Active noise control method, storage medium, electronic device, system and vehicle

Abstract

An active noise control method, a storage medium, an electronic device, a system and a vehicle. The method comprises: acquiring multiple error signals and multiple reference signals at different positions; determining target step size data, wherein the target step size data comprises a convergence step size that corresponds to each reference signal and is from each secondary loudspeaker to each error microphone; determining a coefficient of an adaptive filter of each secondary loudspeaker on the basis of the error signals, the reference signals and the target step size data; and controlling an output signal of each secondary loudspeaker on the basis of the reference signals and the coefficient of the adaptive filter of each secondary loudspeaker.

Description

Active noise control method, storage medium, electronic device, system and vehicle

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This disclosure claims priority to Chinese patent application number 202311813017.4, filed with the China Patent Office on December 25, 2023, entitled “Active Noise Control Method, Storage Medium, Electronic Device, System and Vehicle,” the entire contents of which are incorporated by reference into this disclosure. Technical Field

[0003] The present disclosure relates to the field of vehicles, and in particular, to an active noise control method, a storage medium, an electronic device, a system, and a vehicle. Background Art

[0004] With the development of the automotive industry, people's requirements for vehicle driving experience are constantly increasing, and they are paying more and more attention to the comfort of the acoustic environment inside the car. In existing technologies, passive noise reduction technology has good noise reduction effects in the mid- and high-frequency bands by blocking or absorbing noise, but its impact on low-frequency noise is limited. Active noise control uses the principle of interference cancellation to achieve noise reduction by generating a secondary signal with an opposite phase to the noise. The sound field in the actual vehicle cabin is not uniform, and the frequency characteristics of noise in different locations are different. Existing active noise control algorithms use a single step size to update each filter coefficient, which makes it difficult to achieve ideal noise reduction effects.

[0005] Summary of the Invention

[0006] The present disclosure aims to provide an active noise control method, a storage medium, an electronic device, a system and a vehicle to improve the noise reduction effect.

[0007] In order to achieve the above objectives, the present disclosure provides, in a first aspect, an active noise control method, comprising:

[0008] Acquire multi-path error signals and multi-path reference signals at different positions;

[0009] Determining target step length data, wherein the target step length data includes a convergence step length from each secondary speaker to each error microphone corresponding to each reference signal;

[0010] determining coefficients of an adaptive filter for each secondary speaker based on the error signal, the reference signal, and the target step size data;

[0011] An output signal of each of the secondary speakers is controlled according to the reference signal and a coefficient of an adaptive filter of each of the secondary speakers.

[0012] Optionally, the method further includes:

[0013] Target weight data is determined, wherein the target weight data includes a weight of an adaptive filter from each reference signal to each secondary speaker.

[0014] Optionally, determining the target weight data and the target step size data includes:

[0015] The target weight data and the target step length data are determined according to the secondary speaker position, the error microphone position, and the acceleration sensor position.

[0016] Optionally, determining the target weight data and the target step length data according to the secondary speaker position, the error microphone position, and the acceleration sensor position includes:

[0017] determining a convergence step length in the target step length data according to a first distance between the secondary speaker and the error microphone, wherein the first distance and the convergence step length are negatively correlated;

[0018] The weight in the target weight data is determined according to a second distance between the secondary speaker and the acceleration sensor, wherein the second distance and the weight are negatively correlated.

[0019] Optionally, determining the target weight data and the target step size data includes:

[0020] determining a correlation between the reference signal and the error signal;

[0021] According to the correlation, the weight in the target weight data and the convergence step in the target step data are determined, wherein the weight and the correlation are positively correlated, and the convergence step and the correlation are positively correlated.

[0022] Optionally, determining the target stride length data includes:

[0023] Determining basic step length data, wherein the basic step length data includes a basic convergence step length from each reference signal corresponding to each secondary speaker to each error microphone;

[0024] The basic convergence step size corresponding to the reference signal whose amplitude is smaller than the amplitude threshold is controlled to increase.

[0025] Optionally, increasing the basic convergence step size corresponding to the reference signal whose control amplitude is less than the amplitude threshold includes:

[0026] According to a preset increment, the basic convergence step size corresponding to the reference signal whose amplitude is less than the amplitude threshold is controlled to increase; or,

[0027] The ratio of the amplitude to the power of the reference signal is determined, and according to the ratio, the basic convergence step size corresponding to the reference signal having an amplitude less than an amplitude threshold is controlled to increase.

[0028] Optionally, determining the target weight data and the target step size data includes:

[0029] Determine the vehicle's operating condition based on the vehicle's real-time speed and current road type;

[0030] Target weight data and target step length data corresponding to the vehicle operating condition are determined through a target database.

