Noise control method and apparatus, intelligent device, vehicle, electronic device, and medium

Abstract

The present disclosure provides a noise control method, device, intelligent device, vehicle, electronic device and medium, which can determine a noise reduction mode according to the noise type of the intelligent device, determine a control signal of at least one sound emitting device according to the noise reduction mode and / or a target noise reduction position, control the at least one sound emitting device to output a cancellation signal through the control signal, and form a noise reduction quiet zone at the target noise reduction position. Compared with the related art, the present disclosure can reduce noise of different noise types and achieve accurate noise suppression of a key area without increasing hardware cost by determining a control signal of at least one sound emitting device according to the noise reduction mode and / or a target noise reduction position, and then outputting a cancellation signal to form a noise reduction quiet zone at the target noise reduction position.

Description

Technical Field

[0001] This disclosure relates to the field of human-machine noise control technology, and in particular to a noise control method, apparatus, intelligent device, vehicle, electronic device, and medium. Background Technology

[0002] During vehicle operation, various types of noise are typically generated inside the vehicle. Currently, vehicles can only reduce noise of a single type. To reduce noise of different types, it would be necessary to modify the vehicle's hardware layout, increase hardware costs, and impose requirements on passenger seating posture. Summary of the Invention

[0003] This disclosure provides a noise control method, apparatus, smart device, vehicle, electronic device, and storage medium. Its main objective is to enable the control of different types of noise within a vehicle.

[0004] According to a first aspect of this disclosure, a noise control method is provided, comprising:

[0005] Determine the noise reduction method based on the type of noise from the smart device; Based on the noise reduction method and / or the target noise reduction location, determine the control signal for at least one sound-generating device; By controlling at least one sound-generating device to output a cancellation signal through a control signal, a noise reduction and quiet zone is formed at the target noise reduction location.

[0006] Optionally, the noise type is broadband noise or narrowband noise.

[0007] Optionally, the noise reduction method is determined based on the noise type of the smart device, including at least one of the following: When the noise type is broadband noise, a first noise reduction method is determined, which is a noise reduction method based on the target frequency band. When the noise type is narrowband noise, a second noise reduction method is determined, while the first noise reduction method is a noise reduction method based on the target frequency point; When the noise type is mixed noise, a third noise reduction method is determined. The third noise reduction method is to separate narrowband noise and broadband noise from the mixed noise and reduce the noise of narrowband noise and broadband noise respectively based on the target frequency point and the target frequency band.

[0008] Optionally, based on the noise reduction method and / or the target noise reduction location, a control signal for at least one sound-generating device is determined, including: Obtain the second residual noise estimation signal at the target noise reduction location; Based on the noise reduction method and the second residual noise estimation signal, determine the control signal for at least one sound-generating device.

[0009] Optionally, based on the noise reduction method and the second residual noise estimation signal, a control signal for at least one sound-generating device is determined, including at least one of the following: When the noise reduction method is the first noise reduction method, the control signal of at least one sound-generating device in the target frequency band is determined based on the second residual noise estimation signal and the target frequency domain controller. When the noise reduction method is the second noise reduction method, the control signal of at least one sound-generating device at the target frequency point is determined based on the second residual noise estimation signal and the target control dictionary. When the noise reduction method is the third noise reduction method, the control signal of at least one sound-generating device in the target frequency band is determined based on the second residual noise estimation signal and the target frequency domain controller, and the control signal of at least one sound-generating device at the target frequency point is determined based on the second residual noise estimation signal and the target control dictionary.

[0010] Optionally, based on the second residual noise estimation signal and the target frequency domain controller, a control signal for at least one sound-generating device in the target frequency band is determined, including: Based on the second residual noise estimation signal, the second noise error frequency domain estimation signal is obtained; Based on the second noise error frequency domain estimation signal, obtain the updated target frequency domain signal controller; Based on the updated target frequency domain signal controller, determine the control signal for at least one sound-generating device in the target frequency band.

[0011] Optionally, based on the updated target frequency domain signal controller coefficients, a control signal for at least one sound-generating device in the target frequency band is determined, including: Obtain the reference noise signal; Based on the updated target frequency domain signal controller and the reference noise signal, determine the control signal for at least one sound-generating device in the target frequency band.

[0012] Optionally, based on the second residual noise estimation signal and the target control dictionary, a control signal for at least one sound-generating device at the target frequency point is determined, including: Based on the second residual noise estimation signal, obtain the updated target control dictionary; Based on the updated target control dictionary, determine the control signal for at least one sound-generating device at the target frequency point.

[0013] Optionally, obtaining the second residual noise estimation signal at the target denoising location includes: Acquire the first residual noise signal collected by the pickup device, wherein the pickup device and the target noise reduction location are located at different positions; Based on the first residual noise estimation signal, the second residual noise estimation signal at the target noise reduction location is obtained.

[0014] Optionally, based on the first residual noise estimation signal, obtaining the second residual noise estimation signal at the target denoising location includes: Based on the first residual noise signal, a first noise estimation signal is obtained; Based on the first noise estimation signal, obtain the second noise estimation signal; Based on the second noise estimation signal, the second residual noise estimation signal is obtained.

[0015] Optionally, obtaining a second noise estimation signal based on the first noise estimation signal includes: Based on the first noise estimation signal and the target frequency domain signal estimator, the second noise estimation signal is obtained.

[0016] Optionally, the method further includes: Acquire a first noise signal collected by a first pickup device, and acquire second noise signals collected by a second pickup device at different locations; Based on the first noise signal and the second noise signal, obtain the first frequency domain noise signal and the second frequency domain noise signal; Based on the first frequency domain noise signal and the second frequency domain noise signal, obtain the target frequency domain signal estimator.

[0017] Optionally, the first microphone is fixedly installed on the smart device, while the second microphone is temporarily placed on the smart device.

[0018] Optionally, obtaining a first noise estimation signal based on the first residual noise signal includes: The first noise estimation signal is obtained based on the first residual noise estimation signal and the first cancellation estimation signal.

[0019] Optionally, obtaining a first noise estimation signal based on the first residual noise signal includes: A second residual noise estimation signal is obtained based on the second noise estimation signal and the second cancellation estimation signal.

[0020] Optionally, the target noise reduction location is the location of the user's ear.

[0021] Optionally, the second residual noise estimation signal is the signal after the second cancellation signal cancels the second noise signal, and the first residual noise signal is the noise signal after the first cancellation signal cancels the first noise signal.

[0022] Optionally, the method further includes: The target noise reduction location is obtained based on the user's sitting posture and / or seat position.

[0023] According to a second aspect of this disclosure, a noise control device is provided, comprising: The first determining module is configured to determine the noise reduction method based on the noise type of the smart device; The second determining module is configured to determine a control signal for at least one sound-generating device based on the noise reduction method and / or the target noise reduction location. The control module is configured to control at least one sound-generating device to output a cancellation signal via a control signal, thereby creating a noise reduction and silencing zone at the target noise reduction location.

[0024] Optionally, the noise type is broadband noise or narrowband noise.

[0025] Optionally, the first determining module is further configured to include at least one of the following: When the noise type is broadband noise, a first noise reduction method is determined, which is a noise reduction method based on the target frequency band. When the noise type is narrowband noise, a second noise reduction method is determined, while the first noise reduction method is a noise reduction method based on the target frequency point; When the noise type is mixed noise, a third noise reduction method is determined. The third noise reduction method is to separate narrowband noise and broadband noise from the mixed noise and reduce the noise of narrowband noise and broadband noise respectively based on the target frequency point and the target frequency band.

[0026] Optionally, the second determining module is further configured as follows: Obtain the second residual noise estimation signal at the target noise reduction location; Based on the noise reduction method and the second residual noise estimation signal, determine the control signal for at least one sound-generating device.

[0027] Optionally, the second determining module is further configured to include at least one of the following: When the noise reduction method is the first noise reduction method, the control signal of at least one sound-generating device in the target frequency band is determined based on the second residual noise estimation signal and the target frequency domain controller. When the noise reduction method is the second noise reduction method, the control signal of at least one sound-generating device at the target frequency point is determined based on the second residual noise estimation signal and the target control dictionary. When the noise reduction method is the third noise reduction method, the control signal of at least one sound-generating device in the target frequency band is determined based on the second residual noise estimation signal and the target frequency domain controller, and the control signal of at least one sound-generating device at the target frequency point is determined based on the second residual noise estimation signal and the target control dictionary.

[0028] Optionally, the second determining module is further configured as follows: Based on the second residual noise estimation signal, the second noise error frequency domain estimation signal is obtained; Based on the second noise error frequency domain estimation signal, obtain the updated target frequency domain signal controller; Based on the updated target frequency domain signal controller, determine the control signal for at least one sound-generating device in the target frequency band.

[0029] Optionally, the second determining module is further configured as follows: Obtain the reference noise signal; Based on the updated target frequency domain signal controller and the reference noise signal, determine the control signal for at least one sound-generating device in the target frequency band.

[0030] Optionally, the second determining module is further configured as follows: Based on the second residual noise estimation signal, obtain the updated target control dictionary; Based on the updated target control dictionary, determine the control signal for at least one sound-generating device at the target frequency point.

[0031] Optionally, the second determining module is further configured as follows: Acquire the first residual noise signal collected by the pickup device, wherein the pickup device and the target noise reduction location are located at different positions; Based on the first residual noise estimation signal, the second residual noise estimation signal at the target noise reduction location is obtained.

