A signal processing method, device, storage medium and vehicle

Abstract

The application relates to a signal processing method and device, a storage medium and a vehicle. The method comprises the following steps: receiving a first audio signal at a noise source collected by one or more first sensors; receiving a second audio signal at a human ear collected by one or more second sensors, the first audio signal and the second audio signal are used to determine a parameter for processing the first audio signal according to a first processing mode; and sending a third audio signal determined after processing the first audio signal according to the first processing mode, the third audio signal is used to instruct a loudspeaker to emit sound waves, and the sound waves are used to cancel the noise at the human ear. Therefore, the real-time adjustment capability of the parameter in the noise reduction process can be realized, so that fast noise reduction can be realized, the noise reduction amount is large, and the noise reduction effect is better.

Description

Technical Field

[0001] This application relates to the field of signal processing, and more particularly to a signal processing method, apparatus, storage medium, and vehicle. Background Technology

[0002] Vehicles typically generate significant noise during high-speed driving. This noise includes road noise generated by the interaction between the tires and the road surface, the suspension and the vehicle body, wind noise, and environmental noise, severely impacting the comfort of passengers. Noise reduction methods can reduce the energy of perceived noise and minimize noise interference. Vehicle noise reduction methods generally fall into two categories: passive noise reduction and active noise reduction. Passive noise reduction uses physical methods to reduce vehicle noise, while active noise cancellation (ANC) typically utilizes active noise cancellation (ANC) technology. This involves using speakers to generate audio signals that suppress noise signals. When the noise signal and the suppressed noise signal intersect and superimpose, they neutralize each other, ultimately achieving noise reduction.

[0003] In active noise reduction methods, filters are usually only suitable for noise reduction under a single operating condition. Due to the complex body structure and unstable operating conditions of actual vehicles, the real-time adjustment capability is poor in practical applications, making it impossible to achieve rapid noise reduction or stabilize at a large noise reduction level. Therefore, there is an urgent need for noise reduction solutions with stronger real-time adjustment capabilities and better noise reduction effects. Summary of the Invention

[0004] In view of this, a signal processing method, apparatus, storage medium, and vehicle are proposed.

[0005] In a first aspect, embodiments of this application provide a signal processing method. The method includes: receiving a first audio signal at a noise source acquired by one or more first sensors; receiving a second audio signal at a human ear acquired by one or more second sensors, wherein the first audio signal and the second audio signal are used to determine parameters for processing the first audio signal according to a first processing method; and sending a third audio signal determined after processing the first audio signal according to the first processing method, wherein the third audio signal is used to instruct a loudspeaker to emit sound waves, the sound waves being used to cancel out the noise at the human ear.

[0006] According to the embodiments of this application, by using a second audio signal collected at the human ear for real-time parameter adjustment during the processing of the first audio signal, the current noise reduction state can be taken into account, and the parameters can be adjusted according to the current noise reduction state. Therefore, the first audio signal is processed using the adjusted parameters to obtain a third audio signal, which instructs the speaker to emit sound waves to cancel noise. This achieves faster noise reduction, a greater noise reduction amount, better noise reduction effect, and improves the comfort of passengers.

[0007] According to the first aspect and any possible implementation, the method further includes: processing the first audio signal according to a second processing method to determine a fourth audio signal, the second processing method indicating a transmission method of sound waves from the speaker to the second sensor; determining a fifth audio signal based on the second audio signal and an audio signal after processing the third audio signal according to the second processing method; and determining parameters for processing the first audio signal according to the first processing method based on the fourth audio signal and the fifth audio signal.

[0008] According to an embodiment of this application, a fourth audio signal is determined by estimating the transmission method from the speaker to the second sensor, taking into account the transmission process of the first audio signal. Simultaneously, a fifth audio signal is reconstructed. This allows for the calculation of the initial noise heard by the occupants before noise reduction. Combining these two methods to adjust the parameters enables the corresponding sound wave emitted by the speaker, indicated by the adjusted parameters, to better cancel out the noise, thereby achieving a greater noise reduction. Furthermore, by calculating the parameters in real-time based on the above two methods, the parameters can be adjusted promptly, allowing for faster recovery from noise reduction issues when interference occurs, resulting in stronger robustness and ultimately a better noise reduction effect.

[0009] According to the first aspect and any possible implementation, based on the fourth audio signal and the fifth audio signal, determining the parameters for processing the first audio signal according to the first processing method includes: determining the parameters for processing the first audio signal according to the first processing method based on the autocorrelation matrix of the fourth audio signal and the cross-correlation matrix of the fourth audio signal and the fifth audio signal.

[0010] Therefore, the autocorrelation of the fourth audio signal and the cross-correlation between the fourth and fifth audio signals can be considered simultaneously during parameter adjustment, thus achieving better noise reduction.

[0011] According to the first aspect and any possible implementation, based on the autocorrelation matrix of the fourth audio signal and the cross-correlation matrix of the fourth audio signal and the fifth audio signal, the parameters for processing the first audio signal according to the first processing method are determined, including: determining the direction of change of the parameters based on the autocorrelation matrix, the cross-correlation matrix, and the parameters at the previous time step; and determining the parameters at the current time step based on one or more of the following: the parameters at the previous time step, the signal length when processing according to the first processing method, the direction of change of the parameters, and the magnitude of change of the parameters.

[0012] According to the embodiments of this application, by determining the parameters at each moment, it is possible to achieve stable and large noise reduction while avoiding noise caused by sudden changes in the filter due to untimely parameter updates, thereby improving the comfort of the driving and riding experience.

[0013] According to the first aspect and any possible implementation, according to the second processing method, the first audio signal is processed to determine the fourth audio signal, including: moving a predetermined window distance every so often, processing the first audio signal of a predetermined window length according to the second processing method to determine the fourth audio signal; and according to the second audio signal and the audio signal after processing the third audio signal according to the second processing method, a fifth audio signal is determined, including: moving a predetermined window distance every so often, processing the second audio signal of a predetermined window length and the audio signal after processing the third audio signal according to the second processing method to determine the fifth audio signal.

[0014] According to the embodiments of this application, calculations can be performed using a predetermined window, eliminating the need to determine the fourth and fifth audio signals point by point, thus reducing the amount of computation.

[0015] According to the first aspect and any possible implementation, the method further includes: determining a noise reduction amount based on the second audio signal and the fifth audio signal; and adjusting one or more of the following based on the noise reduction amount: the magnitude of the parameter change, the signal length when processed according to the first processing method, the predetermined window movement distance, and the predetermined window length.