[0031] Optionally, the method further includes:

[0032] Obtain historical road noise and vibration data of the vehicle under different operating conditions;

[0033] training at least one preset model using an error signal as an objective function based on the historical road noise data and the historical vibration data;

[0034] By completing the training of the preset model, the weight data and step length data corresponding to each working condition are obtained;

[0035] A target database is established based on the corresponding relationship between the vehicle operating condition, the weight data and the step data.

[0036] Optionally, determining coefficients of an adaptive filter for each secondary speaker according to the error signal, the reference signal, and the target step size data includes:

[0037] determining a secondary path estimate between each of the secondary speakers and each of the error microphones;

[0038] determining a filtered reference signal obtained by filtering each of the reference signals through each of the secondary path estimation filters;

[0039] The coefficient of the adaptive filter of the secondary speaker is determined according to each error signal, the convergence step size in the target step size data corresponding to each secondary speaker, and the filtering reference signal.

[0040] Optionally, determining coefficients of the adaptive filter of the secondary speaker according to each error signal, the convergence step size in the target step size data corresponding to each secondary speaker, and the filtering reference signal includes:

[0041] The coefficient W of the adaptive filter for the bth secondary speaker is determined by the following formula: b :

[0042] Among them, W b0 is the historical coefficient of the adaptive filter of the bth secondary speaker at the previous moment, SP bc is the secondary path estimate between the bth secondary loudspeaker and the cth error microphone, Estimate SP for the ath reference signal through the secondary path bc The obtained filtered reference signal; μ abc is the convergence step length from the a-th reference signal to the b-th secondary loudspeaker to the c-th error microphone, E c is the cth error signal.

[0043] Optionally, controlling the output signal of each secondary speaker according to the reference signal and a coefficient of an adaptive filter of each secondary speaker includes:

[0044] An output signal of the secondary speaker is determined according to each reference signal, a weight in the target weight data corresponding to each secondary speaker, and a coefficient of the adaptive filter.

[0045] Optionally, determining the output signal of the secondary speaker according to each reference signal, a weight in the target weight data corresponding to each secondary speaker, and a coefficient of the adaptive filter includes:

[0046] The output signal Y of the secondary speaker is determined by the following formula b b :

[0047] Y b =∑U a *β ab *W b

[0048] Among them, U a is the a-th reference signal, β ab is the weight of the adaptive filter from the a-th reference signal to the b-th secondary speaker, W b are the coefficients of the adaptive filter for the bth secondary speaker.

[0049] A second aspect of the present disclosure provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method provided in the first aspect of the present disclosure.

[0050] A third aspect of the present disclosure provides an electronic device, including:

[0051] a memory having a computer program stored thereon;

[0052] A controller, wherein when the computer program is executed by the controller, the steps of the method provided in the first aspect of the present disclosure are implemented.

[0053] A fourth aspect of the present disclosure provides an active noise control system, comprising:

[0054] A plurality of speed sensors arranged at different positions, a plurality of error microphones arranged at different positions, a plurality of secondary speakers arranged at different positions, and the electronic device provided in the third aspect of the present disclosure, wherein the error microphones are used to obtain an error signal, the acceleration sensor is used to obtain a reference signal, and the secondary speaker is used to output a signal that is in antiphase with the noise to offset the source noise.

[0055] A fifth aspect of the present disclosure provides a vehicle, comprising the active noise control system provided in the fourth aspect of the present disclosure.

[0056] In the above technical solution, multiple error signals and multiple reference signals at different locations are obtained; target step data is determined, wherein the target step data includes the convergence step from each reference signal to each secondary speaker to each error microphone; the coefficients of the adaptive filter of each secondary speaker are determined based on the error signal, the reference signal, and the target step data; and the output signal of each secondary speaker is controlled based on the reference signal and the coefficients of the adaptive filter of each secondary speaker. In this way, multiple convergence steps can be determined during the noise reduction process, and the convergence step corresponding to each reference signal and error signal combination can be independently controlled when updating the coefficients of the adaptive filter of each secondary speaker. Differentiated weighting processing is performed on error signals and reference signals from different sources, thereby achieving refined control of each secondary speaker and improving the noise reduction effect of the secondary speaker output signal.

[0057] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following detailed description, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings:

[0059] FIG1 is a flow chart of an active noise control method provided by an exemplary embodiment of the present disclosure.

[0060] FIG2 is a schematic diagram of installation positions of various components on a vehicle provided by an exemplary embodiment of the present disclosure.

[0061] FIG3 is a schematic diagram of installation positions of various components on a vehicle provided by an exemplary embodiment of the present disclosure.

[0062] FIG4 is a logic diagram of a multi-step multi-weight active noise control system provided by an exemplary embodiment of the present disclosure.

[0063] FIG5 is a schematic diagram of a multi-step filter coefficient update logic provided by an exemplary embodiment of the present disclosure.

