Automobile Road Noise Processing Method, Equipment and Filter Training Method

Abstract

The present application discloses a method for processing automotive road noise, a device, and a filter training method, which relate to the field of noise reduction technology. The method includes: performing sub-band decomposition on an input time-domain reference signal and a time-domain error signal respectively by using a preset polyphase analysis filter bank to obtain a sub-band reference signal and a sub-band error signal; updating sub-band adaptive filter coefficients based on the sub-band reference signal and the sub-band error signal; performing sub-band synthesis on the updated sub-band adaptive filter coefficients by using a preset polyphase synthesis filter bank to obtain updated time-domain adaptive filter coefficients; processing a newly input time-domain reference signal based on the updated time-domain adaptive filter coefficients to obtain an anti-noise signal, and controlling road noise based on the anti-noise signal. The present application realizes reducing the computational amount of an automotive road noise control system, thereby reducing the requirement for the computing power of system hardware.

Description

Technical Field

[0001] The present application relates to the technical field of noise reduction, and particularly to a method for processing vehicle road noise, a device, and a filter training method. Background Art

[0002] With the development of the automotive industry, the comfort and quietness inside the vehicle cockpit have received increasing attention from the automotive industry and consumers. Vehicle interior noise, as a key factor affecting the acoustic quietness and riding experience inside the vehicle, has gradually become a pain point issue of concern in the industry. Vehicle interior noise mainly comes from the vehicle's power system and road noise during vehicle driving. With the development of new energy vehicles, electric motors have gradually replaced internal combustion engines, thereby significantly reducing engine noise. Road Noise Cancellation (RNC) technology has become an important means to improve the acoustic comfort inside the vehicle. The RNC technology generates a sound signal opposite to the noise characteristics by combining the reference signal collected by the vibration sensor and the error signal collected by the in-vehicle microphone, and plays it through the in-vehicle speaker to cancel the original noise. Currently, in the RNC technology, a subband adaptive algorithm is adopted. After updating the subband adaptive filter coefficients, the time-domain adaptive filter coefficients are synthesized, which has the advantages of fast convergence, independence between different subbands, the ability to use different adaptive algorithms and filter orders for different subbands, and low sensitivity to changes in the input signal. However, the subband decomposition and subband synthesis methods used in the current subband adaptive algorithm are computationally complex and require high hardware computing power for the RNC system.

[0003] The above content is only used to assist in understanding the technical solution of the present application, and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of the present application is to provide a method for processing vehicle road noise, a device, and a filter training method, aiming to solve the problem that the subband decomposition and subband synthesis methods used in the current subband adaptive algorithm are computationally complex and require high hardware computing power for the RNC system.

[0005] To achieve the above purpose, the present application proposes a method for processing vehicle road noise, and the method for processing vehicle road noise includes:

[0006] Performing subband decomposition on the input time-domain reference signal and time-domain error signal respectively by using a preset polyphase analysis filter bank to obtain a subband reference signal and a subband error signal;

[0007] Updating the subband adaptive filter coefficients based on the subband reference signal and the subband error signal;

[0008] Use a preset polyphase synthesis filter bank to perform sub-band synthesis on the updated sub-band adaptive filter coefficients to obtain the updated time-domain adaptive filter coefficients;

[0009] Process the newly input time-domain reference signal based on the updated time-domain adaptive filter coefficients to obtain an anti-noise signal, and control the road noise based on the anti-noise signal.

[0010] Optionally, the preset polyphase analysis filter bank and the preset polyphase synthesis filter bank are pre-trained, and the training objective is to reduce the signal reconstruction loss and spectral leakage loss of the polyphase analysis filter bank and the polyphase synthesis filter bank.

[0011] Optionally, the signal reconstruction loss is calculated based on the error between the reconstructed signal and a preset first-type signal, and the reconstructed signal is obtained by performing sub-band decomposition and sub-band synthesis on the preset first-type signal using the to-be-trained polyphase analysis filter bank and the to-be-trained polyphase synthesis filter bank.

[0012] Optionally, the spectral leakage loss is calculated based on the target sub-band signal, the target sub-band signal is obtained by performing sub-band decomposition on a preset second-type signal using the to-be-trained polyphase analysis filter bank, the preset second-type signal is a single-frequency signal or a narrow-band signal within a preset frequency band range, and the frequency band range of the target sub-band signal is the range other than the preset frequency band range.

[0013] Optionally, the step of using the preset polyphase analysis filter bank to perform sub-band decomposition on the input time-domain reference signal and time-domain error signal respectively to obtain a sub-band reference signal and a sub-band error signal includes:

[0014] Perform sub-band decomposition on the input time-domain reference signal using the fast Fourier transform algorithm and the preset polyphase analysis filter bank to obtain a sub-band reference signal, and perform sub-band decomposition on the input time-domain error signal using the fast Fourier transform algorithm and the polyphase analysis filter bank to obtain a sub-band error signal;

[0015] And / or,

[0016] The step of using the preset polyphase synthesis filter bank to perform sub-band synthesis on the updated sub-band adaptive filter coefficients to obtain the updated time-domain adaptive filter coefficients includes:

[0017] Perform sub-band synthesis on the updated sub-band adaptive filter coefficients using the inverse fast Fourier transform algorithm and the preset polyphase synthesis filter bank to obtain the updated time-domain adaptive filter coefficients.

[0018] Optionally, the frame index of the input time-domain reference signal and the time-domain error signal is l, and the automotive road noise processing method further includes:

[0019] Execute the tasks allocated to the N interruptions during the N interruptions in the (l + 1)-th frame, where N represents the frame length, and an interruption refers to the period from generating an anti-noise signal based on the time-domain reference signal of each sampling point to receiving the time-domain reference signal of the next sampling point. The tasks allocated to the N interruptions include: the task of calculating the updated time-domain adaptive filter coefficients.

[0020] In addition, to achieve the above object, the present application also proposes a filter training method, which is used to train a polyphase analysis filter bank and a polyphase synthesis filter bank. The polyphase analysis filter bank and the polyphase synthesis filter bank are applied to the automotive road noise processing method as described above. The filter training method includes:

[0021] Perform sub-band decomposition and sub-band synthesis on a preset first type of signal using the to-be-trained polyphase analysis filter bank and the to-be-trained polyphase synthesis filter bank to obtain a reconstructed signal;

[0022] Calculate a signal reconstruction loss according to the error between the reconstructed signal and the preset first type of signal;

[0023] Perform sub-band decomposition on a preset second type of signal using the to-be-trained polyphase analysis filter bank to obtain a target sub-band signal, where the preset second type of signal is a single-frequency signal or a narrow-band signal within a preset frequency band range, and the frequency band range of the target sub-band signal is the range other than the preset frequency band range;

[0024] Calculate a spectrum leakage loss according to the target sub-band signal;

[0025] Optimize the to-be-trained polyphase analysis filter bank and the to-be-trained polyphase synthesis filter bank with the aim of reducing the signal reconstruction loss and the spectrum leakage loss to obtain a trained polyphase analysis filter bank and a trained polyphase synthesis filter bank.

[0026] Optionally, the step of optimizing the to-be-trained polyphase analysis filter bank and the to-be-trained polyphase synthesis filter bank with the aim of reducing the signal reconstruction loss and the spectrum leakage loss to obtain a trained polyphase analysis filter bank and a trained polyphase synthesis filter bank includes:

[0027] Perform weighted summation of the signal reconstruction loss and the spectrum leakage loss according to a preset weight to obtain a total loss;

[0028] Optimize the to-be-trained polyphase analysis filter bank and the to-be-trained polyphase synthesis filter bank with the goal of reducing the total loss, so as to obtain the trained polyphase analysis filter bank and polyphase synthesis filter bank.

[0029] Optionally, the initial coefficients of the to-be-trained polyphase analysis filter bank and the to-be-trained polyphase synthesis filter bank are Hamming windows of a preset length.

[0030] In addition, to achieve the above object, the present application also proposes an automotive road noise processing device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the automotive road noise processing method as described above.

[0031] One or more technical solutions proposed by the present application have at least the following technical effects:

[0032] In the present application, the input time-domain reference signal and time-domain error signal are respectively sub-band decomposed by using a preset polyphase analysis filter bank to obtain a sub-band reference signal and a sub-band error signal; the sub-band adaptive filter coefficients are updated based on the sub-band reference signal and the sub-band error signal; the updated sub-band adaptive filter coefficients are sub-band synthesized by using a preset polyphase synthesis filter bank to obtain updated time-domain adaptive filter coefficients; the new input time-domain reference signal is processed based on the updated time-domain adaptive filter coefficients to obtain an anti-noise signal, and the road noise is controlled based on the anti-noise signal. In the present application, since sub-band decomposition is performed by using a polyphase analysis filter bank and sub-band synthesis is performed by using a polyphase synthesis filter bank, during the process of restoring the filter coefficients to the full band, the synthesis filter bank can be directly used, and only an IFFT (Inverse Fast Fourier Transform) operation needs to be performed after adding a synthesis window to the sub-band domain filter, so as to obtain time-domain filter coefficients that are almost the same as those in the full band, ensuring performance while avoiding additional computational complexity, thereby reducing the hardware computing power requirements for the automotive road noise control system. Description of the Drawings

[0033] The drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0034] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0035] Figure 1Schematic flowchart provided for the first embodiment of the method for processing automotive road noise in this application;

[0036] Figure 2 Schematic flowchart of the prediction model training process involved in an embodiment of this application;

[0037] Figure 3 Schematic diagram of the signal processing process involved in an embodiment of this application;

[0038] Figure 4 Schematic flowchart of the method for processing automotive road noise involved in an embodiment of this application;

[0039] Figure 5 Schematic diagram of the model architecture and parameter matching architecture involved in an embodiment of this application;

[0040] Figure 6 Comparison diagram of filter coefficients involved in an embodiment of this application;

[0041] Figure 7 Schematic diagram of the device structure of the hardware operating environment involved in the method for processing automotive road noise in the embodiments of this application.

[0042] The realization of the purpose, functional characteristics and advantages of this application will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0043] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of this application and are not used to limit this application.

[0044] For a better understanding of the technical solutions of this application, the following will be described in detail in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0045] The RNC technology generates a sound signal opposite to the noise characteristics by combining the reference signal collected by the vibration sensor and the error signal collected by the in-vehicle microphone, and plays it through the in-vehicle speaker to cancel the original noise. Currently, the subband adaptive algorithm is used in the RNC technology. After updating the subband adaptive filter coefficients, the time-domain adaptive filter coefficients are synthesized. It has the advantages of fast convergence, independence between different subbands, the ability to use different adaptive algorithms and filter orders for different subbands, and low sensitivity to changes in the input signal. However, the subband decomposition and subband synthesis methods used in the current subband adaptive algorithm are computationally complex and require high hardware computing power for the RNC system.

[0046] In order to solve the above technical problems, embodiments of the present application propose to perform sub-band decomposition on the input time-domain reference signal and time-domain error signal respectively by using a preset polyphase analysis filter bank to obtain a sub-band reference signal and a sub-band error signal; update the sub-band adaptive filter coefficients based on the sub-band reference signal and the sub-band error signal; use a preset polyphase synthesis filter bank to perform sub-band synthesis on the updated sub-band adaptive filter coefficients to obtain updated time-domain adaptive filter coefficients; process the newly input time-domain reference signal based on the updated time-domain adaptive filter coefficients to obtain an anti-noise signal, and control the road noise based on the anti-noise signal. In the embodiments of the present application, since polyphase analysis filter banks are used for sub-band decomposition and polyphase synthesis filter banks are used for sub-band synthesis, during the process of restoring the filter coefficients to the full band, the synthesis filter bank can be directly used. Only by adding a synthesis window to the sub-band domain filter and performing IFFT (Inverse Fast Fourier Transform) operation can time-domain filter coefficients that are almost the same as those in the full band be obtained, ensuring performance while avoiding additional computational complexity, thereby reducing the hardware computing power requirements for the automotive road noise control system.

[0047] The first embodiment of the automotive road noise processing method of the present application is proposed below. Refer to Figure 1 , Figure 1This is a schematic flowchart of the first embodiment of the method for processing automotive road noise in this application. In this embodiment, the execution subject of the method for processing automotive road noise can be the control unit in the automotive RNC system, but it is not limited to the control unit. For example, it can also be a processing device with data processing and program running functions. In a feasible implementation, the automotive RNC system can include sensors, a control unit, a noise canceller, and other auxiliary devices set as needed; among them, the sensors include a reference sensor for collecting reference signals and an error sensor for collecting error signals, and multiple reference sensors and error sensors can be set; the reference sensor and the error sensor can be vibration sensors (acceleration sensors) or microphones; the control unit runs adaptive algorithms, such as FxLMS (Filtered-x Least Mean Square), FxNLMS (Filtered-X Normalized Least Mean Square), FxAP (Filter-x Affine Projection) and other adaptive algorithms, updates the adaptive filter coefficients based on the reference signal and the error signal, and calculates the anti-noise signal; the noise canceller is used to output the anti-noise signal to cancel the road noise. In a feasible implementation, the reference sensor can adopt a vibration sensor and can be set at the attachment point of the vehicle body and the chassis, and the error sensor can adopt a microphone and can be set inside the vehicle, such as on the roof lining (near the passenger's head area); the noise canceller can adopt a vibration actuator and / or a speaker. The vibration actuator generates reverse mechanical vibrations according to the anti-noise signal to cancel the low-frequency road noise transmitted through the vehicle body, and the speaker emits reverse sound waves according to the anti-noise signal to directly cancel the air-borne noise (such as the high-frequency components of tire noise) entering the passenger compartment.

