Partition control method and device of sound field, electronic equipment and storage medium

Abstract

The invention relates to a sound field partition control method, which comprises the following steps: determining bright areas and dark areas in a sound field of a vehicle, and obtaining a transfer function matrix corresponding to at least one bright area and a transfer function matrix corresponding to at least one dark area; based on the transfer function matrix corresponding to the bright area and the transfer function matrix corresponding to the dark area, determining an error sample corresponding to the bright area and an error sample corresponding to the dark area; based on the error sample corresponding to the bright area and the error sample corresponding to the dark area, correcting the correlation matrix corresponding to the bright area and the correlation matrix corresponding to the dark area; and establishing a sound field partition control model based on the obtained target bright region correlation matrix and the target dark region correlation matrix, and adjusting a driving signal of a loudspeaker array based on the sound field partition control model so as to perform partition control on the sound field of the vehicle. According to the invention, the robustness of the sound field partition control model can be improved, and the partition control of the sound field can be realized under different temperatures and humidity.

Description

Technical Field

[0001] The present invention relates to the technical field of sound fields, and particularly to a method and device for zonal control of a sound field, an electronic device, and a storage medium. Background Art

[0002] When performing zonal control of a sound field, the Pressure Matching (PM) algorithm and the Acoustic Contrast Control (ACC) algorithm are usually used to achieve zonal control of the sound field. When using the above algorithms, it is necessary to control the robustness of the algorithms. In related technologies, a diagonal matrix and a regularization parameter are usually introduced, and the robustness of the algorithm is controlled by a regularization method. However, the regularization parameter is a hyperparameter and needs to be manually set before the algorithm runs. It is difficult to set a suitable regularization parameter in the actual application process, and there are problems such as low accuracy and poor flexibility. Summary of the Invention

[0003] The present application provides a method and device for zonal control of a sound field, an electronic device, and a storage medium to solve the problems of low accuracy and poor flexibility in the zonal control of the vehicle sound field in related technologies.

[0004] To achieve the above object, the present application provides a method for zonal control of a sound field, which is applied to a vehicle. The vehicle is provided with a speaker array for forming the sound field of the vehicle. The method includes:

[0005] Determine a bright area region and a dark area region in the sound field of the vehicle, and obtain a transfer function matrix corresponding to at least one bright area region and a transfer function matrix corresponding to at least one dark area region; wherein, the temperature and humidity corresponding to the transfer function matrix of each bright area region are different, and the temperature and humidity corresponding to the transfer function matrix of each dark area region are different;

[0006] Based on the transfer function matrix corresponding to the bright area region and the transfer function matrix corresponding to the dark area region, determine an error sample corresponding to the bright area region and an error sample corresponding to the dark area region;

[0007] Based on the error sample corresponding to the bright area region and the error sample corresponding to the dark area region, correct the correlation matrix corresponding to the bright area region and the correlation matrix corresponding to the dark area region to obtain a target bright area correlation matrix and a target dark area correlation matrix;

[0008] Establish a sound field zonal control model based on the target bright area correlation matrix and the target dark area correlation matrix, and adjust the driving signal of the speaker array based on the sound field zonal control model to perform zonal control of the sound field of the vehicle.

[0009] According to the above technical means, by obtaining the transfer function matrix corresponding to at least one bright area region with different temperature and humidity and the transfer function matrix corresponding to at least one dark area region in the sound field of the vehicle, and then determining the error sample corresponding to the bright area region and the error sample corresponding to the dark area region according to the transfer function matrix corresponding to the bright area region and the transfer function matrix corresponding to the dark area region, so as to correct the correlation matrix corresponding to the bright area region and the correlation matrix corresponding to the dark area region according to the error sample corresponding to the bright area region and the error sample corresponding to the dark area region, and finally establishing a sound field partition control model to adjust the driving signal of the speaker array to perform partition control on the sound field of the vehicle. On the one hand, measuring the transfer function matrix at different temperatures and humidities can achieve the partition control of the sound field at different temperatures and humidities respectively, improving the flexibility of the sound field partition control method; on the other hand, since the sound field partition control model of the present application is established according to the corrected correlation matrix corresponding to the bright area region and the correlation matrix corresponding to the dark area region, while improving the robustness of the sound field partition control model, it also improves the accuracy of the sound field partition control; on the other hand, compared with the prior art, the model of the present application uses actual error data and does not need to manually set regularization parameters before the algorithm runs, improving the stability of the sound field partition method.