[0064] FIG6 is a schematic diagram of a multi-step filter coefficient update logic provided by an exemplary embodiment of the present disclosure.

[0065] FIG7 is a schematic diagram of a multi-weight output signal logic provided by an exemplary embodiment of the present disclosure.

[0066] FIG8 is a schematic diagram of a multi-weight output signal logic provided by an exemplary embodiment of the present disclosure.

[0067] FIG9 is a flow chart of an active noise control method provided by an exemplary embodiment of the present disclosure.

[0068] FIG10 is a block diagram of an electronic device provided by an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0069] The following describes the specific embodiments of the present disclosure in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present disclosure and are not intended to limit the present disclosure.

[0070] FIG1 is a flow chart of an active noise control method provided by an exemplary embodiment of the present disclosure. The method can be applied to a controller of an active noise control system of a vehicle. As shown in FIG1 , the method may include S101 to S104.

[0071] S101, acquiring multi-path error signals and multi-path reference signals at different positions.

[0072] For example, multiple error microphones can be pre-installed at different positions of the vehicle to obtain multi-path error signals; multiple acceleration sensors can be pre-installed at different positions of the vehicle to obtain multi-path reference signals. The installation positions of the error microphones, acceleration sensors and secondary speakers on the vehicle can be shown in Figures 2 and 3. Figure 2 is a schematic diagram of the components and sound field division in the vehicle cabin, and Figure 3 is a schematic diagram of the vehicle chassis. The error microphones numbered 1-4 can be arranged at the driver's headrest, the co-driver's headrest, the co-driver's rear headrest and the driver's rear headrest, and the corresponding error signals received are E1-E4 respectively. The cabin can be divided into four areas, and each error signal can reflect the noise information of an area. Due to the uneven sound field in the cabin, the noise amplitude and frequency characteristics of the four error signals are different. Secondary speakers numbered 5-9 can output secondary signals Y1-Y5, respectively, to control the sound field in the corresponding areas. Secondary speakers numbered 5-8 can be installed on the four doors (driver's door, front passenger's door, rear passenger's door, and rear driver's door). Numbered 9 can be an additional bass secondary speaker, installed behind the rear seats to better control the noisier rear area. Accelerometers numbered 10-13 are installed near the left front wheel, right front wheel, right rear wheel, and left rear wheel, respectively, to collect reference signals U from the corresponding tires. In this way, based on the multi-path error signals and multi-path reference signals at different locations, the output signals of the secondary speakers can be tailored to the vehicle's sound field.

[0073] S102 : Determine target step length data, wherein the target step length data includes a convergence step length from each secondary loudspeaker to each error microphone corresponding to each reference signal.

[0074] For example, the vehicle operating condition can be determined based on the vehicle's real-time speed and the current road surface type, and target stride length data corresponding to the vehicle operating condition can be determined from a pre-set target database. The target database can be preset based on test results and include a correspondence between the vehicle operating condition and stride length data. The target stride length data corresponding to the current vehicle operating condition can be determined by searching the preset correspondence.

[0075] The number of convergence steps in the target step data is the product of the number of accelerometers, the number of error microphones, and the number of secondary speakers. Taking the number of devices in Figures 2 and 3 as an example, the number of convergence steps in the target step data is 80. This allows for independent control of the convergence step corresponding to each reference signal and error signal combination when updating the coefficients of the adaptive filter for each secondary speaker. This means that, based on reference and error signals at different locations, each secondary speaker on the vehicle can be assigned a convergence step appropriate to its actual state. By applying multiple convergence steps, refined control of each secondary speaker is achieved, improving noise reduction effectiveness.

[0076] S103 , determining coefficients of an adaptive filter for each secondary speaker according to the error signal, the reference signal, and the target step size data.

[0077] S104 , controlling the output signal of each secondary speaker according to the reference signal and the coefficient of the adaptive filter of each secondary speaker.

[0078] For example, taking the systems corresponding to FIG. 2 and FIG. 3 as an example, the output signals of the five secondary speakers numbered 5-9 can be determined respectively according to the error signals E1-E4, the reference signals U1-U4 and the target step data to achieve precise noise reduction.

[0079] Assume U a is the ath reference signal, E c is the cth error signal, SP bc is the secondary path estimate between the bth secondary loudspeaker and the cth error microphone, μ abc The convergence step length of the a-th reference signal from the b-th secondary loudspeaker to the c-th error microphone can be updated in the following way: a Estimating SP through secondary paths bc Filter to obtain the filtered reference signal Filtered reference signal and the cth error signal E c Multiply by the corresponding convergence step μ abc , the coefficient update amount can be obtained All different reference signal-error signal combinations participate in updating the adaptive filter coefficients, that is, each adaptive filter has a*c update amounts in one coefficient update. Then combined with the historical coefficient W of the adaptive filter b0 Calculate the coefficients W of the adaptive filter for the bth secondary speaker b , for example, can be expressed as Calculate W b .

