Noise control method, device and readable storage medium

Abstract

This application discloses a noise control method, apparatus, and readable storage medium, relating to the field of vehicle noise reduction technology. The method includes: acquiring a reference signal and a real error signal collected by a physical error microphone; determining the secondary path transfer parameters corresponding to the physical error microphone 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 based on the real 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; determining the target adaptive filter parameters based on the reference signal, the real error signal, and the virtual error signal; and processing the reference signal based on the target adaptive filter parameters to generate and output a target anti-noise signal through an anti-noise speaker to reduce noise in the target area corresponding to the virtual error point, thereby improving the noise reduction effect in areas far from the physical error microphone.

Description

Technical Field

[0001] This application relates to the field of vehicle noise reduction technology, and in particular to a noise control method, device and readable storage medium. Background Technology

[0002] Road noise refers to the structural and aerodynamic noise generated by the contact between the tires and the road surface during vehicle operation, which is transmitted to the vehicle interior through multiple paths, forming a broadband noise. Road noise can affect the driver's driving state and reduce the comfort of other passengers in the vehicle. Therefore, users have an increasing demand for noise reduction in the vehicle interior.

[0003] Traditional passive noise reduction technology achieves in-vehicle noise reduction through damping, vibration reduction 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 mid-high frequency), specific vehicle speeds or specific areas, and has limited impact on low-frequency noise, making it difficult to meet users' noise reduction needs across the entire frequency band.

[0004] In response, active noise cancellation technology is employed. This technology uses vibration sensors to collect excitation signals between the tires and the road surface, and utilizes error microphones placed inside the vehicle to monitor in-vehicle noise in real time. Based on the excitation signal and the in-vehicle noise, an anti-noise signal with the same amplitude but opposite phase to the in-vehicle noise is generated. This anti-noise signal is then output through a speaker to cancel out the in-vehicle noise, achieving noise reduction across the entire frequency band.

[0005] However, this method has a better noise reduction effect in the area near the error microphone, but a poorer noise reduction effect in the area far away from the error microphone. Summary of the Invention

[0006] This application provides a noise control method, apparatus, and readable storage medium that can improve noise reduction in areas far from error microphones.

[0007] In a first aspect, some embodiments of this application provide a noise control method, comprising: acquiring a reference signal and a real error signal collected by a physical error microphone, wherein the reference signal is a noise source signal outside the vehicle; determining secondary path transfer parameters corresponding to the physical error microphone 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 a virtual error point based on the real 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; determining target adaptive filter parameters based on the reference signal, the real error signal, and the virtual error signal; processing the reference signal based on the target adaptive filter parameters to generate a target anti-noise signal; and outputting the target anti-noise signal through an anti-noise speaker to reduce noise in a target area, wherein the target area is the noise reduction area corresponding to the virtual error point.

[0008] Secondly, some embodiments of this application also provide a noise control device, including: a signal acquisition module for acquiring a reference signal and a real error signal collected by a physical error microphone, wherein the reference signal is a noise source signal outside the vehicle; a first determination module for determining secondary path transmission parameters corresponding to the physical error microphone using a preset calibration wave signal and a target calibration wave signal collected by the physical error microphone; a second determination module for determining a virtual error signal corresponding to a virtual error point based on the real error signal, the secondary path transmission parameters corresponding to the physical error microphone, and the mapping parameters from the physical error microphone to the virtual error point; a third determination module for determining target adaptive filter parameters based on the reference signal, the real error signal, and the virtual error signal; a signal generation module for processing the reference signal based on the target adaptive filter parameters to generate a target anti-noise signal; and a noise reduction module for outputting the target anti-noise signal through an anti-noise speaker to reduce noise in a target area, wherein the target area is the noise reduction area corresponding to the virtual error point.

[0009] Thirdly, some embodiments of this application also provide a noise control system, including: a memory, a processor, and computer instructions stored in the memory and executable on the processor, the computer instructions being configured to implement the steps of the noise control method as described in the first aspect above.

[0010] Fourthly, some embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the noise control method described in the first aspect.

[0011] Fifthly, some embodiments of this application provide a computer program product comprising: computer program code, which, when executed on a display device, causes the display device to perform the noise control method described in the first aspect.

[0012] The beneficial effects of this application embodiment compared with the prior art are as follows: During vehicle operation, the secondary path transfer parameters of the physical error microphone can be measured in real time using a preset calibration wave signal and a target calibration wave signal collected by a physical error microphone arranged inside the vehicle. The obtained secondary path transfer parameters corresponding to the physical error microphone can reflect the current environment of the vehicle. Furthermore, during noise reduction, the real error signal at the physical error microphone is collected using the physical error microphone arranged inside the vehicle. Based on the real-time measured secondary path transfer parameters of the physical error microphone and the mapping parameters 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, which can more accurately simulate the virtual error signal of the virtual error point. 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 the target anti-noise signal. This avoids the problem of the limited noise reduction area of ​​the physical error microphone, improves the noise reduction effect of the virtual error point, and extends the noise reduction area corresponding to the physical error microphone to the virtual error point, thereby improving the noise reduction effect for occupants far from the physical error microphone area and enhancing the user experience. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 A logic block diagram of a noise control method provided in an embodiment of this application;

[0015] Figure 2 A schematic diagram of the internal structure of a vehicle provided in an embodiment of this application;

[0016] Figure 3 One of the flowcharts of the noise control method provided in the embodiments of this application;

[0017] Figure 4 A second schematic flowchart illustrating the noise control method provided in this application embodiment;

[0018] Figure 5 The third schematic flowchart of the noise control method provided in the embodiments of this application;

[0019] Figure 6 Fourth flowchart illustrating the noise control method provided in this application embodiment;

[0020] Figure 7 Fifth of the flowcharts illustrating the noise control method provided in the embodiments of this application;

[0021] Figure 8 Sixth schematic flowchart of the noise control method provided in the embodiments of this application;

[0022] Figure 9 This is a hardware block diagram of a noise control device provided in an embodiment of this application. Detailed Implementation

[0023] The embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described below do not represent all embodiments consistent with this application. They are merely examples of systems and methods consistent with some aspects of this application.

[0024] It should be noted that the brief descriptions of terms in this application are only for the convenience of understanding the embodiments described below, and are not intended to limit the embodiments of this application. Unless otherwise stated, these terms should be understood in their ordinary and common meaning.

[0025] The terms "first," "second," "third," etc., used in this application's description and documentation are used to distinguish similar or related objects or entities, and do not necessarily imply a specific order or sequence, unless otherwise specified. It should be understood that such terms are interchangeable where appropriate.

[0026] The terms “comprising” and “having”, and any variations thereof, are intended to cover but not exclude inclusion, for example, a product or device that includes a range of components is not necessarily limited to all of the components that are clearly listed, but may include other components that are not clearly listed or that are inherent to such product or device.

[0027] The term "module" refers to any known or subsequently developed hardware, software, firmware, artificial intelligence, fuzzy logic, or combination of hardware and / or software code that is capable of performing the functions associated with that element.

[0028] The technical solutions of the embodiments of this application will be described below.

[0029] Road noise refers to the structural and aerodynamic noise generated by the contact between the tires and the road surface during vehicle operation, which is transmitted to the vehicle interior through multiple paths, forming a broadband noise. Road noise can affect the driver's driving state and reduce the comfort of other passengers in the vehicle. Therefore, users have an increasing demand for noise reduction in the vehicle interior.

[0030] Traditional passive noise reduction technology achieves in-vehicle noise reduction through damping, vibration reduction 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 mid-high frequency), specific vehicle speeds or specific areas, and has limited impact on low-frequency noise, making it difficult to meet users' noise reduction needs across the entire frequency band.

[0031] In response, relevant technologies employ active noise cancellation (RNC) to control road noise in real time. For example, RNC technology involves placing vibration sensors near the vehicle's tires to collect excitation signals between the tires and the road surface (i.e., the source excitation signals of road noise). An error microphone inside the vehicle monitors the in-vehicle noise in real time. Based on the excitation signals and the in-vehicle noise, an anti-noise signal with the same amplitude but opposite phase to the in-vehicle noise is generated in real time. This anti-noise signal is then output through a speaker to cancel out the in-vehicle noise, achieving noise reduction across the entire frequency band.

[0032] However, this method achieves effective noise reduction in the area near the error microphone (which can be called the noise reduction "sweet spot"), but the noise reduction effect drops significantly in areas far from the error microphone. For example, placing the error microphone near the car door can lead to reduced noise reduction or even increased noise on the side of the passenger's head away from the error microphone, affecting the passenger's riding experience.

[0033] In some embodiments, increasing the number of error microphones placed inside the vehicle can expand the noise cancellation "sweet spot" and improve the noise cancellation effect; however, this approach leads to increased costs.

[0034] To address the aforementioned technical problems, this application provides a noise control method. During vehicle operation, a preset calibration wave signal and a target calibration wave signal collected by a physical error microphone arranged inside the vehicle are used to measure the secondary path transfer parameters of the physical error microphone in real time. The obtained secondary path transfer parameters corresponding to the physical error microphone reflect the current environment of the vehicle. Furthermore, during noise reduction, the physical error microphone arranged inside the vehicle is used to collect the actual error signal at the physical error microphone. Based on the real-time measured secondary path transfer parameters of the physical error microphone and the mapping parameters from the physical error microphone to the virtual error point, the actual error signal at the physical error microphone is mapped to the virtual error point, enabling a more accurate simulation of the virtual error signal at the virtual error point. Then, based on the reference signal, the actual error signal, and the virtual error signal, 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 avoids the limitation of the noise reduction area of ​​the physical error microphone, improves the noise reduction effect of the virtual error point, and extends the noise reduction area corresponding to the physical error microphone to the virtual error point, thereby improving the noise reduction effect for occupants away from the physical error microphone area and enhancing the user experience.

[0035] The noise control method provided in this application is applied to a noise control system. Before introducing the noise control method provided in this application, the noise control system will be illustrated below.

[0036] Figure 1 This is a schematic diagram of a noise control system provided in an embodiment of this application. Figure 1 As shown, the noise control system may include a vibration sensor, a physical error microphone (also known as a real error microphone), an anti-noise speaker, and a controller (also known as an RNC controller).

[0037] The vibration sensor is mounted on the vehicle's chassis, near the tires. It collects excitation signals generated by the tires' contact with the road surface and the tires' own vibrations, serving as a reference signal for the noise source. For example, the vibration sensor can be an acceleration sensor.