[0010] Further, based on the error sample corresponding to the bright area region and the error sample corresponding to the dark area region, correcting the correlation matrix corresponding to the bright area region and the correlation matrix corresponding to the dark area region includes: determining the intermediate parameter corresponding to the bright area region and the intermediate parameter corresponding to the dark area region based on the error sample corresponding to the bright area region and the error sample corresponding to the dark area region; determining the parameter set corresponding to the bright area region and the parameter set corresponding to the dark area region based on the intermediate parameter corresponding to the bright area region and the intermediate parameter corresponding to the dark area region; wherein, the parameter set includes mean value, covariance matrix and mixing coefficient; correcting the correlation matrix corresponding to the bright area region and the correlation matrix corresponding to the dark area region based on the parameter set corresponding to the bright area region and the parameter set corresponding to the dark area region.

[0011] According to the above technical means, determining the intermediate parameter corresponding to the bright area region and the intermediate parameter corresponding to the dark area region through the error sample corresponding to the bright area region and the error sample corresponding to the dark area region, and then determining the parameter set corresponding to the bright area region and the parameter set corresponding to the dark area region, so as to correct the correlation matrix corresponding to the bright area region and the correlation matrix corresponding to the dark area region according to the parameter set corresponding to the bright area region and the parameter set corresponding to the dark area region, improving the accuracy of the target bright area correlation matrix and the target dark area correlation matrix.

[0012] Further, based on the parameter set corresponding to the bright region and the parameter set corresponding to the dark region, the correlation matrix corresponding to the bright region and the correlation matrix corresponding to the dark region are corrected, including: using the expectation-maximization algorithm, based on the parameter set corresponding to the bright region and the parameter set corresponding to the dark region, determining the probability density function corresponding to the bright region and the probability density function corresponding to the dark region; based on the probability density function corresponding to the bright region and the probability density function corresponding to the dark region, correcting the correlation matrix corresponding to the bright region and the correlation matrix corresponding to the dark region.

[0013] According to the above technical means, the probability density function corresponding to the bright region and the probability density function corresponding to the dark region are determined through the expectation-maximization algorithm, and then the correlation matrix corresponding to the bright region and the correlation matrix corresponding to the dark region are corrected according to the determined probability density function corresponding to the bright region and the probability density function corresponding to the dark region, improving the accuracy of the target bright region correlation matrix and the target dark region correlation matrix.

[0014] Further, based on the probability density function corresponding to the bright region and the probability density function corresponding to the dark region, the correlation matrix corresponding to the bright region and the correlation matrix corresponding to the dark region are corrected, including: based on the probability density function corresponding to the bright region and the probability density function corresponding to the dark region, determining the mathematical expectation of the error samples corresponding to the bright region and the mathematical expectation of the error samples corresponding to the dark region; based on the mathematical expectation of the error samples corresponding to the bright region and the mathematical expectation of the error samples corresponding to the dark region, correcting the correlation matrix corresponding to the bright region and the correlation matrix corresponding to the dark region.

[0015] According to the above technical means, the mathematical expectation of the error samples corresponding to the bright region and the mathematical expectation of the error samples corresponding to the dark region are determined through the probability density function corresponding to the bright region and the probability density function corresponding to the dark region, and then the correlation matrix corresponding to the bright region and the correlation matrix corresponding to the dark region are corrected according to the mathematical expectation of the error samples corresponding to the bright region and the mathematical expectation of the error samples corresponding to the dark region, improving the accuracy of the target bright region correlation matrix and the target dark region correlation matrix.

[0016] Further, the vehicle is also provided with a microphone array, and the microphone array includes at least one microphone. Establishing a sound field partition control model based on the target bright region correlation matrix and the target dark region correlation matrix includes: obtaining the excitation signal of the speaker array, the number of microphones corresponding to the bright region, and the number of microphones corresponding to the dark region; based on the target bright region correlation matrix, the target dark region correlation matrix, the excitation signal of the speaker array, the number of microphones corresponding to the bright region, and the number of microphones corresponding to the dark region, establishing a sound field partition control model.