[0032] Optionally, the second determining module is further configured as follows: Based on the first residual noise signal, a first noise estimation signal is obtained; Based on the first noise estimation signal, obtain the second noise estimation signal; Based on the second noise estimation signal, the second residual noise estimation signal is obtained.

[0033] Optionally, the second determining module is further configured as follows: Based on the first noise estimation signal and the target frequency domain signal estimator, the second noise estimation signal is obtained.

[0034] Optionally, the second determining module is further configured as follows: Acquire a first noise signal collected by a first pickup device, and acquire second noise signals collected by a second pickup device at different locations; Based on the first noise signal and the second noise signal, obtain the first frequency domain noise signal and the second frequency domain noise signal; Based on the first frequency domain noise signal and the second frequency domain noise signal, obtain the target frequency domain signal estimator.

[0035] Optionally, the first microphone is fixedly installed on the smart device, and the second microphone is temporarily placed on the smart device.

[0036] Optionally, the second determining module is further configured as follows: The first noise estimation signal is obtained based on the first residual noise estimation signal and the first cancellation estimation signal.

[0037] Optionally, the second determining module is further configured as follows: A second residual noise estimation signal is obtained based on the second noise estimation signal and the second cancellation estimation signal.

[0038] Optionally, the target noise reduction location is the location of the user's ear.

[0039] Optionally, the second residual noise estimation signal is the signal after the second cancellation signal cancels the second noise signal, and the first residual noise signal is the noise signal after the first cancellation signal cancels the first noise signal.

[0040] Optionally, the second determining module is further configured as follows: The target noise reduction location is obtained based on the user's sitting posture and / or seat position.

[0041] According to a third aspect of this disclosure, a smart device is provided, the smart device including the noise control device as described in the second aspect.

[0042] According to a fourth aspect of this disclosure, a vehicle is provided, the vehicle including a noise control device as described in the second aspect; or, As described in the third aspect, intelligent devices.

[0043] According to a fifth aspect of this disclosure, an electronic device is provided, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect above.

[0044] According to a sixth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are configured to cause the computer to perform the method described in the first aspect above.

[0045] According to a seventh aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method described in the first aspect above.

[0046] The noise control method, apparatus, smart device, vehicle, electronic device, and storage medium disclosed herein can determine a noise reduction method based on the noise type of the smart device; determine a control signal for at least one sound-generating device based on the noise reduction method and / or the target noise reduction location; and control the at least one sound-generating device to output a cancellation signal through the control signal to form a noise-reducing silent zone at the target noise reduction location. Compared with related technologies, this disclosure, by determining the control signal for at least one sound-generating device based on the noise reduction method and / or the target noise reduction location, and then outputting a cancellation signal to form a noise-reducing silent zone at the target noise reduction location, can reduce noise of different noise types without increasing hardware costs, and achieve precise noise suppression in key areas.

[0047] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0048] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein: Figure 1 This is a schematic flowchart of a noise control method provided in an embodiment of the present disclosure; Figure 2 This is a schematic diagram of the structure of a noise control system provided in an embodiment of the present disclosure; Figure 3 This is a schematic flowchart of another noise control method provided in an embodiment of the present disclosure; Figure 4 This is a schematic flowchart of another noise control method provided in an embodiment of the present disclosure; Figure 5 This is a schematic diagram of the structure of a noise control device provided in an embodiment of the present disclosure; Figure 6 This is a schematic diagram of the structure of a smart terminal provided in an embodiment of the present disclosure; Figure 7 A schematic block diagram of an example electronic device provided for embodiments of this disclosure. Detailed Implementation

[0049] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0050] The following description, with reference to the accompanying drawings, describes noise control methods, apparatus, smart devices, vehicles, electronic devices, and media according to embodiments of the present disclosure.

[0051] This disclosure relates to the following specific concepts, including: Active noise control (ANC) refers to the use of secondary sound sources to generate anti-phase sound waves to cancel out target noise. Road noise cancellation (RNC) refers to the active suppression of broadband noise generated by road surface excitation inside the vehicle. Remote Microphone Technique (RMT) is a commonly used technique for virtual sensing that estimates the signal at a virtual error microphone by using the signal from a remote microphone. An observer filter (OF) is used to establish a mapping relationship between far-field microphone signals and near-field microphone signals (or near-field target points near the ear); Near-field microphone: A reference microphone installed near the ear (such as on the headrest), used only in the offline acoustic calibration phase, and not required in the online control phase; Far-field microphone: A vehicle-mounted microphone fixed to the roof of the car, located far from the ear, serving as a physical error sensor during the online control phase; Narrowband Active Noise Cancellation (NANC) generates a cancellation signal with the same amplitude but opposite phase as the noise based on a reference signal. This cancellation signal is then played through a speaker, thereby reducing narrowband line spectrum noise.

[0052] Acoustics Transfer Function Dictionary (ATFD): Based on the in-vehicle acoustic transfer function vector, the response at different frequency points is calculated in advance and stored in the NANC system in the form of a dictionary for updating the control filter, which reduces the complexity of the real-time NANC system.

[0053] Virtual Sensing (VS): Using observation filters, an acoustic mapping relationship is established between the real vehicle microphone (far field) and the virtual target point (near field at the human ear). During real-time control, the noise reduction and silencing zone of the NANC system is transferred from the far field error microphone to the virtual target point without the need to place a physical microphone at the target location.

[0054] Observer Filter (OF): The core component of the virtual sensing system, used to establish a mapping relationship between the noise at the far-field error microphone and the noise at the near-field virtual target point (at the human ear), enabling the NANC system to estimate and suppress the noise at the virtual target point based on the actual error microphone signal.

[0055] min-max criterion: the optimal robust design criterion, which ensures stable performance of the system under various acoustic scenarios (such as different sitting postures) by minimizing the maximum error in all worst-case scenarios.

[0056] Figure 1 This is a flowchart illustrating a noise control method provided in an embodiment of this disclosure. The method of this embodiment is applied to a smart device, which may be a robot, a vehicle, or other smart devices with noise control functions, etc. Specific embodiments of this disclosure do not limit the display scenario.

[0057] Step 101: Determine the noise reduction method based on the noise type of the smart device.

[0058] The noise types include at least one of the following: broadband noise and narrowband noise. Broadband noise can be the sound of air conditioning running, road driving noise, fan hum, etc., which has a wide spectrum distribution and its energy changes slowly over time; narrowband noise can be engine ignition noise, motor harmonics, power supply hum (50 / 60 Hz and its harmonics), range extender noise, which has a distinct fundamental frequency and harmonic structure.

[0059] It should be noted that the noise reduction method includes at least one of the following: a first noise reduction method, wherein the first noise reduction method is a noise reduction method based on the target frequency band; a second noise reduction method, wherein the first noise reduction method is a noise reduction method based on the target frequency point; and a third noise reduction method, wherein the third noise reduction method is a method of separating narrowband noise and broadband noise from mixed noise, and performing noise reduction on the narrowband noise and broadband noise based on the target frequency point and the target frequency band, respectively.

[0060] Step 102: Determine the control signal for at least one sound-generating device based on the noise reduction method and / or the target noise reduction location.

[0061] The target noise reduction location is the area of ​​noise reduction that the user is actually focused on. Specifically, the target noise reduction location can be the driver's ear, the passenger's head, or the area near the ear when using the terminal device. The sound-generating device can be a speaker. Step 103: Control at least one sound-generating device to output a cancellation signal through a control signal to form a noise reduction and quiet zone at the target noise reduction location.

[0062] In this embodiment of the disclosure, a noise reduction method is determined according to the noise type of the smart device, specifically including at least one of the following: when the noise type is broadband noise, a first noise reduction method is determined, which is a noise reduction method based on a target frequency band; when the noise type is narrowband noise, a second noise reduction method is determined, which is a noise reduction method based on a target frequency point; when the noise type is mixed noise, a third noise reduction method is determined, which is a method of separating narrowband noise and broadband noise from the mixed noise and reducing the narrowband noise and broadband noise respectively based on the target frequency point and the target frequency band.

[0063] In this embodiment of the disclosure, determining the control signal of at least one sound-generating device according to the noise reduction method and / or the target noise reduction location specifically includes: acquiring a second residual noise estimation signal at the target noise reduction location; and determining the control signal of at least one sound-generating device according to the noise reduction method and the second residual noise estimation signal.

[0064] In this embodiment of the disclosure, determining the control signal of at least one sound-generating device based on the noise reduction method and the second residual noise estimation signal specifically includes at least one of the following: when the noise reduction method is a first noise reduction method, determining the control signal of at least one sound-generating device in the target frequency band based on the second residual noise estimation signal and the target frequency domain controller; when the noise reduction method is a second noise reduction method, determining the control signal of at least one sound-generating device at the target frequency point based on the second residual noise estimation signal and the target control dictionary; when the noise reduction method is a third noise reduction method, determining the control signal of at least one sound-generating device in the target frequency band based on the second residual noise estimation signal and the target frequency domain controller, and determining the control signal of at least one sound-generating device at the target frequency point based on the second residual noise estimation signal and the target control dictionary.

[0065] In this embodiment of the disclosure, determining the control signal of at least one sound-generating device in the target frequency band based on the second residual noise estimation signal and the target frequency domain controller specifically includes: obtaining a second noise error frequency domain estimation signal based on the second residual noise estimation signal; obtaining an updated target frequency domain signal controller based on the second noise error frequency domain estimation signal; and determining the control signal of at least one sound-generating device in the target frequency band based on the updated target frequency domain signal controller.