[0048] In this embodiment, the method for processing automotive road noise includes steps S10 to S40:

[0049] Step S10, respectively perform sub-band decomposition on the input time-domain reference signal and time-domain error signal by using a preset polyphase analysis filter bank to obtain a sub-band reference signal and a sub-band error signal.

[0050] When multiple reference sensors are set, the input time-domain reference signal is multi-channel. Similarly, when multiple error sensors are set, the input time-domain error signal is also multi-channel. In this embodiment, the number of channels of the time-domain reference signal and the time-domain error signal is not limited. For example, when applied to a MIMO (Multiple Input Multiple Output, MIMO) vehicle-mounted RNC system, both the input and the output are multi-channel.

[0051] The time-domain reference signal and the time-domain error signal collected by the sensor can be directly input into the control unit, or input into the control unit after preprocessing; the control unit can directly perform subband decomposition on the input time-domain reference signal and time-domain error signal, or can also perform preprocessing before subband decomposition. In this embodiment, there is no limitation on whether to perform preprocessing on the time-domain reference signal and the time-domain error signal, nor on what preprocessing operations to perform on the signals. For example, it can include prefiltering, amplitude adjustment, etc. Prefiltering includes high-pass filtering, low-pass filtering, etc., and the preprocessing operations can be set according to needs. In the following embodiments, the preprocessing operation on the signal will not be particularly emphasized anymore. That is, even if it is not written that preprocessing is required, it does not necessarily mean that there is no or no need to perform preprocessing operations.

[0052] The control unit uses a preset polyphase analysis filter bank to perform subband decomposition on the time-domain reference signal to obtain a subband reference signal, and performs subband decomposition on the time-domain error signal to obtain a subband error signal. The polyphase analysis filter bank can be preset according to needs, and there is no limitation on its coefficient setting in this embodiment.

[0053] It can be understood that there are multiple subbands, and there are also multiple subband reference signals and subband error signals obtained through subband decomposition respectively. That is, the subband reference signal and the subband error signal corresponding to each subband are obtained through subband decomposition. In the specific implementation, the subbands can be divided from the full frequency band, but in this embodiment, there is no limitation that they are divided from the full frequency band. They can be divided from a relatively wide frequency band range, and there is no limitation on the number of subbands.

[0054] In a feasible implementation, the control unit can adopt a method without output delay for road noise control, that is: the time-domain reference signal collected by the reference sensor and the time-domain error signal collected by the error sensor are input into the control unit point by point in real time. The control unit generates an anti-noise signal for a single sampling point of the currently input time-domain reference signal in real time according to the current time-domain adaptive filter coefficients, and then outputs the anti-noise signal in real time to achieve the effect of no output delay; on the other hand, the adaptive filter coefficients are updated frame by frame. Specifically, the control unit puts the input time-domain reference signal and time-domain error signal into the corresponding buffer in real time. After each frame of time-domain reference signal and each frame of time-domain error signal are stored, subband decomposition is performed on the frame of time-domain reference signal and the frame of time-domain error signal, and subsequent operations for updating the time-domain adaptive filter coefficients are performed. In other embodiments, a method with output delay can also be adopted, that is, after inputting a plurality of sampling points of the time-domain reference signal, the anti-noise signal starts to be output. In this embodiment, there is no limitation on whether to adopt the method without output delay or the method with output delay. In the specific application scenario, the specific implementation method can be selected according to needs.

[0055] Step S20: Update the sub-band adaptive filter coefficients based on the sub-band reference signal and the sub-band error signal.

[0056] The sub-band adaptive filter coefficients can be updated according to a preset adaptive algorithm. For example, the adaptive algorithm can be FxLMS, FxNLMS, FxAP, etc., which are not limited in this embodiment.

[0057] For example, in a feasible embodiment, the adaptive algorithm can adopt the complex-domain FxLMS algorithm. Specifically, the control unit can convolve the sub-band reference signal with a pre-established secondary path model, and then use the complex-domain LMS algorithm to calculate the updated sub-band adaptive filter coefficients based on the sub-band error signal, the sub-band reference signal after convolving with the secondary path model, and the current sub-band adaptive filter coefficients.

[0058] Step S30: Perform sub-band synthesis on the updated sub-band adaptive filter coefficients using a preset polyphase synthesis filter bank to obtain the updated time-domain adaptive filter coefficients.

[0059] To avoid output delay, filtering output needs to be performed in the time domain (full band). Therefore, the sub-band adaptive filter coefficients can be converted to the full band. By performing sub-band synthesis on the updated sub-band adaptive filter coefficients, the updated time-domain adaptive filter coefficients can be obtained.

[0060] It should be noted that the sub-band adaptive algorithm is adopted in this embodiment, that is, the sub-band adaptive filter coefficients are updated separately and then synthesized into the full-band (time-domain) adaptive filter coefficients. Compared with the method of directly updating the full-band (time-domain) adaptive filter coefficients, in this embodiment, each sub-band adaptive filter coefficient is updated separately, and then the updated sub-band adaptive filter coefficients are synthesized into the updated time-domain adaptive filter coefficients, which has the advantages of fast convergence, independence of different sub-bands, the ability to use different adaptive algorithms and filter orders for different sub-bands, and low sensitivity to changes in the input signal.

[0061] In this embodiment, a preset polyphase synthesis filter bank is used to perform sub-band synthesis on the updated sub-band adaptive filter coefficients to obtain the updated time-domain adaptive filter coefficients. The polyphase synthesis filter bank can be set in advance according to needs, and its coefficient setting is not limited in this embodiment.

[0062] Step S40: Process the newly input time-domain reference signal based on the updated time-domain adaptive filter coefficients to obtain an anti-noise signal, and control the road noise based on the anti-noise signal.

[0063] After updating the time-domain adaptive filter coefficients, the subsequent newly input time-domain reference signal can be processed according to the updated time-domain adaptive filter coefficients to generate an anti-noise signal. After generating the anti-noise signal, the road noise can be controlled based on the anti-noise signal. For example, the anti-noise signal can be played through a speaker set inside the vehicle, or the anti-noise signal can be output after some post-processing operations. The post-processing operations can include, for example, amplitude limiting, etc., which are not limited herein.

[0064] The generated anti-noise signal can be multi-channel. For example, when there are multiple speakers, a multi-channel anti-noise signal is generated and output through multiple speakers. In this embodiment, the number of channels of the anti-noise signal is not limited.

[0065] In this embodiment, since a polyphase analysis filter bank is used for subband decomposition and a polyphase synthesis filter bank is used for subband synthesis, during the process of restoring the filter coefficients to the full band, the synthesis filter bank can be directly used. Only by adding a synthesis window to the subband-domain filter and then performing an IFFT (Inverse Fast Fourier Transform) operation can time-domain filter coefficients that are almost identical to the full band be obtained, ensuring performance while avoiding additional computational complexity.

[0066] In a specific implementation, a polyphase analysis filter bank based on FFT (hereinafter referred to as a polyphase FFT analysis filter bank) and a polyphase synthesis filter bank based on IFFT (hereinafter referred to as a polyphase IFFT synthesis filter bank) can be used to further improve the computational efficiency. Hereinafter, the analysis filter bank can also be referred to as an analysis window, and the synthesis filter bank can be referred to as a synthesis window.

[0067] In a feasible implementation, the preset polyphase analysis filter bank and the preset polyphase synthesis filter bank are pre-trained, and the training objective is to reduce the signal reconstruction loss and spectral leakage loss of the polyphase analysis filter bank and the polyphase synthesis filter bank.

[0068] It should be noted that the method of training with the objective of reducing the signal reconstruction loss and spectral leakage loss of the filter bank is similar to the method of training a neural network model with the objective of reducing the prediction loss of the neural network model. The steps include preparing data for training, designing a loss function, selecting an optimizer for training, etc., which can be specifically referred to the training method of the neural network model. Among them, the signal reconstruction loss refers to the error between the signal obtained by subband decomposing and subband synthesizing the signal through the filter bank and the original signal, and the spectral leakage loss refers to the magnitude of the spectral leakage generated by subband decomposing the signal through the filter bank.

[0069] In a feasible implementation manner, the signal reconstruction loss is calculated according to the error between the reconstructed signal and a preset first type of signal. The reconstructed signal is obtained by performing subband decomposition and subband synthesis on the preset first type of signal by using a multiphase analysis filter bank to be trained and a multiphase synthesis filter bank to be trained. Among them, an initialized multiphase analysis filter bank and a multiphase synthesis filter bank can be set in advance, and the coefficients of the multiphase analysis filter bank and the multiphase synthesis filter bank need to be updated during the training process. The preset first type of signal is a signal preset for calculating the signal reconstruction loss, and can be various types of noise signals, vibration signals, music signals, etc. collected in advance. The preset first type of signal is subjected to subband decomposition by using the multiphase analysis filter bank to be trained, and the decomposed result is then subjected to subband synthesis by using the multiphase synthesis filter bank to be trained to obtain a reconstructed signal, and the error is calculated with the preset first type of signal to obtain the signal reconstruction loss.

[0070] In a feasible implementation manner, the spectral leakage loss is calculated according to the target subband signal. The target subband signal is obtained by performing subband decomposition on a preset second type of signal by using the multiphase analysis filter bank to be trained. The preset second type of signal is a single-frequency signal or a narrowband signal within a preset frequency band range, and the frequency band range of the target subband signal is a range other than the preset frequency band range. Among them, the preset second type of signal is a signal preset for calculating the spectral leakage loss, and can be a single-frequency signal or a narrowband signal within a preset frequency band range collected in advance. The preset frequency band range can be set as needed. The preset second type of signal is subjected to subband decomposition by using the multiphase analysis filter bank to be trained, and the spectral leakage loss is calculated according to the target subband signal in the decomposition result. It can be understood that since the preset second type of signal is a single-frequency signal or a narrowband signal within a preset frequency band range, after the preset second type of signal is subjected to subband decomposition, if there is no spectral leakage, then there will be no signal in the range other than the preset frequency band range in the subband decomposition result, and the frequency band range of the target subband signal is a range other than the preset frequency band range, so the magnitude of the target subband signal can represent the magnitude of the spectral leakage, and the spectral leakage loss can be calculated according to the target subband signal.

[0071] In a feasible implementation manner, the filter bank can be trained in the following manner:

[0072] Step 1: Preparation of data. Two types of data are used for the training of the analysis filter bank and the synthesis filter bank. The first type is various types of noise signals, vibration signals, and music signals. The second type is single-frequency signals or narrowband signals uniformly generated according to a known frequency band range (hereinafter, the set of subband indices within this frequency band range is represented by S). Each of these two types of data accounts for 50% of the total data.

[0073] Step 2: Design of the loss function. The signal reconstruction error and the spectral leakage energy are two criteria for measuring the performance of the analysis window and the synthesis window of the subbands. Therefore, these two metrics are used as the loss function for training.

[0074]

[0075]

[0076]

[0077] Among them, L MSE is the reconstruction loss function of the signal, L leakage is the loss function of the spectral leakage of the signal, α and β are weight coefficients used to adjust the influence ratio of the losses of the two parts, for example, set as α = 0.4 and β = 0.6. z[r] is the preset first type of signal, z'[r] is the signal after subband decomposition and subband synthesis of z[r], that is, the reconstructed signal, R is the length of the signal, for example, it can be set as R = 20000. Z[k] is the subband signal after subband decomposition of the preset second type of signal, k is the index of the subband, and S is the set of subband indices within the known frequency band range in Step 1.

[0078] Step 3: Select an optimizer for training. Calculate L MSE according to the input preset first type of signal, calculate L leakage according to the input preset second type of signal, and combine these two losses as the final loss L total . Pytorch (an open-source deep learning framework for machine learning and deep learning) can be selected as the training environment, and Adam can be selected as the optimizer to perform training iterations on the analysis window and the synthesis window until the loss function converges, and finally obtain the trained analysis window and synthesis window. The downsampling factor can be set to 3, the number of points P of the FFT can be set to 128, so the size of the analysis window and the synthesis window is 128 * 3. During training, a hamming window with a length of 128 * 3 can be used as the initial coefficient.

[0079] In a feasible implementation, the control unit can dynamically determine the filter bank for sub-band decomposition and sub-band synthesis according to the acquired environmental state information. Hereinafter, the determined filter bank is referred to as the target filter bank for distinction. The target filter bank includes a target polyphase analysis filter bank for sub-band decomposition and a target polyphase synthesis filter bank for sub-band synthesis. The target polyphase analysis filter bank is used to perform sub-band decomposition on the time-domain reference signal and the time-domain error signal, and the target polyphase synthesis filter bank is used to perform sub-band synthesis on the updated sub-band adaptive filter coefficients. Since the dynamic change of the acoustic propagation path will occur when the environmental state outside the vehicle or the environmental state inside the vehicle changes, which will affect the stability of the vehicle road noise. If a fixed polyphase analysis filter bank and polyphase synthesis filter bank are used for sub-band decomposition and sub-band synthesis, it may lead to the inability to adapt to the changing vehicle road noise and result in unstable noise reduction effect. Therefore, in this implementation, the target polyphase analysis filter bank and the target polyphase synthesis filter bank for sub-band decomposition and sub-band synthesis are dynamically determined according to the environmental state information to adapt to environmental changes and improve the stability of the noise reduction effect. Different filter banks can be preset for different environmental states in advance. Each filter bank includes a polyphase analysis filter bank and a polyphase synthesis filter bank. Then, when the control unit needs to perform sub-band decomposition on the current time-domain reference signal and time-domain error signal, and needs to perform sub-band synthesis on the current sub-band adaptive filter coefficients, the filter bank preset corresponding to the environmental state characterized by the acquired environmental state information can be determined as the target filter bank. The filter banks set for different environmental states can be filter banks with the best or better noise reduction effect in this environmental state determined in advance through experiments, data statistics, etc.

