: The target virtual sensing parameters include 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. Determining the virtual error signal corresponding to the virtual error point according to the target virtual sensing parameters and the true error signal 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 true 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.

[0147] Exemplarily, such as Figure 5As shown, according to the target virtual sensing parameter and the real error signal, determining the virtual error signal corresponding to the virtual error point may include the following steps: S501. 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.

[0148] 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.

[0149] It can be understood that the secondary path transfer parameter of the virtual error point changes with the change of the vehicle's environment.

[0150] 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 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.

[0151] S502. Determine the primary sound field signal of the physical error microphone according to the real error signal and the secondary sound field signal of the physical error microphone.

[0152] S503. 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.

[0153] S504. 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.

[0154] Among them, for the specific implementation manners of S502 to S504, reference can be made to S403 to S405 above, which will not be elaborated here.

[0155] 2. Determination process of the mapping parameter 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.

[0156] 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 location.

[0157] 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, for example, include 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 location. The weather type can, for example, be 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, for example, be plateau, plain, etc. The wind speed information refers to the wind speed type information. The wind speed type can, for example, be weak wind, strong wind, etc.

[0158] In specific implementation, the external vehicle environment information is obtained through sensors provided on the vehicle. Among them, the sensors can include external lidar, millimeter-wave radar, GPS, external cameras, temperature sensors, air pressure sensors, rain sensors, wind speed sensors, etc.

[0159] The in-vehicle environment information includes the information about the occupants in the vehicle (such as the number of occupants, sitting postures, and positions), the seat states (seat displacements and tilt angles), other sound source information (such as the air conditioner, the entertainment system, and the speaking volume of the occupants), the in-vehicle temperature information, the in-vehicle humidity information, and the microphone occlusion information. Among them, the information about 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 setting, the volume information of the audio-visual entertainment system, and the volume information of the occupants. Among them, the air conditioner setting can be, for example, the high gear, the medium gear, the low gear, etc. The volume information of the audio-visual entertainment system can be, for example, the low volume, the medium volume, and the high volume. The volume information of the occupants can be, for example, the low volume, the medium volume, and the 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.

[0160] 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 rotation encoders.

[0161] 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 whole vehicle closure opening and closing state information. Among them, the window opening and closing state information is, for example, 30% open. The whole vehicle closure opening and closing state information is, for example, 30% open.

[0162] 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 rotation encoders, and CAN signals.

[0163] S602. Determine the mapping parameters from the physical error microphone to the virtual error point according to the environment information.

[0164] 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.

[0165] 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 environment information 1 of the sample environment where the vehicle is located, sample sound signal 1, sample sound signal 2, sample sound signal 3, and sample sound signal 4.

[0166] 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.

[0167] Optionally, based on the preset number of delay sampling points, perform delay processing on sample sound signal 2 to obtain the delayed sample sound signal 2.

[0168] 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.

[0169] Optionally, based on the preset number of delay sampling points, perform delay processing on sample sound signal 4 to obtain the delayed sample sound signal 4.

[0170] After that, input sample environment information 1 into the first initial model, and output the predicted primary path transfer parameter and the predicted secondary path transfer parameter. Use the actual primary path transfer parameter as the supervision information to perform iterative training on the first initial model, and use 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.

[0171] 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 primary path transfer parameter corresponding to the physical error microphone may include: obtaining the environment information of the environment where the vehicle is located; determining the primary path transfer parameter corresponding to the physical error microphone according to the environment information.

[0172] 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.

[0173] 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. When the vehicle is in the 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.

[0174] 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 environment where the vehicle is located; determining the secondary path transfer parameter corresponding to the virtual error point according to the environmental information.

[0175] 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.

[0176] Among them, when training the fourth initial model, when the vehicle is in a sample environment, control the anti-noise speaker to output a sample calibration signal, and obtain the sound signal currently collected by the virtual error microphone arranged at the virtual error point (i.e., the sample sound signal 6). According to the sample calibration signal and the sample sound signal 6, determine the actual secondary path transfer parameter corresponding to the virtual error point. When the vehicle is in the sample environment, obtain the sample environmental information 4, input the sample environmental information 4 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 the supervision information, perform iterative training on the fourth initial model, that is, the fourth parameter prediction module.

[0177] 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. Obtain the environmental information of the environment where the vehicle is located.

[0178] Among them, for the specific implementation process of S701, reference can be made to the specific implementation process of S601 described above, which will not be elaborated here.

[0179] S702. Determine the target noise reduction parameters according to the environmental information. Among them, the target noise reduction parameters include a convergence step size, a first weighting parameter, a second weighting parameter, a leakage factor, a signal gain ratio, and a phase compensation factor.