[0066] In this embodiment of the disclosure, determining the control signal of at least one sound-generating device in the target frequency band based on the updated target frequency domain signal controller coefficients specifically includes: acquiring a reference noise signal; and determining the control signal of at least one sound-generating device in the target frequency band based on the updated target frequency domain signal controller and the reference noise signal.

[0067] In this embodiment of the disclosure, determining the control signal of at least one sound-generating device at a target frequency point based on the second residual noise estimation signal and the target control dictionary specifically includes: obtaining an updated target control dictionary based on the second residual noise estimation signal; and determining the control signal of at least one sound-generating device at the target frequency point based on the updated target control dictionary.

[0068] In this embodiment of the disclosure, obtaining the second residual noise estimation signal at the target noise reduction location specifically includes: obtaining the first residual noise signal collected by the pickup device, wherein the pickup device and the target noise reduction location are at different locations; and obtaining the second residual noise estimation signal at the target noise reduction location based on the first residual noise estimation signal.

[0069] In this system, the distance between the location of the first pickup device and the target noise source is greater than the distance between the second location and the target noise source. The second residual noise estimation signal characterizes the residual noise state after the primary noise and the second cancellation signal are superimposed at the second location. The target noise source can be a major noise source such as engine, fan, or road noise. The second location is the noise reduction target point of interest to the user, such as the driver's ear, the passenger's head position, or the near-ear area when the terminal device is in use.

[0070] It should be noted that the distance between the location of the first pickup device and the target noise source is greater than the distance between the second location and the target noise source. This indicates that the second location is closer to the noise source, and its acoustic environment may have a higher sound pressure level or more complex spectral characteristics. Using the first residual noise signal to estimate the second residual noise signal can also be understood as using the residual noise signal of a far-field physical microphone device to estimate the second residual noise signal system of a near-field virtual microphone device. This approach eliminates the need to deploy multiple near-field physical microphones inside the vehicle, enabling precise noise reduction in near-field areas, such as the user's ear area. In other words, it achieves precise noise suppression in critical areas without increasing hardware costs.

[0071] In this embodiment of the disclosure, obtaining a second residual noise estimation signal at the target noise reduction location based on a first residual noise estimation signal specifically includes: obtaining a first noise estimation signal based on the first residual noise signal; obtaining a second noise estimation signal based on the first noise estimation signal; and obtaining a second residual noise estimation signal based on the second noise estimation signal.

[0072] In this embodiment of the disclosure, obtaining a second noise estimation signal based on a first noise estimation signal specifically includes: obtaining a second noise estimation signal based on the first noise estimation signal and a target frequency domain signal estimator.

[0073] In this embodiment of the disclosure, the noise control method further includes: acquiring a first noise signal collected by a first pickup device, and acquiring a second noise signal collected by a second pickup device at different locations; acquiring a first frequency domain noise signal and a second frequency domain noise signal based on the first noise signal and the second noise signal; and acquiring a target frequency domain signal estimator based on the first frequency domain noise signal and the second frequency domain noise signal.

[0074] The first pickup device is typically one or more microphones. Its physical installation location is defined as the location of the first pickup device. This location can be a structurally stable, easily wired, and representative area within the vehicle, such as the roof or above the dashboard. Specifically, it can be a noise reduction area far from the user's actual focus. The first residual noise signal characterizes the residual noise state after the first noise signal and the first cancellation signal are superimposed at the location of the first pickup device. The first cancellation signal can be a reverse sound wave signal output by the target sound control device according to a corresponding control algorithm, used to cancel the primary noise. The target sound control device can be a loudspeaker. In an ANC system, the first residual noise signal can be used as feedback input to the target controller to continuously adjust the secondary control signal output by the target sound control device to minimize the noise energy at the location of the first pickup device. In other words, the first residual noise signal not only contains environmental noise information but also implies the current operating state of the control system and the cancellation effect.

[0075] It should be noted that since the location of the first sound pickup device is far from the target noise reduction area that the user is actually concerned about, controlling it based solely on the first residual noise signal may result in poor noise reduction effect in the noise reduction area that the user is actually concerned about. Therefore, the subsequent steps of this disclosure can use the first residual noise signal to deduce the noise state at a second location that is closer to the noise source or closer to the human ear through acoustic modeling or signal estimation methods, thereby achieving precise noise suppression for the target area.

[0076] In this embodiment of the disclosure, the first microphone is fixedly installed on the smart device, and the second microphone is temporarily placed on the smart device.

[0077] In this embodiment of the disclosure, obtaining a first noise estimation signal based on a first residual noise signal specifically includes: obtaining a first noise estimation signal based on the first residual noise estimation signal and a first cancellation estimation signal.

[0078] In this embodiment of the disclosure, obtaining a first noise estimation signal based on a first residual noise signal specifically includes: obtaining a second residual noise estimation signal based on a second noise estimation signal and a second cancellation estimation signal.

[0079] In this embodiment of the disclosure, the target noise reduction location is the location of the user's ear.

[0080] In this embodiment of the disclosure, the second residual noise estimation signal is the signal after the second cancellation signal cancels the second noise signal, and the first residual noise signal is the noise signal after the first cancellation signal cancels the first noise signal.

[0081] In this embodiment of the disclosure, the noise control method further includes: obtaining the target noise reduction position based on the user's sitting posture and / or the seat status.

[0082] In this embodiment, based on the obtained second residual noise estimation signal, the ANC system can adjust the amplitude, phase, or filtering parameters of the cancellation signal output by the sound-generating device (speaker), thereby reducing the actual noise level at the second location. That is, by acquiring the second residual noise estimation signal, the limitation of traditional methods that can only optimize the noise reduction effect in the area where the first pickup device is located can be overcome. The target noise reduction area inside the vehicle can be moved from the location of the first pickup device to the second location, and the noise reduction effect actually perceived by passengers inside the vehicle can be improved without changing the existing vehicle hardware layout, enabling precise noise reduction in key areas.

[0083] In this embodiment of the disclosure, obtaining a second residual noise estimation signal at the target noise reduction location based on a first residual noise signal specifically includes: obtaining a first noise estimation signal based on the first residual noise signal; and obtaining a second residual noise estimation signal based on the first noise estimation signal.

[0084] In this embodiment of the disclosure, obtaining a first noise estimation signal based on a first residual noise signal specifically includes: obtaining a first noise estimation signal based on the first residual noise signal and a first cancellation estimation signal.

[0085] In this embodiment of the disclosure, obtaining a second residual noise estimation signal based on a second noise estimation signal specifically includes: obtaining a second residual noise estimation signal based on a second noise estimation signal and a second cancellation estimation signal.

[0086] It should be noted that the first residual noise signal collected by the first pickup device is not the original noise signal, but rather the residual signal resulting from the superposition of the primary noise signal from the target noise source and the cancellation signal output by the loudspeaker at the location of the first pickup device. The first residual noise signal contains both the primary noise and the first secondary noise, and cannot directly represent the original noise. The primary noise is the sound pressure level of the target noise source at the location of the first pickup device, and the first secondary noise is the response of the secondary noise output by the loudspeaker propagating to the location of the first pickup device via a secondary path. In this embodiment, the influence of the first cancellation signal can be removed from the first residual noise signal through secondary path modeling and signal separation to obtain an estimate of the primary noise, i.e., the first noise estimation signal. The first noise estimation signal can be considered an approximate representation of the original noise generated by the target noise source at the location of the first pickup device, and it is unaffected by the cancellation signal.

[0087] For example, a known first-order path estimation model can be utilized. and current control signal Calculate the first cancellation signal estimate ,like Then, the first cancellation signal From the first noise estimation signal Subtracting from the first noise level yields the first noise estimation signal. like Alternatively, methods such as adaptive filtering, blind source separation, and deep learning can be used to directly extract the primary noise components (the first noise estimation signal). ).

[0088] It should also be noted that the second position can be closer to the noise source or the area of ​​interest to the user (such as near the ear), and there are differences in spatial distance and acoustic transmission between it and the location of the first pickup device.

[0089] In this embodiment of the disclosure, obtaining a second residual noise estimation signal based on a first noise estimation signal specifically includes: obtaining a second noise estimation signal based on the first noise estimation signal; and obtaining a second residual noise estimation signal based on the second noise estimation signal.

[0090] In this embodiment of the disclosure, obtaining a second noise estimation signal based on a first noise estimation signal specifically includes: obtaining the second noise estimation signal based on the first noise estimation signal and a target signal estimator.

[0091] Among them, the target signal estimator Characterizing the sound propagation characteristics from the location of the first pickup device to the second location allows the first noise estimation signal to be mapped to the second location, resulting in the second noise estimation signal. , Second noise estimation signal It can also be used as a second primary noise estimate at the second location.

[0092] It should be noted that the ANC system also has a second-stage cancellation signal at the second position. Using known second-order path estimation models Calculate the second cancellation estimation signal , Then, the second noise estimation signal... Second cancellation estimation signal Superposition yields the second residual noise signal. ,like That is, the primary noise component and the secondary noise component at the second position are superimposed to obtain the second residual noise signal. Although the second noise estimation signal is an estimate, it can effectively reflect the residual noise that should be collected if a real microphone were deployed at the second location. It can be used as a target for controller optimization or for generating personalized noise reduction strategies.

[0093] In this embodiment of the disclosure, obtaining a second residual noise estimation signal based on a second noise estimation signal may specifically include: obtaining a second noise estimation signal based on a first noise estimation signal and a target signal estimator.