[0080] In this way, the coefficients of the adaptive filter of the secondary speaker can be updated based on multiple convergence steps, and the error signals and reference signals from different sources can be weighted differently, thereby improving the accuracy of the output signal of each secondary speaker, achieving refined control of each secondary speaker, and improving the noise reduction effect of the secondary speaker output signal.

[0081] In the above technical solution, multiple error signals and multiple reference signals at different locations are obtained; target step data is determined, wherein the target step data includes the convergence step from each reference signal to each secondary speaker to each error microphone; the coefficients of the adaptive filter of each secondary speaker are determined based on the error signal, the reference signal, and the target step data; and the output signal of each secondary speaker is controlled based on the reference signal and the coefficients of the adaptive filter of each secondary speaker. In this way, multiple convergence steps can be determined during the noise reduction process, and the convergence step corresponding to each reference signal and error signal combination can be independently controlled when updating the coefficients of the adaptive filter of each secondary speaker. Differentiated weighting processing is performed on error signals and reference signals from different sources, thereby achieving refined control of each secondary speaker and improving the noise reduction effect of the secondary speaker output signal.

[0082] In an optional embodiment, the active noise control method provided by the present disclosure may further include:

[0083] Target weight data is determined, wherein the target weight data includes weights for the adaptive filter of each reference signal to each secondary speaker.

[0084] For example, the vehicle operating condition can be determined based on the vehicle's real-time speed and the current road type. The target database can also include a correspondence between the vehicle operating condition and the weight data to determine the target weight data corresponding to the vehicle operating condition from the target database.

[0085] The number of weights in the target weight data is the product of the number of accelerometers and the number of secondary speakers. Taking the number of devices in Figures 2 and 3 as an example, the number of convergence steps in the target step data is 20. This optimizes the weighting of each secondary speaker for different reference signals. By applying multiple weights, we can further achieve refined control of each secondary speaker and improve noise reduction effectiveness.

[0086] FIG4 is a logic diagram of a multi-step multi-weight active noise control system provided by an exemplary embodiment of the present disclosure. As shown in FIG4 , the reference signal U a Estimated path SP via secondary path bc Filtering can obtain the corresponding filtering reference signal The filtered reference signal Error signal E c Combined with the multiple convergence steps corresponding to the secondary speaker in the target step data, the coefficient W of the adaptive filter of the secondary speaker can be obtained. b , to update the coefficients of the adaptive filter of the secondary speaker through multiple convergence steps. a , the coefficient W of the adaptive filter of the secondary speakerb , the multiple weights corresponding to the secondary speaker in the target weight data can determine the output signal of the secondary speaker. Assume β ab is the weight of the adaptive filter from the a-th reference signal to the b-th secondary speaker. The output signal of the b-th secondary speaker can be determined in the following way: a With weight β ab Multiply by the coefficient W b The adaptive filter of the b-th secondary speaker is filtered to obtain the output signal Y of the b-th secondary speaker b .

[0087] This allows the system to determine multiple convergence steps and weights tailored to the vehicle's sound field during the noise reduction process. This allows for independent control of the convergence step size for each reference signal and error signal combination when updating the coefficients of each secondary speaker's adaptive filter, optimizing the weighting applied to each secondary speaker for different reference signals. By applying differentiated weighting to error and reference signals from different sources, the system achieves refined control of each secondary speaker and enhances the noise reduction effect of the secondary speaker's output signal.

[0088] In an optional embodiment, determining the target weight data and the target step length data may include:

[0089] Target weight data and target step length data are determined according to the secondary speaker position, the error microphone position, and the acceleration sensor position.

[0090] Specifically, the target weight data and target step data can be determined in the following way:

[0091] determining a convergence step length in the target step length data according to a first distance between the secondary loudspeaker and the error microphone, wherein the first distance and the convergence step length are negatively correlated;

[0092] The weight in the target weight data is determined according to a second distance between the secondary speaker and the acceleration sensor, wherein the second distance and the weight are negatively correlated.

[0093] For example, the basic convergence step and basic weight can be determined first, and then the basic convergence step and basic weight can be adjusted based on the first distance and the second distance. For example, the basic convergence step and basic weight can also be pre-stored fixed values. For another example, the basic weight data and basic step data corresponding to the current vehicle operating condition can be determined by the correspondence between the pre-calibrated vehicle operating condition, weight data and step data, wherein the basic weight data can include the basic weight of the adaptive filter from each reference signal to each secondary speaker, and the basic step data can include the basic convergence step from each secondary speaker to each error microphone corresponding to each reference signal.