[0080] The environmental state information may include information characterizing the environmental state inside and / or outside the vehicle. In a specific implementation, the environmental state inside the vehicle may include, for example, the seat adjustment state, the position of the occupants, the window opening and closing state, etc. The environmental state outside the vehicle may include, for example, the weather condition, the road surface condition, etc., which are not limited in this embodiment.

[0081] In a feasible implementation manner, for each filter bank that can be pre-set corresponding to different environmental states, the filter update delay and / or the spectral leakage are different. The filter update delay and spectral leakage brought by the filter bank refer to the filter update delay and spectral leakage that will occur when sub-band decomposition and sub-band synthesis are performed according to the filter bank. It should be noted that generally, the filter update delay and the spectral leakage brought by the filter bank are negatively correlated. The larger the delay, the smaller the spectral leakage; the smaller the delay, the larger the spectral leakage. And the smaller the delay, the better the robustness and transient response of the system; the smaller the spectral leakage, the better the filtering accuracy and steady-state performance of the system. Since the stability of vehicle road noise is different under different environmental states, and the requirements for the robustness, transient response ability, filtering accuracy, and steady-state performance of the system are different, therefore, dynamically determining the filter bank to be used according to the environmental state information can balance the filter update delay and the spectral leakage, and adopt a delay and spectral leakage suitable for the environmental state, so as to balance the robustness, transient response ability, filtering accuracy, and steady-state performance of the system, and ensure the stability of the noise reduction effect.

[0082] In a feasible implementation manner, it can be set that when the control unit dynamically determines the target filter bank according to the environmental state information, the following rules need to be met: in the environmental state characterized by the environmental state information, the higher the stability of the vehicle road noise, the greater the filter update delay and the smaller the spectral leakage brought by the target filter bank; the lower the stability of the vehicle road noise, the smaller the filter update delay and the larger the spectral leakage brought by the target filter bank.

[0083] It should be noted that if the stability of the vehicle road noise is relatively high in the environmental state characterized by the environmental state information, a filter bank with a larger filter update delay and a smaller spectral leakage can be adopted, focusing on improving the filtering accuracy and steady-state performance to obtain a better noise reduction effect. If the stability of the vehicle road noise is relatively low in the environmental state characterized by the environmental state information, a filter bank with a smaller filter update delay and a larger spectral leakage can be adopted, focusing on improving the robustness and transient response of the system to quickly adapt to environmental changes and maintain system stability.

[0084] In a specific implementation manner, in order to enable the control unit to meet the above rules when dynamically determining the target filter bank according to the environmental state information, the filter bank corresponding to each environmental state can be set according to this rule.

[0085] In a feasible implementation, the environmental state information includes weather condition information and road surface condition information. Among them, the weather condition information can specifically be information related to weather conditions that affect the road surface, such as the dryness of the road surface, rainfall, humidity, etc. The road surface condition information can specifically be information characterizing the type of the road surface, such as asphalt road surface, potholed road surface, brick road surface, etc.

[0086] In a feasible implementation, when the environmental state information indicates that the environment is good and stable (for example: good road conditions, clear weather), the automotive road noise is relatively stable. Then, a filter bank with a larger filter update delay and smaller spectral leakage can be adopted, focusing on improving the filtering accuracy and steady-state performance to obtain a better noise reduction effect. When the environmental state information indicates that the environment is unstable (for example: deteriorating road conditions, bad weather), the stability of the automotive road noise may deteriorate. At this time, a filter bank with a smaller filter update delay and larger spectral leakage can be adopted, focusing on improving the robustness and transient response of the system to quickly adapt to environmental changes and maintain system stability.

[0087] The following Table 1 gives some examples of determining the filter bank based on the weather condition information and the road surface condition information. Table 1 only gives some examples and does not limit the way of determining the target filter bank in this embodiment. The content in parentheses in Table 1 is the explanatory description of the weather condition information, the road surface condition information, etc.

[0088] Table 1 Examples of selection of analysis window and synthesis window

[0089]

[0090] In a feasible implementation, to train filter banks with different spectral leakage sizes and different filter update delays, different weight combinations of signal reconstruction loss and frequency leakage loss can be set during training. For example, by setting a larger weight of signal reconstruction loss and a smaller weight of frequency leakage loss, a filter bank with a smaller delay and larger leakage can be trained. By setting a smaller weight of signal reconstruction loss and a larger weight of frequency leakage loss, a filter bank with a larger delay and smaller leakage can be trained.

[0091] Based on the above first embodiment, the second embodiment of the vehicle road noise processing method of the present application is proposed. In this embodiment, the same or similar content as that in the above first embodiment can be referred to the above introduction and will not be repeated hereinafter. In this embodiment, a load balancing strategy is proposed, so that the vehicle road noise processing algorithm proposed in the above embodiments can be applied to the vehicle RNC system with limited computing power conditions. For example, there are a large number of computational peak demands within a single sampling period in a MIMO vehicle RNC system. If the subband adaptive algorithm without output delay is run in real time on an embedded platform such as a DSP (Digital Signal Processor), the system may not be able to run in real time due to insufficient peak computing power. The load balancing strategy proposed in this embodiment aims to ensure that the subband adaptive algorithm without output delay can be run in real time on an embedded platform such as a DSP, solves the problem of insufficient peak computing power, and provides an important guarantee for practical applications. Specifically, in this embodiment, the vehicle road noise processing method further includes step S50:

[0092] Execute the tasks allocated to the N interrupts among the N interrupts in the (l + 1)-th frame, where N represents the frame length, and the interrupt refers to the period from generating the anti-noise signal based on the time-domain reference signal of each sampling point to receiving the time-domain reference signal of the next sampling point. The tasks allocated to the N interrupts include: the task of calculating the updated time-domain adaptive filter coefficients.

[0093] In this embodiment, the road noise control is carried out in a manner without output delay in the control unit, that is: the time-domain reference signal collected by the reference sensor and the time-domain error signal collected by the error sensor are input into the control unit point by point in real time. The control unit generates a single-sampling-point anti-noise signal for the currently input single-sampling-point time-domain reference signal according to the current time-domain adaptive filter coefficients in real time, and then outputs the anti-noise signal in real time to achieve the effect of no output delay; on the other hand, the adaptive filter coefficients are updated frame by frame. Specifically, the control unit puts the input time-domain reference signal and time-domain error signal into the corresponding buffers in real time. After each buffer is filled with a frame of time-domain reference signal and a frame of time-domain error signal, the subband decomposition of the frame of time-domain reference signal and the frame of time-domain error signal is performed, and subsequent operations for updating the time-domain adaptive filter coefficients are carried out.

[0094] Let the frame index of the "input time-domain reference signal" and the "input time-domain error signal" in step S10 be l. Then, after updating the time-domain adaptive filter coefficients based on the l-th frame time-domain reference signal and time-domain error signal, the updated time-domain adaptive filter coefficients will be used to process each sample point of the l+1-th frame time-domain reference signal point by point to generate a noise-resistant signal of N sample points. Specifically, for each received sample point of the time-domain reference signal, it is processed according to the updated time-domain adaptive filter coefficients to generate a noise-resistant signal of one sample point and output. Then, it can be understood that during the process of processing each sample point of the l+1-th frame time-domain reference signal point by point, it is also necessary to update the time-domain adaptive filter coefficients based on the l+1-th frame time-domain reference signal and the l+1-th frame time-domain error signal, so as to process each sample point of the l+2-th frame time-domain reference signal to generate a noise-resistant signal, and so on.

[0095] It can be understood that during the period from the start of receiving the first sample point of the l+1-th frame to the generation of the noise-resistant signal by processing the N-th sample point of the l+1-th frame, it is necessary to complete an update operation of the time-domain adaptive filter coefficients based on the l-th frame time-domain reference signal and time-domain error signal. In this embodiment, the update task of the adaptive filter coefficients can be distributed to N interrupts for execution, avoiding the execution of the entire adaptive filter coefficient update task in a single interrupt, thereby avoiding the problem of insufficient computing power caused by a sharp increase in computing power.

[0096] The load balancing strategy proposed in this embodiment ensures the full utilization of each interrupt, avoids the situation of any single interrupt being overloaded, optimizes the use of computing resources, and guarantees the high-efficiency performance and response speed of the system in real-time processing. This method of distributing computing tasks to multiple interrupts helps the system to maintain stable operation when facing high computing demands, so as to achieve continuous and efficient operation in various driving environments.

[0097] In this embodiment, there is no limitation on the way of distributing tasks to each interrupt. For example, the computing operations in the task can be divided into multiple subtasks according to the amount of computation, and the amount of computation of each subtask is the same or approximately the same, and each interrupt processes one subtask.

[0098] In a feasible implementation, the update task of the adaptive filter coefficients can be divided into a sub-band decomposition task, a sub-band synthesis task, and multiple sub-band update tasks. Each sub-band update task includes an update task for at least one sub-band adaptive filter coefficient. The sub-band decomposition task is executed during the first preset number of interrupts, the sub-band synthesis task is executed during the second preset number of interrupts, and one of the sub-band update tasks is executed during each of the third preset number of interrupts in the middle. The sum of the preset first number, the preset second number, and the preset third number is N.

[0099] Based on the above first and / or second embodiments, a third embodiment of the vehicle road noise processing method of the present application is proposed. In this embodiment, for the content that is the same as or similar to the above first and second embodiments, reference can be made to the above introduction and will not be elaborated hereinafter. In this embodiment, the update step size of the adaptive filter coefficients used in the adaptive algorithm is dynamically adjusted to balance the adaptive convergence speed and system stability, so as to improve the system's adaptive ability and stability and avoid problems such as delayed response or unstable output. Specifically, the control unit can obtain influence factor information and dynamically determine the target update step size corresponding to the sub-band adaptive filter coefficients according to the influence factor information. Among them, the influence factor information includes environmental state information and / or sensor state information. The environmental state information is information characterizing the environmental state inside and / or outside the vehicle, and the sensor state information is information characterizing the working state of the sensor. The sensor includes a sensor for collecting the time-domain reference signal and / or a sensor for collecting the time-domain error signal.

[0100] The influence factor information is information characterizing the specific manifestation of the influence factor, and the influence factor is a factor that determines the adjustment strategy of the update step size of the sub-band adaptive filter coefficients. That is, the specific manifestation of these factors determines different adjustment strategies for the update step size. In this embodiment, the influence factors can at least include one or more of the environmental state inside the vehicle, the environmental state outside the vehicle, the working state of the reference sensor, and the working state of the error sensor. In other implementation manners, other influence factors may also be included, such as vehicle speed.

[0101] It should be noted that when the environmental state outside the vehicle or the environmental state inside the vehicle changes, it will cause dynamic changes in the acoustic propagation path, which will affect the energy magnitude or stability of the vehicle road noise. The effects on the energy magnitude or stability of different sub-bands of noise are also different. If a fixed step size is used for updating the sub-band adaptive filter coefficients, when the energy magnitude or stability of the vehicle road noise changes, it may be due to an overly large update step size, resulting in too rapid an update of the sub-band adaptive filter coefficients, leading to system misjudgment or poor acoustic effects. It may also be due to an overly small update step size, resulting in a slow convergence rate of the adaptive filter and poor noise reduction effect. Therefore, in the specific implementation, the environmental state outside the vehicle and the environmental state inside the vehicle can be used as influencing factors, and the update step size can be dynamically adjusted based on the environmental state to balance the adaptive convergence rate and system stability, and avoid delayed response or unstable output. When the working states of the reference sensor and the error sensor change, the accuracy of the collected signal will be affected. For example, when there is an abnormality in the reference sensor or the error sensor, the accuracy of the collected signal will decrease, and then the estimation of the vehicle road noise in the signal will be incorrect. If a fixed step size is used for updating the sub-band adaptive filter coefficients and the adaptive filter coefficients are still continuously updated, it will cause the generated anti-noise signal to be unable to accurately cancel the real vehicle road noise, resulting in unstable noise reduction effects. Therefore, in the specific implementation, the working states of the reference sensor and the error sensor can be used as influencing factors, and the update step size can be dynamically adjusted based on the working states of the sensors to balance the adaptive convergence rate and system stability, and avoid delayed response or unstable output.

[0102] In this embodiment, there is no limitation on the way of obtaining the influencing factor information. For example, the environmental state information can be obtained by image analysis of the environmental images collected by the camera, and the sensor state information can be determined by outlier analysis of the signals collected by the sensors.

[0103] The influencing factor information can be obtained every once in a while. The shorter the time interval, the higher the real-time degree. In this embodiment, there is no limitation on the acquisition frequency. For example, in a feasible implementation, the influencing factor information can be obtained every time the adaptive filter coefficients are updated.

[0104] Dynamically determining the update step size of the sub-band adaptive filter coefficients means that after each new influencing factor information is obtained, the update step size is determined according to the new influencing factor information, so that the update step size of the adaptive filter coefficients changes according to the specific performance of the influencing factors. By dynamically adjusting the update step size, the update speed of the adaptive filter coefficients is adjusted to adapt to the changes in the internal or external environment state of the vehicle, or to adapt to the changes in the working state of the sensor, balancing the adaptive convergence speed and system stability, and avoiding problems such as misjudgment, poor acoustic effects, and unstable noise reduction effects caused by too fast or too slow update speed of the adaptive filter coefficients, ensuring the robustness and safety of the system, and avoiding delayed response or unstable output.