[0180] In some examples, S702 may include: determining the target noise reduction parameters according to the environmental information and a pre-trained second parameter prediction model.

[0181] Exemplarily, the second parameter prediction model is obtained by training a second initial model based on multiple sets of second sample data. Among them, each set of second sample data includes 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, the sample environmental information 3 is input into the second initial model, and a set of predicted noise reduction parameters is output. Using the reference noise reduction parameters as supervision information, the second initial model is iteratively trained, that is, the second parameter prediction module.

[0182] Among them, the reference noise reduction parameters may include a reference first weighting parameter.

[0183] Specifically, taking the reference signal as a multi-channel reference signal as an example, the process of determining the reference first weighting parameter may include: S801. Perform a Fourier transform on the reference signal to obtain a frequency-domain reference signal.

[0184] S802. Determine the power spectral information of the frequency-domain reference signal.

[0185] Specifically, the power spectral information of the frequency-domain reference signal can be calculated according to the following formula (12): (12) Among them, represents the power spectral information of the frequency-domain reference signal; represents the frequency-domain reference signal.

[0186] S803. Determine the reference first weighting parameter of the reference signal according to the preset peak frequency, the preset power spectral information, and the power spectral information of the frequency-domain reference signal.

[0187] Among them, the preset peak frequency refers to the peak frequency point of the road noise signal (i.e., the reference reference signal).

[0188] Specifically, the reference first weighting parameter of the reference signal can be calculated according to the following formula (13): (13) Among them, represents the reference first weighting parameter corresponding to the reference signal of the i-th channel in the multi-channel reference signal; represents the power spectral signal of the reference signal of the i-th channel in the multi-channel reference signal; represents the power spectral information of the reference reference signal; represents a preset peak frequency. Among them, the reference reference signal can be a reference signal measured in a sample environment in advance.

[0189] 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 weighting parameter, second weighting parameter) and target virtual sensing parameters (mapping parameters, also called observation filter parameters).

[0190] Table 1 below gives a schematic description of the first weighting parameter of the reference signal corresponding to the vibration sensor, the second weighting parameter of the true error signal collected by the physical error microphone, and the observation filter parameter when the vehicle is in different environments.

[0191] Table 1 shows the target noise reduction parameters and target virtual sensing parameters when the vehicle is in different environments

[0192] Next, a specific example is used to schematically illustrate the noise control method provided by the embodiments of the present application.

[0193] Exemplarily, as Figure 8 shown, the noise control method may include the following steps: S901. After the vehicle starts, obtain the reference signal 1 collected by the vibration sensor.

[0194] S902. Start the RNC controller, and process the reference signal 1 according to the initial adaptive filter parameters to generate the anti-noise signal 1.

[0195] S903. Output the anti-noise signal 1 through the anti-noise speaker.

[0196] S904. During the driving of the vehicle, obtain the reference signal 2 collected by the vibration sensor and the true error signal 1 collected by the physical error microphone.

[0197] S905. Obtain the environmental information 1 of the environment where the vehicle is located. The environmental information 1 may include in-vehicle environmental information, out-of-vehicle environmental information, and vehicle status information.

[0198] S906. Determine the noise reduction parameter 1 based on the environmental information 1 and the second parameter prediction model. The noise reduction parameter 1 includes the convergence step size 1.

[0199] S907. Determine the target virtual sensing parameter 1 based on the environmental information 1 and the first parameter prediction model. The target virtual sensing parameter 1 includes the mapping parameter 1 from the physical error microphone to the virtual error point. Among them, the mapping parameter 1 includes the secondary path mapping parameter 1 and the primary path mapping parameter 1.

[0200] S908. Measure the secondary path transfer parameter 1 corresponding to the physical error microphone using the preset calibration wave signal.

[0201] S909. Determine the virtual error signal 1 of the virtual error point based on the real error signal 1, the mapping parameter 1, and the secondary path transfer parameter 1 corresponding to the physical error microphone.

[0202] S910. Determine the adaptive filter parameter 1 based on the reference signal 2, the real error signal 1, the virtual error signal 1, and the convergence step size 1.

[0203] S911. Process the reference signal 2 based on the adaptive filter parameter 1 to generate the anti-noise signal 2.

[0204] S912. Output the anti-noise signal 2 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.

[0205] S913. During the vehicle driving process, obtain the reference signal 3 collected by the vibration sensor and the real error signal 2 collected by the physical error microphone.