[0016] According to the embodiments of this application, by calculating the noise reduction amount and adjusting one or more of the following based on the noise reduction amount: the change range of parameters, signal length, predetermined window movement distance, and predetermined window length, it is possible to adapt to different noise reduction environments and states during the noise reduction process, thereby achieving better noise reduction effects and improving the user experience.

[0017] According to the first aspect and any possible implementation, in the sixth possible implementation of the signal processing method, the first processing method is Wiener filtering.

[0018] This allows for greater noise reduction and faster noise reduction.

[0019] Secondly, embodiments of this application provide a signal processing apparatus. The apparatus includes: a first receiving module for receiving a first audio signal from a noise source collected by one or more first sensors; a second receiving module for receiving a second audio signal from a human ear collected by one or more second sensors, wherein the first and second audio signals are used to determine parameters for processing the first audio signal according to a first processing method; and a transmitting module for transmitting a third audio signal determined after processing the first audio signal according to the first processing method, wherein the third audio signal is used to instruct a loudspeaker to emit sound waves, and the sound waves are used to cancel out the noise at the human ear.

[0020] According to the second aspect and any possible implementation, the device further includes: a first determining module, configured to process the first audio signal according to a second processing method to determine a fourth audio signal, wherein the second processing method indicates the transmission method of sound waves from the speaker to the second sensor; a second determining module, configured to determine a fifth audio signal based on the second audio signal and an audio signal after processing the third audio signal according to the second processing method; and a third determining module, configured to determine parameters for processing the first audio signal according to the first processing method based on the fourth audio signal and the fifth audio signal.

[0021] According to the second aspect and any possible implementation, the third determining module includes: determining parameters for processing the first audio signal according to the first processing method based on the autocorrelation matrix of the fourth audio signal and the cross-correlation matrix of the fourth audio signal and the fifth audio signal.

[0022] According to the second aspect and any possible implementation, based on the autocorrelation matrix of the fourth audio signal and the cross-correlation matrix of the fourth audio signal and the fifth audio signal, the parameters for processing the first audio signal according to the first processing method are determined, including: determining the direction of change of the parameters based on the autocorrelation matrix, the cross-correlation matrix, and the parameters at the previous time step; and determining the parameters at the current time step based on one or more of the following: the parameters at the previous time step, the signal length when processing according to the first processing method, the direction of change of the parameters, and the magnitude of change of the parameters.

[0023] According to the second aspect and any possible implementation, the first determining module includes: moving a predetermined window distance every so often, processing the first audio signal of a predetermined window length according to the second processing method to determine a fourth audio signal; the second determining module includes: moving a predetermined window distance every so often, determining a fifth audio signal based on the second audio signal of a predetermined window length and the audio signal after processing the third audio signal according to the second processing method.

[0024] According to the second aspect and any possible implementation, the device further includes: a fourth determining module, configured to determine a noise reduction amount based on the second audio signal and the fifth audio signal; and an adjusting module, configured to adjust one or more of the following based on the noise reduction amount: the magnitude of the parameter change, the signal length when processed according to the first processing method, the predetermined window movement distance, and the predetermined window length.

[0025] According to the second aspect and any possible implementation, the first processing method is Wiener filtering.

[0026] Thirdly, embodiments of this application provide a signal processing apparatus, which includes: a processor and a memory; the memory is used to store a program; the processor is used to execute the program stored in the memory, so that the apparatus implements the signal processing method in the first aspect or any possible implementation of the first aspect.

[0027] Fourthly, embodiments of this application provide a terminal device that can execute the signal processing method in the first aspect or any possible implementation of the first aspect.

[0028] Fifthly, embodiments of this application provide a computer-readable storage medium having program instructions stored thereon, which, when executed by a computer, cause the computer to implement the method described in the first aspect or any possible implementation of the first aspect.

[0029] To achieve the above objectives, a sixth aspect of this application provides a computer program product comprising program instructions that, when executed by a computer, cause the computer to implement the signal processing method in the first aspect or any possible implementation thereof.

[0030] In a seventh aspect, embodiments of this application provide a vehicle, the vehicle including a processor, the processor being configured to execute the signal processing method described in the first aspect or any possible implementation thereof.

[0031] These and other aspects of this application will become more apparent in the description of the following embodiments(s). Attached Figure Description

[0032] The accompanying drawings, which are included in and form part of this specification, illustrate exemplary embodiments, features, and aspects of this application together with the specification and serve to explain the principles of this application.

[0033] Figure 1 A schematic diagram illustrating an application scenario according to an embodiment of this application is shown.

[0034] Figure 2 A flowchart of a signal processing method according to an embodiment of this application is shown.

[0035] Figure 3 A schematic diagram of a sliding window according to an embodiment of this application is shown.

[0036] Figure 4 A flowchart illustrating a signal processing method according to an embodiment of this application is shown.

[0037] Figure 5 A flowchart illustrating a signal processing method according to an embodiment of this application is shown.

[0038] Figure 6 A flowchart illustrating a signal processing method according to an embodiment of this application is shown.

[0039] Figure 7 A flowchart illustrating a signal processing method according to an embodiment of this application is shown.

[0040] Figure 8 A structural diagram of a signal processing apparatus according to an embodiment of this application is shown.

[0041] Figure 9 A structural diagram of a signal processing apparatus according to an embodiment of this application is shown. Detailed Implementation

[0042] Various exemplary embodiments, features, and aspects of this application will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0043] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0044] Furthermore, to better illustrate this application, numerous specific details are provided in the following detailed embodiments. Those skilled in the art should understand that this application can be implemented without certain specific details. In some instances, methods, means, components, and circuits well-known to those skilled in the art have not been described in detail in order to highlight the main points of this application.

[0045] Figure 1 A schematic diagram illustrating an application scenario according to an embodiment of this application is shown. For example... Figure 1 As shown, the signal processing method of this application embodiment can be used to reduce the noise heard by drivers and passengers in a vehicle. The signal processing system of this application embodiment can be installed in a vehicle and includes a speaker, a sensor, and a processor.

[0046] The loudspeaker is used to emit sound waves corresponding to the audio signal to cancel out noise near the ears of the occupants, thereby reducing the noise heard by the occupants inside the vehicle. There can be one or more loudspeakers.

[0047] The sensor may include a first sensor and a second sensor.