[0094] The secondary speaker has the greatest influence on the nearest error microphone, and its output signal also has a greater impact on the noise reduction effect. Therefore, when updating the adaptive filter coefficients, the smaller the distance between the secondary speaker and the error microphone, the larger the convergence step size assigned to the corresponding error signal can be, thereby obtaining the convergence step size in the target step size data based on the base convergence step size. For the secondary speaker and accelerometer, the closer the distance between them, the greater the mutual influence between them. Therefore, when determining the output signal of the secondary speaker, the smaller the distance between the secondary speaker and the accelerometer, the larger the weight assigned to the corresponding reference signal can be, thereby obtaining the weight in the target step size data based on the base weight.

[0095] In this way, by combining the positions of the acceleration sensor, the secondary speaker, and the error microphone, the secondary speaker can control the sound field at the corresponding position more specifically.

[0096] In an optional embodiment, determining the target weight data and the target step length data may include:

[0097] determining a correlation between a reference signal and an error signal;

[0098] According to the correlation, the weight in the target weight data and the convergence step in the target step data are determined, wherein the weight and the correlation are positively correlated, and the convergence step and the correlation are positively correlated.

[0099] For example, the basic weight data and basic step size data can be determined in the manner described above, and the basic convergence step size and basic weight can be adjusted based on the correlation between the reference signal and the error signal. The stronger the correlation between the reference signal and the error signal, the better the noise reduction effect of the active noise control system. A correlation test can be performed first to determine the correlation between the reference signal and the error signal. For example, the basic weight and basic convergence step size corresponding to the reference signal and the error signal whose correlation is greater than the correlation threshold can be controlled to increase, and the basic weight and basic convergence step size corresponding to the reference signal and the error signal whose correlation is less than the correlation threshold can be controlled to decrease. In this way, the noise reduction effect can be further improved.

[0100] In an optional embodiment, in S102, determining the target stride length data may include:

[0101] Determine basic step length data;

[0102] The basic convergence step size corresponding to the reference signal whose amplitude is smaller than the amplitude threshold is controlled to increase.

[0103] The basic step length data includes a basic convergence step length from each secondary speaker to each error microphone corresponding to each reference signal. For example, the basic step length data can be determined using the method described above. Controlling the increase of the basic convergence step length corresponding to a reference signal with an amplitude less than an amplitude threshold can include:

[0104] According to the preset increment, the basic convergence step size corresponding to the reference signal whose amplitude is less than the amplitude threshold is controlled to increase; or,

[0105] The ratio of the amplitude and power of the reference signal is determined, and according to the ratio, the basic convergence step corresponding to the reference signal whose amplitude is less than the amplitude threshold is controlled to increase.

[0106] For example, for a base convergence step corresponding to a reference signal with an amplitude less than an amplitude threshold, the sum of a preset increment and the base convergence step can be determined as the corresponding convergence step in the target step data. Alternatively, the ratio of the reference signal's amplitude to its power can be determined. Reference signals with smaller amplitudes also have smaller powers. If the power of the reference signal is placed in the denominator and the resulting ratio is greater than 1, the product of the base convergence step corresponding to the reference signal and the ratio can be determined as the corresponding convergence step in the target step data to increase the convergence step of the reference signal. In this way, by increasing the convergence step of reference signals with smaller amplitudes, the convergence speed can be increased, allowing the active noise control system to enter the optimal noise reduction state more quickly after being activated.

[0107] In an optional embodiment, determining the target weight data and the target step length data may include:

[0108] Determine the vehicle's operating condition based on the vehicle's real-time speed and current road type;

[0109] The target weight data and target step length data corresponding to the vehicle operating condition are determined through the target database.

[0110] For example, the vehicle's real-time speed can be acquired via a pre-installed speed sensor; the current road surface type can be determined using images of the vehicle's external environment captured by a pre-installed camera. Road surface types may include asphalt, cement, and gravel roads. The correspondence between vehicle speed, road surface type, and vehicle operating condition can be preset based on test results. This correspondence can be represented, for example, in a mapping table. By searching this preset correspondence, the vehicle operating condition corresponding to the vehicle's real-time speed and current road surface type can be quickly and easily determined, thereby improving the operational stability of the active noise control system.

[0111] For example, the target database can be established in the following manner:

[0112] Obtain historical road noise and vibration data of the vehicle under different operating conditions;

[0113] Training at least one preset model using an error signal as an objective function based on historical road noise data and historical vibration data;

[0114] By completing the training of the preset model, the weight data and step length data corresponding to each working condition are obtained;

[0115] Based on the correspondence between vehicle operating conditions, weight data and step data, a target database is established.