[0017] According to the above technical means, by obtaining the target bright area correlation matrix, the target dark area correlation matrix, the excitation signal of the speaker array, the number of microphones corresponding to the bright area, and the number of microphones corresponding to the dark area, a sound field partition control model is established, and using the actual error data, without manually setting the regularization parameter before the algorithm runs, the stability of the sound field partition method is improved.

[0018] Further, based on the transfer function matrix corresponding to the bright area and the transfer function matrix corresponding to the dark area, the error samples corresponding to the bright area and the error samples corresponding to the dark area are determined, including: determining the ideal transfer function matrix of the bright area from the transfer function matrix corresponding to the bright area, and determining the ideal transfer function matrix of the dark area from the transfer function matrix corresponding to the dark area; determining the error matrix corresponding to the bright area based on the first deviation information between the ideal transfer function matrix of the bright area and the transfer function matrix corresponding to the bright area; determining the error matrix corresponding to the dark area based on the second deviation information between the ideal transfer function matrix of the dark area and the transfer function matrix corresponding to the dark area; determining the error samples corresponding to the bright area and the error samples corresponding to the dark area based on the error matrix corresponding to the bright area and the error matrix corresponding to the dark area.

[0019] According to the above technical means, the error matrix corresponding to the bright area is determined by the first deviation information between the ideal transfer function matrix of the bright area and the transfer function matrix corresponding to the bright area, and the error matrix corresponding to the dark area is determined by the second deviation information between the ideal transfer function matrix of the dark area and the transfer function matrix corresponding to the dark area, so as to determine the error samples corresponding to the bright area and the error samples corresponding to the dark area according to the error matrix corresponding to the bright area and the error matrix corresponding to the dark area, improving the accuracy of the error samples corresponding to the bright area and the error samples corresponding to the dark area.

[0020] Further, based on the error matrix corresponding to the bright area and the error matrix corresponding to the dark area, the error samples corresponding to the bright area and the error samples corresponding to the dark area are determined, including: respectively performing vectorization processing on the error matrix corresponding to the bright area and the error matrix corresponding to the dark area to obtain the error vector corresponding to the bright area and the error vector corresponding to the dark area; determining the error samples corresponding to the bright area and the error samples corresponding to the dark area based on the error vector corresponding to the bright area and the error vector corresponding to the dark area.

[0021] According to the above technical means, the error vector corresponding to the bright area and the error vector corresponding to the dark area are obtained by performing vectorization processing on the error matrix corresponding to the bright area and the error matrix corresponding to the dark area, so as to determine the error samples corresponding to the bright area and the error samples corresponding to the dark area, improving the accuracy of the error samples corresponding to the bright area and the error samples corresponding to the dark area.

[0022] Further, obtaining the transfer function matrix corresponding to at least one of the bright region areas and the transfer function matrix corresponding to at least one of the dark region areas includes: obtaining temperature parameters and humidity parameters; wherein, the temperature parameters include a temperature range and a temperature acquisition interval, and the humidity parameters include a humidity range and a humidity acquisition interval; based on the temperature parameters and the humidity parameters, obtaining the transfer function matrix corresponding to at least one bright region area and the transfer function matrix corresponding to at least one dark region area.

[0023] According to the above technical means, by obtaining the temperature parameters and humidity parameters to obtain the transfer function matrix corresponding to at least one bright region area and the transfer function matrix corresponding to at least one dark region area, the transfer function matrices at different temperatures and different humidities can be obtained, and the zonal control of the sound field can be realized respectively at different temperatures and humidities, improving the flexibility of the sound field zonal control method.