[0206] S914. Obtain the environmental information 2 of the vehicle's location. The environmental information 2 can include the in-vehicle environmental information, the out-of-vehicle environmental information, and the vehicle status information.

[0207] S915. Determine whether the environmental information 2 is the same as the environmental information 1. If so, execute S916; otherwise, execute S918.

[0208] S916. Process the reference signal 3 based on the adaptive filter parameter 1 to generate the anti-noise signal 3.

[0209] S917. Output the anti-noise signal 3 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.

[0210] S918. Determine the noise reduction parameter 2 based on the environmental information 2 and the second parameter prediction model. The noise reduction parameter 2 includes the convergence step size 2.

[0211] S919. Determine the target virtual sensing parameter 2 according to the environmental information 2 and the first parameter prediction model. The target virtual sensing parameter 2 includes the mapping parameter 2 from the physical error microphone to the virtual error point. The mapping parameter 2 includes the secondary path mapping parameter 2 and the primary path mapping parameter 2.

[0212] S920. Measure the secondary path transfer parameter 2 corresponding to the physical error microphone by using a preset calibration wave signal.

[0213] S921. Determine the virtual error signal 2 of the virtual error point according to the true error signal 2, the mapping parameter 2, and the secondary path transfer parameter 2 corresponding to the physical error microphone.

[0214] S922. Determine the adaptive filter parameter 2 according to the reference signal 3, the true error signal 2, the virtual error signal 2, and the convergence step 2.

[0215] S923. Process the reference signal 3 based on the adaptive filter parameter 2 to generate an anti-noise signal 4.

[0216] S924. Output the anti-noise signal 4 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.

[0217] 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 third determination module 904, a signal generation module 905, and a noise reduction module 906. The signal acquisition module 901 is used to acquire a reference signal and a true error signal collected by a physical error microphone. The reference signal is a noise source signal outside the vehicle. The first determination module 902 is used to determine the secondary path transfer parameter corresponding to the physical error microphone by using a preset calibration wave signal and a target calibration wave signal collected by the physical error microphone. The second determination module 903 is used to determine the virtual error signal corresponding to the virtual error point according to the true error signal, the secondary path transfer parameter corresponding to the physical error microphone, and the mapping parameter from the physical error microphone to the virtual error point. The third determination module 904 is used to determine the target adaptive filter parameter according to the reference signal, the true error signal, and the virtual error signal. The signal generation module 905 is used to process the reference signal based on the target adaptive filter parameter to generate a target anti-noise signal. The noise reduction module 906 is used to 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.

[0218] Optionally, the mapping parameters may include secondary path mapping parameters and primary path mapping parameters. The second determination module 903 is specifically configured to: 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; determine 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 parameters; determine the primary sound field signal of the virtual error point according to the real error signal, the primary path mapping parameters, and the secondary sound field signal of the physical error microphone; determine the virtual error signal according to the secondary sound field signal and the primary sound field signal of the virtual error point.

[0219] Exemplarily, the second determination module 903 is specifically configured to: perform a time delay process on the secondary path mapping parameters according to the number of time delay sampling points to obtain the time delay processed secondary path mapping parameters; determine the secondary sound field signal of the virtual error point according to the secondary sound field signal of the physical error microphone and the time delay processed secondary path mapping parameters.

[0220] Exemplarily, the second determination module 903 is specifically configured to: determine the primary sound field signal of the physical error microphone according to the real error signal and the secondary sound field signal of the physical error microphone; perform a time delay process on the primary path mapping parameters according to the number of time delay sampling points to obtain the time delay processed primary path mapping parameters; 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 time delay processed primary path mapping parameters.

[0221] Exemplarily, the first determination module 902 is specifically configured to: when the anti-noise speaker outputs the first audio signal, obtain the first audio signal and the target calibration wave signal collected by the physical error microphone; if the first audio signal includes the preset frequency band information, use the first audio signal as the preset calibration wave signal, and determine the secondary path transfer parameter corresponding to the physical error microphone according to the preset calibration wave signal and the target calibration wave signal; if the first audio signal does not include the preset frequency band information, control the anti-noise speaker to output the preset calibration wave signal, and determine the secondary path transfer parameter corresponding to the physical error microphone according to the preset calibration wave signal and the target calibration wave signal.

[0222] Exemplarily, the first determination module 902 is specifically configured to: when the anti-noise speaker does not output the first audio signal, control the anti-noise speaker to output the preset calibration wave signal, and obtain the target calibration wave signal collected by the physical error microphone; determine the secondary path transfer parameter corresponding to the physical error microphone according to the preset calibration wave signal and the target calibration wave signal.