[0048] The first sensor can be one or more, and may include an accelerometer, vehicle-mounted radar (such as millimeter-wave radar, lidar, ultrasonic radar, etc.), rain sensor, camera, vehicle attitude sensor (such as gyroscope), inertial measurement unit (IMU), etc. The first sensor can be placed near the noise source on the vehicle to collect reference signals. The reference signals can be used to indicate the noise level near the noise source. For example, the reference signal may include the acceleration signal collected by the accelerometer. Since this acceleration signal is proportional to the vehicle vibration amplitude, the magnitude of the noise near the noise source can be determined using this acceleration signal.

[0049] The second sensor may include a microphone, which can be positioned near the ears of vehicle occupants to collect residual signals. The residual signals can be used to indicate the residual noise heard by the vehicle occupants after the sound waves emitted by the speaker cancel out the noise near their ears. In one possible implementation, if there are multiple occupants in the vehicle, multiple second sensors can be set up for each occupant to collect corresponding residual signals.

[0050] The processor, such as a digital signal processor (DSP) chip, can be integrated into the vehicle's infotainment system (or audio system) as an in-vehicle computing unit. It can perform calculations based on signals collected by sensors to determine the audio signal. Alternatively, the processor can be externally located in a cloud server. The server and vehicle can communicate wirelessly, using technologies such as 2G / 3G / 4G / 5G mobile communication, as well as wireless communication methods like Wi-Fi, Bluetooth, FM, digital radio, and satellite communication. Through this communication, the server can collect and calculate the signals from the sensors and then send the results back to the vehicle.

[0051] In one possible implementation, the signal processing system of this application embodiment may further include a preamplifier and a power amplifier. The preamplifier can be used to amplify the residual signal acquired by the second sensor to a certain level range, and the power amplifier can be used to amplify the audio signal to drive the speaker to emit corresponding sound waves.

[0052] In the process of active noise cancellation in vehicles, due to the complex structure and unstable operating conditions of the vehicle body, current in-vehicle noise cancellation methods cannot effectively adjust in real time based on the current noise reduction effect, resulting in poor noise reduction performance. The signal processing method of this application, in the process of processing the reference signal to determine the audio signal for active noise cancellation, also utilizes the residual signal to adjust the parameters during processing. This allows for real-time adjustment based on the current noise reduction effect, dynamically changing the audio signal. Therefore, the dynamically adjusted audio signal can cancel out the noise signal at the human ear, achieving a greater noise reduction amount, rapid noise reduction, and a better noise reduction effect.

[0053] The following is Figures 2-3 For example, based on the above signal processing system, the signal processing method of this application embodiment will be described in detail:

[0054] Figure 2 A flowchart of a signal processing method according to an embodiment of this application is shown. This method can be used in the aforementioned signal processing system. Figure 2 The method may include:

[0055] Step S201: The first sensor acquires a reference signal.

[0056] The first sensor can be one or more. The reference signal acquired by the first sensor can be seen in x(n) in the figure. n can correspond to the current time and represent the sequence number in the signal sequence, that is, the signal acquired at the current time is the nth signal in the signal sequence. The reference signal can be a multi-channel signal, that is, one x(n) can correspond to a group of signals, where one signal in each group corresponds to one channel.

[0057] Step S202: The second sensor acquires the residual signal.

[0058] See Figure 2 ∑ can refer to the summation of the reference signal and the audio signal at the error point, meaning the reference signal and the audio signal cancel each other out at the error point, and the remaining signal after cancellation is the residual signal. The error point is the location where the second sensor is placed, which can be any location near the ear, such as any location near the left ear and / or the right ear. Correspondingly, one or more second sensors can be set. If there are multiple occupants, multiple corresponding second sensors can be set. In the figure, d(n) can represent the actual primary noise signal, corresponding to the noise that actually reaches the error point. In the figure, G can represent the actual secondary path, which can refer to the path of sound wave transmission from the speaker to the error point.

[0059] In a real vehicle, the audio signal emitted by the speaker (y(n) in the figure) reaches the error point via the secondary path G and cancels out d(n). After cancellation, the signal that the second sensor can collect is the residual signal, which can be seen as e(n) in the figure. e(n) indicates the noise actually heard by the driver and passengers after noise reduction. In this application, by using e(n) collected by the second sensor at the error point, we can better understand the current noise reduction effect, which helps to adjust relevant parameters more effectively and improve the noise reduction effect.

[0060] In this application, after obtaining the reference signal and the residual signal, the reference signal filtered by the filter corresponding to the transfer function of the secondary path can be determined by combining the known audio signal, and the primary noise signal can be calculated. These two signals are then used to update the relevant parameters of the Wiener filter (which can be represented by W) used to obtain the audio signal. This allows for dynamic adjustment of the output audio signal.

[0061] In one possible implementation, the sliding window algorithm can be used to calculate the relevant parameters during the update of W's parameters. That is, for some parameters, it is not necessary to calculate them point by point, thus saving computational resources.

[0062] Figure 3 A schematic diagram of a sliding window according to an embodiment of this application is shown. Figure 3As shown in the figure, the rectangular boxes correspond to sliding windows, and each point on the coordinate axis corresponds to a signal. Here, n corresponds to the current signal, for example, x(n). N represents the length of the sliding window, meaning one sliding window corresponds to N signals. M represents the distance the sliding window moves, meaning the corresponding parameters are calculated every M signals.

[0063] exist Figure 3 In the process shown, the sliding window moved twice, first from the position corresponding to signal nM to the position corresponding to signal n, and then from the position corresponding to signal n to the position corresponding to signal n+M. During this process, three calculations can be performed for each of the three positions.

[0064] The sliding window algorithm can be used in the relevant processes of steps S203-S208 below. See also... Figure 3 During the process of updating the parameters of W, the relevant processes in steps S203-S208 can be executed once every M signals to recalculate the relevant parameters based on N signals, including the current signal and N-1 signals before the current signal.

[0065] In step S203, the processor filters the reference signal according to the transfer function of the secondary path to determine the filtered reference signal.

[0066] Since the reference signal, after being filtered to obtain the audio signal, still needs to pass through a secondary path to reach the error point, this application first calculates the reference signal after passing through the secondary path (i.e., the filtered reference signal, see...). Figure 2 Chinese x g (n)). Then, the filtering parameters of the audio signal are adjusted based on this signal. In this process, the influence of the secondary path on the noise reduction effect is taken into account, thereby achieving a better noise reduction effect.

[0067] The transfer function for the secondary path can be found in [reference needed]. Figure 2 middle In one possible implementation, a white noise signal can be played through a loudspeaker, and the signal acquired by a second sensor can be recorded to estimate the transfer function of the secondary path. The transfer function of the secondary path can be obtained by using the least mean square (LMS) algorithm or Wiener filtering, or other methods can be employed.