[0116] For example, while the vehicle is driving on an actual road surface and active noise cancellation is disabled, road noise data collected in real time by a standard microphone can be recorded and used as historical road noise data. Vibration data collected by an accelerometer (i.e., a reference signal) can be recorded and used as historical vibration data and stored. When the vehicle is stationary, the transfer function from the secondary speaker to the error microphone (i.e., a secondary path estimate) can be determined based on white noise. A preset model can be trained based on this data. The algorithm in the preset model can include any one or more of a supervised learning model, a reinforcement learning model, and a genetic algorithm model. For a supervised learning model, the error signal can be used as the objective function to establish a model. Training is performed using historical road noise and vibration data. The supervised learning model predicts the step size and weight that minimizes the error signal. For a reinforcement learning model, a reduction in the error signal can be used as a reward signal, while an unchanged or increased error signal can be used as a penalty signal. The model can be established and trained using historical road noise and vibration data. The computer obtains rewards or penalties by varying the step size and weight. After repeatedly trying different parameter combinations, the step size and weight that maximize the reward are ultimately selected. For the genetic algorithm model, different step lengths and weights can be used as chromosome parameter combinations. By simulating operations such as crossover, mutation, and natural selection in evolution, a new noise reduction effect can be calculated based on historical road noise data and historical vibration data. Combinations with poor noise reduction effects are eliminated, and combinations with good noise reduction effects are retained. The step lengths and weights with good effects are used as the parent generation to continue to pass on excellent characteristics to the offspring, and finally the optimal step length and weight are determined.

[0117] For example, obtaining weight data and step size data corresponding to each operating condition by training a preset model may include: determining, for each operating condition, the preset model with the smallest objective function value as the target model; and establishing a correspondence between the vehicle operating condition, weight data, and step size data based on the vehicle operating condition and the target model. The smaller the objective function value, the more reliable the trained model, and the more accurate the correspondence between the vehicle operating condition, weight data, and step size data.

[0118] This allows us to calculate the optimal parameter combinations for different operating conditions based on the measured data. This means we can determine the optimal combination of weights and step sizes for each operating condition, thereby achieving the optimal noise reduction effect. Using the target database, we can quickly and easily determine the target weights and step sizes for each vehicle operating condition, eliminating the need to manually set numerous parameters.

[0119] In an optional embodiment, in S103, determining the coefficients of the adaptive filter of each secondary speaker according to the error signal, the reference signal and the target step size data may include:

[0120] determining a secondary path estimate between each secondary loudspeaker and each error microphone;

[0121] Determine a filtered reference signal obtained by filtering each reference signal through each secondary path estimation;

[0122] The coefficient of the adaptive filter of the secondary speaker is determined according to each error signal, the convergence step size in the target step size data corresponding to each secondary speaker, and the filtering reference signal.

[0123] For example, the secondary path estimation can be determined by the method of determining the secondary path estimation in the related art, which will not be described in detail here. The coefficient W of the adaptive filter of the b-th secondary speaker can be determined by the following formula: b :

[0124] Among them, W b0 is the historical coefficient of the adaptive filter of the bth secondary speaker at the previous moment, SP bc is the secondary path estimate between the bth secondary loudspeaker and the cth error microphone, Estimate SP for the ath reference signal through the secondary path bc The obtained filtered reference signal; μ abc is the convergence step length from the a-th reference signal to the b-th secondary loudspeaker to the c-th error microphone, E c is the cth error signal.

[0125] In an optional embodiment, in S104, controlling the output signal of each secondary speaker according to the reference signal and the coefficient of the adaptive filter of each secondary speaker may include:

[0126] The output signal of the secondary speaker is determined according to each reference signal, the weight in the target weight data corresponding to each secondary speaker, and the coefficient of the adaptive filter.

[0127] For example, the output signal Y of the secondary speaker can be determined by the following formula b: b :

[0128] Y b =∑U a *β ab *W b

[0129] Among them, U a is the a-th reference signal, β ab is the weight of the adaptive filter from the a-th reference signal to the b-th secondary speaker, W b are the coefficients of the adaptive filter for the bth secondary speaker.

[0130] For ease of understanding, taking the vehicle as an example with two accelerometers, two error microphones, and two secondary speakers, the corresponding multi-step filter coefficient update logic can be shown in Figures 5 and 6, and the corresponding multi-weight output signal logic can be shown in Figures 7 and 8. Among them, the reference signals U1 and U2 can be obtained respectively through the two accelerometers; the error signals E1 and E2 can be obtained respectively through the two error microphones; and the signals output by the two secondary speakers are Y1 and Y2 respectively. There are a total of four secondary path estimates between the secondary speaker and the error microphone, including SP 11 、SP 12 、SP 21 and SP 22 , the coefficients of the adaptive filters of the two secondary speakers are W1 and W2 respectively, and the weights include β 11 , β 12 , β 21 and β 22 , the convergence step size includes μ 111 -μ 222 .