[0105] The specific method of determining the update step size of the sub-band adaptive filter coefficients according to the influencing factor information is not limited in this embodiment. For example, the mapping relationship between the influencing factor information and the update step size of the sub-band adaptive filter coefficients can be set in advance as needed. After obtaining the influencing factor information, the control unit uses the update step size corresponding to the influencing factor information as the target update step size according to this mapping relationship. In this embodiment, the mapping relationship is not limited. For example, the mapping relationship can be represented by a mapping table, a relational expression, etc., and the mapping relationship can be determined by experiments, data statistics or other methods.

[0106] It should be noted that since there are multiple sub-bands, there are multiple sub-band adaptive filters, and the target update step sizes of the coefficients of each sub-band adaptive filter may be the same or different, which is not limited in this embodiment.

[0107] In this embodiment, by dynamically determining the update step size of the sub-band adaptive filter coefficients according to the obtained environmental state information and / or sensor state information, updating the sub-band adaptive filter coefficients according to the determined update step size, and then converting the sub-band adaptive filter coefficients into time-domain adaptive filter coefficients, using the time-domain adaptive filter coefficients to process the time-domain reference signal to generate an anti-noise signal, so as to control the road noise based on the anti-noise signal, achieving adaptation to the changes in the internal or external environment state of the vehicle, or adaptation to the changes in the working state of the sensor, balancing the adaptive convergence speed and system stability, and avoiding problems such as misjudgment, poor acoustic effects, and unstable noise reduction effects caused by too fast or too slow update speed of the adaptive filter coefficients, ensuring the robustness and safety of the system, and avoiding delayed response or unstable output.

[0108] In a feasible implementation manner, to improve the noise reduction effect of the system and ensure the noise reduction stability, it is set that when the control unit dynamically determines the target update step size according to the influencing factor information, it can conform to one or more of the following rules:

[0109] 1. Under the environmental state characterized by the environmental state information, the target update step size corresponding to the sub-band with higher automotive road noise energy or higher stability level is larger than the target update step size corresponding to the sub-band with lower automotive road noise energy or lower stability level.

[0110] Under the same environmental state, the distribution of automotive road noise on each sub-band may be different; for the sub-band with higher noise energy, a larger update step size can be adopted to specifically enhance the noise reduction intensity of this sub-band, improve the noise reduction effect, and avoid delayed response; for the sub-band with lower noise energy, a smaller update step size can be adopted to ensure the stability of the noise reduction effect and avoid unstable output. Under the same environmental state, the stability level of automotive road noise on each sub-band may be different; for the sub-band with higher stability level, a larger update step size can be adopted to ensure the adaptive convergence speed of the system and a lower steady-state error level, and avoid delayed response; for the sub-band with lower stability level, a smaller update step size can be adopted to avoid misjudgment of the system or generation of adverse acoustic effects, and ensure the robustness and safety of the system.

[0111] It should be noted that the stability level of automotive road noise refers to the stability level of the change in noise energy.

[0112] 2. For any target sub-band, under the environmental state characterized by the environmental state information, the higher the energy or the higher the stability level of the automotive road noise in the target sub-band, the larger the target update step size corresponding to the target sub-band; the lower the energy or the lower the stability level of the automotive road noise in the target sub-band under the environmental state characterized by the environmental state information, the smaller the target update step size corresponding to the target sub-band.

[0113] For the same sub-band, the energy of vehicle road noise in this sub-band may be different under different environmental conditions. Therefore, for a certain sub-band (referred to as the target sub-band for distinction), if the energy of vehicle road noise in the target sub-band is high under a certain environmental condition, a larger update step size can be adopted for the target sub-band to specifically enhance the noise reduction intensity of this sub-band, improve the noise reduction effect, and avoid delayed response. If the energy of vehicle road noise in the target sub-band is low under a certain environmental condition, a smaller update step size can be adopted for the target sub-band to ensure the stability of the noise reduction effect. For the same sub-band, the stability degree of vehicle road noise in this sub-band may be different under different environmental conditions. Therefore, for a certain sub-band (referred to as the target sub-band for distinction), if the stability degree of vehicle road noise in the target sub-band is high under a certain environmental condition, a larger update step size can be adopted for the target sub-band to ensure the adaptive convergence speed of the system and a lower steady-state error level, and avoid delayed response. If the energy of vehicle road noise in the target sub-band is low under a certain environmental condition, a smaller update step size can be adopted for the target sub-band to avoid misjudgment of the system or generation of adverse acoustic effects, and ensure the robustness and safety of the system.

[0114] 3. According to the abnormal degrees of each sensor characterized by the sensor status information, the target update step size corresponding to the sub-band where the sensor with a higher abnormal degree is located is smaller than the target update step size corresponding to the sub-band where the sensor with a lower abnormal degree is located.

[0115] The sub-band where the sensor is located refers to the sub-band in which the signal collected by the sensor is distributed or mainly distributed, and the sub-bands where different sensors are located may be different.

[0116] The sensor status information is used to characterize the working state of the sensor. In the specific implementation manner, the working state of the sensor can be divided into two types, namely normal operation and abnormal operation. The sensor status information can be the information used to indicate whether the sensor belongs to the normal working state or the abnormal working state. Then, it can be understood that the abnormal degree of the sensor with the working state of "normal operation" is lower than that of the sensor with the working state of "abnormal operation". Or, the working state of the sensor can also be divided into more than two types. For example, different abnormal degrees are represented by values within 0-1, and the sensor status information can be the value indicating the abnormal degree of the sensor.

[0117] Based on the acquired sensor status information, the abnormality degree of each sensor can be determined, and the abnormality degrees of the sensors may be different; for the sensor with a higher abnormality degree, a smaller update step size can be adopted for the sub-band where the sensor is located to avoid misjudgment of the system and produce poor acoustic effects. For the sensor with a lower abnormality degree, a larger update step size can be adopted for the sub-band where the sensor is located to ensure the noise reduction effect of the system and avoid delayed response.

[0118] 4. For any target sub-band, the higher the abnormality degree of the sensor belonging to the target sub-band, the smaller the target update step size corresponding to the target sub-band, and the lower the abnormality degree of the sensor belonging to the target sub-band, the larger the target update step size corresponding to the target sub-band.

[0119] For a certain sub-band (hereinafter referred to as the target sub-band for distinction), the sensors belonging to the target sub-band refer to the sensors whose signals are all or mainly distributed in this sub-band.

[0120] For the target sub-band, different sensor status information is acquired, and the abnormality degrees of the sensors belonging to the target sub-band may be different; if the abnormality degree of the sensors belonging to the target sub-band is higher, a smaller update step size can be adopted for the target sub-band to avoid misjudgment of the system and produce poor acoustic effects; if the abnormality degree of the sensors belonging to the target sub-band is lower, a larger update step size can be adopted for the target sub-band to ensure the noise reduction effect of the system and avoid delayed response.

[0121] In a feasible implementation manner, the influencing factor information may further include the real-time vehicle speed of the vehicle. When the vehicle speed changes, it will affect the energy magnitude or stability of the vehicle road noise. When the update step size of the sub-band adaptive filter coefficient adopts a fixed step size when the vehicle speed changes, it may lead to misjudgment or poor acoustic effects due to too large an update step size and too fast update of the sub-band adaptive filter coefficient when the energy magnitude or stability of the vehicle road noise changes. Therefore, the vehicle speed can be used as an influencing factor.

[0122] In a feasible implementation manner, when the influencing factor information further includes the vehicle speed, when dynamically determining the target update step size according to the influencing factor information, the following rule or rules may also be met:

[0123] 5. The higher the vehicle speed stability, the larger the target update step size corresponding to each sub-band, and the lower the vehicle speed stability, the smaller the target update step size corresponding to each sub-band.

[0124] The vehicle speed stability refers, for example, to the change rate of the vehicle speed. The larger the change rate, the lower the vehicle speed stability, and the smaller the change rate, the higher the vehicle speed stability.

[0125] The higher the vehicle speed stability, the higher the stability of the vehicle road noise. At this time, a larger update step size can be adopted to ensure the adaptive convergence speed of the system and a lower steady-state error level, and avoid delayed response. The lower the vehicle speed stability, the lower the stability of the vehicle road noise. At this time, a smaller update step size can be adopted to avoid misjudgment of the system or generation of adverse acoustic effects, and ensure the robustness and safety of the system.

[0126] 6. When the vehicle speed is outside the preset range, the target update step size corresponding to each sub-band is smaller than the target update step size corresponding to each sub-band when the vehicle speed is within the preset range.

[0127] The preset range indicates that the vehicle speed is at a normal level. When the vehicle speed is outside the preset range, it represents a relatively extreme or abnormal situation, and the vehicle road noise may have relatively extreme or abnormal manifestations. For such extreme or abnormal situations, a smaller update step size can be adopted to avoid misjudgment or generation of adverse acoustic effects.

[0128] In a specific implementation manner, in order to enable the control unit to conform to one or more of the above rules when dynamically determining the target update step size according to the influence factor information, a mapping relationship that satisfies one or more of the above rules can be set. The control unit determines the update step size corresponding to the influence factor information according to this mapping relationship as the target update step size. The mapping relationship can be represented by a mapping table, a relational expression, etc. It should be noted that when setting to conform to multiple of the above rules, there can be a certain priority order between multiple rules to avoid conflicts. The priority order can be specifically set according to needs and is not limited here. For example, the priority of determining the update step size according to the sensor status information can be higher than the priority of determining the update step size according to the environmental status information.

[0129] In a feasible implementation manner, the environmental status information includes weather condition information and road surface condition information. Among them, the weather condition information can specifically be information related to the weather condition that affects the road surface, such as the dryness of the road surface, rainfall, humidity, etc. The road surface condition information can specifically be information characterizing the type of the road surface, such as asphalt road surface, potholed road surface, brick road surface, etc.

[0130] In a feasible implementation, when the environmental status information and sensor status information indicate that the environment is good and stable (for example: good road conditions, clear weather, normal sensors, medium vehicle speed), the vehicle road noise is relatively stable, and there will be no particularly abnormal and prominent noise in each sub-band. At this time, a moderate or larger update step size can be adopted to ensure the adaptive convergence speed of the system and a low steady-state error level. When the environmental status information and sensor status information indicate that the environment deteriorates or becomes unstable (for example: deteriorating road conditions, bad weather, abnormal sensors, drastic changes in vehicle speed), the stability of the vehicle road noise may deteriorate. At this time, a smaller update step size can be adopted to improve the system stability. When the environmental status information and sensor status information indicate the existence of specific types of noise (for example: waterlogged road surface, brick road surface, wind noise), the step sizes of different frequency sub-bands can be adjusted differentially. A larger update step size can be adopted for the sub-bands with high noise energy to specifically enhance or weaken the filtering intensity of specific frequency bands and optimize the noise reduction effect. When the environmental status information and sensor status information indicate an extremely harsh environment or system anomaly (for example: extremely harsh road conditions, serious sensor failure, extremely low speed), the update step size can be significantly reduced or even the update can be frozen to avoid system misjudgment or adverse acoustic effects and ensure the robustness and safety of the system.

[0131] The following Table 2 gives some examples of determining the update step sizes for the low-frequency, medium-frequency, and high-frequency bands based on weather condition information, road surface condition information, sensor status information, and the signal characteristics (acceleration amplitude, energy, sound pressure signal amplitude) of the vibration sensor. Among them, the low-frequency, medium-frequency, and high-frequency can be divided as needed. It can be understood that each of the three frequency bands contains one or more sub-bands. Table 2 only gives some examples and does not limit the way of determining the target update step size in this embodiment. The content in parentheses in Table 2 is an explanatory description of weather condition information, road surface condition information, sensor status information, update step size, etc.

[0132] Table 2 Examples of update step size selection

[0133]

[0134] Based on the above first, second, and / or third embodiments, a fourth embodiment of the vehicle road noise processing method of the present application is proposed. In this embodiment, the same or similar content as in the above first, second, and third embodiments can be referred to the above introduction and will not be repeated hereinafter. In this embodiment, it is proposed to predict the environmental status information and / or sensor status information based on the input reference signal and error signal through a prediction model. Specifically, a preset prediction model is used to perform prediction based on the input time-domain reference signal, the input time-domain error signal, the sub-band reference signal, and the sub-band error signal to obtain the environmental status information and / or the sensor status information, where the prediction model is pre-trained.

[0135] In the specific implementation manner, the prediction model can be implemented by using a neural network model, which can be pre-trained and then deployed in the control unit. The control unit calls the prediction model for prediction when it needs to obtain environmental state information or sensor state information. In this embodiment, the implementation manner of the prediction model is not limited.

[0136] In the specific implementation manner, multiple prediction models can be set to respectively output different environmental state information or sensor state information, or a prediction model can be set to output multiple types of information in the mode of multi-output tasks.

[0137] In this embodiment, since the changes in the environmental state and the sensor state will be reflected in the signals collected by the sensor, the time-domain reference signal, the time-domain error signal, the sub-band reference signal, and the sub-band error signal are used as the input data of the prediction model. Through the pre-trained prediction model, the relationship between these input data and the environmental state information or the sensor state information can be learned. Furthermore, the prediction model can be used to predict the environmental state information or the sensor state information based on these input data. On the one hand, by obtaining the environmental state information or the sensor state information through model prediction, the existing reference signals and error signals can be utilized without the need to additionally set up a data acquisition module. On the other hand, the complex situations of automotive road noise under different environmental states or sensor states can all be reflected in the signals collected by the sensor. Therefore, using the signals collected by the sensor can predict more complex and accurate environmental state information and sensor state information, and further enable the dynamic determination of the update step size of the sub-band adaptive filter based on these information, which can greatly improve the system's ability to cope with complex environments. In addition, using the time-domain signal and the sub-band signal together as the prediction basis takes into account the importance of the frequency-domain characteristics and can accurately detect and match more scenarios with small differences in time-domain characteristics.