[0038] Optionally, one or more vibration sensors can be used. For example, multiple vibration sensors can be installed on the vehicle chassis. Multiple vibration sensors can collect multiple reference signals.

[0039] The physical error microphone is installed inside the vehicle. The physical error microphone is used to collect the actual error signal at its location. The true error signal refers to the residual noise signal resulting from the superposition of the noise signal transmitted from the noise source outside the vehicle (i.e., the vibration sensor) to the physical error microphone and the anti-noise signal output by the anti-noise speaker inside the vehicle.

[0040] Of course, one or more physical error microphones can be set. For example, Figure 2 As shown, a physical error microphone is placed in the driver's area (such as on the driver's seat), a physical error microphone is placed in the passenger area (such as on the passenger seat), a physical error microphone is placed in the right rear area (such as on the right rear seat), and a physical error microphone is placed in the left rear area (such as on the right left seat).

[0041] Virtual error points are selected for different areas inside the vehicle. For example, ... Figure 2 As shown, two virtual error points are selected in the driver's side area, two virtual error points are selected in the passenger side area, two virtual error points are selected in the right rear area, and two virtual error points are selected in the left rear area.

[0042] Noise-resistant speakers are used to output noise-resistant signals. It's understood that noise-resistant speakers can reuse the speakers in a vehicle's audio-visual entertainment system. That is, noise-resistant speakers can also be used to output audio signals. Additionally, noise-resistant speakers are also used to output preset calibration waveform signals.

[0043] like Figure 2 As shown, a noise-canceling speaker is placed in the driver's area (such as on the driver's seat), a noise-canceling speaker is placed in the passenger area (such as on the passenger seat), a noise-canceling speaker is placed in the right rear area (such as on the right rear seat), a noise-canceling speaker is placed in the left rear area (such as on the right left seat), and a noise-canceling speaker is placed near the rear window.

[0044] like Figure 1 As 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 collect external environment information. Such as road type information. The in-vehicle environment information acquisition module M2 is used to collect in-vehicle environment information and vehicle status information (collectively referred to as...). ).

[0045] The external environment information acquisition module includes, but is not limited to, external LiDAR, millimeter-wave radar, GPS, external cameras, temperature sensors, rain sensors, and other external condition recognition sensor modules. The internal environment information acquisition module M2 includes, but is not limited to, seat pressure sensors, internal cameras, motor rotary encoders, CAN signals, and other internal environment monitoring sensors.

[0046] like Figure 1 As shown, the noise control system may further include a data update module M3, a rapid response module M4, a noise reduction parameter module M5, a dynamic calibration module M6, and a fast dynamic virtual sensing module FD-VS. 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 rapid response module M4 is used to match the 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) based on environmental information. The noise reduction parameter module M5 is used to update the adaptive filter parameters. The dynamic calibration module M6 is used to generate calibration wave signals. The fast dynamic virtual sensing module FD-VS is used to determine the virtual error signal of the virtual error point based on the target virtual sensing parameters and the real error signal.

[0047] During the noise reduction process, the external environment information acquisition module M1 collects information about the external environment. The in-vehicle environment information acquisition module M2 is used to collect in-vehicle environment information and vehicle status information (collectively referred to as...). The data update module M3 is used to acquire information about the external environment of the vehicle. In-vehicle environment information and vehicle status information (collectively referred to as...) ), and send external environment information to the rapid response module M4. In-vehicle environment information and vehicle status information (collectively referred to as...) The rapid response module M4, based on information about the external environment, In-vehicle environment information and vehicle status information (collectively referred to as...) The target noise reduction parameters are determined, including the convergence step size. The fast response module M4 sends the target noise reduction parameters to the noise reduction parameter module M5. The dynamic calibration module M6 generates the calibration waveform signal. And control the noise-canceling speaker to output a preset calibration wave signal. The Fast Dynamic Virtual Sensing Module (FD-VS) uses the initial noise immunity signal output by the noise immunity speaker. Secondary path transfer parameters of the physical error microphone Determine the secondary sound field signal corresponding to the physical error microphone. Based on the actual error signal and the secondary sound field signal of the microphone due to physical error. And mapping parameters, determine the primary sound field signal of the virtual error point. Secondary sound field signal of virtual error points The primary sound field signal of the virtual error point Secondary sound field signal of virtual error points By superimposing the signals, the virtual error signals of the virtual error points are obtained. According to the secondary path transmission of the microphone based on physical error. Secondary path transfer parameters corresponding to virtual error points For reference signal The reference signal is processed (i.e., filtered) to obtain the processed reference signal. Using the LMS algorithm, based on the true error signal... Virtual error signal Processed reference signal The convergence step size is adjusted, and the adaptive filter parameters are updated. The noise reduction parameter module M5 applies the adaptive filter parameters to the processed reference signal. The signal is processed to generate the target anti-noise signal. The noise reduction parameter module M5 controls the anti-noise speaker to output the target anti-noise signal through the power amplifier chip.

[0048] The noise control method provided in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0049] Figure 3 This is a flowchart illustrating a noise control method provided in an embodiment of this application. Figure 3 As shown, the noise control method may include the following steps:

[0050] S301. Acquire the reference signal and the actual error signal collected by the physical error microphone. The reference signal is the noise source signal outside the vehicle.

[0051] The reference signal refers to a signal related to external noise. In this embodiment, the reference signal (i.e., the noise source signal) refers to the excitation signal generated by the contact between the vehicle's tires and the road surface, as well as the vibration of the tires themselves, collected by a vibration sensor.

[0052] For example, when the vehicle starts, the controller in the noise control system acquires a reference signal collected by the vibration sensor. For example, during vehicle operation, the controller acquires the reference signal collected by the vibration sensor. It is understood that the acquired reference signal is the reference signal collected by the vibration sensor at the current moment. Furthermore, the reference signal refers to a time-domain reference signal.

[0053] In some examples, such as the description of the noise control system above, the noise control system may include multiple vibration sensors mounted on the vehicle chassis and located near the tires. For example, one vibration sensor may be positioned near each tire. When executing S301, in the case of multiple vibration sensors, multiple reference signals collected by the multiple vibration sensors at the current moment are acquired. These multiple reference signals can be understood as multi-channel reference signals.

[0054] It should be noted that the number of reference signal channels is related to the number of vibration sensors deployed. In practical applications, the number of vibration sensors can be set according to actual needs. Therefore, in this embodiment, the number of reference signal channels is not specifically limited.

[0055] The true error signal refers to the residual noise signal resulting from the superposition of the in-vehicle noise signal and the secondary sound field signal at the physical error microphone. The in-vehicle noise signal at the physical error microphone is the noise signal transmitted to the physical error microphone from the external noise source signal (i.e., the excitation signal collected by the vibration sensor). The secondary sound field signal at the physical error microphone is the sound field signal generated at the physical error microphone by the initial anti-noise signal output by the anti-noise speaker. 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 activated.

[0056] For example, when the vehicle starts, the controller in the noise control system acquires the actual error signal collected by the physical error microphone. For example, during vehicle operation, the controller acquires the actual error signal collected by the physical error microphone. It is understood that the acquired actual error signal is the actual error signal collected by the physical error microphone at the current moment. Furthermore, the actual error signal refers to the actual error signal in the time domain.

[0057] In some examples, such as the description above regarding noise control systems, multiple physical error microphones can be arranged inside the vehicle. For instance, physical error microphones can be arranged for different areas within the vehicle. Figure 2 As shown, a physical error microphone is placed in the driver's area (e.g., on the driver's seat), a physical error microphone is placed in the passenger's area (e.g., on the passenger's seat), a physical error microphone is placed in the right rear area (e.g., on the right rear seat), and a physical error microphone is placed in the left rear area (e.g., on the right left seat). When executing S301, for the case where multiple physical error microphones are set, multiple real error signals collected by the multiple physical error microphones at the current time are acquired. These multiple real error signals can be understood as multi-channel real error signals.

[0058] It should be noted that the number of channels for the actual error signal is related to the number of physical error microphones deployed. In practical applications, the number of physical error microphones can be set according to actual needs. Therefore, in this embodiment, the number of channels for the actual error signal is not specifically limited.

[0059] In some examples, based on the in-vehicle environment information, the area to be noise-reduced is determined, and the physical error microphone located in the area to be noise-reduced from among multiple physical error microphones is identified as the target physical error microphone, and the real error signal collected by the target physical error microphone is obtained.

[0060] S302. Using the preset calibration wave signal and the target calibration wave signal collected by the physical error microphone, determine the secondary path transmission parameters corresponding to the physical error microphone.

[0061] The preset calibration wave signal is an audio signal used to measure the physical error of the microphone. Optionally, the preset calibration wave signal may include audio signals with preset frequency band (low frequency) information.

[0062] The target calibration wave signal refers to the sound signal currently collected by the physical error microphone when the noise-canceling speaker outputs a preset calibration wave signal. In this embodiment, the secondary path transfer parameters corresponding to the physical error microphone are related to the vehicle's environment, that is, the target virtual sensing parameters change with the vehicle's environment. Therefore, when determining the secondary path transfer parameters corresponding to the physical error microphone, the noise-canceling 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 can be adaptively processed, and then the secondary path transfer parameters corresponding to the physical error microphone can be determined based on 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 noise-canceling speaker and the target calibration wave signal currently collected by the physical error microphone can be used to measure the secondary path transfer parameters corresponding to the physical error microphone in real time. The obtained secondary path transfer parameters corresponding to the physical error microphone can reflect the current environment of the vehicle (such as the external environment, the internal environment and the vehicle status). Based on the real-time measured secondary path transfer parameters corresponding to the physical error microphone, the virtual error signal of the virtual error point can be simulated more accurately.

[0063] In one possible implementation, when the noise-canceling speaker outputs a first audio signal (i.e., when the vehicle's audio-visual entertainment system is working), the first audio signal output by the noise-canceling speaker of the vehicle is acquired; if the first audio signal includes preset frequency band information, the first audio signal is used as a preset calibration wave signal, and the target calibration wave signal currently collected by the physical error microphone is acquired; based on the preset calibration wave signal and the target calibration wave signal, the secondary path transmission parameters corresponding to the physical error microphone are determined.

[0064] It is understandable that the speakers of the vehicle's in-vehicle audio-visual entertainment system can be used as noise-canceling speakers, meaning that the vehicle's in-vehicle speakers are used to output audio signals from the audio-visual entertainment system, as well as noise-canceling signals (including an initial noise-canceling signal). For example, the first audio signal refers to the audio signal currently output by the noise-canceling speaker. For instance, the audio signal output by the noise-canceling speaker when the audio-visual entertainment system is playing media resources. These media resources could be, for example, music media resources, news media resources, navigation voice information, etc.