[0131] Figure 9 is a flow chart of an active noise control method provided by an exemplary embodiment of the present disclosure. Figure 9 provides a clearer understanding of the implementation process of the active noise control method provided by the present disclosure. As shown in Figure 9, the method may include steps S601 to S607.

[0132] S601, obtaining multiple error signals at different locations, multiple reference signals, the real-time speed of the vehicle, and the current road type.

[0133] S602: Determine the vehicle operating condition based on the vehicle's real-time speed and the current road type.

[0134] S603: Determine target weight data and target step length data corresponding to the vehicle operating condition from a target database.

[0135] The target weight data includes the weight of the adaptive filter from each reference signal to each secondary speaker, and the target step data includes the convergence step from each reference signal corresponding to each secondary speaker to each error microphone.

[0136] In step S604 , the active noise control system operates with the target weight data and the target step size data to achieve an optimal noise reduction effect until the vehicle operating condition changes.

[0137] S605 , controlling the vehicle to travel on different roads at different speeds to generate historical road noise data and historical vibration data.

[0138] S606: Training at least one preset model based on historical road noise data and historical vibration data.

[0139] S607 , selecting at least one preset model, and determining the optimal weighted combination of weight data and step data under different vehicle operating conditions to establish a database.

[0140] In this way, the convergence step size corresponding to each reference signal and error signal combination when updating the coefficients of the adaptive filter of each secondary speaker that is adapted to the vehicle operating conditions can be quickly determined from the target database. The weight of each secondary speaker for different reference signals can be selected, and the error signals and reference signals from different sources can be differentially weighted, thereby achieving refined control of each secondary speaker and improving the noise reduction effect of the secondary speaker output signal.

[0141] Figure 10 is a block diagram of an electronic device according to an exemplary embodiment of the present disclosure. As shown in Figure 10 , the electronic device 700 may include a processor 701 and a memory 702. The electronic device 700 may also include one or more of a multimedia component 703, an input / output (I / O) interface 704, and a communication component 705.

[0142] The processor 701 is used to control the overall operation of the electronic device 700 to complete all or part of the steps in the active noise control method described above. The memory 702 is used to store various types of data to support the operation of the electronic device 700. Such data may include, for example, instructions for any application or method operating on the electronic device 700, as well as application-related data, such as contact information, sent and received messages, pictures, audio, video, etc. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 703 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 702 or transmitted via the communication component 705. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 704 provides an interface between the processor 701 and other interface modules. The aforementioned other interface modules may be a keyboard, a mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more thereof, is not limited here. Therefore, the corresponding communication component 705 may include: a Wi-Fi module, a Bluetooth module, an NFC module, etc.

[0143] In an exemplary embodiment, the electronic device 700 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-mentioned active noise control method.

[0144] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided. When executed by a processor, the program instructions implement the steps of the active noise control method described above. For example, the computer-readable storage medium may be the aforementioned memory 702 including the program instructions. The program instructions may be executed by the processor 701 of the electronic device 700 to perform the active noise control method described above.

[0145] The present disclosure also provides an active noise control system, comprising:

[0146] A plurality of speed sensors disposed at different positions, a plurality of error microphones disposed at different positions, a plurality of secondary speakers disposed at different positions, and the electronic device 700 as described above.

[0147] The error microphone is used to obtain an error signal, the accelerometer is used to obtain a reference signal, and the secondary speaker is used to output a signal that is in antiphase with the noise to offset the source noise.

[0148] The present disclosure also provides a vehicle, comprising the active noise control system as described above.

[0149] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings. However, the present disclosure is not limited to the specific details of the above embodiments. Within the technical concept of the present disclosure, various simple modifications can be made to the technical solutions of the present disclosure, and these simple modifications all fall within the scope of protection of the present disclosure.

[0150] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0151] In addition, the various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

Claims

1. An active noise control method, characterized in that, The method includes: Obtaining multiple error signals and multiple reference signals at different positions; Determining target step data, where the target step data includes the convergence step of each reference signal corresponding to each secondary speaker to each error microphone; Determining the coefficients of the adaptive filter of each secondary speaker according to the error signal, the reference signal, and the target step data; Controlling the output signal of each secondary speaker according to the reference signal and the coefficients of the adaptive filter of each secondary speaker.

2. The method according to claim 1, characterized in that, The method further includes: Determining target weight data, where the target weight data includes the weights of the adaptive filters of each reference signal to each secondary speaker.

3. The method according to claim 2, wherein Determining the target weight data and the target step data includes: Determining the target weight data and the target step data according to the positions of the secondary speakers, the positions of the error microphones, and the positions of the acceleration sensors.