[0138] In a feasible implementation manner, the prediction model may include a first feature extraction module, a second feature extraction module, a third feature extraction module, a feature fusion module, and a prediction module. The step of using the preset prediction model to perform prediction based on the input time-domain reference signal, the input time-domain error signal, the sub-band reference signal, and the sub-band error signal to obtain the environmental state information and / or the sensor state information includes: inputting the sub-band reference signal and the sub-band error signal into the first feature extraction module for feature extraction to obtain a first feature representation; inputting the input time-domain reference signal into the second feature extraction module for feature extraction to obtain a second feature representation; inputting the input time-domain error signal into the third feature extraction module for feature extraction to obtain a third feature representation; splicing the first feature representation, the second feature representation, and the third feature representation and inputting the spliced result into the feature fusion module for feature fusion to obtain a fused feature representation; and inputting the fused feature representation into the prediction module for prediction to obtain the environmental state information and / or the sensor state information.

[0139] It should be noted that, to improve the accuracy of the prediction result and fully extract the information related to the environmental state and the sensor state in the signal, in this implementation manner, feature extraction modules are respectively set for the sub-band reference signal and the sub-band error signal, the time-domain reference signal, and the time-domain error signal to extract the signal features of the three signals. For the sake of distinction, they are respectively called the first feature extraction module, the second feature extraction module, and the third feature extraction module; a feature extraction module based on a neural network structure can be used to implement it. The results output by the three feature extraction modules, that is, the feature representations, are respectively called the first feature representation, the second feature representation, and the third feature representation for the sake of distinction. The feature representation can be in the form of a feature vector or a feature map, which is not limited in this implementation manner. The feature fusion module can specifically be a module for simply splicing the three feature representations, or can also include a module for deeply fusing the spliced features. The module for performing deep fusion can also be specifically implemented based on a neural network structure, which is not limited here. The prediction module can be designed according to the type of information to be output and the specific form of each type of information, which is not limited in this implementation manner.

[0140] In a feasible implementation manner, the first feature extraction module, the second feature extraction module, and the third feature extraction module each include at least one convolutional layer, and the convolutional layer is used to extract features from the signal; the feature fusion module includes a frequency-axis gated recurrent unit (FGRU), a time-axis gated recurrent unit (TGRU), and at least one transposed convolutional layer connected in sequence. The frequency-axis gated recurrent unit is used to process data along the frequency axis to capture the correlation in the frequency dimension, the time-axis gated recurrent unit is used to process the output of the frequency-axis gated recurrent unit along the time axis to capture the dynamic features in the time dimension, and the transposed convolutional layer is used to gradually upsample and restore to the original size; the prediction module includes a plurality of task output layers, and each task output layer is respectively used to output one item of the environmental state information or the sensor state information. Hereinafter, FGRU and TGRU may also be collectively referred to as FT-GRU.

[0141] In a feasible implementation, a prediction model can be set up based on an improved U-Net (a convolutional neural network based on deep learning) architecture, integrating a frequency-axis gated recurrent unit and a time-axis gated recurrent unit, specifically designed to process complex acoustic and vibration signals. The prediction model includes an Encoder and a Decoder, which are specifically designed to process acoustic and vibration signals. The Encoder performs progressive downsampling through multiple layers of convolution, batch normalization, and ReLU activation functions to effectively extract features; the Decoder uses multiple layers of transposed convolution and skip connections for progressive upsampling to prepare features for the terminal classification task. In addition, FGRU and TGRU are integrated into the network structure to enhance the processing ability of multi-dimensional data, especially in time series and frequency analysis, to adapt to real-time changing environmental conditions. This comprehensive design not only captures complex features spatially but also improves the adaptability and prediction accuracy of the model to complex changes in the vehicle's interior and exterior environments by dynamically adjusting the GRU layer responses. Specifically, the Encoder includes a first feature extraction module, a second feature extraction module, and a third feature extraction module. The first feature extraction module may include three convolutional layers, followed by a max-pooling layer after the first two convolutional layers, and the third convolutional layer is used to adjust the dimension of the output feature representation so that the feature representation output by the first feature extraction module is convenient for splicing with the feature representations output by the other two feature extraction modules. The dimensions of the input and output data of the first feature extraction module and the input and output data of its respective convolutional layers and max-pooling layers can be set as needed and are not limited here. The second feature extraction module and the third feature extraction module perform feature extraction for time-domain signals and can be set to include a one-dimensional convolutional layer, a pooling layer, and a convolutional layer for adjusting the dimension of the output feature representation respectively. The dimensions of the input and output data of the second feature extraction module and the third feature extraction module and the input and output data of their respective convolutional layers and pooling layers can be set as needed and are not limited here. After the feature representations output by the three feature extraction modules are spliced, they are input into the frequency-axis gated recurrent unit. The splicing method can be, for example, splicing along the channel dimension, which is not limited here. The role of the frequency-axis gated recurrent unit is to process data along the frequency axis and capture the correlations in the frequency dimension; the spliced feature representations can be divided into multiple time steps and are sequentially input into the frequency-axis gated recurrent unit. After cyclic calculation by the frequency-axis gated recurrent unit, the hidden states corresponding to each time step are output and sequentially input into the time-axis gated recurrent unit. The role of the time-axis gated recurrent unit is to process the output of the frequency-axis gated recurrent unit along the time axis and capture the dynamic features in the time dimension; the time-axis gated recurrent unit performs cyclic calculation on the hidden states of each sequentially input time step, and the output result is further used as the input of the transposed convolutional layer. The dimensions of the input and output data of the frequency-axis gated recurrent unit and the time-axis gated recurrent unit are also not limited here.The Decoder after the temporal-axis gated recurrent unit can be set to include two transposed convolutional layers and a convolutional layer for adjusting the dimension of the output data. The dimensions of the specific input and output data are not restricted herein. The Decoder is followed by a multi-task output layer, which can include a shared feature extraction layer and output layers respectively set for each piece of information. For example, if it is necessary to output weather condition information, road surface condition information, and sensor status information, an output layer can be respectively set for this kind of information. Among them, the shared feature extraction layer can include one or more convolutional layers, and a pooling layer can be connected after the convolutional layer, whose function is to extract general features and reduce the spatial dimension. The output layers respectively set for each piece of information can include a convolutional layer, a global average pooling layer, and a fully connected layer. An activation function is set after the fully connected layer, which is used to output the confidence levels or probability distributions corresponding to the respective value categories of each piece of information, and is used to determine the final value of each piece of information. The dimensions of the input and output data of the multi-task output layer and the dimensions of the input and output data of each layer therein are also not restricted. In this embodiment, not only the independent processing of each task is ensured, but also the overall performance and resource utilization rate of the model are optimized through the sharing of underlying features and the specialization of top-level decisions.

[0142] In a feasible embodiment, a lightweight, real-time, low-parameter-frequency-time-structured U-Net network can be set as the prediction model, which is specifically designed to accurately process and analyze various frequency-domain and time-domain data, ensuring the effective fusion of sub-band domain and time-domain multi-source information, effectively improving the recognition speed and accuracy of complex acoustic environments and road conditions, and improving the robustness of the model to changes in the vehicle interior and exterior environments. Specifically, the dimensions of the input and output data of the prediction model and the input and output data of each layer in the model can all conform to the definition of [batch size, feature dimension, number of channels]. The dimension of the input data of the first feature extraction module can be set to [1, 65, 11], where the batch size is 1, indicating that a single sample is processed each time, the feature dimension is 65, corresponding to the frequency-domain features extracted by sub-band analysis (i.e., 65 sub-bands), and the number of channels is 11, including 9 vibration sensor channels and 2 error microphone channels. The dimension of the input data of the second feature extraction module can be set to [1, 64, 9], the batch size is 1, the time step is 64, indicating the length of the time-domain signal (i.e., the frame length is 64), and the number of channels is 9, corresponding to 9 vibration sensors. The dimension of the input data of the third feature extraction module is set to [1, 64, 2], the batch size is 1, the time step is 64, and the number of channels is 2, corresponding to 2 error microphones. When training the prediction model, a sequence containing T frames can be input, such as [1, T, 65, 11], so that the FT-GRU can learn the inter-frame relationship; during inference, frames can be input one by one, and the FT-GRU realizes inter-frame modeling through state transfer.

[0143] The Encoder includes a first feature extraction module, a second feature extraction module, and a third feature extraction module.

[0144] The first feature extraction module may include:

[0145] The first convolutional layer: the convolutional kernel dimension is 3x3, the number of input channels is 11, the number of output channels is 16, the padding is 1 ("same" padding), and the output dimension is [1, 65, 16].

[0146] The max pooling layer: the pooling window is 2x1, the stride is 2, and the output dimension is [1, 32, 16].

[0147] The second convolutional layer: the convolutional kernel dimension is 3x3, the number of input channels is 16, the number of output channels is 32, the padding is 1 ("same" padding), and the output dimension is [1, 32, 32].

[0148] The max pooling layer: the pooling window is 2x1, the stride is 2, and the output dimension is [1, 16, 32].

[0149] The 1D convolutional layer: 1D convolution, the convolutional kernel dimension is 4x1, the number of input channels is 32, the number of output channels is 32, the stride is 2x1, the padding is "valid", and the output dimension is [1, 29, 32].

[0150] The final output dimension of the first feature extraction module: [1, 29, 32].

[0151] The second feature extraction module includes:

[0152] The one-dimensional convolutional layer: the convolutional kernel dimension is 3, the number of input channels is 9, the number of output channels is 16, the padding is 1 ("same" padding), and the output dimension is [1, 64, 16].

[0153] The pooling layer: the pooling window is 2, the stride is 2, and the output dimension is [1, 32, 16].

[0154] The 1D convolutional layer: 1D convolution, the convolutional kernel dimension is 4, the number of input channels is 16, the number of output channels is 32, the stride is 1, the padding is "valid", and the output dimension is [1, 29, 32].

[0155] The third feature extraction module includes:

[0156] The one-dimensional convolutional layer: the convolutional kernel dimension is 3, the number of input channels is 2, the number of output channels is 32, the padding is 1 ("same" padding), and the output dimension is [1, 64, 32].

[0157] The pooling layer: the pooling window is 2, the stride is 2, and the output dimension is [1, 32, 32].

[0158] 1D Convolutional Layer: 1D convolution, with a convolutional kernel dimension of 4, 32 input channels, 32 output channels, a stride of 1, padding of "valid", and an output dimension of [1, 29, 32].

[0159] Concatenation Operation in the Feature Fusion Stage: The outputs of the three branches are all [1, 29, 32]. They are concatenated along the channel dimension, i.e., 32 + 32 + 32 = 96, and the output dimension is [1, 29, 96].

[0160] The FT-GRU layer follows the Encoder. The input dimension of the FT-GRU layer is [1, 29, 96], where 29 represents the feature dimension (or time step), and 96 represents the number of input feature channels. The FT-GRU layer includes:

[0161] The First Layer: Freq GRU (GRU on the frequency axis), Function: Process data along the frequency axis to capture correlations in the frequency dimension. Input dimension: [1, 29, 96], where 29 represents the time step and 96 represents the feature channel dimension. Processing Method: For each time step (a total of 29), Freq GRU treats the 96-dimensional features as an input sequence (length 96) for cyclic calculation and outputs the hidden state. Assuming the number of hidden units is H (e.g., H = 32), the output is [1, 29, H].

[0162] The Second Layer: Time GRU (GRU on the time axis), Function: Process the output of Freq GRU along the time axis to capture dynamic features in the time dimension. Input dimension: [1, 29, H]. Processing Method: Cycle through 29 time steps, with each time step inputting H-dimensional features and outputting the final features. Assuming the number of target output channels is 32, the output dimension is [1, 29, 32].

[0163] The Decoder after the FT-GRU layer includes two transposed convolutional layers and a convolutional layer for adjusting the output data dimension.

[0164] The input dimension of the first transposed convolutional layer is [1, 29, 32], the size of the transposed convolutional kernel is 4x1, the stride is 2, the padding is 1, the number of output channels is 32, and the output dimension is [1, 58, 32].

[0165] The input dimension of the second transposed convolutional layer is [1, 58, 32], the size of the transposed convolutional kernel is 8x1, the stride is 1, the padding is 0, the number of output channels is 16, and the output dimension is [1, 65, 16].

[0166] The input dimension of the third convolutional layer is [1, 65, 16], the size of the convolutional kernel is 1x1, the stride is 1, the number of output channels is 1, and the output dimension is [1, 65, 1].

[0167] The decoder is followed by a multi-task output layer, which includes a shared feature extraction layer and multiple output layers (pavement condition classification branch, weather condition classification branch, and sensor status monitoring branch).

[0168] The input dimension of the shared feature extraction layer is [1, 65, 1], and the shared feature extraction layer includes:

[0169] Convolutional layer: The convolutional kernel size is 3x1, the input channels are 1, the output channels are 32, the padding is 1 ("same" padding), and the output is [1, 65, 32]. Max pooling layer: The pooling window is 2x1, the stride is 2, and the output is [1, 32, 32]. Its function is to extract general features and reduce the spatial dimension.

[0170] The pavement condition classification branch, weather condition classification branch, and sensor status monitoring branch connected after the shared feature extraction layer.