[0065] In another possible implementation, if the first audio signal does not include preset frequency band information, the noise-canceling speaker is controlled to output a preset calibration wave signal, and the target calibration wave signal currently collected by the physical error microphone is acquired; based on the preset calibration wave signal and the target calibration wave signal, the secondary path transmission parameters corresponding to the physical error microphone are determined.

[0066] In another possible implementation, when the noise-canceling speaker is not outputting the first audio signal (i.e., when the vehicle's audio-visual entertainment system is not working), the noise-canceling speaker is controlled to output a preset calibration wave signal, and the target calibration wave signal currently collected by the physical error microphone is acquired. Based on the preset calibration wave signal and the target calibration wave signal, the secondary path transmission parameters corresponding to the physical error microphone are determined. It can be understood that when controlling the noise-canceling speaker to output the preset calibration wave signal, the preset calibration wave signal is superimposed on the first audio signal for output.

[0067] In this embodiment, when the secondary path transfer parameters corresponding to the physical error microphone are measured in real time using a preset calibration wave signal, the first audio signal currently output by the noise-canceling speaker can be obtained. When the first audio signal includes preset frequency band information, the secondary path transfer parameters corresponding to the physical error microphone are determined based on the first audio signal and the target calibration wave signal currently collected by the physical error microphone. In this way, the secondary path transfer parameters matching the current environment of the vehicle can be measured in real time, and there is no need to control the noise-canceling speaker to output other calibration wave signals, so as not to affect the audio-visual entertainment experience of the occupants in the vehicle.

[0068] Furthermore, when the first audio signal does not include information from a preset frequency band, or when the noise-canceling speaker is not outputting the first audio signal, the noise-canceling speaker can be controlled to superimpose a preset calibration wave signal onto the first audio signal. This allows the use of the preset calibration wave signal and the target calibration wave signal currently collected by the physical error microphone to determine the secondary path transmission parameters corresponding to the physical error microphone, improving the accuracy of parameter determination. Simultaneously, this will not affect the audio-visual entertainment experience of the vehicle occupants.

[0069] S303. Based on the real error signal, the secondary path transmission parameters corresponding to the physical error microphone, and the mapping parameters from the physical error microphone to the virtual error point, determine the virtual error signal corresponding to the virtual error point.

[0070] Specifically, the target virtual sensing parameters are obtained, including the mapping parameters from the physical error microphone to the virtual error point; based on the real error signal, the secondary path transmission parameters corresponding to the physical error microphone, and the mapping parameters from the physical error microphone to the virtual error point, the virtual error signal corresponding to the virtual error point is determined.

[0071] Virtual error points refer to measurement points of error signals that are far from the location of the physical error microphone, i.e., virtual error points located outside the noise reduction area corresponding to the physical error microphone.

[0072] Optionally, one or multiple virtual error points can be set. In practical applications, virtual error points can be selected according to actual needs. For example, virtual error points can be selected for different areas inside the vehicle. Figure 2 As shown, two virtual error points are selected in the driver's side area, two virtual error points are selected in the passenger side area, two virtual error points are selected in the right rear area, and two virtual error points are selected in the left rear area.

[0073] The virtual error signal refers to the error signal collected from a simulated virtual error point. In other words, the virtual error signal is the residual noise signal resulting from the superposition of the in-vehicle noise signal and the secondary sound field signal at the virtual error point. Specifically, the in-vehicle noise signal at the virtual error point is the noise signal transmitted to the virtual error point from the external noise source signal (i.e., the excitation signal collected by the vibration sensor). The secondary sound field signal at the virtual error point is the sound field signal generated at the virtual error point by the initial noise reduction signal output by the noise-reducing speaker.

[0074] The mapping parameters from the physical error microphone to the virtual error point can include secondary path mapping parameters and primary path mapping parameters. Secondary path mapping parameters refer to the transmission characteristics of the noise-resistant signal emitted by the noise-resistant loudspeaker from the physical error microphone to the virtual error point. Primary path mapping parameters refer to the transmission characteristics of the noise source signal from the physical error microphone to the virtual error point.

[0075] In this embodiment, unlike the secondary path transmission parameters corresponding to the physical error microphone, determining the mapping parameters from the physical error microphone to the virtual error point requires deploying an error microphone at the virtual error point. The mapping parameters are then determined based on the actual error signal collected by the physical error microphone and the error signal collected by the error microphone at the virtual error point. However, in actual noise reduction, error microphones are not deployed at the virtual error point. In other words, the mapping parameters from the physical error microphone to the virtual error point cannot be measured in real time. Therefore, in this embodiment, the mapping parameters from the physical error microphone to the virtual error point can be obtained in advance.

[0076] In some examples, the mapping parameters from the physical error microphone to the virtual error point are related to the vehicle's environment; that is, the mapping parameters change with the vehicle's environment. Therefore, different mapping parameters can be pre-set based on different vehicle environments. During noise reduction, environmental information about the vehicle's environment can be acquired, and the mapping parameters from the physical error microphone to the virtual error point can be determined based on this information. Thus, during vehicle movement, by determining the mapping parameters from the physical error microphone to the virtual error point based on the environmental information, and then using the secondary path transmission parameters corresponding to the physical error microphone and the mapping parameters from the physical error microphone to the virtual error point, the virtual error signal of the virtual error point can be determined. This improves the accuracy of the virtual error signal determination, thereby enhancing the noise reduction effect on the target area corresponding to the virtual error point.

[0077] In one possible implementation, when mapping parameters from the physical error microphone to the virtual error point, environmental information about the vehicle's environment can be obtained, and the mapping parameters from the physical error microphone to the virtual error point can be determined based on the environmental information and a pre-trained first parameter prediction model.

[0078] In this embodiment of the 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 data processing efficiency and obtain the mapping parameters from the physical error microphone to the virtual error point more quickly.

[0079] The process of determining the mapping parameters from the physical error microphone to the virtual error point based on environmental information and a pre-trained first-parameter prediction model can be found in the section below on determining the mapping parameters from the physical error microphone to the virtual error point; it will not be repeated here.

[0080] In some examples, determining the virtual error signal corresponding to the virtual error point based on the real 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 based on the initial noise immunity signal output by the noise immunity speaker and the secondary path transfer parameters of the physical error microphone; determining the secondary sound field signal of the virtual error point based on the secondary sound field signal of the physical error microphone and the secondary path mapping parameters; determining the primary sound field signal of the virtual error point based on the real error signal, the primary path mapping parameters, and the secondary sound field signal of the physical error microphone; and determining the virtual error signal based on the secondary sound field signal and the primary sound field signal of the virtual error point.

[0081] The specific implementation method for determining the virtual error signal can be found in the following text on the determination process of the virtual error signal (method 1), which will not be repeated here.

[0082] In other examples, the target virtual sensing parameters include secondary path transfer parameters corresponding to the virtual error point and primary path mapping parameters from the physical error microphone to the virtual error point.

[0083] In other words, the secondary sound field signal of the virtual error point can be determined based on the initial noise immunity signal output by the noise immunity speaker and the secondary path transmission parameters of the virtual error point; the primary sound field signal of the virtual error point can be determined based on the real error signal, the primary path mapping parameters, and the secondary sound field signal of the physical error microphone; and the virtual error signal can be determined based on the secondary sound field signal and the primary sound field signal of the virtual error point.

[0084] The specific implementation method for determining the virtual error signal can be found in the following text, which describes the determination process of the virtual error signal (method 2). It will not be repeated here.

[0085] Among them, the primary path mapping parameter refers to the transmission characteristic parameter of the noise source signal from the physical error microphone to the virtual error point.

[0086] It is understandable that the primary path mapping parameters are related to the vehicle's environment; that is, the primary path mapping parameters change with the vehicle's environment. Therefore, different primary path mapping parameters can be pre-set according to different vehicle environments. During noise reduction, environmental information of the vehicle's environment can be acquired, and the primary path mapping parameters can be determined based on this information. Thus, during vehicle movement, the primary path mapping parameters are determined based on the environmental information. Then, using the secondary path transfer parameters corresponding to the virtual error point and the primary path mapping parameters from the physical error microphone to the virtual error point, the virtual error signal of the virtual error point can be determined. This improves the accuracy of virtual error signal determination, thereby enhancing the noise reduction effect in the target area corresponding to the virtual error point.

[0087] In one possible implementation, when determining the primary path mapping parameters, environmental information of the vehicle's environment can be obtained, and the primary path mapping parameters can be determined based on the environmental information and the pre-trained first parameter prediction model.

[0088] In this embodiment, a pre-trained first parameter prediction model can be used to determine the primary path mapping parameters, which can improve data processing efficiency and obtain the mapping parameters from the physical error microphone to the virtual error point more quickly.

[0089] The process of determining the primary path mapping parameters based on environmental information and a pre-trained first-parameter prediction model can be found in the section below on determining the mapping parameters from the physical error microphone to the virtual error point; this will not be repeated here.

[0090] The secondary path transfer parameters of a virtual error point refer to the transfer characteristic parameters from the noise-canceling loudspeaker to the virtual error point. It can be understood that the secondary path transfer parameters of a virtual error point are related to the vehicle's environment; that is, the secondary path transfer parameters of a virtual error point change with the vehicle's environment. Therefore, different secondary path transfer parameters of virtual error points can be pre-set according to different vehicle environments. During noise reduction, environmental information about the vehicle's environment can be obtained, and the secondary path transfer parameters of the virtual error points can be determined based on this information.

[0091] For example, using environmental information and a pre-trained fourth-parameter prediction model, the secondary path propagation parameters of virtual error points are determined.

[0092] The process of determining the secondary path propagation parameters of virtual error points based on environmental information and a pre-trained fourth-parameter prediction model can be found in the section below on the determination process of secondary path propagation parameters of virtual error points, and will not be repeated here.

[0093] S304. Determine the target adaptive filter parameters based on the reference signal, the real error signal, and the virtual error signal.

[0094] Here, the adaptive filter coefficients refer to the weighting coefficients of the adaptive filter. The adaptive filter parameters are used to track the characteristics of the reference signal and the error signal (real error signal and virtual error signal) in real time to generate an inverse cancellation signal (i.e., an anti-noise signal). Correspondingly, the target adaptive filter parameters are dynamically adjusted based on the reference signal, real error signal, and virtual error signal at the current moment; that is, the adaptive filter parameters at the current moment.