[0068] In one possible implementation, a sliding window algorithm can be used to filter the reference signal every M reference signals according to the transfer function of the secondary path to determine the filtered reference signal.

[0069] In step S204, the processor determines the primary noise signal based on the residual signal and the audio signal after filtering the audio signal according to the transfer function of the secondary path.

[0070] Determine the filtered reference signal x g After (n), this application also needs to calculate the primary noise signal d(n) at the error point. Therefore, when updating the filtering parameters of the audio signal, the correlation between the filtered reference signal and the primary noise signal can be used to adjust the parameters so that the final determined audio signal can better cancel the noise signal at the human ear.

[0071] In practice, the primary noise signal is canceled out by the audio signal during active noise reduction and cannot be directly acquired. To dynamically adjust the parameters of the Wiener filter used to determine the audio signal, this application reconstructs the primary noise signal based on the acquired residual signal and audio signal, using the transfer function of the determined secondary path (see [link to relevant documentation]). Figure 2 middle ).

[0072] One way to determine the primary noise signal is shown in formula (1):

[0073]

[0074] Where J can represent the transfer function of the secondary path. The maximum value of the corresponding filter length, where the filter length represents the number of signals filtered in one pass (i.e., the number of input signal sampling points processed by the filter in one pass). I can represent the maximum value of the Wiener filter W used to determine the audio signal, and can also represent the number of signals filtered in one pass of the Wiener filter. This can represent the audio signal y(n) obtained after filtering the reference signal x(n) by W. It can be represented that y(n) passes through The filtered audio signal reaches the error point. Therefore, the primary noise signal can be deduced from the acquired residual signal.

[0075] In one possible implementation, a sliding window algorithm can be used to determine a primary noise signal every M residual signals and the audio signal after filtering the audio signal according to the transfer function of the secondary path.

[0076] In step S205, the processor determines the autocorrelation matrix based on the filtered reference signal.

[0077] One method for determining the autocorrelation matrix can be found in formula (2):

[0078]

[0079] Among them, R xx (n) can represent x g The autocorrelation matrix corresponding to (n) can indicate x g The degree of autocorrelation of (n). It can represent x g The transpose of (n).

[0080] In one possible implementation, utilizing Figure 3 The sliding window algorithm shown can be applied every M cycles. The filtered reference signal is used to obtain the first N signals to calculate x. g The autocorrelation matrix corresponding to (n).

[0081] One method for determining the autocorrelation matrix using the sliding window algorithm can be found in formula (3):

[0082]

[0083] Where N can represent the length of the sliding window.

[0084] In step S206, the processor determines the cross-correlation matrix based on the filtered reference signal and the primary noise signal.

[0085] One method for determining the cross-correlation matrix is ​​shown in formula (4):

[0086]

[0087] Among them, R xd (n) can represent x g (n) and The corresponding autocorrelation matrix can indicate x g (n) and The degree of correlation between them. It can represent The transpose of .

[0088] In one possible implementation, utilizing Figure 3 The sliding window algorithm shown can be applied every M cycles. The filtered reference signal is calculated every M points. And obtain the first N of the above signals to calculate x. g (n) and The corresponding cross-correlation matrix.

[0089] One method for determining the cross-correlation matrix using the sliding window algorithm can be found in formula (5):

[0090]

[0091] Where N can represent the length of the sliding window.

[0092] In step S207, the processor updates the coefficients of the Wiener filter used to determine the audio signal based on the autocorrelation matrix and the cross-correlation matrix.

[0093] The target direction W of the Wiener filter coefficients can be determined based on the autocorrelation matrix and the cross-correlation matrix. wn (n). Determine W wn One method for (n) can be found in formula (6):

[0094]

[0095] in, R can be represented xx The inverse matrix of (n).

[0096] Next, the target direction W of the Wiener filter can be determined. wn The difference between (n) and the previous Wiener filter coefficient W(n-1) determines the direction of filter variation ΔW(n), see formula (7):

[0097] ΔW(n)=W wn (n)-W(n-1) (7)

[0098] Therefore, the coefficient W(n) corresponding to the current W can be determined. One method for determining W(n) can be found in formula (8):

[0099]

[0100] Here, μ can represent the amplitude of the Wiener filter change. L can represent the length of the Wiener filter, and the value of L can be consistent with the value of the sliding window length N, thereby achieving better noise reduction effect.

[0101] It should be noted that, in order to prevent jump noise, the processor needs to update the coefficients W(n) corresponding to W point by point. For example, in this application, a sliding window algorithm can be used to update ΔW(n) every M points. However, in order to prevent jump noise, for each reference signal, the corresponding Wiener filter W(n) needs to be updated using the current ΔW(n) (regardless of whether it has been updated), μ, and L.

[0102] In step S208, the processor determines the noise reduction amount based on the primary noise signal and the residual signal, and adjusts the parameters of the Wiener filter and the sliding window based on the determined noise reduction amount.

[0103] In this application, in order to adjust the noise reduction state according to the current noise reduction effect and obtain a larger noise reduction amount and a faster, more responsive noise reduction speed, the noise reduction amount can be determined first, and the relevant parameters can be adjusted accordingly.

[0104] One method for determining the noise reduction (NR) is shown in formula (9):

[0105]

[0106] Among them, P d (n) and P e (n) can be represented respectively as The power corresponding to e(n). Determine P. d (n) and P e One method for (n) can be found in formulas (10) and (11) respectively:

[0107]

[0108] P e (n)=(1-α)P e (n-1)-αe 2 (n) (11)

[0109] Here, α can represent a parameter used to control the sliding speed, which can be preset, and the value of α is, for example, 0.01.

[0110] In one possible implementation, the parameters of the Wiener filter and the sliding window can be adjusted based on the calculated noise reduction amount. Adjustable parameters may include, for example, the variation amplitude μ of the Wiener filter coefficients, the length L of the Wiener filter, the movement distance M of the sliding window, and the length N of the sliding window. The noise reduction amount can be inversely proportional to μ and directly proportional to L, M, and N.

[0111] The values ​​of the above parameters can also be determined based on the noise reduction amount NR. One method for determining L based on NR is shown in formula (12):

[0112] L=r(NR)*I (12)

[0113] Here, I can represent the maximum value corresponding to the Wiener filter length, which can be preset. In one possible implementation, M can be determined based on the value of L, and the values ​​of M and L can be equal. r(NR) can represent the scaling factor determined based on NR.

[0114] One method for determining N based on NR is shown in formula (13):

[0115] N = r(NR) * N0 (13)

[0116] The value of N0 can represent the maximum value corresponding to the length of the sliding window, and can be preset.