[0094] The target signal estimator is used to model or compensate for acoustic paths, such as secondary paths or error paths. It can be an adaptive filter, a transfer function model, or a neural network-based estimation module. Specifically, a first noise estimation signal can be input to the target signal estimator, which then generates a second noise estimation signal based on acoustic transfer characteristics. The second noise estimation signal reflects the expected residual noise or noise components to be canceled at the target control point (e.g., the ear position). This second noise estimation signal can be used to subsequently generate a cancellation signal or update the parameters of the target controller to improve noise suppression accuracy.

[0095] In some embodiments of this disclosure, the function of building an offline target signal estimator can also be supported. The noise control method further includes: acquiring a first noise signal collected by a first pickup device and acquiring a second noise signal collected by a second pickup device temporarily placed at different locations; and building a target signal estimator based on the acquired first noise signal and second noise signal.

[0096] It should be noted that the temporary placement of the second microphone can be adjusted according to the user's sitting posture. Different seat positions / different user sitting postures correspond to different positions of the second microphone. Then, the first and second microphones synchronously acquire the first and second noise signals. For example, N typical sitting postures are selected (e.g., the front, middle, and back of the seat; upright and tilted back, etc.). In each posture, a second microphone (near-field reference microphone) is temporarily placed near the user's ear. Under different dynamic calibration conditions, the noise signals from the far-field microphone and the near-field microphone are synchronously acquired. Then, a mapping relationship between the noise signals from the far-field and near-field microphones is established to construct a target signal estimator. The target signal estimator can be an adaptive filter.

[0097] In some embodiments of this disclosure, to reduce the computational load of signal estimation or mapping and to meet the real-time requirements of the vehicle-mounted embedded platform, signal estimation or mapping can also be performed in the frequency domain. Based on the acquired first noise estimation signal and second noise estimation signal, a target frequency domain signal estimator is constructed. Specifically, when the target signal estimator is a target frequency signal estimator, the acquired first noise estimation signal and second noise estimation signal are subjected to frequency domain transformation respectively to obtain a first frequency domain signal and a second frequency domain signal; based on the first frequency domain signal and the second frequency domain signal, the target frequency domain signal estimator is constructed. Specifically, the target frequency domain signal estimator can perform filtering calculations in the target frequency band and bypass frequency components outside the target frequency band.

[0098] For example, the target frequency band can be 20–250Hz. Modeling methods can employ Wiener solutions, least squares, LMS-type adaptive filtering algorithms, etc., and are not limited to these embodiments in this disclosure. The target frequency domain signal estimator directly sets frequency components outside the target frequency band to zero, excluding them from calculation, thus avoiding the introduction of high-frequency band estimation errors and reducing computational load.

[0099] In this embodiment of the disclosure, a target frequency domain signal estimator is constructed based on a first frequency domain signal and a second frequency domain signal. Specifically, this includes: solving for the frequency domain signal estimator coefficients that make the estimation error frequency domain signal at different positions meet the target conditions based on the first frequency domain signal and the second frequency domain signal; and constructing the target frequency domain signal estimator based on the frequency domain signal estimator coefficients.

[0100] In this embodiment of the disclosure, controlling the noise at the second location based on the second residual noise estimation signal specifically includes: updating the target signal controller based on the second residual noise estimation signal; and controlling the noise at the second location based on the updated target signal controller.

[0101] Optionally, the target signal controller is updated based on the second residual noise estimation signal, specifically including: performing a frequency domain transformation on the second residual noise estimation signal to obtain a second noise error frequency domain estimation signal; and updating the target frequency domain signal controller based on the second noise error frequency domain estimation signal.

[0102] In this embodiment of the disclosure, controlling the noise at the second position based on the updated target signal controller specifically includes: generating a second-stage cancellation signal based on the reference noise signal collected by the updated target signal controller and the third pickup device; and controlling the sound-generating device to output the cancellation signal based on the cancellation signal to suppress the noise at the second position.

[0103] Optionally, the first pickup device is a first physical pickup device. Based on the first residual noise signal collected by the first pickup device, the acquisition of a second residual noise estimation signal includes: based on the first residual noise signal collected by the first physical pickup device, the acquisition of a second residual noise estimation signal at a second virtual pickup device.

[0104] In this embodiment, the first pickup device can be a physically installed far-field error microphone or a remote physical microphone; the second pickup device can be a near-field error microphone or a near-field virtual microphone virtually constructed through signal processing. To better understand the noise control of this disclosure, this embodiment provides a schematic diagram of another noise control system, such as... Figure 2 As shown. The system includes the following functional modules: primary noise source, reference sensor, ANC controller, secondary speaker, far-field error microphone (remote physical microphone), and near-field error microphone (near-field virtual microphone).

[0105] It should be noted that the primary noise source can represent the physical source of external environmental noise, such as engine, tire and road surface excitation, wind noise, etc., and the sound waves generated by them propagate through the air to form a primary sound field. The reference sensor can be placed close to the noise source to collect a reference signal strongly correlated with the primary noise, serving as the feedforward input to the ANC controller. The ANC controller, the core processing unit, receives the reference signal and error feedback signal, runs an adaptive algorithm, and generates control signals to drive the secondary speaker. The secondary speaker can generate inverted sound waves based on the control signal to cancel out the primary noise in the target area. The primary noise source can be referred to as the acoustic propagation path from the primary noise source to the error microphone, including effects such as air medium, vehicle body structure reflection and absorption; the secondary path can be referred to as the acoustic transmission path from the secondary loudspeaker to the error microphone, which usually needs to be identified online to ensure control accuracy; the far-field error microphone can be placed in a fixed position far away from the user's ear, such as the center of the roof, to acquire the global residual noise signal as the feedback input of the traditional ANC system; the near-field error microphone can be placed in the target area close to the user's ear, such as the side of the headrest or a temporary wearable microphone, to collect the noise signal actually perceived by the human ear.

[0106] To better understand the noise control content of this disclosure, in conjunction with the above... Figure 2 Correspondingly, this disclosure provides a schematic diagram of another noise control method, such as... Figure 3 As shown, in Figure 3 The meanings of each parameter are as follows: 1. It is the original noise signal at the far-field error microphone, i.e., the first noise signal; 2. It is the noise estimation signal at the far-field error microphone, i.e., the first noise estimation signal; 3. It is the noise estimation signal at the near-field error microphone, i.e., the second noise estimation signal; 4. It is the noise signal at the near-field error microphone, i.e., the second noise signal; 5. It is the noise signal at the near-field error microphone, i.e., the second residual noise signal; 6. It is a far-field physical secondary path model used to filter the cancellation signal to simulate the acoustic response generated when the cancellation signal propagates through the secondary path to the corresponding far-field error microphone. 7. It is a near-field virtual secondary path estimation model used to filter the cancellation estimation signal to simulate the acoustic response generated when the signal propagates through the secondary path to the corresponding far-field error microphone; 8. It is a near-field virtual secondary path estimation model used to filter the cancellation estimation signal to simulate the acoustic response generated when the signal propagates through the physical secondary path to the corresponding near-field error microphone; 9. It is a target signal estimator used to obtain other noise estimation signals based on known noise signals; 10. It is a target signal controller used to generate control signals based on noise estimation signals; 11. It is a sound-generating device used to output a cancellation signal according to a control signal; 12. It is a reference noise signal.

[0107] Apart from Figure 3 The following additional concepts are provided for the various concepts or meanings mentioned in the text: 13. It is the cancellation estimation signal at the far-field error microphone, i.e., the first cancellation estimation signal; 14. It is the cancellation signal at the near-field error microphone, i.e., the second-stage cancellation signal; 15. It is the cancellation estimation signal at the near-field error microphone, i.e., the second-stage residual noise signal.

[0108] To better understand the noise control content of this disclosure, in conjunction with the above... Figure 1 Correspondingly, this disclosure provides a schematic diagram of another noise control method, such as... Figure 4 As shown.

[0109] It should be noted that traditional RNC systems use physical error microphones placed near the ear (such as in the headrest) to acquire residual error signals. Although this can achieve optimal noise reduction near the ear, the solution requires integrating the microphone into the headrest, which increases hardware costs and is sensitive to the passenger's posture, thus encroaching on passenger space.

[0110] To address the aforementioned issues, some solutions utilize a roof-mounted microphone (far-field microphone) as an error sensor, avoiding modifications to the headrest hardware. However, its noise-canceling quiet zone is only located at the microphone (far field), far from the ear, resulting in a significant reduction in actual noise reduction near the ear and poor perceived noise reduction for passengers. While virtual sensing (RMT) technology can shift the noise reduction target point closer to the ear, existing virtual sensing solutions suffer from the following core drawbacks: (1) Excessive computation: Traditional time-domain observation filters are numerous, and the computational workload of real-time time-domain convolution exceeds the upper limit of the computing power of the vehicle-mounted embedded platform in broadband road noise scenarios; (2) Poor high frequency estimation accuracy: Due to the complexity of the sound field modes and weak correlation in the high frequency band, the estimation error of the observation filter for high frequency noise is large. If the entire frequency band is processed, it will not only waste computing power, but also introduce high frequency noise estimation error, leading to instability of the control system. (3) Insufficient robustness to changes in sitting posture: Adjusting the passenger seat forward and backward, changing the backrest angle, etc. will cause significant changes in the acoustic transmission path between the near field and the far field. The fixed observation filter cannot adapt to this dynamic change, resulting in distortion of virtual sensing estimation or even system divergence.

[0111] In summary, existing technologies are unable to achieve practical broadband road noise virtual sensing active control while simultaneously considering both computational power constraints and stability requirements.