[0095] For example, the adaptive filter coefficients can be dynamically adjusted using an adaptive filtering algorithm to obtain the target adaptive filter parameters, thereby minimizing the error between the noise signal and the reference signal.

[0096] Optionally, the adaptive filtering algorithm may be, for example, the Filtered-X Least Mean Squares (Fxlms) algorithm, the Filtered-X Normalized Least Mean Squares (Fxnlms) algorithm, or the Filtered-X Recursive Least Squares (Fxrls) algorithm. This application does not specifically limit the type of multi-channel adaptive filtering algorithm used in its embodiments.

[0097] In some examples, determining the target adaptive filter parameters based on the reference signal, the true error signal, and the virtual error signal may include: obtaining initial adaptive filter parameters and target noise reduction parameters, the target noise reduction parameters including the convergence step size; and adjusting the initial adaptive filter parameters based on the reference signal, the true error signal, the virtual error signal, and the convergence step size to obtain the target adaptive filter parameters.

[0098] The initial adaptive filter parameters can be preset. The initial adaptive filter parameters can also be understood as the adaptive filter parameters from the previous time step.

[0099] Understandably, after obtaining the initial adaptive filter parameters, these parameters can be adjusted based on the reference signal, the true error signal, and the virtual error signal to obtain the target adaptive filter parameters. The reference signal, the true error signal, and the virtual error signal are related to the vehicle's environment. Changes in the vehicle's environment will cause changes in the acquired reference signal, the acquired true error signal, and the simulated virtual error signal. Therefore, when the vehicle's environment changes, the target adaptive filter parameters from the previous moment can be adjusted based on the currently acquired reference signal, the acquired true error signal, and the simulated virtual error signal to obtain the target adaptive filter parameters for the current moment. In other words, the target adaptive filter parameters are continuously iterated during vehicle operation to ensure good noise reduction performance despite changes in the vehicle's environment.

[0100] For example, the target adaptive filter parameters can be calculated according to the following formulas (1) and (2):

[0101] (1)

[0102] in, This represents the error signal matrix formed by the conjugation of the real error signal and the virtual error signal; This represents the actual error signal collected by the physical error microphone at the current moment. The virtual error signal representing the virtual error point at the current moment; The secondary sound field signal represents the virtual error point at the current moment; n represents the discrete time index, i.e., the current moment, which is also the moment when the current reference signal and the real error signal are collected.

[0103] (2)

[0104] in, This represents the target adaptive filter parameters (i.e., the adaptive filter parameters at the current moment). This represents the initial adaptive filter parameters (i.e., the adaptive filter parameters from the previous time step before the update). This represents the reference signal matrix (i.e., the reference signal for multiple channels). This represents the convergence step size of the adaptive filter parameters; This represents the error signal matrix formed by the conjugation of the real error signal and the virtual error signal.

[0105] In some examples, the target noise reduction parameters may also include a first weighting parameter (also called a first pre-weighting parameter). The value of the first weighting parameter is related to the vehicle's environment at the current moment when the reference signal is acquired. Specifically, on the one hand, when the vehicle's environment (such as the external environment) 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 broadband impact noise will increase. Similarly, under some severe weather conditions (such as strong winds, heavy rain, etc.), the energy of the noise source signal will also increase significantly. On the other hand, when the vehicle's environment (such as the external environment and / or the internal environment) changes, the acoustic propagation path (i.e., the transmission path) will also change, which will cause changes in the signal (noise source signal or anti-noise signal) transmitted to the physical error microphone or virtual error point. Therefore, before determining the target adaptive filter parameters, the first weighting parameter can be determined to weight the reference signal according to the first weighting parameter.

[0106] In some examples, when determining the parameters of the target adaptive filter, the reference signal is weighted according to the first weighting parameter to obtain the weighted reference signal; the parameters of the target adaptive filter are then determined based on the real error signal, the virtual error signal, and the weighted reference signal.

[0107] Optionally, the first weighting parameter is related to the environmental information of the vehicle's environment. Therefore, when determining the first weighting parameter, it can also be determined based on the environmental information of the vehicle's environment.

[0108] It is understandable that when the reference signal is a multi-channel reference signal, a corresponding first weighting parameter can be determined for each channel of the reference signal, that is, the first weighting parameter matrix corresponding to the multi-channel reference signal can be determined. The specific implementation method for determining the first weighting parameter of the reference signal can be found in the relevant explanation of the weighting parameter determination process below, and will not be repeated here.

[0109] Optionally, before weighting the reference signal according to the first weighting parameter to obtain the weighted reference signal, the first weighting parameter is processed according to the primary path transmission parameter corresponding to the physical error microphone to obtain the processed first weighting parameter; the reference signal is then weighted according to the processed first weighting parameter to obtain the weighted reference signal.

[0110] In this example, the target adaptive filter parameters can be calculated according to the following formula (3):

[0111] (3)

[0112] in, This represents the target adaptive filter parameters (i.e., the adaptive filter parameters at the current moment). This represents the initial adaptive filter parameters (i.e., the adaptive filter parameters from the previous time step before the update). This represents the reference signal matrix (i.e., the reference signal for multiple channels). This represents the convergence step size of the adaptive filter parameters; This represents the first weighted parameter matrix corresponding to the multi-channel reference signal; This represents the error signal matrix formed by the conjugation of the real error signal and the virtual error signal.

[0113] Among them, the primary path transmission parameters corresponding to the physical error microphone refer to the transmission characteristic parameters from the noise source to the physical error microphone.

[0114] In some examples, the primary path transfer parameters corresponding to the physical error microphone are related to the vehicle's environment; that is, the primary path transfer parameters of the physical error microphone change with the vehicle's environment. Therefore, different primary path transfer parameters corresponding to the physical error microphone can be pre-set according to different vehicle environments. During noise reduction, environmental information of the vehicle's environment can be acquired, and the primary path transfer parameters corresponding to the physical error microphone can be determined based on this information. Thus, during vehicle operation, the primary path transfer parameters corresponding to the physical error microphone are determined based on the environmental information of the vehicle's environment. Then, the primary path transfer parameters corresponding to the physical error microphone and the first weighting parameter are used to weight the currently acquired reference signal. Based on the weighted reference signal, the real error signal, and the virtual error signal, the adaptive filter parameters are updated. In updating the adaptive filter parameters, the influence of various factors such as the actual road noise source excitation characteristics, the contribution degree of each channel, and the driver's position is comprehensively considered. The reference signals of each channel are amplified or attenuated, thereby improving the noise reduction effect. Determining the virtual error signal of the virtual error point can improve the accuracy of virtual error signal determination, thereby improving the noise reduction effect of the target area corresponding to the virtual error point.

[0115] In one possible implementation, when obtaining the primary path transfer parameters corresponding to the physical error microphone, environmental information of the vehicle's environment can be obtained, and the primary path transfer parameters corresponding to the physical error microphone can be determined based on the environmental information and the pre-trained third parameter prediction model.

[0116] In this embodiment, a pre-trained third-parameter prediction model can be used to determine the primary path transmission parameters corresponding to the physical error microphone, which can improve data processing efficiency and obtain the primary path transmission parameters corresponding to the physical error microphone more quickly.

[0117] In some examples, before weighting the reference signal according to the first weighting parameter to obtain the weighted reference signal, the method further includes filtering the reference signal to obtain a filtered reference signal; and weighting the filtered reference signal according to the first weighting parameter to obtain the weighted reference signal.

[0118] In some examples, the target noise reduction parameters may also include a second weighting parameter (also known as a second pre-weighting parameter). Therefore, before determining the target adaptive filter parameters, the second weighting parameter can be determined to weight the true error signal according to the second weighting parameter, resulting in a weighted true error signal.

[0119] In this embodiment, on the one hand, the energy of the noise source signal (i.e., road noise) changes when the vehicle's environment (e.g., the external environment). For example, when the vehicle travels on a bumpy road, the energy of broadband impact noise increases. Similarly, under adverse weather conditions (e.g., strong winds, heavy rain), the energy of the noise source signal also increases significantly. On the other hand, when the vehicle's environment (e.g., the external environment and / or the internal environment) changes, the acoustic propagation path (i.e., the transmission path) also changes, leading to changes in the signal transmitted to the physical error microphone (noise source signal or anti-noise signal). Furthermore, the characteristics of the actual error signal itself also affect the noise reduction effect. For example, if the amplitude of the actual error signal is too large, it will affect the accuracy of the adaptive filter parameter determination, thus affecting the noise reduction effect. In other words, the second weighting parameter is related to both the vehicle's environment and the signal characteristics of the actual error signal itself. Therefore, the second weighting parameter can be set for the actual error signal based on the environmental information of the vehicle's environment and the signal characteristics of the actual error signal.

[0120] It is understandable that when the true error signal is a multi-channel true error signal, the corresponding second weighting parameter can be determined for each channel's true error signal, that is, the second weighting parameter matrix corresponding to the multi-channel true error signal can be determined.

[0121] 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 a filtered true error signal; and then weighting the filtered true error signal according to the second weighting parameter to obtain a weighted true error signal. This can filter out signals in the true error signal that are outside the set range (such as the amplitude range of a loudspeaker), improve the accuracy of determining the target adaptive filter parameters, and thus enhance the noise reduction effect.

[0122] In some examples, when determining the virtual error signal corresponding to a virtual error point, the virtual error signal can be determined based on the weighted real 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. This can improve the accuracy of predicting the virtual error signal corresponding to the virtual error point, thereby enhancing the noise reduction effect of the virtual error point.

[0123] Optionally, after determining the virtual error signal corresponding to the virtual error point, the method further includes: filtering the virtual error signal to obtain a filtered virtual error signal. This can filter out signals in the virtual error signal that are not within a set range (such as the amplitude range of a loudspeaker), improve the accuracy of determining the target adaptive filter parameters, and thus enhance the noise reduction effect.

[0124] In other words, when determining the target adaptive filter parameters based on the reference signal, the true error signal, and the virtual error signal, the reference signal can be processed according to the first weighting parameter to obtain the weighted reference signal; the true error signal can be processed according to the second weighting parameter to obtain the weighted true error signal; the virtual error signal corresponding to the virtual error point can be determined according to the 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; and the target adaptive filter parameters can be determined according to the weighted reference signal, the weighted true error signal, and the virtual error signal of the virtual error point.