[0117] One method for determining μ based on NR is shown in formula (14):

[0118]

[0119] Here, μ0 can represent the maximum value corresponding to the variation of the Wiener filter coefficients, and can be related to a preset value. μ (NR) can represent the scaling factor determined based on NR.

[0120] One method for determining r(NR) based on NR is shown in formula (15):

[0121]

[0122] Here, β1, β2, and β3 are preset parameters. β1 can be used to constrain the lower limit of r(NR), and the value range of β1 is, for example, 0.05-0.1. β2 can be used to adjust the slope of the function; the larger the value of β2, the faster the corresponding parameter adjusts with the noise reduction amount. β3 can be used to determine the value of r(NR) when the noise reduction amount is 0, and is used to indicate the initial noise reduction amount when r(NR) starts to increase.

[0123] r is determined based on NR. μ One method for (NR) can be found in formula (16):

[0124]

[0125] Among them, β4, β5, and β6 are preset parameters. β1 can be used to constrain r. μ The lower limit of (NR), β4, has a range of values, for example, 0.05-0.1. β5 can be used to adjust the slope of the function; a larger value of β5 indicates that the corresponding parameter adjusts more quickly with the noise reduction amount. β6 can be used to determine r when the noise reduction amount is 0. μ The value of (NR) is used to indicate r μ (NR) is the initial noise reduction amount when it starts to increase.

[0126] It should be noted that the values ​​of β1 and β4 can be the same or different. The same applies to β2 and β5, and β3 and β6.

[0127] In step S209, the processor performs Wiener filtering on the reference signal according to the determined coefficients of the Wiener filter to determine the audio signal, and emits the sound wave corresponding to the audio signal through the speaker.

[0128] One method for determining the audio signal y(n) by applying Wiener filter coefficients to the reference signal is shown in formula (17):

[0129]

[0130] Where I can represent the maximum value corresponding to the Wiener filter length L, which can be preset.

[0131] Therefore, the determined corresponding audio signal can cancel out the noise signal at the human ear, thus achieving the effect of noise reduction.

[0132] Figure 4 A flowchart illustrating a signal processing method according to an embodiment of this application is shown. This method can be used in the aforementioned signal processing system. Figure 4 As shown, the method includes:

[0133] Step S401: Receive a first audio signal from a noise source collected by one or more first sensors;

[0134] Step S402: Receive a second audio signal at the ear collected by one or more second sensors, wherein the first audio signal and the second audio signal are used to determine parameters for processing the first audio signal according to a first processing method;

[0135] Step S403: Send a third audio signal determined after processing the first audio signal according to the first processing method. The third audio signal is used to instruct the speaker to emit sound waves, which are used to cancel out noise at the human ear.

[0136] According to the embodiments of this application, during the processing of the first audio signal, the parameters of the second audio signal collected at the human ear are adjusted in real time. The second audio signal is the actual noise at the human ear, that is, the noise remaining after being canceled by the sound waves generated by the third audio speaker (i.e., the residual signal mentioned above). Thus, the first audio signal is processed using the adjusted parameters to obtain the third audio signal, which instructs the speaker to emit sound waves to cancel the noise. This can achieve faster noise reduction, a larger noise reduction amount, better noise reduction effect, and improve the comfort of the driver and passengers.

[0137] The first audio signal can be the aforementioned reference signal x(n), the second audio signal can be the aforementioned residual signal e(n), and the third audio signal can be the aforementioned audio signal y(n). The second audio signal at the human ear can be the residual signal collected at any position within a preset range near the ear of the vehicle occupant. Multiple first sensors can be set in different positions, and multiple second sensors can also be set in different positions.

[0138] In one possible implementation, the first processing method is Wiener filtering. This allows for greater noise reduction and faster noise reduction. The first processing method could also be any other processing method that processes the first audio signal to determine the third audio signal.

[0139] The parameters used to process the first audio signal according to the first processing method can be, for example, the coefficients of the Wiener filter W described above.

[0140] When the sound waves emitted by the loudspeaker cancel out the noise at the human ear, the noise level heard by the occupants of the vehicle will decrease.

[0141] An example of step S401 can be found here. Figure 2 Examples of steps S201 and S402 can be found in [reference needed]. Figure 2 Examples of steps S202 and S403 can be found in [reference needed]. Figure 2 The relevant description in step S209.

[0142] Figure 5 A flowchart illustrating a signal processing method according to an embodiment of this application is shown. Figure 5 As shown, the method also includes:

[0143] Step S501: According to the second processing method, the first audio signal is processed to determine the fourth audio signal. The second processing method indicates the transmission method of the sound wave from the speaker to the second sensor.

[0144] Step S502: Determine the fifth audio signal based on the second audio signal and the audio signal after processing the third audio signal according to the second processing method;

[0145] Step S503: Based on the fourth audio signal and the fifth audio signal, determine the parameters for processing the first audio signal according to the first processing method.

[0146] According to an embodiment of this application, a fourth audio signal is determined by estimating the transmission method from the speaker to the second sensor, taking into account the transmission process of the first audio signal. Simultaneously, a fifth audio signal is reconstructed. This allows for the calculation of the initial noise heard by the occupants before noise reduction. Combining these two methods to adjust the parameters enables the corresponding sound wave emitted by the speaker, indicated by the adjusted parameters, to better cancel out the noise, thereby achieving a greater noise reduction. Furthermore, by calculating the parameters in real-time based on the above two methods, the parameters can be adjusted promptly, allowing for faster recovery from noise reduction issues when interference occurs, resulting in stronger robustness and ultimately a better noise reduction effect.

[0147] The second processing method can be, for example, the transfer function of the secondary path mentioned above. The fourth audio signal can be the reference signal x after filtering the reference signal according to the transfer function of the secondary path. g (n). The fifth audio signal can be the primary noise signal calculated above.

[0148] An example of step S501 can be found here. Figure 2 Examples of steps S203 and S502 can be found in [reference needed]. Figure 2 Examples of steps S204 and S503 can be found in [reference needed]. Figure 2 The relevant descriptions in steps S205-S207.

[0149] In one possible implementation, determining the parameters for processing the first audio signal according to the first processing method based on the fourth audio signal and the fifth audio signal includes: determining the parameters for processing the first audio signal according to the first processing method based on the autocorrelation matrix of the fourth audio signal and the cross-correlation matrix of the fourth audio signal and the fifth audio signal.

[0150] Therefore, the autocorrelation of the fourth audio signal and the cross-correlation between the fourth and fifth audio signals can be considered simultaneously during parameter adjustment, thus achieving better noise reduction.