[0024] A zonal control device for a sound field, applied to a vehicle, the vehicle is provided with a speaker array for forming the sound field of the vehicle, the device includes:

[0025] A determination unit, configured to determine a bright region area and a dark region area in the sound field of the vehicle, and obtain the transfer function matrix corresponding to at least one bright region area and the transfer function matrix corresponding to at least one dark region area; wherein, the temperature and humidity corresponding to the transfer function matrix of each bright region area are different, and the temperature and humidity corresponding to the transfer function matrix of each dark region area are different; based on the transfer function matrix corresponding to the bright region area and the transfer function matrix corresponding to the dark region area, determining the error sample corresponding to the bright region area and the error sample corresponding to the dark region area;

[0026] A correction unit, configured to correct the correlation matrix corresponding to the bright region area and the correlation matrix corresponding to the dark region area based on the error sample corresponding to the bright region area and the error sample corresponding to the dark region area, to obtain a target bright region correlation matrix and a target dark region correlation matrix;

[0027] An adjustment unit, configured to establish a sound field zonal control model based on the target bright region correlation matrix and the target dark region correlation matrix, and adjust the driving signal of the speaker array based on the sound field zonal control model to perform zonal control on the sound field of the vehicle.

[0028] An electronic device includes a processor and a memory, the memory stores a computer program that can run on the processor, and when the processor executes the computer program, the above-mentioned method is implemented.

[0029] A computer-readable storage medium, characterized in that a computer program is stored thereon, and when the computer program is executed by a processor, the above-mentioned method is implemented.

[0030] A computer program product includes a computer program or instructions, characterized in that when the computer program or instructions are executed by a processor, the steps in any of the above methods are implemented.

[0031] Advantages of this application:

[0032] (1) By measuring the transfer function matrices corresponding to the bright area and the transfer function matrices corresponding to the dark area under different temperatures and humidities, the zonal control of the sound field can be realized respectively under different temperatures and humidities, improving the flexibility of the sound field zonal control method;

[0033] (2) Since the sound field zonal control model of this application is established based on the correlation matrices corresponding to the corrected bright area and the correlation matrices corresponding to the dark area, while improving the robustness of the sound field zonal control model, the accuracy of the sound field zonal control is also improved;

[0034] (3) By using the Expectation-Maximization algorithm to determine the probability density functions corresponding to the bright area and the probability density functions corresponding to the dark area, the correlation matrices corresponding to the bright area and the correlation matrices corresponding to the dark area are corrected, improving the accuracy of the target bright area correlation matrix and the target dark area correlation matrix;

[0035] (4) By obtaining the target bright area correlation matrix, the target dark area correlation matrix, the excitation signal of the speaker array, the number of microphones corresponding to the bright area, and the number of microphones corresponding to the dark area, a sound field zonal control model is established, and using the actual error data, without manually setting the regularization parameter before the algorithm runs, improving the stability of the sound field zonal control method. Description of the Drawings

[0036] Figure 1 Schematic diagram of the implementation process of a sound field zonal control method provided by an embodiment of this application Figure 1 ;

[0037] Figure 2 Schematic diagram of a speaker array provided by an embodiment of this application;

[0038] Figure 3 Schematic diagram of a microphone array provided by an embodiment of this application;

[0039] Figure 4 Schematic diagram of the implementation process of a sound field zonal control method provided by an embodiment of this application Figure 2 ;

[0040] Figure 5 Schematic diagram of a sound field zonal control system provided by an embodiment of this application;

[0041] Figure 6Schematic diagram of the sound energy contrast between the bright area and the dark area provided by the embodiments of the present application Figure 1 ;

[0042] Figure 7 Schematic diagram of the range difference of the bright area provided by the embodiments of the present application Figure 1 ;

[0043] Figure 8 Schematic diagram of the sound energy contrast between the bright area and the dark area provided by the embodiments of the present application Figure 2 ;

[0044] Figure 9 Schematic diagram of the range difference of the bright area provided by the embodiments of the present application Figure 2 ;

[0045] Figure 10 Schematic diagram of the composition structure of a sound field partition control device provided by the embodiments of the present application;

[0046] Figure 11 Schematic diagram of the hardware entity of an electronic device provided by the embodiments of the present application. Detailed implementation manners

[0047] The following will illustrate the implementation manners of the present application with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be understood that the preferred embodiments are only for illustrating the present application and not for limiting the protection scope of the present application.