[0125] In some examples, the target adaptive filter parameters are determined based on the weighted reference signal, the weighted true error signal, and the virtual error signal. This includes: processing the weighted reference signal based on the primary path transfer parameters corresponding to the physical error microphone to obtain the target reference signal; and determining the target adaptive filter parameters based on the target reference signal, the weighted true error signal, and the target virtual error signal.

[0126] It is understandable that, based on the above description of the 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. In other words, in some examples, the environmental information of the vehicle's environment can be obtained, and the target noise reduction parameters can be determined based on the environmental information of the vehicle's environment. The target noise reduction parameters may include the convergence step size, the first weighting parameter, and the second weighting parameter.

[0127] Optionally, the target noise reduction parameters may also include leakage factor, signal gain ratio, and phase compensation factor. The leakage factor is a coefficient attenuation factor introduced during adaptive filtering to prevent filter coefficients from overflowing or diverging due to continuous updates. The signal gain ratio refers to the amplitude adjustment ratio of the target signal and the residual noise signal by the noise control system. The phase compensation factor is a correction parameter for the signal phase delay to ensure that the noise control system's anti-noise signal is accurately aligned with the in-vehicle noise signal.

[0128] In some examples, when determining the target noise reduction parameters based on environmental information of the vehicle's environment, the target noise reduction parameters can be determined based on a pre-trained second parameter prediction model and environmental information.

[0129] The specific implementation method for determining the target noise reduction parameters can be found in the process of determining the target noise reduction parameters below, and will not be repeated here.

[0130] S305. Based on the target adaptive filter parameters, the reference signal is processed to generate the target anti-noise signal.

[0131] S306. Output the target noise reduction signal through the noise reduction speaker to reduce noise in the target area, where the target area is the noise reduction area corresponding to the virtual error point.

[0132] For example, the target area can be... Figure 2 The light gray area shown.

[0133] In this embodiment, a physical error microphone arranged inside the vehicle is used to collect the actual error signal at the physical error microphone. Using a preset calibration wave signal and the target calibration wave signal collected by the physical error microphone, the secondary path transmission parameters of the physical error microphone are determined. Based on the secondary path transmission parameters of the physical error microphone and the mapping parameters from the physical error microphone to the virtual error point, the actual error signal at the physical error microphone is mapped to the virtual error point, thus simulating the virtual error signal of the virtual error point. Then, based on the reference signal, the actual error signal, and the virtual error signal, the target adaptive filter parameters are determined. Based on the target adaptive filter parameters, the reference signal is processed to generate the target anti-noise signal. This avoids the problem of limited noise reduction area of ​​the physical error microphone, improves the noise reduction effect of the virtual error point, and extends the noise reduction area corresponding to the physical error microphone to the virtual error point, thereby improving the noise reduction effect for occupants away from the physical error microphone area and enhancing the user experience.

[0134] The above is a schematic description of the overall scheme of the noise control method provided in the embodiments of this application. The following sections will describe in detail the determination process of the virtual error signal, the determination process of the mapping parameters from the physical error microphone to the virtual error point, the determination process of the primary path transfer parameters corresponding to the physical error microphone, the determination process of the secondary path transfer parameters corresponding to the virtual error point, and the determination process of the target noise reduction parameters.

[0135] 1. The process of determining the virtual error signal

[0136] Method 1: The target virtual sensing parameters include 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. Based on the target virtual sensing parameters and the real error signal, the virtual error signal corresponding to the virtual error point is determined (i.e., S303 above). This can include: determining the secondary sound field signal of the physical error microphone based on the initial noise immunity signal output by the noise immunity speaker and the secondary path transfer parameters of the physical error microphone; determining the secondary sound field signal of the virtual error point based on the secondary sound field signal of the physical error microphone and the secondary path mapping parameters; determining the primary sound field signal of the virtual error point based on the real error signal, the primary path mapping parameters, and the secondary sound field signal of the physical error microphone; and determining the virtual error signal based on the secondary sound field signal and the primary sound field signal of the virtual error point.

[0137] For example, such as Figure 4 As shown, determining the virtual error signal corresponding to the virtual error point based on the target virtual sensing parameters and the real error signal can include the following steps:

[0138] S401. Determine the secondary sound field signal of the physical error microphone based on the initial noise immunity signal output by the noise immunity speaker and the secondary path transmission parameters of the physical error microphone.

[0139] The initial noise immunity signal can be the noise immunity signal output by the noise immunity loudspeaker based on the initial noise reduction parameters and the reference signal.

[0140] The secondary sound field signal of the physical error microphone is the sound field signal generated at the physical error microphone by the initial noise immunity signal output by the noise immunity speaker.

[0141] It is understandable that the secondary path transmission parameters of the physical error microphone will change with the changes in the vehicle's environment.

[0142] Specifically, the secondary sound field signal of the physical error microphone can be calculated according to the following formula (4):

[0143] (4)

[0144] in, The secondary sound field signal of the microphone represents the physical error at the current moment; This represents the initial noise immunity signal output by the noise immunity loudspeaker; This represents the secondary path transmission parameter of the physical error microphone; n represents the discrete time index, i.e., the current time, which is the time when the current reference signal and the actual error signal are acquired.

[0145] S402. Determine the secondary sound field signal of the virtual error point based on the secondary sound field signal and secondary path mapping parameters of the physical error microphone.

[0146] 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 loudspeaker at the virtual error point.

[0147] Understandably, the secondary path mapping parameters will change as the vehicle's environment changes.

[0148] Specifically, the secondary sound field signal of the virtual error point can be calculated according to the following formula (5):

[0149] (5)

[0150] in, The secondary sound field signal representing the virtual error point at the current moment; The secondary path mapping parameters represent the path from the physical error microphone to the virtual error point; The secondary sound field signal of the microphone represents the physical error at the current moment; n represents the discrete time index, i.e., the current moment, which is the moment when the current reference signal and the actual error signal are collected.

[0151] Optionally, when determining the secondary sound field signal of the virtual error point based on the secondary sound field signal and secondary path mapping parameters of the physical error microphone, the secondary sound field signal of the virtual error point is determined based on the secondary sound field signal of the physical error microphone, the secondary path mapping parameters, and the number of time delay sampling points.

[0152] Specifically, the secondary sound field signal of the virtual error point can be calculated according to the following formula (6):

[0153] (6)

[0154] in, The secondary sound field signal representing the virtual error point at the current moment; The secondary path mapping parameters represent the path from the physical error microphone to the virtual error point; The secondary sound field signal of the microphone represents the physical error at the current moment; This indicates the number of time delay sampling points; n represents the discrete time index, i.e., the current time, which is the time when the current reference signal and the actual error signal are collected.

[0155] In this embodiment, considering that the noise immunity signal transmitted to the virtual error point may be transmitted earlier than the noise immunity signal transmitted to the physical error microphone during the actual sound signal transmission process, when determining the secondary sound field signal of the virtual error point, the secondary sound field signal of the physical error microphone, the secondary path mapping parameters, and the number of time delay sampling points can be combined to determine the secondary 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 mapping the virtual error signal more accurately.

[0156] S403. Determine the primary sound field signal of the physical error microphone based on the actual error signal and the secondary sound field signal of the physical error microphone.

[0157] Among them, the primary sound field signal of the physical error microphone is the noise signal transmitted from the noise source signal to the physical error microphone.

[0158] Specifically, the primary sound field signal of the physical error microphone can be calculated according to the following formula (7):

[0159] (7)

[0160] in, The primary sound field signal of the microphone represents the physical error at the current moment; This represents the actual error signal collected by the physical error microphone at the current moment. The secondary sound field signal of the microphone represents the physical error at the current moment; n represents the discrete time index, i.e., the current moment, which is the moment when the current reference signal and the actual error signal are collected.

[0161] S404. Determine the primary sound field signal of the virtual error point based on the primary sound field signal and primary path mapping parameters of the physical error microphone.

[0162] Among them, the primary sound field signal of the virtual error point is the noise signal transmitted from the noise source to the virtual error point.

[0163] Understandably, the primary path mapping parameters will change as the vehicle's environment changes.

[0164] Specifically, the primary sound field signal of the virtual error point can be calculated according to the following formula (8):

[0165] (8)

[0166] in, The primary sound field signal representing the virtual error point at the current moment; The primary path mapping parameters represent the physical error microphone to the virtual error point; The primary sound field signal of the microphone represents the physical error at the current moment; n represents the discrete-time index, i.e., the current moment, which is also the moment when the current reference signal and the actual error signal are acquired.

[0167] Optionally, when determining the primary sound field signal of the virtual error point based on the primary sound field signal and primary path mapping parameters of the physical error microphone, the primary sound field signal of the virtual error point is determined based on the primary sound field signal of the physical error microphone, the primary path mapping parameters, and the number of time delay sampling points.

[0168] The primary sound field signal of the virtual error point can be calculated according to the following formula (9):

[0169] (9)

[0170] in, The primary sound field signal representing the virtual error point at the current moment; The primary path mapping parameters represent the physical error microphone to the virtual error point; The primary sound field signal of the microphone represents the physical error at the current moment; This indicates the number of time delay sampling points; n represents the discrete time index, i.e., the current time, which is the time when the current reference signal and the actual error signal are collected.

[0171] In this embodiment, considering that the noise source signal transmitted to the virtual error point may be transmitted earlier than the noise source signal transmitted to the physical error microphone during the actual sound signal transmission process, when determining the primary sound field signal of the virtual error point, the primary sound field signal of the virtual error point can be determined by combining the primary sound field signal of the physical error microphone, the primary path mapping parameters, and the number of time delay sampling points, so as to ensure the accuracy of the determination of the secondary sound field signal of the virtual error point and thus map the virtual error signal more accurately.

[0172] S405. Determine the virtual error signal of the virtual error point based on the secondary sound field signal and the primary sound field signal of the virtual error point.

[0173] Specifically, the virtual error signal of the virtual error point can be calculated according to the following formula (10):

[0174] (10)

[0175] in, The virtual error signal representing the virtual error point at the current moment; The secondary sound field signal representing the virtual error point at the current moment; The primary sound field signal represents the virtual error point at the current moment; n represents the discrete time index, i.e., the current moment, which is also the moment when the current reference signal and the real error signal are acquired.

[0176] Method 2: The target virtual sensing parameters include the secondary path transfer parameters corresponding to the virtual error point and the primary path mapping parameters from the physical error microphone to the virtual error point. Based on the target virtual sensing parameters and the real error signal, the virtual error signal corresponding to the virtual error point is determined. This can include: determining the secondary sound field signal of the virtual error point based on the initial noise immunity signal output by the noise immunity speaker and the secondary path transfer parameters of the virtual error point; determining the primary sound field signal of the virtual error point based on the real error signal, the primary path mapping parameters, and the secondary sound field signal of the physical error microphone; and determining the virtual error signal based on both the secondary and primary sound field signals of the virtual error point.