[0112] To address the core problems of existing broadband path noise virtual sensing active control systems, such as limited computing power, poor high-frequency estimation accuracy, and insufficient robustness to sitting posture, this disclosure proposes a broadband path noise virtual sensing ANC system based on frequency domain observation filters and min-max robust optimization. (1) Effective suppression of broadband road noise at the ear: Using virtual sensing technology, the noise reduction and silencing zone is moved from the far-field vehicle microphone to the vicinity of the ear, achieving efficient suppression of broadband road noise of 20–250 Hz near the ear without the need to deploy a physical microphone at the headrest; (2) Reduce system computing power requirements: Convert the observation filter from the time domain to the frequency domain and combine it with the frequency domain block adaptive filtering framework. Only perform filtering calculations on the key target frequency band of 20–250 Hz. While ensuring the noise reduction effect, the amount of calculation is greatly reduced, which meets the real-time requirements of the vehicle embedded platform. (3) Improve the stability of the high-frequency band system: By limiting the observation filter to work only in the target low-frequency band, the estimation error caused by the complexity of the high-frequency sound field modes is avoided, thereby avoiding the risk of high-frequency control divergence; (4) Enhance robustness under different sitting postures: During the offline calibration stage, acoustic measurements are performed on various typical sitting postures (fore-and-aft position of the seat, backrest angle, etc.). A robust observation filter is designed based on the min-max criterion to ensure that the system can operate stably and effectively under various sitting posture conditions.

[0113] The system hardware includes: an accelerometer (mounted on the chassis to collect road noise reference signals) X(Z) The system includes a far-field in-vehicle microphone (fixed on the roof, used to acquire error signals during online control), a near-field microphone (temporarily installed near the ear on the headrest during offline calibration, and cannot be deployed during online operation), and an in-vehicle speaker (used to output secondary control sound waves). X(Z) accelerometer - W(Z) control filter; Y(Z) speaker output signal; Offline acoustic calibration stage 1. Acoustic measurements in multiple seated positions.

[0114] N typical sitting postures were selected (e.g., forward, middle, and rearmost seat positions; upright and tilted back). For each posture, a near-field reference microphone was temporarily placed near the ear. Under different dynamic calibration conditions, the far-field vehicle-mounted microphone signal d_m(t) and the near-field target microphone signal d_v(t) (i = 1, …, N) were simultaneously acquired. The far-field signal was from the top of the head, and the near-field signal was from the headrest; different seat positions corresponded to different microphone signals. 2. Frequency domain observation filter identification.

[0115] The original noise signals from the synchronously acquired far-field and near-field microphones are subjected to a frequency domain Fourier transform (FFT) to obtain the frequency domain signals D_m(f) and D_v(f). Within the target frequency band F_target = [20 Hz, 250 Hz], a frequency domain mapping relationship from the far-field microphone to the near-field microphone is established: D_hat_v(f) = O_mv(f) · D_M(f) Where O_mv(f) is the multi-channel frequency domain observation filter to be identified, and the modeling method can employ Wiener solution, least squares, LMS-type adaptive filtering algorithms, etc. Frequency components outside the target frequency band are directly set to zero and not included in the calculation, thus avoiding the introduction of high-frequency band estimation errors and reducing computational load. U 3. Optimization of robust observation filter based on min-max criterion.

[0116] To ensure good estimation performance of the observation filter under all sitting postures, the min-max robust optimization criterion is used to solve for the optimal frequency domain filter coefficients that minimize the maximum estimation error under each sitting posture: O (f) = arg min_O max_{i=1,...,N}||D_v(f)-O_mv(f)·D_m(f)||^2,f∈F_target The above optimization can be transformed into a convex optimization problem, which can be solved during the offline calibration stage to obtain the robust observation filter O. (f). After inverse IFFT transformation and truncation, the corresponding multi-channel time-domain FIR filter coefficients O are obtained. (t), stored internally within the system.

[0117] Online real-time control stage The online real-time control system is based on a frequency domain block adaptive filtering framework and mainly includes the following three modules: 1. Virtual error signal estimation module.

[0118] During the online control phase, the residual error signal e_m(n) is acquired using the far-field vehicle microphone. The original noise signal d_hat_m(n) at the far-field microphone is then estimated using the IMC method. Finally, this signal is compared with the frequency domain observation filter O obtained offline. (f) Perform frequency domain filtering within the target frequency band to estimate the virtual residual error signal at the near-field ear target point: d_hat_v(n)=IFFT(O (f)·FFT(d_hat_m(n))), f∈F_target The estimated signal d_hat_v(n) represents the original noise signal at the near-field microphone obtained by estimation. Then, the result of convolving the speaker output signal with the secondary path is superimposed to calculate the residual error signal e_hat_v(n) at the near-field virtual position when the RNC system is working, and it is used to update the adaptive control filter.

[0119] 2. Frequency domain adaptive control module.

[0120] The system processes the input signal in blocks and performs adaptive updates of the control filter coefficients in the frequency domain. The reference signal x(n) is provided by the accelerometer, and the update formula for the control filter W(f) is: W_{k+1}(f) = W_k(f) + μ·X (f)·FFT(e_hat_v(n)),f ∈ F_target Where μ is the step size and X(f) is the frequency domain representation of the reference signal. This frequency domain controlled filter can be transformed back to the time domain by IFFT, and then convolved with the reference signal vector to output a secondary output signal y(n) on a sample-by-sample basis. This signal ultimately drives the in-vehicle speakers to emit a secondary cancellation signal to suppress low-frequency road noise.

[0121] 3. Target frequency band filtering and bypass processing module For frequency components outside the range of F_target, the system directly bypasses them (the output of the observation filter is set to zero) without active control, thereby preventing high-frequency estimation errors from causing system divergence, ensuring overall system stability, and saving computing power.

[0122] The embodiments disclosed herein are applicable to broadband active road noise control in enclosed spaces such as automobiles, and are particularly suitable for scenarios where it is not possible to routinely place physical microphones near passengers' ears. Through virtual sensing and frequency domain robust optimization, effective suppression of low-frequency road noise near the ear can be achieved.

[0123] 1. Broadband road noise virtual sensing noise reduction area migration.

[0124] This disclosure applies broadband virtual sensing technology to in-vehicle road noise active control, breaking the limitation of traditional RNC systems that rely on physical error microphones on headrests. By using offline identified frequency domain observation filters, the noise reduction and quieting zone is shifted from the far-field vehicle microphone to the vicinity of the ear, significantly improving the noise reduction effect actually perceived by the passenger without changing the existing vehicle hardware layout.

[0125] 2. Low-computing-power implementation of frequency domain observation filter + target frequency band limitation.

[0126] By converting the observation filter to the frequency domain and combining it with the frequency domain block adaptive filter algorithm framework, virtual sensing operations are performed in the key target frequency band of 20–250 Hz. This effectively avoids the problems of difficult sound field modeling and large estimation errors in the high-frequency band, while reducing the computational load to a level that meets the real-time processing requirements of the vehicle-mounted embedded platform.

[0127] 3. Design of a robust observation filter for multiple sitting postures based on the min-max criterion. Deviations for all sitting postures. To address acoustic path disturbances caused by changes in passenger posture, a robust observation filter optimization method based on the min-max criterion is proposed. Through offline acoustic calibration covering various typical sitting postures, the optimal frequency domain filter that minimizes the worst-case estimation error is solved, ensuring stable and reliable system operation across the entire sitting posture range.

[0128] 1. Frequency domain block virtual error estimation.

[0129] Within the frequency domain block adaptive filter control framework, the virtual error estimation process is implemented entirely in the frequency domain, sharing computational resources with the adaptive update of the control filter, thus avoiding the computational overhead caused by additional time-domain convolution.

[0130] 2. Target frequency band bypass design.

[0131] In the observation filter processing module, frequency components outside 20–250 Hz are bypassed, which reduces invalid calculations and prevents high-frequency estimation errors from being fed back to the control loop. This is a key design feature to ensure system stability.

[0132] 3. Multi-position acoustic calibration and min-max robust optimization.

[0133] In the offline calibration stage, the near-field and far-field acoustic transmission relationships under N typical sitting postures are systematically measured. The min-max optimization problem is solved in the frequency domain to generate a robust observation filter with both wide adaptability and noise reduction performance.

[0134] A method and system for active control of in-vehicle broadband road noise based on virtual sensing, characterized by comprising the following: (1) Offline acoustic calibration stage: For various typical sitting postures, near-field ear microphone and far-field vehicle microphone signals are collected, and frequency domain observation filters are identified based on the min-max robust criterion within the target frequency band (20–250 Hz); (2) Frequency domain observation filter: Establish frequency domain mapping from far-field vehicle microphone signal to near-field target signal, which is effective only in the low-frequency band of the target and has zero bypass on the target frequency band; (3) Online real-time control: Based on the frequency domain block adaptive filtering framework, the virtual ear residual error signal is estimated by the frequency domain observation filter, and the control filter is adaptively updated to suppress broadband path noise at the human ear; (4) Robustness guarantee mechanism: The min-max robust observation filter ensures that the system can operate stably under different sitting postures without the need for online re-identification of the transfer function.

[0135] In existing in-vehicle range extender narrowband noise active control (NANC) systems, the built-in microphones of the in-vehicle multimedia system (vehicle infotainment microphones) are typically used as error microphones. These microphones are mounted on the roof, far from the passengers' ears, and are considered far-field microphones. The system uses an adaptive filtering algorithm to drive the secondary speakers in the doors, creating a noise reduction "silent zone" at the far-field error microphones, thereby suppressing the narrowband line spectrum noise generated by the range extender.