[0223] In some examples, the noise control device may further include a first parameter acquisition module configured to acquire a target noise reduction parameter, where the target noise reduction parameter includes a first weighting parameter. Specifically, the third determination module 904 is configured to: perform weighting processing on the reference signal according to the first weighting parameter to obtain a weighted reference signal; and determine target adaptive filter parameters according to the weighted reference signal, the true error signal, and the virtual error signal.

[0224] In some examples, specifically, the third determination module 904 is configured to: acquire a target virtual sensing parameter, where the target virtual sensing parameter includes a primary path transfer parameter corresponding to a physical error microphone; 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; and perform weighting processing on the reference signal according to the processed first weighting parameter to obtain a weighted reference signal.

[0225] In some examples, the target noise reduction parameter further includes a second weighting parameter. Specifically, the second determination module 903 is configured to: perform weighting processing on the true error signal according to the second weighting parameter to obtain a weighted true error signal; and determine the virtual error signal according to the weighted true error signal, the secondary path transfer parameter corresponding to the physical error microphone, and the mapping parameter from the physical error microphone to the virtual error point. Specifically, the third determination module 904 is configured to: determine target adaptive filter parameters according to the weighted reference signal, the weighted true error signal, and the virtual error signal.

[0226] In some examples, specifically, the third determination module 904 is 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; and determine target adaptive filter parameters according to the target reference signal, the weighted true error signal, and the target virtual error signal.

[0227] In some examples, specifically, the third determination module 904 is configured to: acquire initial adaptive filter parameters and a target noise reduction parameter, where the target noise reduction parameter includes a convergence step size; and 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.

[0228] In some examples, the noise control device may further include: an environment information acquisition module and a second parameter acquisition module. The environment information acquisition module is configured to acquire environment information of the vehicle environment, where the environment information includes at least one of external vehicle environment information, internal vehicle environment information, and vehicle state information. The second parameter acquisition module is configured to determine a mapping parameter from the physical error microphone to the virtual error point according to the environment information.

[0229] In some examples, the second parameter acquisition module is specifically configured to determine the mapping parameter from the physical error microphone to the virtual error point according to the environmental information and the first parameter prediction model.

[0230] In some examples, the first parameter acquisition module is specifically configured to determine the target noise reduction parameters according to the environmental information, and the target noise reduction parameters at least include a convergence step size, a first weighting parameter, and a second weighting parameter.

[0231] In some examples, the first parameter acquisition module is specifically configured to determine the target noise reduction parameters according to the environmental information and the second parameter prediction model.

[0232] 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 elaborated here. Among them, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc, etc.

[0233] The present disclosure provides a computer program product, which includes a computer program. When the computer program runs on a computer, the computer implements the above noise control method.

[0234] For the sake of convenience of explanation, the above description has been made in combination 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 practical applications, so that those skilled in the art can better use the embodiments and various different variations of the embodiments suitable for specific use considerations.

[0235] 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 items 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.

[0236] It should be understood that in various embodiments of the present application, the magnitudes of the serial numbers of the above processes do not imply the sequence of execution. The execution sequence 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.

[0237] 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 the present application.

[0238] Those skilled in the art can clearly understand that for the convenience and simplicity 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.

[0239] In several embodiments provided by the present 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 couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms.

[0240] 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 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.

[0241] In addition, the functional units in various embodiments of the present 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.

[0242] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all should be covered by the protection scope of the present 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; Determine the secondary path transfer parameter corresponding to the physical error microphone by using a preset calibration wave signal and a target calibration wave signal collected by the physical error microphone; Determine the virtual error signal corresponding to the virtual error point according to the true error signal, the secondary path transfer parameter corresponding to the physical error microphone, and the mapping parameter from the physical error microphone to the virtual error point; Determine the target adaptive filter parameter according to the reference signal, the true error signal, and the virtual error signal; Process the reference signal based on the target adaptive filter parameter 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 the noise reduction area corresponding to the virtual error point.

2. The method according to claim 1, characterized in that, The mapping parameter includes a secondary path mapping parameter and a primary path mapping parameter; the determining the virtual error signal corresponding to the virtual error point according to the true error signal, the secondary path transfer parameter corresponding to the physical error microphone, and the mapping parameter from the physical error microphone to the virtual error point includes: 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; Determine 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; Determine 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; Determine 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.

3. The method according to claim 2, characterized in that, The 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 includes: Perform time delay processing on the secondary path mapping parameter according to the number of time delay sampling points to obtain the time delay processed 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 and the time delay processed secondary path mapping parameter.