[0171] The input dimension of the pavement condition classification branch is [1, 32, 32], and the output dimension is [1, N], which is the probability distribution of N pavement conditions. The pavement condition classification branch includes:

[0172] Convolutional layer: 1x1 convolution, the output channels are 64, and the output dimension is [1, 32, 64].

[0173] Global average pooling: Reducing the dimension to [1, 64].

[0174] Fully connected layer: Output N (the number of pavement condition categories), and the activation function is Softmax.

[0175] The input dimension of the weather condition classification branch is [1, 32, 32], and the output dimension is [1, M], which is the probability distribution of M weather conditions. The weather condition classification branch includes:

[0176] Convolutional layer: 1x1 convolution, the output channels are 64, and the output dimension is [1, 32, 64].

[0177] Global average pooling: Reducing the dimension to [1, 64].

[0178] Fully connected layer: Output M (the number of weather condition categories), and the activation function is Softmax.

[0179] The input dimension of the sensor status monitoring branch is [1, 32, 32], and the output dimension is [1, 11]. Each value is between 0 and 1, representing the confidence of 11 sensors. The sensor status monitoring branch includes:

[0180] Convolutional layer: 1x1 convolution, the output channels are 128, and the output dimension is [1, 32, 128].

[0181] Global average pooling: dimensionality reduction to [1,128].

[0182] Fully connected layer: output 11 (corresponding to 11 sensors), activation function Sigmoid.

[0183] In general, the prediction model outputs the following results:

[0184] Road condition classification: [1,N], probability distribution of N road conditions.

[0185] Weather condition classification: [1,M], probability distribution of M weather conditions.

[0186] Sensor status monitoring: [1,11], confidence level (0-1) of 11 sensors.

[0187] According to the above results output by the prediction model, road condition information for characterizing road conditions, weather condition information for characterizing weather conditions, and sensor status information for characterizing sensor status can be obtained. For example, in a feasible implementation, the category corresponding to the largest probability distribution in each probability distribution in the road condition classification result can be used as the road condition information. For another example, the confidence level in the sensor status monitoring result can be set in advance to indicate the degree of sensor abnormality. The sensor status information can be binary classification information indicating whether the sensor is in a normal state or an abnormal state, or information indicating the degree of sensor abnormality. It can be set as needed in the specific implementation.

[0188] In one possible implementation, referring to Figure 2 , the prediction model can be trained in advance in the following ways.

[0189] Step 1: Data collection. Install accelerometers and microphones with sensitivity and frequency response that meet the test requirements at the locations where acceleration sensors and microphones are required on the vehicle to fully capture vibration and sound data under different road conditions and speeds.

[0190] Step 2: Data labeling. Labeling can be done synchronously during data recording. All data labeling can be done directly by the labeler during data recording, and the road conditions in the data can be labeled in real time and in detail. Specifically, when the microphone and vibration sensor collect data inside and outside the vehicle, the labeler will instantly classify and describe the current road conditions in detail. For example, these descriptions may include but are not limited to: road type (such as highways, urban roads, rural roads), lane markings, road texture, potholes, obstacle distribution, and road slipperiness. The labeler will also record and label weather conditions that affect road conditions (such as rainy or snowy days).

[0191] Step 3: Data Augmentation. To improve the model's ability to recognize abnormal situations, sensor faults and noises can be artificially introduced. First, the robustness of the model can be enhanced by simulating various sensor faults and environmental interferences. For example, covering the microphone with cloth, plastic or metal sheets to simulate the blockage of dust and water, and simulating abnormal impacts by tapping the accelerometer. In addition, digital signal processing techniques can also be used to add various synthetic noises to the normal data, such as white noise, natural environmental noise, and signals simulating electromagnetic interference. When introducing these faults and interferences, the data can be systematically segmented by time and classified and labeled, such as "microphone occlusion - cloth" or "accelerometer impact interference", to ensure the richness of the dataset and the generalization ability of the trained model. These refined operations not only increase the model's adaptability to complex real-world situations, but also help maintain the performance and stability of the system in various environments.

[0192] Step 4: Dataset Balancing. To address the problem of uneven data distribution in the collected data, data analysis can be performed to identify which categories or scenarios have too little or too much data. Then, data augmentation techniques can be used to increase the number of samples in the scarce categories, such as generating new data by changing speed, adding noise, or using synthetic techniques. For the overabundant categories, a downsampling strategy can be adopted to selectively reduce the samples, or representative samples can be selected through clustering analysis. In addition, resampling techniques can also be used to balance the entire dataset to ensure that the data of each category is evenly distributed in the training set, thereby improving the generalization ability and performance of the model under all conditions. This step can also include applying statistical methods to evaluate the changes before and after data balancing to verify the effectiveness of the balancing strategy and adjust the balancing parameters as needed.

[0193] Step 5: Model Architecture Setting. For example, the model architecture in the above embodiments can be set.

[0194] Step 6: Model Training. Customized loss functions can be used for model training to handle different classification tasks. Cross-entropy loss can be used for multi-class classification tasks (such as road surface conditions and weather conditions) to ensure that the model can effectively distinguish different categories; for the binary classification problem of sensor status, binary cross-entropy loss can be adopted to improve the sensitivity of recognizing normal and abnormal states. During the training process, the Adam optimizer (Adaptive Moment Estimation) can be selected, and its adaptive learning rate mechanism helps to better handle the problem of gradient sparsity. At the same time, SGD (Stochastic Gradient Descent) with momentum can also be considered to stabilize the training process. In addition, early stopping mechanisms and regularization techniques can also be adopted to prevent overfitting and ensure that the model can perform well on unseen data.

[0195] Step Seven: Verification and Testing. To ensure the effectiveness and reliability of the model in practical applications, the dataset can be divided into a training set, a validation set, and an independent test set. During the model development process, the validation set is used for periodic evaluation to monitor the training progress and adjust the hyperparameters. The test set is used for the final evaluation after the model training is completed to test the performance of the model on completely unknown data. Various performance metrics, such as accuracy, precision, recall, and F1-score, are applied to evaluate the classification task, while the ROC (Receiver Operating Characteristic) curve and AUC (Area Under Curve) value are used to measure the performance of the model for sensor status anomaly detection.

[0196] Based on the above first, second, third, and / or fourth embodiments, the fifth embodiment of the vehicle road noise processing method of the present application is proposed. In this embodiment, the content that is the same as or similar to the above first, second, third, and / or fourth embodiments can be referred to the above introduction and will not be elaborated hereinafter. In this embodiment, the target amplitude limiting parameter value can also be dynamically determined according to the obtained environmental status information and / or sensor status information. After generating the anti-noise signal, the target amplitude limiting parameter value is used to limit the amplitude of the anti-noise signal, and the anti-noise signal after amplitude limiting is output to control the road noise. That is, post-processing operations can be performed on the anti-noise signal. The post-processing operations include amplitude limiting the anti-noise signal, and the amplitude limiting is performed according to the target amplitude limiting parameter value determined according to the environmental status information and / or sensor status information. The amplitude limiting parameter value is a parameter value used to limit the degree of amplitude limiting. What specific parameter it is is not limited in this embodiment. For example, it can be an upper amplitude value, indicating that the amplitude value of the finally output signal is not allowed to exceed this upper amplitude value.

[0197] It should be noted that when the environmental status outside the vehicle or inside the vehicle changes, it will cause dynamic changes in the acoustic propagation path, which will affect the stability of the vehicle road noise. If a fixed amplitude limiting parameter value is used for amplitude limiting of the anti-noise signal, when the stability of the vehicle road noise changes, it may not be able to fully cancel the noise due to excessive amplitude limiting, or it may not be able to avoid noise or distortion caused by misjudgment or overcompensation due to too small amplitude limiting. Therefore, in the specific implementation, the amplitude limiting parameter value can be dynamically adjusted according to the environmental status information, so as to dynamically control the output amplitude of the anti-noise signal, adapt to different environmental statuses, ensure a good noise reduction effect, and avoid introducing noise or distortion due to misjudgment or overcompensation, and ensure the stability and safety of the system.

[0198] When the operating states of the reference sensor and the error sensor change, the accuracy of the collected signal will be affected. For example, when there is an abnormality in the reference sensor or the error sensor, the accuracy of the collected signal will decrease, and then the estimation of the vehicle road noise in the signal will be incorrect, and the generated anti-noise signal may not only fail to cancel the noise but also introduce noise. Therefore, in the specific implementation, the limit amplitude parameter value can be dynamically adjusted according to the sensor status information, so as to dynamically control the output amplitude of the anti-noise signal. For example, when the sensor is abnormal, the output amplitude can be reduced to avoid introducing noise or distortion due to misjudgment or overcompensation.

[0199] The specific manner of determining the target limit amplitude parameter value according to the environmental state information and / or the sensor status information is not limited in this embodiment. For example, the mapping relationship between the environmental state information and / or the sensor status information and the limit amplitude parameter value can be set in advance. After obtaining the environmental state information and / or the sensor status information, the control unit uses the limit amplitude parameter value corresponding to the environmental state information and / or the sensor status information as the target limit amplitude parameter value according to this mapping relationship. In this embodiment, this mapping relationship is not limited. For example, this mapping relationship can be represented by a mapping table, a relational expression, etc., and this mapping relationship can be determined by experiments, data statistics or other means.

[0200] In a feasible implementation, it can be set that when the control unit determines the target limit amplitude parameter value according to the influence factor information, it conforms to one or more of the following rules:

[0201] 1. The higher the stability of the vehicle road noise in the environmental state characterized by the environmental state information, the smaller the degree of amplitude limitation of the anti-noise signal according to the target limit amplitude parameter value; the lower the stability of the vehicle road noise in the environmental state characterized by the environmental state information, the greater the degree of amplitude limitation of the anti-noise signal according to the target limit parameter.

[0202] If the stability of the vehicle road noise is relatively high in the environmental state characterized by the environmental state information, a limit amplitude parameter value with a smaller degree of amplitude limitation for the anti-noise signal can be adopted, that is, an anti-noise signal with a larger allowable output amplitude can be output, so as to more fully cancel the noise and ensure a good noise reduction effect. If the stability of the vehicle road noise is relatively low in the environmental state characterized by the environmental state information, a limit amplitude parameter value with a larger degree of amplitude limitation for the anti-noise signal can be adopted, that is, the amplitude of the output signal is limited to a greater extent, so as to avoid introducing noise or distortion due to system misjudgment or overcompensation and ensure system stability.

[0203] 2. The higher the degree of abnormality of the sensor characterized by the sensor status information, the greater the degree of amplitude limitation of the anti-noise signal according to the target amplitude limitation parameter; the lower the degree of abnormality of the sensor characterized by the sensor status information, the smaller the degree of amplitude limitation of the anti-noise signal according to the target amplitude limitation parameter.

[0204] When the sensor abnormality is higher, the degree of amplitude limitation of the anti-noise signal is greater, thus avoiding introducing noise or distortion due to misjudgment or overcompensation.

[0205] In a feasible implementation manner, based on the environmental status information and / or the sensor status information, the target amplitude limitation parameter value can also be determined according to the vehicle speed. When determining the target amplitude limitation parameter value according to the vehicle speed, the following rules are followed:

[0206] 3. The lower the vehicle speed stability, the greater the degree of amplitude limitation of the anti-noise signal according to the target amplitude limitation parameter; the higher the vehicle speed stability, the smaller the degree of amplitude limitation of the anti-noise signal according to the target amplitude limitation parameter.

[0207] The vehicle speed stability refers, for example, to the change rate of the vehicle speed. The greater the change rate, the lower the vehicle speed stability; the smaller the change rate, the higher the vehicle speed stability.

[0208] When the vehicle speed stability is relatively high, the stability of the vehicle road noise is relatively high. At this time, an amplitude limitation parameter value with a relatively small degree of amplitude limitation of the anti-noise signal can be adopted, that is, an anti-noise signal with a larger output amplitude is allowed, so as to more fully cancel the noise and ensure a good noise reduction effect. When the vehicle speed stability is relatively low, the stability of the vehicle road noise is relatively low. At this time, an amplitude limitation parameter value with a relatively large degree of amplitude limitation of the anti-noise signal can be adopted, that is, the amplitude of the output signal is limited to a greater extent, to avoid introducing noise or distortion due to system misjudgment or overcompensation and ensure system stability.

[0209] In a specific implementation manner, in order to enable the control unit to determine the target amplitude limitation parameter value to conform to one or more of the above rules, a mapping relationship that satisfies one or more of the above rules can be set. The control unit determines the amplitude limitation parameter value corresponding to the environmental status information, the sensor status information, and the vehicle speed according to this mapping relationship as the target amplitude limitation parameter value. The mapping relationship can be represented by means of a mapping table, a relational expression, etc. It should be noted that when setting to conform to multiple above rules, a certain priority order can be followed among the multiple rules to avoid conflicts. The priority order can be specifically set according to needs and is not limited here. For example, the priority of determining the amplitude limitation parameter value according to the sensor status information can be higher than the priority of determining the amplitude limitation parameter value according to the environmental status information.

[0210] In a feasible implementation, when the environmental state information and the sensor state information indicate that the environment is good and stable (for example: good road conditions, clear weather, normal sensors, medium vehicle speed), the vehicle road noise is relatively stable. At this time, a clipping parameter value with a smaller amplitude limit for the anti-noise signal can be adopted, allowing a larger amplitude anti-noise signal to be output, so as to more fully cancel the noise and ensure a good noise reduction effect. When the environmental state information and the sensor state information indicate that the environment deteriorates or becomes unstable (for example: deteriorating road conditions, bad weather, abnormal sensors, drastic vehicle speed changes), the stability of the vehicle road noise may deteriorate. At this time, a clipping parameter value with a larger amplitude limit for the anti-noise signal can be adopted, that is, the amplitude of the output signal is limited to a greater extent, to avoid introducing noise or distortion due to system misjudgment or overcompensation and ensure system stability. When the environmental state information and the sensor state information indicate an extremely harsh environment or system abnormality (for example: extremely harsh road conditions, severe sensor failure, extremely low speed), the amplitude of the output signal can be greatly limited to give priority to ensuring system safety.