[0151] An example of the process for determining the autocorrelation matrix can be found in [link to documentation]. Figure 2 An example of step S205, the process of determining the cross-correlation matrix, can be found in [reference needed]. Figure 2 Step S206. An example of determining the parameters for processing the first audio signal according to the first processing method based on the autocorrelation matrix of the fourth audio signal and the cross-correlation matrix of the fourth and fifth audio signals can be found in [link to example]. Figure 2 The relevant description in step S207.

[0152] Figure 6 A flowchart illustrating a signal processing method according to an embodiment of this application is shown. Figure 6 As shown, based on the autocorrelation matrix of the fourth audio signal and the cross-correlation matrix of the fourth and fifth audio signals, the parameters for processing the first audio signal according to the first processing method are determined, including:

[0153] Step S601: Determine the direction of change of the parameters based on the autocorrelation matrix, the cross-correlation matrix, and the parameters from the previous time step;

[0154] Step S602: Determine the parameters at the current moment based on one or more of the following: the parameters at the previous moment, the signal length when processed according to the first processing method, the direction of change of the parameters, and the magnitude of change of the parameters.

[0155] According to the embodiments of this application, by determining the parameters at each moment, it is possible to achieve stable and large noise reduction while avoiding noise caused by sudden changes in the filter due to untimely parameter updates, thereby improving the comfort of the driving and riding experience.

[0156] The direction of parameter change can be ΔW(n) as described above. The signal length during processing according to the first processing method can refer to the signal length at each processing stage, or the length L of the Wiener filter as described above. The magnitude of parameter change can be μ as described above. The parameter at the current moment can be the coefficient W(n) of the current Wiener filter as described above. The previous moment can refer to the moment before the current moment, and the parameter at the previous moment can be W(n-1) as described above.

[0157] Examples of steps S601-S602 can be found above. Figure 2 Step S207.

[0158] In one possible implementation, according to the transmission method and according to the second processing method, the first audio signal is processed to determine the fourth audio signal, including: moving a predetermined window distance every so often, processing the first audio signal of a predetermined window length according to the second processing method, and determining the fourth audio signal;

[0159] Determining a fifth audio signal based on the second audio signal and the audio signal after processing the third audio signal according to the second processing method includes: moving a distance every predetermined window, and determining the fifth audio signal based on the second audio signal of the predetermined window length and the audio signal after processing the third audio signal according to the second processing method.

[0160] According to the embodiments of this application, calculations can be performed using a predetermined window, eliminating the need to determine the fourth and fifth audio signals point by point, thus reducing the amount of computation.

[0161] The window can be found in the text above. Figure 3 The sliding window shown can have a predetermined window movement distance of M (as mentioned above) and a predetermined window length of N (as mentioned above).

[0162] The above process can be found in [reference]. Figure 2 Examples of steps S203-S204.

[0163] Figure 7A flowchart illustrating a signal processing method according to an embodiment of this application is shown. Figure 7 As shown, the method also includes:

[0164] Step S701: Determine the noise reduction amount based on the second audio signal and the fifth audio signal;

[0165] Step S702: Adjust one or more of the following according to the noise reduction amount: the change range of the parameter, the signal length when processed according to the first processing method, the predetermined window movement distance, and the predetermined window length.

[0166] According to the embodiments of this application, by calculating the noise reduction amount and adjusting one or more of the following based on the noise reduction amount: the change range of parameters, signal length, predetermined window movement distance, and predetermined window length, it is possible to adapt to different noise reduction environments and states during the noise reduction process, thereby achieving better noise reduction effects and improving the user experience.

[0167] The noise reduction amount can be, for example, NR as mentioned above. A smaller noise reduction amount indicates a higher power requirement for noise reduction and a faster noise reduction speed. After adjustment, a smaller noise reduction amount corresponds to a larger change in the parameters, a smaller signal length, a smaller predetermined window movement distance, and a smaller predetermined window length; conversely, a larger noise reduction amount corresponds to a smaller change in the parameters, a larger signal length, a larger predetermined window movement distance, and a larger predetermined window length.

[0168] Examples of steps S701-S702 can be found in the relevant description in step S208 above.

[0169] Figure 8 A structural diagram of a signal processing apparatus according to an embodiment of this application is shown. Figure 8 As shown, the device includes:

[0170] The first receiving module 801 is used to receive a first audio signal from a noise source collected by one or more first sensors;

[0171] The second receiving module 802 is used to receive a second audio signal at the ear collected by one or more second sensors, wherein the first audio signal and the second audio signal are used to determine parameters for processing the first audio signal according to a first processing method;

[0172] The transmitting module 803 is used to transmit a third audio signal determined after processing the first audio signal according to the first processing method. The third audio signal is used to instruct the speaker to emit sound waves, which are used to cancel out noise at the human ear.

[0173] According to the embodiments of this application, by using a second audio signal collected at the human ear for real-time parameter adjustment during the processing of the first audio signal, the current noise reduction state can be taken into account, and the parameters can be adjusted according to the current noise reduction state. Therefore, the first audio signal is processed using the adjusted parameters to obtain a third audio signal, which instructs the speaker to emit sound waves to cancel noise. This achieves faster noise reduction, a greater noise reduction amount, better noise reduction effect, and improves the comfort of passengers.

[0174] In one possible implementation, the first processing method is Wiener filtering.

[0175] This allows for greater noise reduction and faster noise reduction.

[0176] In one possible implementation, the device further includes: a first determining module, configured to process the first audio signal according to a second processing method to determine a fourth audio signal, wherein the second processing method indicates the transmission method of sound waves from the speaker to the second sensor; a second determining module, configured to determine a fifth audio signal based on the second audio signal and an audio signal obtained by processing the third audio signal according to the second processing method; and a third determining module, configured to determine parameters for processing the first audio signal according to the first processing method based on the fourth audio signal and the fifth audio signal.

[0177] According to an embodiment of this application, a fourth audio signal is determined by estimating the transmission method from the speaker to the second sensor, taking into account the transmission process of the first audio signal. Simultaneously, a fifth audio signal is reconstructed. This allows for the calculation of the initial noise heard by the occupants before noise reduction. Combining these two methods to adjust the parameters enables the corresponding sound wave emitted by the speaker, indicated by the adjusted parameters, to better cancel out the noise, thereby achieving a greater noise reduction. Furthermore, by calculating the parameters in real-time based on the above two methods, the parameters can be adjusted promptly, allowing for faster recovery from noise reduction issues when interference occurs, resulting in stronger robustness and ultimately a better noise reduction effect.