4. The method according to claim 3, wherein The determining the target weight data and the target step data according to the positions of the secondary speakers, the positions of the error microphones, and the positions of the acceleration sensors includes: Determining the convergence step in the target step data according to the first distance between the secondary speaker and the error microphone, where the first distance and the convergence step are negatively correlated; Determining the weight in the target weight data according to the second distance between the secondary speaker and the acceleration sensor, where the second distance and the weight are negatively correlated.

5. The method according to claim 2, characterized in that, Determining the target weight data and the target step data includes: Determining the correlation between the reference signal and the error signal; Determining the weight in the target weight data and the convergence step in the target step data according to the correlation, where the weight is positively correlated with the correlation, and the convergence step is positively correlated with the correlation.

6. The method according to claim 1, wherein The determining the target step data includes: Determining basic step data, where the basic step data includes the basic convergence step of each reference signal corresponding to each secondary speaker to each error microphone; Controlling the basic convergence step corresponding to the reference signal with an amplitude less than the amplitude threshold to increase.

7. The method according to claim 6, characterized in that, The controlling the basic convergence step corresponding to the reference signal with an amplitude less than the amplitude threshold to increase includes: Controlling the basic convergence step corresponding to the reference signal with an amplitude less than the amplitude threshold to increase according to a preset increment; or, Determining the ratio of the amplitude and power of the reference signal, and controlling the basic convergence step corresponding to the reference signal with an amplitude less than the amplitude threshold to increase according to the ratio.

8. The method according to claim 2, wherein Determining the target weight data and the target step data includes: Determining the vehicle condition according to the real-time driving speed of the vehicle and the current road surface type; Determining the target weight data and the target step data corresponding to the vehicle condition through a target database.

9. The method according to claim 8, characterized in that, The method further includes: Obtaining historical road noise data and historical vibration data of the vehicle under different conditions; Training at least one preset model with the error signal as the objective function according to the historical road noise data and the historical vibration data; Obtain the weight data and step data corresponding to each working condition through a preset model that has completed training; Based on the corresponding relationship among the vehicle working condition, the weight data, and the step data, establish a target database.

10. The method according to any one of claims 1-9, characterized in that, The determining the coefficients of the adaptive filter of each secondary speaker according to the error signal, the reference signal, and the target step data includes: Determine the secondary path estimation from each of the secondary speakers to each of the error microphones; Determine the filtered reference signal obtained by filtering each of the reference signals through each of the secondary path estimations; According to each of the error signals, the convergence step in the target step data corresponding to each of the secondary speakers, and the filtered reference signal, determine the coefficients of the adaptive filter of the secondary speaker.

11. The method according to claim 10, wherein The determining the coefficients of the adaptive filter of the secondary speaker according to each of the error signals, the convergence step in the target step data corresponding to each of the secondary speakers, and the filtered reference signal includes: Determine the coefficients \(W\) of the adaptive filter of the \(b\)th secondary loudspeaker through the following formula b : Among them, W b0 is the historical coefficient of the adaptive filter of the b-th secondary speaker at the previous moment, and SP bc is the secondary path estimation between the b-th secondary speaker and the c-th error microphone. is the secondary path estimation SP for the a-th reference signal bc The filtered reference signal obtained; μ abc is the convergence step size of the a-th reference signal corresponding to the b-th secondary speaker to the c-th error microphone, E c is the c-th error signal.

12. The method according to any one of claims 2-11, characterized in that, The controlling the output signal of each secondary speaker according to the reference signal and the coefficients of the adaptive filter of each secondary speaker includes: According to each of the reference signals, the weights in the target weight data corresponding to each of the secondary speakers, and the coefficients of the adaptive filter, determine the output signal of the secondary speaker.

13. The method according to claim 12, characterized in that, The determining the output signal of the secondary speaker according to each of the reference signals, the weights in the target weight data corresponding to each of the secondary speakers, and the coefficients of the adaptive filter includes: Determine the output signal Y of the secondary speaker through the following formula for the b-th one b : Y b = ∑U a * β ab * W b Among them, U a is the reference signal of the a-th path, and β ab is the weight of the adaptive filter from the reference signal of the a-th path to the b-th secondary speaker, and W b is the coefficient of the adaptive filter of the b-th secondary speaker.

14. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the steps of the method according to any one of claims 1-13.

15. An electronic device, characterized in that, Including: A memory, on which a computer program is stored; A controller, when the computer program is executed by the controller, it implements the steps of the method according to any one of claims 1-13.

16. An active noise control system, characterized in that, The system includes: A plurality of speed sensors arranged at different positions, a plurality of error microphones arranged at different positions, a plurality of secondary speakers arranged at different positions, and the electronic device according to claim 15, wherein the error microphones are used to obtain error signals, the acceleration sensors are used to obtain reference signals, and the secondary speakers are used to output signals that are out of phase with the noise to cancel the source noise.

17. A vehicle, characterized in that, The vehicle includes the active noise control system according to claim 16.

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