[0211] The following Table 3 gives some examples of determining the output clipping based on the weather condition information, road surface condition information, sensor state information, and signal characteristics (acceleration amplitude, energy, sound pressure signal amplitude) of the vibration sensor. Table 3 only gives some examples and does not limit the method of determining the target clipping parameter value in this embodiment. The content in parentheses in Table 3 is an explanatory description of the weather condition information, road surface condition information, sensor state information, output clipping, etc.

[0212] Table 3 Examples of Output Clipping Selection

[0213]

[0214] Based on the above first, second, third, fourth, and / or fifth embodiments, the sixth embodiment of the vehicle road noise processing method of the present application is proposed. In this embodiment, the same or similar content as in the above first, second, third, fourth, and / or fifth embodiments can be referred to the above introduction and will not be repeated hereinafter. In this embodiment, the pre-weighting weights corresponding to each sensor can also be determined according to the obtained environmental state information and / or sensor state information. After the reference signal and the error signal are weighted with the pre-weighting weights corresponding to their respective sensors, they are used to generate the anti-noise signal and update the coefficients of the adaptive filter. That is, a preprocessing operation is performed on the input reference signal and error signal, and the preprocessing operation includes amplitude adjustment, and the amplitude adjustment is performed according to the pre-weighting weights corresponding to each sensor determined according to the environmental state information and / or sensor state information.

[0215] It should be noted that when the environmental state outside the vehicle or the environmental state inside the vehicle changes, it will cause dynamic changes in the acoustic propagation path, which will affect the energy of the vehicle road noise. The influence on the energy of the vehicle road noise in different frequency bands may also be different. If a fixed amplitude adjustment strategy is adopted for the reference signal and the error signal, when the energy of the vehicle road noise changes, it may be impossible to generate a more targeted anti-noise signal because the noise signals in the reference signal and the error signal are weak, resulting in poor noise reduction effect. Therefore, in the specific implementation manner, the pre-weighting weights of each sensor can be dynamically adjusted according to the environmental state information. Since the frequency bands of the signals collected by different sensors may be different, by adjusting the pre-weighting weights of the sensors, the signals in each frequency band can be enhanced or weakened specifically. For example, the sensors corresponding to the sub-bands with stronger vehicle road noise energy can be set with higher pre-weighting weights, so that a more targeted anti-noise signal can be generated and the noise reduction effect can be improved.

[0216] When the working state of the reference sensor and the working state of the error sensor change, the accuracy of the collected signal will be affected. For example, when there is an abnormality in the reference sensor or the error sensor, the accuracy of the collected signal will decrease, and then the vehicle road noise in the signal will be estimated incorrectly, and the generated anti-noise signal may not only fail to cancel the noise but also introduce noise. Therefore, in the specific implementation manner, the pre-weighting weights of each sensor can be dynamically adjusted according to the sensor state information. For example, the pre-weighting weight of the abnormal sensor can be reduced, so as to avoid introducing noise or distortion due to misjudgment or overcompensation, and ensure the safety and robustness of the system.

[0217] The specific manner of determining the pre-weighting weight according to the environmental state information and / or the sensor state information is not limited in this embodiment. For example, the mapping relationship between the environmental state information and / or the sensor state information and the pre-weighting weight can be set in advance. After obtaining the environmental state information and / or the sensor state information, the control unit uses the pre-weighting weight corresponding to the environmental state information and / or the sensor state information as the target pre-weighting weight according to this mapping relationship. In this embodiment, the mapping relationship is not limited. For example, the mapping relationship can be represented by a mapping table, a relational expression, etc., and the mapping relationship can be determined by experiments, data statistics or other methods.

[0218] In a feasible implementation manner, it can be set that when the control unit determines the pre-weighting weight according to the environmental state information and / or the sensor state information, it conforms to one or more of the following rules:

[0219] 1. Under the environmental state characterized by the environmental state information, the pre-weighting weight of the sensor corresponding to the sub-band with higher energy of the vehicle road noise is greater than that of the sensor corresponding to the sub-band with lower energy of the vehicle road noise.

[0220] The sensor corresponding to the sub-band refers to the sensor whose signal is all or mainly distributed in this sub-band.

[0221] Under the same environmental state, the distribution of vehicle road noise in each sub-band may be different; for the sensor corresponding to the sub-band with higher noise energy, a larger pre-weighting weight can be adopted to specifically enhance the noise reduction intensity of this sub-band and improve the noise reduction effect; for the sub-band with lower noise energy, a smaller pre-weighting weight can be adopted to avoid introducing noise or distortion due to misjudgment or over-compensation.

[0222] 2. For any target sub-band, under the environmental state characterized by the environmental state information, the higher the energy of the vehicle road noise in the target sub-band, the greater the pre-weighting weight of the sensor corresponding to the target sub-band, and the lower the energy of the vehicle road noise in the target sub-band under the environmental state characterized by the environmental state information, the smaller the pre-weighting weight of the sensor corresponding to the target sub-band.

[0223] For the same sub-band, under different environmental states, the energy level of the vehicle road noise in this sub-band may be different; so for a certain sub-band (referred to as the target sub-band for distinction), if the energy of the vehicle road noise in the target sub-band is relatively high under a certain environmental state, a larger pre-weighting weight can be adopted for the sensor corresponding to the target sub-band to specifically enhance the noise reduction intensity of this sub-band and improve the noise reduction effect; if the energy of the vehicle road noise in the target sub-band is relatively low under a certain environmental state, a smaller pre-weighting weight can be adopted for the sensor corresponding to the target sub-band to avoid introducing noise or distortion due to misjudgment or over-compensation.

[0224] 3. According to the degree of abnormality of each sensor characterized by the sensor state information, the higher the degree of abnormality of the sensor, the lower the corresponding pre-weighting weight, and the lower the degree of abnormality of the sensor, the higher the corresponding pre-weighting weight.

[0225] According to the obtained sensor state information, the degree of abnormality of each sensor can be determined, and the degree of abnormality of each sensor may be different; for the sensor with a higher degree of abnormality among them, a smaller pre-weighting weight can be adopted to avoid introducing noise or distortion due to misjudgment or over-compensation, and for the sensor with a lower degree of abnormality among them, a larger pre-weighting weight can be adopted to ensure the noise reduction effect of the system.

[0226] In a specific implementation manner, in order for the control unit to conform to one or more of the above rules when determining the pre-weighting weights of the sensors, a mapping relationship that conforms to one or more of the above rules can be set. The control unit determines the pre-weighting weight corresponding to the environmental state information and / or the sensor state information as the target pre-weighting weight according to this mapping relationship. The mapping relationship can be represented by means of a mapping table, a relational expression, etc. It should be noted that when setting to conform to multiple above rules, there can be a certain priority order among the multiple rules to avoid conflicts. The priority order can be specifically set according to needs and is not limited here. For example, the priority of determining the pre-weighting weight according to the sensor state information can be higher than the priority of determining the pre-weighting weight according to the environmental state information.

[0227] In a feasible implementation manner, the environmental state information includes weather condition information and road surface condition information. Among them, the weather condition information can specifically be information related to the weather condition that affects the road surface, such as the dryness of the road surface, rainfall, humidity, etc. The road surface condition information can specifically be information representing the type of the road surface, such as asphalt road surface, potholed road surface, brick road surface, etc.

[0228] In a feasible implementation manner, three frequency bands, namely low frequency, medium frequency, and high frequency, can be divided; when the environmental state information indicates that the low-frequency noise energy is high (such as in wet and waterlogged weather), a higher pre-weighting weight can be adopted for the signals collected by the low-frequency sensors (sensors whose collected signals are mainly distributed in the low-frequency range). For example, the pre-weighting of the signals collected by the low-frequency sensors is increased by 20%, and the remaining sensor channels maintain a 1.0-fold reference; when the environmental state information indicates that the medium-frequency noise energy is high (such as on a brick road surface and in dry weather), a higher pre-weighting weight can be adopted for the signals collected by the medium-frequency sensors (sensors whose collected signals are mainly distributed in the medium-frequency range). For example, the pre-weighting of the signals collected by the medium-frequency sensors is increased by 20%, and the remaining sensor channels maintain a 1.0-fold reference.

[0229] The following Table 4 gives some examples of determining the pre-weighting strategy based on the weather condition information, road surface condition information, sensor state information, and signal characteristics (acceleration amplitude, energy, sound pressure signal amplitude) of the vibration sensor. Table 4 only gives some examples and does not limit the way of determining the pre-weighting weight in this embodiment. The content in parentheses in Table 4 is the explanatory description of the weather condition information, road surface condition information, sensor state information, pre-weighting strategy, etc.

[0230] Table 4 Examples of Selecting Pre-Weighting Strategy

[0231]

[0232] To help understand the implementation process of the above-mentioned automotive road noise processing methods in each embodiment, an implementation example is given.Figure 3 This is a signal processing flowchart for the automotive road noise processing method in this example. Figure 4 Flow schematic diagram of the automotive road noise processing method in this example.

[0233] Step 1:

[0234] After pre-filtering, amplitude adjustment and other preprocessing operations on the J-channel time-domain vibration reference signal x(n), convolve with the time-domain adaptive filter coefficient W full (l), where l represents the frame index, indicating that it is updated in units of frames rather than the sampling time n. The convolved signal is y(n) of the Q-channel, and after post-processing operations such as amplitude limiting, it is sent to Q speakers for output respectively. The amplitude adjustment operation can be performed on the signal x(n) according to the pre-weighting weight determined in Step 3. The amplitude limiting operation can be performed on the signal y(n) according to the target clipping parameter value T(l) determined in Step 3.

[0235] Step 2:

[0236] Put the J-channel time-domain vibration reference signal x(n) and the M-channel time-domain error microphone signal e(n) into two buffers respectively, with lengths of J*N and M*N. N is the frame length. In this example, J = 9, M = 2, Q = 5, and N = 64. When the buffers are full, a frame of J-channel time-domain vibration reference signal x(l) and a frame of M-channel time-domain error microphone signal e(l) are obtained. Use the FFT-based polyphase analysis filter bank to perform subband decomposition on the above signals x(l) and e(l) respectively, to obtain a frame of J-channel subband vibration reference signal X k (l):

[0237] X k (l) = [X k 1 (l), X k 2 (l)…X k J (l)]

[0238] and a frame of M-channel subband error microphone signal E k (l):

[0239] E k (l) = [E k 1 (l), E k 2 (l)…E k M (l)]

[0240] Among them, k = 1, 2... K, where K is the total number of subbands. In this example, K = 65. In addition, a polyphase analysis filter bank trained by the training method in the fourth embodiment above can be used as the prototype low-pass filter in subband decomposition:

[0241] H G（l） (d) = [h1(d), h2(d)... h P (d)]

[0242] H G（l） (d) is the target polyphase analysis filter bank matched according to G(l), and G(l) is the identifier of the analysis filter bank and the synthesis filter bank determined according to the environmental state information. The formula for subband decomposition of the time-domain vibration reference signal x(l) is as follows:

[0243] (1)

[0244] (2)

[0245] Among them, D is the decimation factor, and P is the length of the FFT. In this example, D = 3 and P = 128. v = -2πikp / P in formula (2). Formula (1) is the process of polyphase filtering the signal x(l). Specifically, it first performs the operation of adding a subband analysis window to the signal x(l), and then accumulates the windowed results to obtain x'(l); formula (2) is to perform DFT (Discrete Fourier Transform) on the signal x'(l) to obtain the subband vibration reference signal X k (l). In actual calculation, FFT can be used to improve the calculation speed. The formula for subband decomposition of the time-domain error microphone signal e(l) will not be elaborated here.

[0246] Step Three:

[0247] Refer to Figure 5 and input a frame of J-channel subband vibration reference signals X1(l)... X K (l), a frame of M-channel subband error microphone signals E1(l)... E K (l), a frame of J-channel time-domain vibration reference signal x(l), and a frame of M-channel time-domain error microphone signal e(l) into a prediction model (U-Net Encoder + FT-GRU + U-Net Decoder + multi-task output layer) based on a neural network structure to predict the road surface condition information, weather condition information, and sensor status information. Corresponding to Figure 5 the function of the parameter matching control module in: dynamically determine the update step sizes μ1(l)... μ of the subband adaptive filter coefficients based on the road surface condition information, weather condition information, and sensor status information K(l); Dynamically determine the target filter bank based on road surface condition information and weather condition information, and use G(l) to represent the identifier of the analysis filter bank and the synthesis filter bank determined according to road surface condition information and weather condition information; Determine the pre-weighting weights of each sensor and the target clipping parameter value T(l) based on road surface condition information, weather condition information, and sensor status information.

[0248] Step Four:

[0249] Since the subbands decomposed by the polyphase analysis filter bank based on FFT are subbands in the complex domain, the complex-domain FxLMS algorithm is used to update the subband adaptive filter coefficients W k (l), and the size of each W k (l) is C*J*Q, where C is the length of the subband adaptive filter coefficients. In this example, C = 4. In the complex-domain FxLMS algorithm, first convolve the input subband vibration reference signal X k (l) with the secondary path model S(k), and this convolution is a complex convolution:

[0250]

[0251] Then use the complex-domain LMS algorithm to update the subband adaptive filter coefficients W k (l):

[0252]

[0253] where q = 1, 2…Q, Q is the number of speaker signal channels. m = 1, 2…M, M is the number of error microphone signal channels, j = 1, 2…J, J is the number of channels of the vibration reference signal. k = 1, 2…K, K is the number of subbands. μ k (l) is the update step size of the subband adaptive filter coefficients determined according to the influencing factor information, which is used to control the update speed of the filter.