[0136] However, traditional methods have the following main drawbacks: 1. The silent zone does not coincide with the human ear: Existing NANC systems establish the silent zone at the far-field microphone, rather than at the passenger's ear. Due to the significant acoustic differences between the far-field microphone and the human ear, the noise reduction effect at the far field cannot effectively reflect the actual noise suppression at the human ear, resulting in limited improvement in the passenger's subjective hearing experience. During the real-time control phase, due to limitations in user experience and engineering implementation conditions, it is impossible to deploy physical error microphones at the passenger's ear in real time, therefore, ear noise cannot be directly used as the control target.

[0137] 2. High computational complexity of multi-channel observation filters: Virtual sensing systems require the introduction of multi-channel long-order observation filters. Traditional implementations use full-order FIR vector convolution, which involves a large amount of computation and is difficult to meet real-time requirements on automotive embedded platforms.

[0138] 3. Changes in sitting posture lead to insufficient system robustness: The passenger's sitting posture (such as backrest angle, head position, seat adjustment height, etc.) will change the acoustic transfer function between the human ear and the microphone, causing the offline calibrated acoustic model to be mismatched with the real-time operating conditions, affecting the system stability and noise reduction effect.

[0139] The purpose of this disclosure is to provide a method and system for active noise control of in-vehicle range extenders based on virtual sensing, in order to solve problems such as the noise reduction and quiet zone not coinciding with the position of the human ear in existing NANC systems, the high computational complexity of multi-channel observation filters, and insufficient system robustness caused by changes in sitting posture.

[0140] This disclosure employs narrowband virtual sensing technology. By establishing an offline observation filter dictionary, the noise reduction quiet zone is shifted from the far-field vehicle microphone to a near-field virtual target point near the ear, effectively suppressing range extender noise at the ear without requiring real-time deployment of ear microphones. To address the computational complexity of multi-channel long-order observation filters, an Acoustic Transfer Function Dictionary (ATFD) method based on Discrete Fourier Transform (DFT) is used. The sine and cosine components of the observation filters at frequencies corresponding to different engine speeds are pre-modeled offline, significantly reducing real-time computing power requirements. To address stability issues caused by changes in seating posture, acoustic calibration is performed for different seating states, and robust optimization of the observation filters is conducted based on the Min-Max criterion, thereby improving the system's stability and noise reduction consistency under different operating conditions.

[0141] The multi-channel NANC system based on virtual sensing in this disclosure introduces a virtual sensing module into the traditional NANC system, shifting the noise reduction target from far-field error microphones (M) to near-field virtual target points (V, corresponding to the passenger's ear position). The system also acquires the range extender speed information (RPM) via the CAN bus, converts it into the fundamental frequency F and its various harmonic frequencies (F, 2F, 3F, etc.) as reference signals, and drives S door secondary speakers for active noise reduction.

[0142] The technical solution of this disclosure is divided into two parts: an offline calibration stage and a real-time control stage.

[0143] Offline calibration stage (1) Secondary path calibration: Measure and record the acoustic transfer function vectors of the secondary paths between S secondary loudspeakers and M far-field error microphones. _{s,m} (s=1,...,S; m=1,...,M), and the virtual secondary path acoustic transfer function vectors between S secondary loudspeakers and V near-field virtual target points (at the human ear). _{s,v}(s=1,...,S; v=1,...,V).

[0144] (2) Construction of the ATFD dictionary for the observation filter: The classic full-order FIR observation filter can be established by temporarily placing microphones at the target virtual point, and then using the multi-channel Wiener solution or LMS-type algorithms to obtain the transfer function between the far-field physical microphone and the near-field virtual microphone. Then, for the harmonic frequency set {ω_k} corresponding to different speed ranges of the range extender, the sine component OF_{m,v}^{sin}(ω_k) and the cosine component OF_{m,v}^{cos}(ω_k) of the observation filter OF_{m,v} from the M channel to the V channel at that frequency point are calculated respectively, and stored offline as ATFD_OF in dictionary form to replace real-time convolution operation. The calculation formulas for the cosine and sine components of the observation filter are as follows.

[0145]

[0146] Similarly, construct the ATFD_SP dictionary for secondary paths.

[0147] (3) Multi-position acoustic calibration and robust observation filter design: For P typical sitting postures (such as different backrest angles, headrest heights, etc.), the acoustic transmission relationship between M far-field microphones and V near-field virtual target points (ears) under each sitting posture is measured to obtain the ideal observation filter OF_p (p=1,...,P) corresponding to each sitting posture. Based on the Min-Max criterion, the robust observation filter OF is solved. To minimize the virtual sensing estimation error under the worst-case sitting posture: OF = argmin_{OF} max_{p=1,...,P} ||OF - OF_p||² Finally, the OF obtained from the equilibrium will be... Perform ATFD dictionary-based processing and storage.

[0148] [Real-time control phase] (1) Reference signal generation: The RPM is acquired in real time through the CAN bus, the current harmonic frequencies ω_k of each order are calculated, and the corresponding reference sine and cosine signals x_k(n) = [cos(ω_k·n), sin(ω_k·n)].

[0149] (2) Virtual sensing error estimation based on ATFD dictionary: According to the current frequency ω_k, look up the ATFD_OF table to obtain the sine and cosine components of the robust observation filter at that frequency point. Then, perform a weighted summation of the measured error signals e_m from the M far-field microphones to estimate the virtual error signals ê_v from the V virtual target points (at the ear). ê_v(n) = Σ_m [OF _{m,v}^{cos}(ω_k)·Re{e_m(n)} + OF _{m,v}^{sin}(ω_k)·Im{e_m(n)}] (3) ATFD dictionary-based control filter update: According to the current frequency ω_k, look up the ATFD_SP table to obtain the sine and cosine components of the secondary path at this frequency point. The classic narrowband FxLMS algorithm is used with the virtual error signal ê_v as the optimization target. The traditional vector convolution is replaced by scalar dot product to update the control filter weights and drive the S secondary speakers to output cancellation signals. Finally, a noise reduction and quiet zone is formed at the virtual target point (at the ear) to suppress the narrowband noise of the range extender.

[0150] The embodiments disclosed herein can be used for active control of low-frequency line spectrum narrowband noise in automobile range extenders / engines and large rotating equipment, and have a wide range of applications.

[0151] This embodiment of the disclosure applies narrowband virtual sensing technology to the in-vehicle range extender NANC system. By observing the filter, an acoustic mapping is established between the far-field vehicle microphone and the near-field human ear virtual target point. Without increasing hardware costs, the noise reduction and quiet zone is accurately transferred from the far-field microphone to the human ear position, significantly improving the passenger's subjective noise reduction experience.

[0152] To address the high computational bottleneck of multi-channel long-order observation filters in virtual sensing, a dictionary-based observation filter scheme based on the ATFD dictionary is proposed for the first time. The sine and cosine components of the observation filter at the corresponding frequency points of each RPM are pre-calculated and stored offline. Real-time calculation only requires scalar table lookup and dot multiplication, which greatly reduces the computational complexity.

[0153] To address the system robustness issue caused by changes in sitting posture, a multi-sitting posture robust observation filter optimization method based on the Min-Max criterion is proposed. By performing acoustic calibration on various typical sitting postures and solving for the optimal robust solution, the stability and noise reduction consistency of the system under different sitting conditions are improved.

[0154] ATFD dictionaryization of observation filters: Using DFT approximation, the multi-channel observation filter is decomposed into sine and cosine components at each frequency point. After offline modeling, it is stored in dictionary form. Real-time control only requires table lookup and scalar operation, which solves the computational bottleneck of multi-channel convolution in virtual sensing.

[0155] Min-Max Robust Observation Filter Design: By performing acoustic calibration on various preset typical sitting postures, the Min-Max optimization criterion is used to solve for the robust observation filter with the smallest estimation error under the worst sitting posture, ensuring that the system has good noise reduction effect and stability under different sitting postures.

[0156] Co-design of virtual sensing and NANC controller: The virtual sensing module is seamlessly integrated into the narrowband noise active control framework. The virtual error signal replaces the actual far-field error signal to drive the controller update, realizing the system-level transfer of the control target from the far field to the near field.

[0157] An offline modeling method for narrowband virtual sensing observation filters based on ATFD dictionary and its application in a real-time NANC system.

[0158] A robust observation filter design method based on multi-position acoustic calibration and the Min-Max criterion.

[0159] A complete system implementation scheme integrating the above-mentioned virtual sensing method with a narrowband NANC controller and applying it to the active noise control of in-vehicle range extenders.

[0160] Improved noise reduction: The quiet zone precisely covers the ear position, resulting in a higher level of noise suppression perceived by passengers' ears compared to traditional far-field NANC solutions.

[0161] Low computational cost implementation: The dictionary-based observation filter replaces multi-channel long-order convolution with scalar dot product, meeting the real-time requirements of automotive embedded platforms.

[0162] Strong robustness: The robust observation filter has good adaptability to changes in sitting posture and can maintain stable noise reduction performance under different sitting conditions.

[0163] No additional hardware cost: Make full use of existing in-vehicle microphones, eliminating the need for additional sensors installed in the ear, and can be directly integrated into the existing NANC hardware platform.

[0164] In some examples, embodiments of this application may include, but are not limited to, the following: Example 1: A noise control method, comprising: determining a noise reduction method based on the noise type of a smart device; Based on the noise reduction method and / or the target noise reduction location, determine the control signal for at least one sound-generating device; By controlling at least one sound-generating device to output a cancellation signal through a control signal, a noise reduction and quiet zone is formed at the target noise reduction location.

[0165] Example 2: The noise type is broadband noise or narrowband noise.