[0177] For example, such as Figure 5 As shown, determining the virtual error signal corresponding to the virtual error point based on the target virtual sensing parameters and the real error signal can include the following steps:

[0178] S501. Determine the secondary sound field signal of the virtual error point based on the initial noise immunity signal output by the noise immunity speaker and the secondary path transmission parameters of the virtual error point.

[0179] The initial noise immunity signal can be the noise immunity signal output by the noise immunity loudspeaker based on the initial noise reduction parameters and the reference signal. The secondary sound field signal at the virtual error point is the sound field signal generated by the initial noise immunity signal output by the noise immunity loudspeaker at the virtual error point.

[0180] Understandably, the secondary path propagation parameters of virtual error points will change with the changes in the vehicle's environment.

[0181] Specifically, the secondary sound field signal of the virtual error point can be calculated according to the following formula (11):

[0182] (11)

[0183] in, The secondary sound field signal representing the virtual error point at the current moment; This represents the initial noise immunity signal output by the noise immunity loudspeaker; The secondary path propagation parameter represents the virtual error point; n represents the discrete time index, i.e., the current time, which is the time when the current reference signal and the real error signal are acquired.

[0184] S502. Determine the primary sound field signal of the physical error microphone based on the actual error signal and the secondary sound field signal of the physical error microphone.

[0185] S503. Determine the primary sound field signal of the virtual error point based on the primary sound field signal and primary path mapping parameters of the physical error microphone.

[0186] S504. Determine the virtual error signal of the virtual error point based on the secondary sound field signal and the primary sound field signal of the virtual error point.

[0187] The specific implementation methods of S502 to S504 can be found in S403 to S405 above, and will not be repeated here.

[0188] 2. The process of determining the mapping parameters from the physical error microphone to the virtual error point.

[0189] The mapping parameters from the physical error microphone to the virtual error point include secondary path mapping parameters and primary path mapping parameters. Secondary path mapping parameters refer to the transmission characteristics of the noise-canceling signal emitted by the noise-canceling loudspeaker from the physical error microphone to the virtual error point. Primary path mapping parameters refer to the transmission characteristics of the noise source signal from the physical error microphone to the virtual error point.

[0190] like Figure 6 As 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:

[0191] S601. Obtain environmental information about the vehicle's surroundings.

[0192] The environmental information includes external environmental information, internal environmental information, and vehicle status information. External environmental information includes road type information (also known as road condition information), weather information, temperature information, air pressure information, and wind speed information. Road type information indicates the road surface type on which the vehicle is traveling. Examples of road types include smooth asphalt roads, rough asphalt roads, roads with speed bumps, and roads with potholes (such as severe potholes). Weather information indicates the weather type of the vehicle's environment. Examples of weather types include sunny, rainy (such as light rain, moderate rain, heavy rain), and snowy (such as light snow, moderate snow, heavy snow). Temperature information refers to the ambient temperature outside the vehicle. For example, the ambient temperature is 20°C. Air pressure information indicates the type of air pressure environment. Examples of air pressure environment types include plateau and plain. Wind speed information refers to wind speed type information. Examples of wind speed types include weak wind and strong wind.

[0193] In practice, sensors installed on the vehicle acquire information about the external environment. These sensors may include external lidar, millimeter-wave radar, GPS, external cameras, temperature sensors, barometric pressure sensors, rain sensors, and wind speed sensors.

[0194] In-vehicle environmental information includes overall occupant seating information (such as number of occupants, seating posture, and position), seat status (seat displacement and tilt angle), other sound source information (such as air conditioning, entertainment system, and occupant speaking volume), in-vehicle temperature information, in-vehicle humidity information, and microphone obstruction information. Overall occupant seating information can include the number of occupants and their individual seating postures. Seat status can include the seat position and angle of each seat. Other sound source information can include air conditioning setting, audio-visual entertainment system volume information, and occupant volume information. Air conditioning setting can be, for example, high, medium, or low. Audio-visual entertainment system volume information can be, for example, low, medium, or high. Occupant volume information can be, for example, low, medium, or high. In-vehicle temperature information refers to the in-vehicle temperature. For example, an in-vehicle temperature of 20°C. In-vehicle humidity information refers to the in-vehicle humidity. For example, an in-vehicle humidity of 50%. Microphone obstruction information indicates the degree of microphone obstruction, such as slight obstruction.

[0195] In practice, sensors installed inside the vehicle are used to acquire information about the in-vehicle environment. These sensors may include seat pressure sensors, physical error microphones, temperature sensors, humidity sensors, in-vehicle cameras, and motor rotary encoders.

[0196] Vehicle status information includes vehicle attribute information, such as tire pressure, vehicle speed, interior wear information, and engine status. Interior wear information indicates the degree of wear inside the vehicle, for example, moderate wear. Engine status can be low, medium, or high RPM. Vehicle status information may also include opening / closing information, such as window opening / closing status, sunroof opening / closing status, and the opening / closing status of all vehicle closures. For example, window opening / closing status might be 30% open. Similarly, the opening / closing status of all vehicle closures might be 30% open.

[0197] In practice, vehicle status information is acquired through sensors installed on the vehicle. These sensors may include pressure gauges, in-vehicle cameras, vibration sensors, physical error microphones, motor rotary encoders, and CAN signals.

[0198] S602. Based on the environmental information, determine the mapping parameters from the physical error microphone to the virtual error point.

[0199] In some examples, S602 may include: determining the mapping parameters from the physical error microphone to the virtual error point based on environmental information and a pre-trained first-parameter prediction model.

[0200] For example, the first parameter prediction model is obtained by training the first initial model based on multiple sets of first sample data, wherein each set of first sample data includes sample environment information 1 of the sample environment in which the vehicle is located, sample sound signal 1, sample sound signal 2, sample sound signal 3 and sample sound signal 4.

[0201] During the training of the first initial model, with the vehicle in a sample environment (i.e., the reference signal collected by the vibration sensor is the sample reference signal), 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 are acquired. 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. Based on sample sound signal 1 and sample sound signal 2, the actual primary path transmission parameters are determined.

[0202] Optionally, based on a preset number of time-delay sampling points, the sample audio signal 2 is subjected to time-delay processing to obtain the time-delayed sample audio signal 2.

[0203] With the vehicle in the sample environment, 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) are acquired. Sample sound signal 3 is the sound signal transmitted from the speaker output to the physical error microphone. Sample sound signal 4 is the sound signal transmitted from the speaker output to the virtual error microphone. Based on sample sound signal 3 and sample sound signal 4, the actual secondary path transmission parameters are determined.

[0204] Optionally, based on a preset number of time-delay sampling points, the sample audio signal 4 is subjected to time-delay processing to obtain the time-delayed sample audio signal 4.

[0205] Next, the sample environment information 1 is input into the first initial model, which outputs the predicted primary path transfer parameters and the predicted secondary path transfer parameters. The first initial model is iteratively trained using the actual primary path transfer parameters as supervision information, and the first initial model is iteratively trained using the actual secondary path transfer parameters as supervision information, to obtain the trained model, namely the first parameter prediction module, thus completing the training of the first initial model.

[0206] 3. The process of determining the primary path transmission parameters corresponding to the physical error microphone.

[0207] In this noise control method, the process of determining the primary path transfer parameters corresponding to the physical error microphone may include: acquiring environmental information of the vehicle's environment; and determining the primary path transfer parameters corresponding to the physical error microphone based on the environmental information.

[0208] In some examples, determining the primary path transfer parameters corresponding to the physical error microphone based on environmental information may include: determining the primary path transfer parameters corresponding to the physical error microphone based on environmental information and a pre-trained third-parameter prediction model.

[0209] In the training of the third initial model, under the condition that the vehicle is in the sample environment, sample reference signals collected by vibration sensors and sample sound signals 5 collected by physical error microphones are acquired. Based on the sample reference signals and sample sound signals 5, the actual primary path transfer parameters corresponding to the physical error microphones are determined. Under the condition that the vehicle is in the sample environment, sample environment information 2 is acquired and input into the third initial model, outputting the predicted primary path transfer parameters corresponding to the physical error microphones. The actual primary path transfer parameters are used as supervision information to iteratively train the third initial model, i.e., the third parameter prediction module.

[0210] 4. The process of determining the secondary path transfer parameters corresponding to the virtual error point.

[0211] In this noise control method, the process of determining the secondary path transfer parameters corresponding to the virtual error point may include: obtaining environmental information of the vehicle's environment; and determining the secondary path transfer parameters corresponding to the virtual error point based on the environmental information.

[0212] In some examples, determining the secondary path propagation parameters corresponding to the virtual error point based on environmental information may include: determining the secondary path propagation parameters corresponding to the virtual error point based on environmental information and a pre-trained fourth-parameter prediction model.

[0213] In the training of the fourth initial model, when the vehicle is in the sample environment, the noise-canceling speaker outputs a sample calibration signal, and the sound signal currently collected by the virtual error microphone placed at the virtual error point (i.e., sample sound signal 6) is acquired. Based on the sample calibration signal and sample sound signal 6, the actual secondary path transmission parameters corresponding to the virtual error point are determined. When the vehicle is in the sample environment, sample environment information 4 is acquired and input into the fourth initial model, outputting the predicted secondary path transmission parameters corresponding to the virtual error point. The actual secondary path transmission parameters corresponding to the virtual error point are used as supervision information to iteratively train the fourth initial model, i.e., the fourth parameter prediction module.

[0214] 5. The process of determining the target noise reduction parameters

[0215] like Figure 7 As shown, the process of determining the target noise reduction parameters in this noise control method may include the following steps:

[0216] S701. Obtain environmental information about the vehicle's surroundings.

[0217] The specific implementation process of S701 can be found in the aforementioned specific implementation process of S601, and will not be repeated here.

[0218] S702. Based on the environmental information, determine the target noise reduction parameters. These parameters include the convergence step size, the first weighting parameter, the second weighting parameter, the leakage factor, the signal gain ratio, and the phase compensation factor.

[0219] In some examples, S702 may include: determining the target noise reduction parameters based on environmental information and a pre-trained second-parameter prediction model.