[0254] Step Five: Synthesize the time-domain adaptive filter coefficients from the subband adaptive filter coefficients.

[0255] Use the polyphase synthesis filter bank based on IFFT for subband synthesis. Compared with the weight stacking method with relatively large computational complexity and a large number of FFT and IFFT operations, this method only has one set of IFFT and adding synthesis window operations, which is simple and efficient. Among them, the polyphase synthesis filter bank can adopt the polyphase analysis filter bank trained according to the training method in the above-mentioned fourth embodiment, and can be dynamically determined according to the environmental state information. As Figure 6 shown, in this example, the full-band (time-domain) adaptive filter coefficients synthesized based on the subband adaptive filter coefficients can achieve almost the same effect as the target full-band (time-domain) adaptive filter coefficients. Figure 6The abscissa is the index of the filter length, and the ordinate is the value of the coefficient corresponding to each tap.

[0256] Step Six: Load balancing strategy. Decompose the subbands, synthesize the subbands, and allocate the tasks of updating the coefficients of the subband adaptive filter to each interruption, and allocate the neural network model prediction task to each interruption.

[0257] It should be noted that the sequence numbers of the above steps do not constitute a limitation on the sequence order between the steps. In addition, the above examples are only for understanding this application and do not constitute a limitation on the method for processing automotive road noise of this application. Based on this technical concept, more forms of simple transformations are within the protection scope of this application.

[0258] Based on the above embodiments of the method for processing automotive road noise, embodiments of the filter training method of this application are proposed. The filter training method of this embodiment is used to train the polyphase analysis filter bank and the polyphase synthesis filter bank, and the trained polyphase analysis filter bank and polyphase synthesis filter bank can be applied to the method for processing automotive road noise of the above embodiments. The filter training method includes:

[0259] A10. Use the polyphase analysis filter bank to be trained and the polyphase synthesis filter bank to be trained to perform subband decomposition and subband synthesis on the preset first type of signal to obtain a reconstructed signal;

[0260] A20. Calculate the signal reconstruction loss according to the error between the reconstructed signal and the preset first type of signal;

[0261] A30. Use the polyphase analysis filter bank to be trained to perform subband decomposition on the preset second type of signal to obtain a target subband signal, where the preset second type of signal is a single-frequency signal or a narrowband signal within a preset frequency band range, and the frequency band range of the target subband signal is a range other than the preset frequency band range;

[0262] A40. Calculate the spectrum leakage loss according to the target subband signal;

[0263] A50. Optimize the polyphase analysis filter bank to be trained and the polyphase synthesis filter bank to be trained with the goal of reducing the signal reconstruction loss and the spectrum leakage loss to obtain a trained polyphase analysis filter bank and a trained polyphase synthesis filter bank.

[0264] The specific implementation details of steps A10 to A50 can refer to the above first embodiment and will not be elaborated here.

[0265] In this embodiment, the multi-phase analysis filter bank and the multi-phase synthesis filter bank to be trained are trained by a training method similar to that of a neural network model. By calculating the signal reconstruction loss and the spectrum leakage loss, and aiming to reduce the signal reconstruction loss and the spectrum leakage loss, the multi-phase analysis filter bank to be trained and the multi-phase synthesis filter bank to be trained are optimized, so that the trained multi-phase analysis filter bank and multi-phase synthesis filter bank can bring less spectrum leakage and can ensure the accuracy of signal reconstruction. Therefore, when the trained multi-phase analysis filter bank and multi-phase synthesis filter bank are applied to the method for processing automotive road noise, the system calculation amount can be reduced and the spectrum leakage can be reduced.

[0266] In a feasible implementation manner, step A50 includes:

[0267] Step A501, performing weighted summation on the signal reconstruction loss and the spectrum leakage loss according to a preset weight to obtain a total loss;

[0268] Step A502, optimizing the multi-phase analysis filter bank to be trained and the multi-phase synthesis filter bank to be trained with the aim of reducing the total loss, so as to obtain a trained multi-phase analysis filter bank and a multi-phase synthesis filter bank.

[0269] The preset weight can be set in advance according to needs. For example, the weight of the signal reconstruction loss is set to 0.4, and the weight of the spectrum leakage loss is set to 0.6.

[0270] An embodiment of the present application provides an automotive road noise processing device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the automotive road noise processing method in the first embodiment above.

[0271] Reference is made below to Figure 7 , which shows a schematic structural diagram of an automotive road noise processing device suitable for implementing the embodiments of the present application. The automotive road noise processing device in the embodiments of the present application may be a control unit in an RNC system. Figure 7 The shown automotive road noise processing device is only an example and should not impose any limitation on the functions and usage scope of the embodiments of the present application.

[0272] As Figure 7As shown, the vehicle road noise processing device may include a processing device 1001 (such as a DSP processor, etc.), which may perform various appropriate actions and processes according to the program stored in the read-only memory 1002 or the program loaded from the storage device 1003 into the random access memory 1004. In the random access memory 1004, various programs and data required for the operation of the vehicle road noise processing device are also stored. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are connected to each other through a bus 1005. The input / output interface 1006 is also connected to the bus. Generally, the following systems may be connected to the input / output interface 1006: an input device 1007 including, for example, a microphone, an accelerometer, etc.; an output device 1008 including, for example, a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 may allow the vehicle road noise processing device to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows a vehicle road noise processing device having various systems, it should be understood that it is not required to implement or have all the systems shown. Instead, more or fewer systems may be implemented or had.

[0273] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program codes for performing the methods shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from the network through the communication device, or installed from the storage device 1003, or installed from the read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the methods of the embodiments disclosed in the present application are executed.

[0274] Compared with the prior art, the beneficial effects of the vehicle road noise processing device provided by the embodiments of the present application are the same as those of the vehicle road noise processing method provided by the above embodiments, and other technical features in the vehicle road noise processing device are the same as the features disclosed in the method of the previous embodiment, which will not be elaborated herein.

[0275] It should be understood that the various parts disclosed in the embodiments of the present application may be implemented by hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in a suitable manner in any one or more embodiments or examples.

[0276] An embodiment of the present application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the vehicle road noise processing method in the above embodiment.

[0277] The computer-readable storage medium provided by the embodiment of the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM) or a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium can be transmitted by any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0278] The above computer-readable storage medium may be included in the vehicle road noise processing device; or it may exist separately without being assembled into the vehicle road noise processing device.

[0279] The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed by the vehicle road noise processing device, the vehicle road noise processing device is caused to execute the above functions defined in the method of the disclosed embodiment of the present application.

[0280] Computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN: Local Area Network) or a wide area network (WAN: Wide Area Network), or it can be connected to an external computer (for example, by connecting through an Internet service provider using the Internet).

[0281] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and this module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutively represented blocks can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0282] The modules involved in the embodiments of this application can be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation on the unit itself in some cases.

[0283] The readable storage medium provided by the embodiments of this application is a computer-readable storage medium. The computer-readable storage medium stores computer-readable program instructions (i.e., computer programs) for performing the above-mentioned automotive road noise processing method. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the embodiments of this application are the same as those of the automotive road noise processing method provided by the above embodiments, and will not be elaborated here.

[0284] An embodiment of the present application further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned automotive road noise processing method are implemented.

[0285] Compared with the prior art, the beneficial effects of the computer program product provided by the embodiment of the present application are the same as those of the automotive road noise processing method provided by the above embodiment, and will not be elaborated here.

[0286] The above are only partial embodiments of the present application, and do not limit the patent scope of the present application accordingly. Any equivalent structural transformation made under the technical concept of the present application by using the content of the specification and drawings of the present application, or any direct / indirect application in other related technical fields, is included in the patent protection scope of the present application.

Claims

1. A method for processing automotive road noise, characterized in that, The method for processing automotive road noise includes: Performing sub-band decomposition on the input time-domain reference signal and time-domain error signal respectively using a preset polyphase analysis filter bank to obtain sub-band reference signals and sub-band error signals; Updating the sub-band adaptive filter coefficients based on the sub-band reference signals and the sub-band error signals; Performing sub-band synthesis on the updated sub-band adaptive filter coefficients using a preset polyphase synthesis filter bank to obtain updated time-domain adaptive filter coefficients; Processing the newly input time-domain reference signal based on the updated time-domain adaptive filter coefficients to obtain an anti-noise signal, and controlling the road noise based on the anti-noise signal; The preset polyphase analysis filter bank and the preset polyphase synthesis filter bank are pre-trained, and the training objective is to reduce the signal reconstruction loss and spectral leakage loss of the polyphase analysis filter bank and the polyphase synthesis filter bank. The signal reconstruction loss refers to the error between the signal obtained by performing sub-band decomposition and sub-band synthesis on the signal through the filter bank and the original signal, and the spectral leakage loss refers to the magnitude of the spectral leakage generated by performing sub-band decomposition on the signal through the filter bank.

2. The method for processing automotive road noise according to claim 1, characterized in that, The signal reconstruction loss is calculated based on the error between the reconstructed signal and a preset first-type signal. The reconstructed signal is obtained by performing sub-band decomposition and sub-band synthesis on the preset first-type signal using the polyphase analysis filter bank to be trained and the polyphase synthesis filter bank to be trained.

3. The method for processing automotive road noise according to claim 1, characterized in that, The spectral leakage loss is calculated based on the target sub-band signal. The target sub-band signal is obtained by performing sub-band decomposition on a preset second-type signal using the polyphase analysis filter bank to be trained. The preset second-type signal is a single-frequency signal or a narrow-band signal within a preset frequency band range, and the frequency band range of the target sub-band signal is the range other than the preset frequency band range.

4. The method for processing automobile road noise according to claim 1, wherein The step of performing sub-band decomposition on the input time-domain reference signal and time-domain error signal respectively using a preset polyphase analysis filter bank to obtain sub-band reference signals and sub-band error signals includes: Performing sub-band decomposition on the input time-domain reference signal using the fast Fourier transform algorithm and the preset polyphase analysis filter bank to obtain sub-band reference signals, and performing sub-band decomposition on the input time-domain error signal using the fast Fourier transform algorithm and the polyphase analysis filter bank to obtain sub-band error signals; and / or The step of performing sub-band synthesis on the updated sub-band adaptive filter coefficients using a preset polyphase synthesis filter bank to obtain updated time-domain adaptive filter coefficients includes: Performing sub-band synthesis on the updated sub-band adaptive filter coefficients using the inverse fast Fourier transform algorithm and the preset polyphase synthesis filter bank to obtain updated time-domain adaptive filter coefficients.

5. The method for processing automotive road noise according to any one of claims 1 to 4, characterized in that, The frame index of the input time-domain reference signal and time-domain error signal is l, and the method for processing automotive road noise further includes: Execute the tasks allocated to the N interruptions in the N interruptions of the (l + 1)-th frame, where N represents the frame length, and an interruption refers to the period from generating an anti-noise signal based on the time-domain reference signal of each sampling point to before receiving the time-domain reference signal of the next sampling point. The tasks allocated to the N interruptions include: the task of calculating the updated time-domain adaptive filter coefficients.

6. A filter training method, characterized in that, The filter training method is used to train a polyphase analysis filter bank and a polyphase synthesis filter bank. The polyphase analysis filter bank and the polyphase synthesis filter bank are applied to the automotive road noise processing method as described in Claim 1. The filter training method includes: Perform sub-band decomposition and sub-band synthesis on a preset first type of signal using the to-be-trained polyphase analysis filter bank and the to-be-trained polyphase synthesis filter bank to obtain a reconstructed signal; Calculate a signal reconstruction loss based on the error between the reconstructed signal and the preset first type of signal; Perform sub-band decomposition on a preset second type of signal using the to-be-trained polyphase analysis filter bank to obtain a target sub-band signal, where the preset second type of signal is a single-frequency signal or a narrow-band signal within a preset frequency band range, and the frequency band range of the target sub-band signal is the range other than the preset frequency band range; Calculate a spectrum leakage loss based on the target sub-band signal; Optimize the to-be-trained polyphase analysis filter bank and the to-be-trained polyphase synthesis filter bank with the goal of reducing the signal reconstruction loss and the spectrum leakage loss to obtain a trained polyphase analysis filter bank and a trained polyphase synthesis filter bank.

7. The filter training method according to claim 6, wherein The step of optimizing the to-be-trained polyphase analysis filter bank and the to-be-trained polyphase synthesis filter bank with the goal of reducing the signal reconstruction loss and the spectrum leakage loss to obtain a trained polyphase analysis filter bank and a trained polyphase synthesis filter bank includes: Perform weighted summation of the signal reconstruction loss and the spectrum leakage loss according to a preset weight to obtain a total loss; Optimize the to-be-trained polyphase analysis filter bank and the to-be-trained polyphase synthesis filter bank with the goal of reducing the total loss to obtain a trained polyphase analysis filter bank and a trained polyphase synthesis filter bank.

8. The filter training method according to claim 6, wherein The initial coefficients of the to-be-trained polyphase analysis filter bank and the to-be-trained polyphase synthesis filter bank are Hamming windows of a preset length.

9. An automobile road noise treatment device, characterized in that, The device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor. The computer program is configured to implement the steps of the automotive road noise processing method as described in any one of Claims 1 to 5.

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