[0178] In one possible implementation, the third determining module includes: determining the parameters for processing the first audio signal according to the first processing method based on the autocorrelation matrix of the fourth audio signal and the cross-correlation matrix of the fourth audio signal and the fifth audio signal.

[0179] Therefore, the autocorrelation of the fourth audio signal and the cross-correlation between the fourth and fifth audio signals can be considered simultaneously during parameter adjustment, thus achieving better noise reduction.

[0180] In one possible implementation, determining the parameters for processing the first audio signal according to the first processing method based on the autocorrelation matrix of the fourth audio signal and the cross-correlation matrix of the fourth audio signal and the fifth audio signal includes: determining the direction of change of the parameters based on the autocorrelation matrix, the cross-correlation matrix, and the parameters at the previous time step; and determining the parameters at the current time step based on one or more of the following: the parameters at the previous time step, the signal length when processing according to the first processing method, the direction of change of the parameters, and the magnitude of change of the parameters.

[0181] According to the embodiments of this application, by determining the parameters at each moment, it is possible to achieve stable and large noise reduction while avoiding noise caused by sudden changes in the filter due to untimely parameter updates, thereby improving the comfort of the driving and riding experience.

[0182] In one possible implementation, the first determining module includes: moving a predetermined window distance every so often, processing the first audio signal of a predetermined window length according to the second processing method to determine a fourth audio signal; the second determining module includes: moving a predetermined window distance every so often, determining a fifth audio signal based on the second audio signal of a predetermined window length and the audio signal after processing the third audio signal according to the second processing method.

[0183] According to the embodiments of this application, calculations can be performed using a predetermined window, eliminating the need to determine the fourth and fifth audio signals point by point, thus reducing the amount of computation.

[0184] In one possible implementation, the device further includes: a fourth determining module, configured to determine a noise reduction amount based on the second audio signal and the fifth audio signal; and an adjusting module, configured to adjust one or more of the following based on the noise reduction amount: the magnitude of the parameter change, the signal length when processed according to the first processing method, the predetermined window movement distance, and the predetermined window length.

[0185] According to the embodiments of this application, by calculating the noise reduction amount and adjusting one or more of the following based on the noise reduction amount: the change range of parameters, signal length, predetermined window movement distance, and predetermined window length, it is possible to adapt to different noise reduction environments and states during the noise reduction process, thereby achieving better noise reduction effects and improving the user experience.

[0186] Figure 9 A structural diagram of a signal processing apparatus according to an embodiment of this application is shown. This signal processing apparatus is applicable to… Figure 1 In the signal processing system shown, the above is performed. Figures 2-7 Any of the signal processing methods shown in the examples.

[0187] like Figure 9 As shown, the signal processing device 900 may include a processor 901 and a transceiver 902. Optionally, the signal processing device 900 may include a memory 903. The processor 901 is coupled to the transceiver 902 and the memory 903, for example, they can be connected via a communication bus.

[0188] The following is combined with Figure 9 The various components of the signal processing device 900 will be described in detail.

[0189] The processor 901 described above is the control center of the signal processing device 900. It can be a single processor or a collective term for multiple processing elements. For example, the processor 901 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement the embodiments of this application, such as one or more microprocessors, or one or more field-programmable gate arrays (FPGAs).

[0190] Optionally, the processor 901 can perform various functions of the signal processing device 900 by running or executing software programs stored in the memory 903 and calling data stored in the memory 903.

[0191] In a specific implementation, as one example, the processor 901 may include one or more CPUs, for example... Figure 9 CPU0 and CPU1 are shown in the diagram.

[0192] In one possible implementation, the signal processing device 900 may also include multiple processors, for example... Figure 9 The processors 901 and 904 are shown in the diagram. Each of these processors can be a single-core processor (CPU) or a multi-core processor (CPU). Here, "processor" can refer to one or more communication devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0193] Optionally, transceiver 902 may include a receiver and a transmitter. Figure 9 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the sending function.

[0194] Alternatively, the transceiver 902 can be integrated with the processor 901 or exist independently, and can be connected via the input / output port of the signal processing device 900. Figure 9 (Not shown in the image) is coupled to the processor 901, but this embodiment of the application does not limit this.

[0195] The aforementioned memory 903 can be used to store software programs that execute the scheme of this application, and the processor 901 controls the execution. For specific implementation methods, please refer to the above method embodiments, which will not be repeated here.

[0196] The memory 903 can be a read-only memory (ROM) or other type of static storage communication device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage communication device capable of storing information and instructions, or it can be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage communication device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited to these. It should be noted that the memory 903 can be integrated with the processor 901 or exist independently, and can be accessed through the input / output ports of the signal processing device 900. Figure 9 (Not shown in the image) is coupled to the processor 901, but this embodiment of the application does not limit this.

[0197] It should be noted that, Figure 9 The structure of the signal processing device 900 shown does not constitute a limitation on the implementation of the signal processing device. The actual signal processing device may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0198] An embodiment of this application provides a signal processing apparatus, including: a processor and a memory; the memory is used to store a program; the processor is used to execute the program stored in the memory to enable the apparatus to implement the above-described method.

[0199] Embodiments of this application provide a computer-readable storage medium having program instructions stored thereon, which, when executed by a computer, cause the computer to perform the above-described method.

[0200] An embodiment of this application provides a terminal device that can perform the above-described method.

[0201] An embodiment of this application provides a computer program product including program instructions that, when executed by a computer, cause the computer to perform the above-described method.

[0202] An embodiment of this application provides a vehicle that includes a processor for performing the methods described above.

[0203] A computer-readable storage medium can be a tangible device capable of holding and storing instructions used by an instruction execution device. For example, a computer-readable storage medium can be an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof.

[0204] The computer-readable program instructions or code described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0205] Various aspects of this application are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0206] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0207] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0208] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved.

[0209] It should also be noted that each block in the block diagram and / or flowchart, as well as combinations of blocks in the block diagram and / or flowchart, can be implemented using hardware (such as circuits or ASICs (Application Specific Integrated Circuits)) that performs the corresponding function or action, or using a combination of hardware and software, such as firmware.