4. The method according to claim 2, wherein The 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: 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; Perform time delay processing on the primary path mapping parameter according to the number of time delay sampling points to obtain the time delay processed 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 and the time delay processed primary path mapping parameter.

5. The method according to claim 1, wherein Determining the secondary path transfer parameter corresponding to the physical error microphone by using the preset calibration wave signal and the target calibration wave signal collected by the physical error microphone includes: When the anti-noise speaker outputs a first audio signal, obtaining the first audio signal and the target calibration wave signal collected by the physical error microphone; If the first audio signal includes preset frequency band information, using the first audio signal as the preset calibration wave signal, and determining the secondary path transfer parameter corresponding to the physical error microphone according to the preset calibration wave signal and the target calibration wave signal; If the first audio signal does not include the preset frequency band information, controlling the anti-noise speaker to output the preset calibration wave signal, and determining the secondary path transfer parameter corresponding to the physical error microphone according to the preset calibration wave signal and the target calibration wave signal.

6. The method according to claim 5, wherein Determining the secondary path transfer parameter corresponding to the physical error microphone by using the preset calibration wave signal and the target calibration wave signal collected by the physical error microphone includes: When the anti-noise speaker does not output a first audio signal, controlling the anti-noise speaker to output the preset calibration wave signal, and obtaining the target calibration wave signal collected by the physical error microphone; Determining the secondary path transfer parameter corresponding to the physical error microphone according to the preset calibration wave signal and the target calibration wave signal.

7. The method according to claim 1, wherein Before determining the target adaptive filter parameter according to the reference signal, the true error signal, and the virtual error signal, the method further includes: Obtaining a target noise reduction parameter, where the target noise reduction parameter includes a first weighting parameter; Determining the target adaptive filter parameter according to the reference signal, the true error signal, and the virtual error signal includes: Performing weighting processing on the reference signal according to the first weighting parameter to obtain a weighted reference signal; Determining the target adaptive filter parameter according to the weighted reference signal, the true error signal, and the virtual error signal.

8. The method according to claim 7, wherein Performing weighting processing on the reference signal according to the first weighting parameter to obtain a weighted reference signal includes: Obtaining a target virtual sensing parameter, where the target virtual sensing parameter includes the primary path transfer parameter corresponding to the physical error microphone; Processing the first weighting parameter according to the primary path transfer parameter corresponding to the physical error microphone to obtain a processed first weighting parameter; Performing weighting processing on the reference signal according to the processed first weighting parameter to obtain the weighted reference signal.

9. The method according to claim 8, wherein The target noise reduction parameter further includes a second weighting parameter. Determining the virtual error signal corresponding to the virtual error point according to the true error signal, the secondary path transfer parameter corresponding to the physical error microphone, and the mapping parameter from the physical error microphone to the virtual error point includes: Performing 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 according to the weighted true error signal, the secondary path transfer parameter corresponding to the physical error microphone, and the mapping parameter from the physical error microphone to the virtual error point; The determining the target adaptive filter parameters according to the 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.

10. The method according to claim 9, 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.

11. The method according to any one of claims 1-10, 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.

12. The method according to any one of claims 1-10, characterized in that, The method further includes: Obtain environmental information of the vehicle environment, where the environmental information includes at least one of external vehicle environment information, internal vehicle environment information, and vehicle state information; Determine the mapping parameter from the physical error microphone to the virtual error point according to the environmental information.

13. The method according to claim 12, characterized in that, The determining the mapping parameter from the physical error microphone to the virtual error point according to the environmental information includes: Determine the mapping parameter from the physical error microphone to the virtual error point according to the environmental information and a first parameter prediction model.

14. The method according to any one of claims 1 to 10, characterized in that The method further includes: Obtain environmental information of the vehicle environment, where the environmental information includes at least one of external vehicle environment information, internal vehicle environment information, and vehicle state information; Determine target noise reduction parameters according to the environmental information, where the target noise reduction parameters at least include a convergence step size, a first weighting parameter, and a second weighting parameter.

15. The method according to claim 14, wherein The determining the target noise reduction parameters according to the environmental information includes: Determine the target noise reduction parameters according to the environmental information and a second parameter prediction model.

16. 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 the secondary path transfer parameter corresponding to the physical error microphone by using a preset calibration wave signal and a target calibration wave signal collected by the physical error microphone; A second determination module, configured to determine a virtual error signal corresponding to the virtual error point according to the true error signal, a secondary path transfer parameter corresponding to the physical error microphone, and a mapping parameter from the physical error microphone to the virtual error point; A third 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.

17. 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 15 is implemented.

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