[0220] For example, the second parameter prediction model is obtained by training the second initial model based on multiple sets of second sample data. Each set of second sample data includes sample environment information 3 of the vehicle's sample environment and a set of reference denoising parameters corresponding to the sample environment information 3. When training the second initial model, the sample environment information 3 is input into the second initial model, and a set of predicted denoising parameters is output. The reference denoising parameters are used as supervision information to iteratively train the second initial model, which is the second parameter prediction module.

[0221] The reference noise reduction parameters may include the reference first weighted parameters.

[0222] Specifically, taking a multi-channel reference signal as an example, the process of determining the first weighting parameter can include:

[0223] S801. Perform a Fourier transform on the reference signal to obtain the frequency domain reference signal.

[0224] S802. Determine the power spectrum information of the frequency domain reference signal.

[0225] Specifically, the power spectrum information of the frequency domain reference signal can be calculated according to the following formula (12):

[0226] (12)

[0227] in, Represents the power spectrum information of the frequency domain reference signal; This represents the frequency domain reference signal.

[0228] S803. Determine the first reference weighting parameter of the reference signal based on the preset peak frequency, preset power spectrum information and power spectrum information of the frequency domain reference signal.

[0229] The preset peak frequency refers to the peak frequency point of the road noise signal (i.e., the reference signal).

[0230] Specifically, the first weighted parameter of the reference signal can be calculated according to the following formula (13):

[0231] (13)

[0232] in, This represents the first reference weighting parameter corresponding to the reference signal of the i-th channel in the multi-channel reference signal. This represents the power spectrum of the reference signal in the i-th channel of a multi-channel reference signal. This indicates the power spectrum information of the reference signal; This indicates the preset peak frequency. The reference signal can be a reference signal measured beforehand in the sample environment.

[0233] It should be noted that the first parameter prediction model and the second parameter prediction model mentioned above can be two sub-models of the parameter prediction model. That is, the parameter prediction model obtained through training can output target noise reduction parameters (such as convergence step size, first weighted parameter, and second weighted parameter) and target virtual sensing parameters (mapping parameters, which can also be called observation filter parameters).

[0234] Table 1 below provides a schematic diagram of the first weighted parameter of the reference signal corresponding to the vibration sensor, the second weighted parameter of the real error signal collected by the physical error microphone, and the observation filter parameters when the vehicle is in different environments.

[0235] Table 1 shows the target noise reduction parameters and target virtual sensing parameters of the vehicle under different environments.

[0236]

[0237] The following is a specific example illustrating the noise control method provided in the embodiments of this application.

[0238] For example, such as Figure 8 As shown, the noise control method may include the following steps:

[0239] S901. After the vehicle is started, acquire the reference signal 1 collected by the vibration sensor.

[0240] 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.

[0241] S903, outputs noise immunity signal 1 through noise immunity speaker.

[0242] S904. During vehicle operation, acquire the reference signal 2 collected by the vibration sensor and the real error signal 1 collected by the physical error microphone.

[0243] S905. Obtain environmental information 1 of the environment in which the vehicle is located. The environmental information 1 may include in-vehicle environment information, out-of-vehicle environment information and vehicle status information.

[0244] S906. Based on environmental information 1 and the second parameter prediction model, determine noise reduction parameter 1, which includes convergence step size 1.

[0245] S907. Based on environmental information 1 and the first parameter prediction model, determine the target virtual sensing parameter 1, which includes a mapping parameter 1 from the physical error microphone to the virtual error point. The mapping parameter 1 includes a secondary path mapping parameter 1 and a primary path mapping parameter 1.

[0246] S908. Using a preset calibration wave signal, measure the secondary path transmission parameter 1 corresponding to the physical error microphone.

[0247] S909. Based on the real error signal 1, mapping parameter 1, and secondary path transmission parameter 1 corresponding to the physical error microphone, determine the virtual error signal 1 of the virtual error point.

[0248] 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.

[0249] S911. Based on the adaptive filter parameter 1, the reference signal 2 is processed to generate the anti-noise signal 2.

[0250] S912. Output noise reduction signal 2 through noise reduction speaker to reduce noise in target area, where target area is the noise reduction area corresponding to virtual error point.

[0251] S913. During vehicle operation, a reference signal 3 collected by a vibration sensor and a real error signal 2 collected by a physical error microphone are acquired.

[0252] S914. Obtain environmental information 2 of the environment in which the vehicle is located. The environmental information 2 may include in-vehicle environment information, out-of-vehicle environment information and vehicle status information.

[0253] S915. Determine whether environment information 2 is the same as environment information 1. If so, execute S916; otherwise, execute S918.

[0254] S916. Based on the adaptive filter parameter 1, the reference signal 3 is processed to generate an anti-noise signal 3.

[0255] S917. Output noise reduction signal 3 through noise reduction speaker to reduce noise in target area, where target area is the noise reduction area corresponding to virtual error point.

[0256] S918. Based on environmental information 2 and the second parameter prediction model, determine noise reduction parameter 2, which includes convergence step size 2.

[0257] S919. Based on environmental information 2 and the first parameter prediction model, determine the target virtual sensing parameter 2, which includes a mapping parameter 2 from the physical error microphone to the virtual error point. The mapping parameter 2 includes a secondary path mapping parameter 2 and a primary path mapping parameter 2.

[0258] S920. Using a preset calibration wave signal, measure the secondary path transmission parameter 2 corresponding to the physical error microphone.

[0259] S921. Based on the real error signal 2, the mapping parameter 2, and the secondary path transmission parameter 2 corresponding to the physical error microphone, determine the virtual error signal 2 of the virtual error point.

[0260] S922. Determine the adaptive filter parameters 2 based on the reference signal 3, the real error signal 2, the virtual error signal 2, and the convergence step size 2.

[0261] S923. Based on the adaptive filter parameter 2, the reference signal 3 is processed to generate the anti-noise signal 4.

[0262] S924. Output noise reduction signal 4 through noise reduction speaker to reduce noise in target area, where target area is the noise reduction area corresponding to virtual error point.

[0263] In some embodiments, this application also provides a noise control device. For example, such as... Figure 9As 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 acquires 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. The first determination module 902 determines the secondary path transmission parameters corresponding to the physical error microphone using a preset calibration wave signal and a target calibration wave signal collected by the physical error microphone. The second determination module 903 determines the virtual error signal corresponding to the virtual error point based on the real error signal, the secondary path transmission parameters corresponding to the physical error microphone, and the mapping parameters from the physical error microphone to the virtual error point. The third determination module 904 determines the target adaptive filter parameters based on the reference signal, the real error signal, and the virtual error signal. The signal generation module 905 processes the reference signal based on the target adaptive filter parameters to generate a target anti-noise signal. The noise reduction module 906 is used to output the target noise reduction signal through the noise reduction speaker to reduce noise in the target area, which is the noise reduction area corresponding to the virtual error point.

[0264] Optionally, the mapping parameters may include secondary path mapping parameters and primary path mapping parameters. The second determining module 903 is specifically used for: determining the secondary sound field signal of the physical error microphone based on the initial noise immunity signal output by the noise immunity speaker and the secondary path transmission parameters of the physical error microphone; determining the secondary sound field signal of the virtual error point based on the secondary sound field signal of the physical error microphone and the secondary path mapping parameters; determining the primary sound field signal of the virtual error point based on the real error signal, the primary path mapping parameters, and the secondary sound field signal of the physical error microphone; and determining the virtual error signal based on the secondary sound field signal and the primary sound field signal of the virtual error point.

[0265] For example, the second determining module 903 is specifically used to: perform time delay processing 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; and 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.

[0266] For example, the second determining module 903 is specifically used to: determine the primary sound field signal of the physical error microphone based on the real error signal and the secondary sound field signal of the physical error microphone; perform time delay processing on the primary path mapping parameters based on the number of time delay sampling points to obtain the time delay processed primary path mapping parameters; and 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 time delay processed primary path mapping parameters.

[0267] For example, the first determining module 902 is specifically used to: when the noise-canceling speaker outputs a first audio signal, acquire 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, use the first audio signal as the preset calibration wave signal, and determine the secondary path transmission parameters corresponding to the physical error microphone based on the preset calibration wave signal and the target calibration wave signal; if the first audio signal does not include preset frequency band information, control the noise-canceling speaker to output the preset calibration wave signal, and determine the secondary path transmission parameters corresponding to the physical error microphone based on the preset calibration wave signal and the target calibration wave signal.

[0268] For example, the first determining module 902 is specifically used to: control the noise-canceling speaker to output a preset calibration wave signal when the noise-canceling speaker does not output a first audio signal, and acquire the target calibration wave signal collected by the physical error microphone; and determine the secondary path transmission parameters corresponding to the physical error microphone based on the preset calibration wave signal and the target calibration wave signal.

[0269] In some examples, the noise control device may further include a first parameter acquisition module, which is used to acquire target noise reduction parameters, including first weighting parameters. The third determination module 904 is specifically used to: perform weighted processing on the reference signal according to the first weighting parameters to obtain a weighted reference signal; and determine the target adaptive filter parameters according to the weighted reference signal, the true error signal, and the virtual error signal.

[0270] In some examples, the third determining module 904 is specifically used to: acquire target virtual sensing parameters, including primary path transfer parameters corresponding to the physical error microphone; process the first weighted parameters according to the primary path transfer parameters corresponding to the physical error microphone to obtain processed first weighted parameters; and perform weighted processing on the reference signal according to the processed first weighted parameters to obtain a weighted reference signal.

[0271] In some examples, the target noise reduction parameters also include a second weighting parameter. The second determining module 903 is specifically used to: weight the real error signal according to the second weighting parameter to obtain a weighted real error signal; and determine the virtual error signal based on the weighted 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. The third determining module 904 is specifically used to: determine the target adaptive filter parameters based on the weighted reference signal, the weighted real error signal, and the virtual error signal.

[0272] In some examples, the third determining module 904 is specifically used to: process the weighted reference signal based on the primary path transmission parameters corresponding to the physical error microphone to obtain the target reference signal; and determine the target adaptive filter parameters based on the target reference signal, the weighted real error signal, and the target virtual error signal.

[0273] In some examples, the third determining module 904 is specifically used to: obtain initial adaptive filter parameters and target noise reduction parameters, the target noise reduction parameters including a convergence step size; and adjust 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.

[0274] In some examples, the noise control device may further include: an environmental information acquisition module and a second parameter acquisition module, wherein the environmental information acquisition module is used to acquire environmental information of the vehicle's environment, including at least one of external environmental information, internal environmental information, and vehicle status information. The second parameter acquisition module is used to determine the mapping parameters from the physical error microphone to the virtual error point based on the environmental information.