[0210] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0211] The various embodiments of this application have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A signal processing method, characterized in that, The method includes: Receive a first audio signal from a noise source, acquired by one or more first sensors; Receive a second audio signal at the human ear collected by one or more second sensors, wherein the first audio signal and the second audio signal are used to determine parameters for processing the first audio signal according to a first processing method; Send a third audio signal determined after processing the first audio signal according to the first processing method, the third audio signal being used to instruct the speaker to emit sound waves, the sound waves being used to cancel out noise at the human ear; Wherein, the parameters for processing the first audio signal according to the first processing method are determined based on the autocorrelation matrix of the fourth audio signal and the cross-correlation matrix of the fourth audio signal and the fifth audio signal; The fourth audio signal is obtained by processing the first audio signal according to the second processing method, wherein the second processing method indicates the transmission method of the sound wave from the speaker to the second sensor; The fifth audio signal is the result of processing the third audio signal according to the second audio signal and the second processing method.

2. The method according to claim 1, characterized in that, The method further includes: According to the second processing method, the first audio signal is processed to determine the fourth audio signal, and the second processing method indicates the transmission method of the sound wave from the speaker to the second sensor; A fifth audio signal is determined based on the second audio signal and the audio signal obtained by processing the third audio signal according to the second processing method; Based on the fourth audio signal and the fifth audio signal, determine the parameters for processing the first audio signal according to the first processing method.

3. The method according to claim 2, characterized in that, The step of determining the parameters for processing the first audio signal according to the first processing method based on the fourth audio signal and the fifth audio signal includes: Based on the autocorrelation matrix of the fourth audio signal and the cross-correlation matrix of the fourth audio signal and the fifth audio signal, the parameters for processing the first audio signal according to the first processing method are determined.

4. The method according to claim 3, characterized in that, The step of determining the parameters for processing the first audio signal according to the first processing method based on the autocorrelation matrix of the fourth audio signal and the cross-correlation matrix of the fourth audio signal and the fifth audio signal includes: The direction of change of the parameters is determined based on the autocorrelation matrix, the cross-correlation matrix, and the parameters from the previous time step. The parameters at the current moment are determined based on one or more of the following: the parameters at the previous moment, the signal length when processed according to the first processing method, the direction of change of the parameters, and the magnitude of change of the parameters.

5. The method according to any one of claims 2-4, characterized in that, The step of processing the first audio signal according to the second processing method to determine the fourth audio signal includes: The first audio signal is processed according to the second processing method at predetermined window lengths by moving a distance every predetermined window to determine the fourth audio signal; Determining the fifth audio signal based on the second audio signal and the audio signal obtained by processing the third audio signal according to the second processing method includes: Every predetermined window movement distance, the fifth audio signal is determined based on the second audio signal of the predetermined window length and the audio signal after processing the third audio signal according to the second processing method.

6. The method according to claim 5, characterized in that, The method further includes: The noise reduction amount is determined based on the second audio signal and the fifth audio signal; Based on the noise reduction amount, one or more of the following are adjusted: the change range of the parameter, the signal length when processing according to the first processing method, the predetermined window movement distance, and the predetermined window length.

7. The method according to claim 1, characterized in that, The first processing method is Wiener filtering.

8. A signal processing apparatus, characterized in that, The device includes: The first receiving module is used to receive the first audio signal from the noise source collected by one or more first sensors; The second receiving module is used to receive a second audio signal from the ear collected by one or more second sensors, wherein the first audio signal and the second audio signal are used to determine parameters for processing the first audio signal according to a first processing method; The transmitting module is configured to transmit a third audio signal determined after processing the first audio signal according to a first processing method, wherein the third audio signal is used to instruct the speaker to emit sound waves, and the sound waves are used to cancel out noise at the human ear; Wherein, the parameters for processing the first audio signal according to the first processing method are determined based on the autocorrelation matrix of the fourth audio signal and the cross-correlation matrix of the fourth audio signal and the fifth audio signal; The fourth audio signal is obtained by processing the first audio signal according to the second processing method, wherein the second processing method indicates the transmission method of the sound wave from the speaker to the second sensor; The fifth audio signal is the result of processing the third audio signal according to the second audio signal and the second processing method.

9. The apparatus according to claim 8, characterized in that, The device further includes: The first determining module is used to process the first audio signal according to the second processing method to determine the fourth audio signal, wherein the second processing method indicates the transmission method of the sound wave from the speaker to the second sensor; The second determining module is used to determine the fifth audio signal based on the second audio signal and the audio signal after processing the third audio signal according to the second processing method; The third determining module is used to determine, based on the fourth audio signal and the fifth audio signal, the parameters for processing the first audio signal according to the first processing method.

10. The apparatus according to claim 9, characterized in that, The third determining module includes: Based on the autocorrelation matrix of the fourth audio signal and the cross-correlation matrix of the fourth audio signal and the fifth audio signal, the parameters for processing the first audio signal according to the first processing method are determined.

11. The apparatus according to claim 10, characterized in that, The step of determining the parameters for processing the first audio signal according to the first processing method based on the autocorrelation matrix of the fourth audio signal and the cross-correlation matrix of the fourth audio signal and the fifth audio signal includes: The direction of change of the parameters is determined based on the autocorrelation matrix, the cross-correlation matrix, and the parameters from the previous time step. The parameters at the current moment are determined based on one or more of the following: the parameters at the previous moment, the signal length when processed according to the first processing method, the direction of change of the parameters, and the magnitude of change of the parameters.

12. The apparatus according to any one of claims 9-11, characterized in that, The first determining module includes: The first audio signal is processed according to the second processing method at predetermined window lengths by moving a distance every predetermined window to determine the fourth audio signal; The second determining module includes: Every predetermined window movement distance, the fifth audio signal is determined based on the second audio signal of the predetermined window length and the audio signal after processing the third audio signal according to the second processing method.

13. The apparatus according to claim 12, characterized in that, The device further includes: The fourth determining module is used to determine the noise reduction amount based on the second audio signal and the fifth audio signal; The adjustment module is used to adjust one or more of the following according to the noise reduction amount: the change range of the parameter, the signal length when processed according to the first processing method, the predetermined window movement distance, and the predetermined window length.

14. The apparatus according to claim 8, characterized in that, The first processing method is Wiener filtering.

15. A signal processing apparatus, characterized in that, include: Processor and memory; The memory is used to store programs; The processor is used to execute the program stored in the memory to enable the device to implement the method according to any one of claims 1-7.

16. A computer-readable storage medium having program instructions stored thereon, characterized in that, When the program instructions are executed by a computer, the computer causes the computer to perform the method described in any one of claims 1-7.

17. A computer program product comprising program instructions, characterized in that, When the program instructions are executed by a computer, the computer causes the computer to perform the method described in any one of claims 1-7.

18. A vehicle, characterized in that, The vehicle includes a processor for performing the method as described in any one of claims 1-7.

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