[0166] Example 3: When the noise type is broadband noise, a first noise reduction method is determined, which is a noise reduction method based on the target frequency band; when the noise type is narrowband noise, a second noise reduction method is determined, which is a noise reduction method based on the target frequency point; when the noise type is mixed noise, a third noise reduction method is determined, which is a method of separating narrowband noise and broadband noise from the mixed noise and reducing narrowband noise and broadband noise respectively based on the target frequency point and the target frequency band.

[0167] Example 4: Obtain the second residual noise estimation signal at the target noise reduction location; determine the control signal of at least one sound-generating device based on the noise reduction method and the second residual noise estimation signal.

[0168] Example 5: When the noise reduction method is the first noise reduction method, the control signal of at least one sound-generating device in the target frequency band is determined based on the second residual noise estimation signal and the target frequency domain controller; when the noise reduction method is the second noise reduction method, the control signal of at least one sound-generating device at the target frequency point is determined based on the second residual noise estimation signal and the target control dictionary; when the noise reduction method is the third noise reduction method, the control signal of at least one sound-generating device in the target frequency band is determined based on the second residual noise estimation signal and the target frequency domain controller, and the control signal of at least one sound-generating device at the target frequency point is determined based on the second residual noise estimation signal and the target control dictionary.

[0169] Example 6: Based on the second residual noise estimation signal, a second noise error frequency domain estimation signal is obtained; based on the second noise error frequency domain estimation signal, an updated target frequency domain signal controller is obtained; based on the updated target frequency domain signal controller, a control signal for at least one sound-generating device in the target frequency band is determined.

[0170] Example 7: Obtain a reference noise signal; determine the control signal of at least one sound-generating device in the target frequency band based on the updated target frequency domain signal controller and the reference noise signal.

[0171] Example 8: Based on the second residual noise estimation signal, obtain the updated target control dictionary; based on the updated target control dictionary, determine the control signal of at least one sound-generating device at the target frequency point.

[0172] Example 9: Obtain the first residual noise signal collected by the pickup device, wherein the pickup device and the target noise reduction location are at different positions; based on the first residual noise estimation signal, obtain the second residual noise estimation signal at the target noise reduction location.

[0173] Example 10: Based on the first residual noise signal, obtain a first noise estimation signal; based on the first noise estimation signal, obtain a second noise estimation signal; based on the second noise estimation signal, obtain a second residual noise estimation signal.

[0174] Example 11: Obtain the second noise estimation signal based on the first noise estimation signal and the target frequency domain signal estimator.

[0175] Example 12: Acquire a first noise signal collected by a first pickup device, and acquire a second noise signal collected by a second pickup device at different locations; acquire a first frequency domain noise signal and a second frequency domain noise signal based on the first noise signal and the second noise signal; acquire a target frequency domain signal estimator based on the first frequency domain noise signal and the second frequency domain noise signal.

[0176] Example 13: The first pickup device is fixedly installed on the smart device, and the second pickup device is temporarily placed on the smart device.

[0177] Example 14: Obtain the first noise estimation signal based on the first residual noise estimation signal and the first cancellation estimation signal.

[0178] Example 15: Obtain the second residual noise estimation signal based on the second noise estimation signal and the second cancellation estimation signal.

[0179] Example 16: The target noise reduction location is the location of the user's ear.

[0180] Example 17: The second residual noise estimation signal is the signal after the second cancellation signal cancels the second noise signal, and the first residual noise signal is the noise signal after the first cancellation signal cancels the first noise signal.

[0181] Example 18: Obtain the target noise reduction position based on the user's sitting posture and / or seat status.

[0182] It should be noted that the noise control method involved in the above embodiments may include at least one of Embodiments 1 to 18. For example, Embodiment 1 can be implemented as an independent embodiment, Embodiment 1+2 can be implemented as an independent embodiment, Embodiment 1+3 can be implemented as an independent embodiment, Embodiment 1+2+3 can be implemented as an independent embodiment, Embodiment 1+4 can be implemented as an independent embodiment, Embodiment 1+2+4 can be implemented as an independent embodiment, Embodiment 1+3+4 can be implemented as an independent embodiment, Embodiment 1+2+3+4 can be implemented as an independent embodiment, Embodiment 1+5 can be implemented as an independent embodiment, Embodiment 1+2+5 can be implemented as an independent embodiment, Embodiment 1+3+5 can be implemented as an independent embodiment, Embodiment 1+2+3+5 can be implemented as an independent embodiment, Embodiment 1+3+4+5 can be implemented as an independent embodiment, Embodiment 1+2+3+4+5 can be implemented as an independent embodiment, and Embodiment 1+6 can be implemented as an independent embodiment. Examples 1+2+6 can be implemented as independent embodiments, examples 1+3+6 can be implemented as independent embodiments, examples 1+2+3+6 can be implemented as independent embodiments, examples 1+3+4+6 can be implemented as independent embodiments, examples 1+2+3+4+6 can be implemented as independent embodiments, examples 1+2+4+5+6 can be implemented as independent embodiments, examples 1+3+4+5+6 can be implemented as independent embodiments, examples 1+2+3+4+5+6 can be implemented as independent embodiments, examples 1+7 can be implemented as independent embodiments, examples 1+2+7 can be implemented as independent embodiments, examples 1+3+7 can be implemented as independent embodiments, examples 1+2+3+7 can be implemented as independent embodiments, examples 1+4+7 can be implemented as independent embodiments, and so on, and are not limited to these, and will not be listed one by one here.

[0183] Furthermore, as Figure 1 The specific implementation of the method shown in this disclosure provides a noise control device, such as... Figure 5 As shown, the device includes: a first determining module 31, a second determining module 32, and a control module 33.

[0184] The first determining module 31 is configured to determine the noise reduction method based on the noise type of the smart device; The second determining module 32 is configured to determine a control signal for at least one sound-generating device based on the noise reduction method and / or the target noise reduction position. The control module 33 is configured to control at least one sound-generating device to output a cancellation signal via a control signal to form a noise reduction and silencing zone at the target noise reduction location.

[0185] In some examples of this embodiment, the noise type is broadband noise or narrowband noise.

[0186] In some examples of this embodiment, the first determining module 31 is further configured to include at least one of the following: When the noise type is broadband noise, a first noise reduction method is determined, which is a noise reduction method based on the target frequency band. When the noise type is narrowband noise, a second noise reduction method is determined, while the first noise reduction method is a noise reduction method based on the target frequency point; When the noise type is mixed noise, a third noise reduction method is determined. The third noise reduction method is to separate narrowband noise and broadband noise from the mixed noise and reduce the noise of narrowband noise and broadband noise respectively based on the target frequency point and the target frequency band.

[0187] In some examples of this embodiment, the second determining module 32 is further configured as follows: Obtain the second residual noise estimation signal at the target noise reduction location; Based on the noise reduction method and the second residual noise estimation signal, determine the control signal for at least one sound-generating device.

[0188] In some examples of this embodiment, the second determining module 32 is further configured to include at least one of the following: When the noise reduction method is the first noise reduction method, the control signal of at least one sound-generating device in the target frequency band is determined based on the second residual noise estimation signal and the target frequency domain controller. When the noise reduction method is the second noise reduction method, the control signal of at least one sound-generating device at the target frequency point is determined based on the second residual noise estimation signal and the target control dictionary. When the noise reduction method is the third noise reduction method, the control signal of at least one sound-generating device in the target frequency band is determined based on the second residual noise estimation signal and the target frequency domain controller, and the control signal of at least one sound-generating device at the target frequency point is determined based on the second residual noise estimation signal and the target control dictionary.

[0189] In some examples of this embodiment, the second determining module 32 is further configured as follows: Based on the second residual noise estimation signal, the second noise error frequency domain estimation signal is obtained; Based on the second noise error frequency domain estimation signal, obtain the updated target frequency domain signal controller; Based on the updated target frequency domain signal controller, determine the control signal for at least one sound-generating device in the target frequency band.

[0190] In some examples of this embodiment, the second determining module 32 is further configured as follows: Obtain the reference noise signal; Based on the updated target frequency domain signal controller and the reference noise signal, determine the control signal for at least one sound-generating device in the target frequency band.

[0191] In some examples of this embodiment, the second determining module 32 is further configured as follows: Based on the second residual noise estimation signal, obtain the updated target control dictionary; Based on the updated target control dictionary, determine the control signal for at least one sound-generating device at the target frequency point.

[0192] In some examples of this embodiment, the second determining module 32 is further configured as follows: Acquire the first residual noise signal collected by the pickup device, wherein the pickup device and the target noise reduction location are located at different positions; Based on the first residual noise estimation signal, the second residual noise estimation signal at the target noise reduction location is obtained.

[0193] In some examples of this embodiment, the second determining module 32 is further configured as follows: Based on the first residual noise signal, a first noise estimation signal is obtained; Based on the first noise estimation signal, obtain the second noise estimation signal; Based on the second noise estimation signal, the second residual noise estimation signal is obtained.

[0194] In some examples of this embodiment, the second determining module 32 is further configured as follows: Based on the first noise estimation signal and the target frequency domain signal estimator, the second noise estimation signal is obtained.

[0195] In some examples of this embodiment, the second determining module 32 is further configured as follows: Acquire a first noise signal collected by a first pickup device, and acquire second noise signals collected by a second pickup device at different locations; Based on the first noise signal and the second noise signal, obtain the first frequency domain noise signal and the second frequency domain noise signal; Based on the first frequency domain noise signal and the second frequency domain noise signal, obtain the target frequency domain signal estimator.