[0275] In some examples, the second parameter acquisition module is specifically used to determine the mapping parameters from the physical error microphone to the virtual error point based on environmental information and the first parameter prediction model.

[0276] In some examples, the first parameter acquisition module is specifically used to determine the target noise reduction parameters based on environmental information. The target noise reduction parameters include at least the convergence step size, the first weighted parameter, and the second weighted parameter.

[0277] In some examples, the first parameter acquisition module is specifically used to determine the target noise reduction parameters based on environmental information and the second parameter prediction model.

[0278] This disclosure provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the various processes of the Bluetooth connection method described above and achieves the same technical effects. To avoid repetition, further details are omitted here. The computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0279] This disclosure provides a computer program product that includes a computer program that, when run on a computer, causes the computer to implement the noise control method described above.

[0280] For ease of explanation, the above description has been provided in conjunction with specific embodiments. However, the discussion in some embodiments above is not intended to be exhaustive or to limit the embodiments to the specific forms disclosed above. Various modifications and variations can be obtained based on the above teachings. The selection and description of the above embodiments are for the purpose of better explaining the principles and practical applications, thereby enabling those skilled in the art to better utilize the embodiments and various different variations of the embodiments suitable for specific application considerations.

[0281] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0282] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0283] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0284] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0285] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for example, the division of units is merely a logical functional division, and there may be other division methods in actual implementation; for example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0286] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0287] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0288] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A noise control method, characterized in that, include: Acquire a reference signal and a real error signal collected by a physical error microphone, wherein the reference signal is a noise source signal outside the vehicle; The secondary path transmission parameters corresponding to the physical error microphone are determined by using the preset calibration wave signal and the target calibration wave signal collected by the physical error microphone. The secondary sound field signal of the physical error microphone is determined based on the initial noise immunity signal output by the noise immunity speaker and the secondary path transfer parameters of the physical error microphone. The secondary sound field signal of the virtual error point is determined based on the secondary sound field signal and secondary path mapping parameters of the physical error microphone. The secondary path mapping parameters are the transmission characteristic parameters of the noise-resistant signal emitted by the noise-resistant speaker from the physical error microphone to the virtual error point. The primary sound field signal of the physical error microphone is determined based on the actual error signal and the secondary sound field signal of the physical error microphone. Based on the primary sound field signal and primary path mapping parameters of the physical error microphone, the primary sound field signal of the virtual error point is determined. The primary path mapping parameters are the transmission characteristic parameters of the noise source signal from the physical error microphone to the virtual error point. The secondary path mapping parameters and the primary path mapping parameters are determined during the noise reduction process based on the environmental information of the vehicle's environment. The environmental information includes external environmental information, internal environmental information, and vehicle status information. The virtual error signal is determined based on the secondary sound field signal and the primary sound field signal of the virtual error point. The target adaptive filter parameters are determined based on the reference signal, the real error signal, and the virtual error signal. Based on the target adaptive filter parameters, the reference signal is processed to generate a target anti-noise signal; The target noise reduction signal is output through a noise-canceling speaker to reduce noise in the target area, which is the noise reduction area corresponding to the virtual error point.

2. The method according to claim 1, characterized in that, The step of determining the secondary sound field signal of the virtual error point based on the secondary sound field signal of the physical error microphone and the secondary path mapping parameters includes: Based on the number of delay sampling points, the secondary path mapping parameters are subjected to delay processing to obtain the delay-processed secondary path mapping parameters; The secondary sound field signal of the virtual error point is determined based on the secondary sound field signal of the physical error microphone and the secondary path mapping parameters after time delay processing.

3. The method according to claim 1, characterized in that, Based on the primary sound field signal and primary path mapping parameters of the physical error microphone, the primary sound field signal of the virtual error point is determined, including: Based on the number of delay sampling points, the primary path mapping parameters are subjected to delay processing to obtain the delay-processed primary path mapping parameters; The primary sound field signal of the virtual error point is determined based on the primary sound field signal of the physical error microphone and the primary path mapping parameters after time delay processing.

4. The method according to claim 1, characterized in that, The step of determining the secondary path transfer parameters corresponding to the physical error microphone using a preset calibration wave signal and a target calibration wave signal acquired by the physical error microphone includes: When the noise-canceling speaker outputs a first audio signal, the first audio signal and the target calibration wave signal collected by the physical error microphone are acquired. If the first audio signal includes preset frequency band information, then the first audio signal is used as the preset calibration wave signal, and the secondary path transmission parameters corresponding to the physical error microphone are determined based on the preset calibration wave signal and the target calibration wave signal. If the first audio signal does not include the preset frequency band information, the noise-canceling speaker is controlled to output the preset calibration wave signal, and the secondary path transmission parameters corresponding to the physical error microphone are determined based on the preset calibration wave signal and the target calibration wave signal.

5. The method according to claim 4, characterized in that, The step of determining the secondary path transfer parameters corresponding to the physical error microphone using a preset calibration wave signal and a target calibration wave signal acquired by the physical error microphone includes: When the noise-canceling speaker does not output the first audio signal, the noise-canceling speaker is controlled to output the preset calibration wave signal, and the target calibration wave signal collected by the physical error microphone is acquired. Based on the preset calibration wave signal and the target calibration wave signal, the secondary path transmission parameters corresponding to the physical error microphone are determined.

6. The method according to claim 1, characterized in that, Before determining the target adaptive filter parameters based on the reference signal, the true error signal, and the virtual error signal, the method further includes: Obtain target noise reduction parameters, wherein the target noise reduction parameters include a first weighting parameter; Determining the target adaptive filter parameters based on the reference signal, the true error signal, and the virtual error signal includes: The reference signal is weighted according to the first weighting parameter to obtain the weighted reference signal; The target adaptive filter parameters are determined based on the weighted reference signal, the real error signal, and the virtual error signal.

7. The method according to claim 6, characterized in that, The step of weighting the reference signal according to the first weighting parameter to obtain the weighted reference signal includes: Obtain target virtual sensing parameters, the target virtual sensing parameters including the primary path transfer parameters corresponding to the physical error microphone; Based on the primary path transmission parameters corresponding to the physical error microphone, the first weighted parameters are processed to obtain the processed first weighted parameters; The reference signal is weighted according to the first weighting parameter after processing to obtain the weighted reference signal.

8. The method according to claim 7, characterized in that, The target noise reduction parameters further include a second weighting parameter. Determining the virtual error signal corresponding to the virtual error point based on 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 includes: The true error signal is weighted according to the second weighting parameter to obtain the weighted true error signal. The virtual error signal is determined based on the weighted real error signal, the secondary path transmission parameters corresponding to the physical error microphone, and the mapping parameters from the physical error microphone to the virtual error point. Determining the target adaptive filter parameters based on the reference signal, the true error signal, and the virtual error signal includes: The target adaptive filter parameters are determined based on the weighted reference signal, the weighted true error signal, and the virtual error signal.

9. The method according to claim 8, characterized in that, Determining the target adaptive filter parameters based on the weighted reference signal, the weighted true error signal, and the virtual error signal includes: Based on the primary path transmission parameters corresponding to the physical error microphone, the weighted reference signal is processed to obtain the target reference signal; The target adaptive filter parameters are determined based on the target reference signal, the weighted true error signal, and the virtual error signal.

10. The method according to any one of claims 1-9, characterized in that, Determining the target adaptive filter parameters based on the reference signal, the true error signal, and the virtual error signal includes: Obtain initial adaptive filter parameters and target noise reduction parameters, wherein the target noise reduction parameters include the convergence step size; The initial adaptive filter parameters are adjusted based on the reference signal, the real error signal, the virtual error signal, and the convergence step size to obtain the target adaptive filter parameters.

11. The method according to claim 1, characterized in that, The step of determining the mapping parameters from the physical error microphone to the virtual error point based on the environmental information includes: Based on the environmental information and the first parameter prediction model, the mapping parameters from the physical error microphone to the virtual error point are determined.

12. The method according to any one of claims 1-9, characterized in that, The method further includes: Obtain environmental information about the vehicle's surroundings, including at least one of external environmental information, internal environmental information, and vehicle status information; Based on the environmental information, target noise reduction parameters are determined, and the target noise reduction parameters include at least a convergence step size, a first weighting parameter, and a second weighting parameter.

13. The method according to claim 12, characterized in that, The step of determining the target noise reduction parameters based on the environmental information includes: The target noise reduction parameters are determined based on the environmental information and the second parameter prediction model.

14. A noise control device, characterized in that, include: The signal acquisition module is used to acquire a reference signal and a real error signal collected by a physical error microphone, wherein the reference signal is a noise source signal outside the vehicle; The first determining module is used to determine the secondary path transmission parameters corresponding to the physical error microphone by using a preset calibration wave signal and the target calibration wave signal collected by the physical error microphone. The second determining module is used to determine the secondary sound field signal of the physical error microphone based on the initial noise immunity signal output by the noise immunity speaker and the secondary path transmission parameters of the physical error microphone. The second determining module is further configured to determine the secondary sound field signal of the virtual error point based on the secondary sound field signal and secondary path mapping parameters of the physical error microphone, wherein the secondary path mapping parameters are the transmission characteristic parameters of the noise-resistant signal emitted by the noise-resistant speaker from the physical error microphone to the virtual error point; The second determining module is further configured to determine the primary sound field signal of the physical error microphone based on the actual error signal and the secondary sound field signal of the physical error microphone; The second determining module is further configured to determine the primary sound field signal of the virtual error point based on the primary sound field signal and the primary path mapping parameters of the physical error microphone. The primary path mapping parameters are the transmission characteristic parameters of the noise source signal from the physical error microphone to the virtual error point. The secondary path mapping parameters and the primary path mapping parameters are both determined based on the environmental information of the vehicle's environment during the noise reduction process. The environmental information includes external environmental information, internal environmental information, and vehicle status information. The second determining module is further configured to determine a virtual error signal based on the secondary sound field signal of the virtual error point and the primary sound field signal of the virtual error point; The third determining module is used to determine the target adaptive filter parameters based on the reference signal, the real error signal, and the virtual error signal; The signal generation module is used to process the reference signal based on the target adaptive filter parameters to generate a target anti-noise signal; The noise reduction module is used to output the target noise reduction signal through the noise-canceling speaker to reduce noise in the target area, where the target area is the noise reduction area corresponding to the virtual error point.

15. A readable storage medium, characterized in that, The readable storage medium stores a computer program that, when executed, implements the method as described in any one of claims 1 to 13.

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