[0196] In some examples of this embodiment, the first microphone is fixedly installed on the smart device, and the second microphone is temporarily placed on the smart device.

[0197] In some examples of this embodiment, the second determining module 32 is further configured as follows: The first noise estimation signal is obtained based on the first residual noise estimation signal and the first cancellation estimation signal.

[0198] In some examples of this embodiment, the second determining module 32 is further configured as follows: A second residual noise estimation signal is obtained based on the second noise estimation signal and the second cancellation estimation signal.

[0199] In some examples of this embodiment, the target noise reduction location is the location of the user's ear.

[0200] In some examples of this embodiment, the second residual noise estimation signal is the signal after the second cancellation signal cancels the second noise signal, and the first residual noise signal is the noise signal after the first cancellation signal cancels the first noise signal.

[0201] In some examples of this embodiment, the second determining module 32 is further configured as follows: The target noise reduction location is obtained based on the user's sitting posture and / or seat position.

[0202] It should be noted that other corresponding descriptions of the functional units involved in the resource display device provided in this embodiment can be found in [reference]. Figure 1 The corresponding descriptions in [the document] will not be repeated here.

[0203] Based on the above, Figure 1 Accordingly, this embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. Figure 1 The method shown.

[0204] Figure 6 A schematic block diagram of an example smart terminal that can be used to implement embodiments of the present disclosure is shown.

[0205] Figure 7 A schematic block diagram of an example electronic device 400 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0206] like Figure 7As shown, the electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes based on a computer program stored in ROM (Read-Only Memory) 402 or a computer program loaded from storage unit 408 into RAM (Random Access Memory) 403. The RAM 403 may also store various programs and data required for the operation of the electronic device 400. The computing unit 401, ROM 402, and RAM 403 are interconnected via a bus 404. An I / O (Input / Output) interface 405 is also connected to the bus 404.

[0207] Multiple components in electronic device 400 are connected to I / O interface 405, including: input unit 406, such as keyboard, mouse, etc.; output unit 407, such as various types of displays, speakers, etc.; storage unit 408, such as disk, optical disk, etc.; and communication unit 409, such as network card, modem, wireless transceiver, etc. Communication unit 409 allows electronic device 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0208] The computing unit 401 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, CPUs (Central Processing Units), GPUs (Graphics Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processors), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as noise control methods. For example, in some embodiments, the noise control method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by the computing unit 401, one or more steps of the methods described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the aforementioned noise control method by any other suitable means (e.g., by means of firmware).

[0209] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application-Specific Standard Products), SOCs (System-on-Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0210] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0211] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0212] To provide noise control for the user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide noise control for the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0213] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with the implementations of the systems and technologies described herein for noise control), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include LANs (Local Area Networks), WANs (Wide Area Networks), the Internet, and blockchain networks.

[0214] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically communicate over a network with noise control. The client-server relationship is established by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, also known as cloud computing servers or cloud hosts, a hosting product within the cloud computing service ecosystem that addresses the management difficulties and weak business scalability inherent in traditional physical hosts and VPS (Virtual Private Server) services. Servers can also be servers for distributed systems or servers integrated with blockchain technology.

[0215] It's important to note that artificial intelligence (AI) is the study of enabling computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and navigation). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily include computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graph technologies.

[0216] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0217] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A noise control method, characterized in that, include: Determine the noise reduction method based on the type of noise from the smart device; Based on the noise reduction method and / or the target noise reduction position, determine the control signal for at least one sound-generating device; The control signal controls the at least one sound-generating device to output a cancellation signal, thereby forming a noise reduction and quiet zone at the target noise reduction location.

2. The noise control method according to claim 1, characterized in that, The noise type is either broadband noise or narrowband noise.

3. The noise control method according to claim 1 or 2, characterized in that, The method of determining noise reduction based on the noise type of the smart device includes at least one of the following: When the noise type is broadband noise, a first noise reduction method is determined, wherein the first noise reduction method is a noise reduction method based on the target frequency band; When the noise type is narrowband noise, a second noise reduction method is determined, wherein the first noise reduction method is a noise reduction method based on the target frequency point; When the noise type is mixed noise, a third noise reduction method is determined. The third noise reduction method is to separate narrowband noise and broadband noise from the mixed noise and reduce the narrowband noise and broadband noise respectively based on the target frequency point and the target frequency band.

4. The noise control method according to any one of claims 1-3, characterized in that, The step of determining the control signal for at least one sound-generating device based on the noise reduction method and / or the target noise reduction position includes: Obtain the second residual noise estimation signal at the target noise reduction location; Based on the noise reduction method and the second residual noise estimation signal, a control signal for at least one sound-generating device is determined.

5. The noise control method according to any one of claims 4, characterized in that, The step of determining a control signal for at least one sound-generating device based on the noise reduction method and the second residual noise estimation signal includes at least one of the following: When the noise reduction method is the first noise reduction method, the control signal of at least one sound-generating device in the target frequency band is determined based on the second residual noise estimation signal and the target frequency domain controller; When the noise reduction method is the second noise reduction method, the control signal of at least one sound-generating device at the target frequency point is determined based on the second residual noise estimation signal and the target control dictionary; When the noise reduction method is the third noise reduction method, the control signal of at least one sound-generating device in the target frequency band is determined based on the second residual noise estimation signal and the target frequency domain controller, and the control signal of at least one sound-generating device at the target frequency point is determined based on the second residual noise estimation signal and the target control dictionary.

6. The noise control method according to claim 5, characterized in that, The step of determining the control signal for at least one sound-generating device in the target frequency band based on the second residual noise estimation signal and the target frequency domain controller includes: Based on the second residual noise estimation signal, the second noise error frequency domain estimation signal is obtained; Based on the second noise error frequency domain estimation signal, obtain the updated target frequency domain signal controller; Based on the updated target frequency domain signal controller, determine the control signal for at least one sound-generating device in the target frequency band.

7. The noise control method according to claim 6, characterized in that, The step of determining the control signal for at least one sound-generating device in the target frequency band based on the updated target frequency domain signal controller coefficients includes: Obtain the reference noise signal; Based on the updated target frequency domain signal controller and the reference noise signal, a control signal for at least one sound-generating device in the target frequency band is determined.

8. The noise control method according to claim 5, characterized in that, Based on the second residual noise estimation signal and the target control dictionary, determine the control signal of at least one sound-generating device at the target frequency point. Based on the second residual noise estimation signal, obtain the updated target control dictionary; Based on the updated target control dictionary, determine the control signal for at least one sound-generating device at the target frequency point.

9. The noise control method according to any one of claims 4-8, characterized in that, The acquisition of the second residual noise estimation signal at the target noise reduction location includes: Acquire the first residual noise signal collected by the pickup device, wherein the pickup device and the target noise reduction location are located at different positions; Based on the first residual noise estimation signal, the second residual noise estimation signal at the target noise reduction location is obtained.

10. The noise control method according to claim 9, characterized in that, The step of obtaining a second residual noise estimation signal at the target noise reduction location based on the first residual noise estimation signal includes: Based on the first residual noise signal, a first noise estimation signal is obtained; Based on the first noise estimation signal, a second noise estimation signal is obtained; Based on the second noise estimation signal, the second residual noise estimation signal is obtained.

11. The noise control method according to claim 9, characterized in that, The step of obtaining a second noise estimation signal based on the first noise estimation signal includes: The second noise estimation signal is obtained based on the first noise estimation signal and the target frequency domain signal estimator.

12. The noise control method according to any one of claims 1-11, characterized in that, The method further includes: Acquire a first noise signal collected by a first pickup device, and acquire second noise signals collected by a second pickup device at different locations; Based on the first noise signal and the second noise signal, obtain the first frequency domain noise signal and the second frequency domain noise signal; Based on the first frequency domain noise signal and the second frequency domain noise signal, a target frequency domain signal estimator is obtained.

13. The noise control method according to any one of claims 1-13, characterized in that, The first microphone is fixedly installed on the smart device, and the second microphone is temporarily placed on the smart device.

14. The noise control method according to claim 10, characterized in that, The step of obtaining a first noise estimation signal based on the first residual noise signal includes: Based on the first residual noise estimation signal and the first cancellation estimation signal, a first noise estimation signal is obtained.

15. The noise control method according to claim 10, characterized in that, The step of obtaining a first noise estimation signal based on the first residual noise signal includes: Based on the second noise estimation signal and the second cancellation estimation signal, a second residual noise estimation signal is obtained.

16. The noise control method according to any one of claims 1-15, characterized in that, The target noise reduction location is the location of the user's ear.

17. The noise control method according to any one of claims 1-16, characterized in that, The second residual noise estimation signal is the signal after the second cancellation signal cancels the second noise signal, and the first residual noise signal is the noise signal after the first cancellation signal cancels the first noise signal.

18. The noise control method according to any one of claims 1-17, characterized in that, The method further includes: The target noise reduction position is obtained based on the user's sitting posture and / or seat status.

19. A noise control device, characterized in that, include: The estimation module is configured to determine the noise reduction method based on the noise type of the smart device; The determining module is configured to determine a control signal for at least one sound-generating device based on the noise reduction method and / or the target noise reduction position. The control module is configured to control the at least one sound-generating device to output a cancellation signal via the control signal to form a noise reduction and silencing zone at the target noise reduction location.

20. A smart device, characterized in that, The smart device includes the travel service device as described in claim 18.

21. A vehicle, characterized in that, The vehicle includes the mobility service device as described in claim 18; or, The smart device as described in claim 19.

22. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-18.

23. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-18.

24. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1-18.

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