[0048] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present application in a schematic manner. Therefore, only the components related to the present application are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and ratio of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0049] In the following description, "some embodiments" are involved, which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0050] In the following description, the terms "first / second / third" only distinguish similar objects and do not represent a specific order for the objects. It can be understood that "first / second / third" can be interchanged in a specific order or sequence when permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0052] The application of the sound field zoning control technology in the automotive intelligent cockpit audio system has received extensive attention. Passengers have personalized requirements for the acoustic environment in the cockpit, and passengers in different seats expect to have different and non-interfering sound fields. The goal of sound field zoning control is to solve the problem of forming different sound fields in different regions of the space and provide a private sound environment for listeners. When performing sound field zoning control, the PM algorithm and the ACC algorithm are usually used to achieve sound field zoning control. When using the above algorithms, it is necessary to control the robustness of the algorithms. The robustness of an algorithm refers to the degree of loss of each index of the algorithm compared with the ideal situation when the actual data has errors, that is, the sensitivity of the algorithm to errors. The robustness control of the algorithm can reduce the sensitivity of the algorithm to errors, thereby improving the stability of the algorithm.

[0053] In related technologies, a diagonal matrix and a regularization parameter are usually introduced, and the robustness control of the algorithm is achieved through a regularization method. Among them, the regularization parameter is intended to adjust the robustness of the algorithm. However, the regularization parameter is a hyperparameter and needs to be set manually before the algorithm runs. If the regularization parameter is too large, the solution of the algorithm will be inaccurate, resulting in poor indicators obtained by the algorithm. If the regularization parameter is too small, the robustness of the algorithm will be poor, resulting in a large loss of the indicators obtained by the algorithm. It is very difficult to set a suitable regularization parameter in the actual application process, and there are problems such as low accuracy and poor flexibility.

[0054] An embodiment of the present application provides a method for zonal control of a sound field. By obtaining a transfer function matrix corresponding to at least one bright zone area and a transfer function matrix corresponding to at least one dark zone area with different temperatures and humidities in the sound field of a vehicle, and then determining an error sample corresponding to the bright zone area and an error sample corresponding to the dark zone area according to the transfer function matrix corresponding to the bright zone area and the transfer function matrix corresponding to the dark zone area, so as to correct the correlation matrix corresponding to the bright zone area and the correlation matrix corresponding to the dark zone area according to the error sample corresponding to the bright zone area and the error sample corresponding to the dark zone area. Finally, a sound field zonal control model is established to adjust the driving signal of the speaker array to perform zonal control on the sound field of the vehicle. On the one hand, by measuring the transfer function matrices at different temperatures and humidities, zonal control of the sound field can be realized respectively at different temperatures and humidities, improving the flexibility of the sound field zonal control method; on the other hand, since the sound field zonal control model of the present application is established according to the corrected correlation matrix corresponding to the bright zone area and the correlation matrix corresponding to the dark zone area, while improving the robustness of the sound field zonal control model, it also improves the accuracy of the sound field zonal control; on the third hand, compared with the prior art, the model of the present application uses actual error data and does not require manual setting of regularization parameters before the algorithm runs, improving the stability of the sound field zonal control method. The method provided by the embodiment of the present application can be executed by an electronic device, and the electronic device can be a car machine in the vehicle.

[0055] Next, the technical solutions in the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present application.

[0056] Figure 1 Schematic implementation process of a method for zonal control of a sound field provided by an embodiment of the present application Figure 1 , as Figure 1 shown, this method is applied to a vehicle, and the vehicle is provided with a speaker array for forming the sound field of the vehicle. This method includes steps S101 to S104, where:

[0057] Step S101, determine a bright zone area and a dark zone area in the sound field of the vehicle, and obtain a transfer function matrix corresponding to at least one bright zone area and a transfer function matrix corresponding to at least one dark zone area; wherein, the temperature and humidity corresponding to the transfer function matrix of each bright zone area are different, and the temperature and humidity corresponding to the transfer function matrix of each dark zone area are different.

[0058] Here, the sound field refers to the area where sound waves exist in the medium. The bright zone area refers to the area in the vehicle where a sound field is desired to be generated. The bright zone area can be any suitable area, for example, the front row area, the driver's area, etc. The dark zone area refers to the area in the vehicle where a sound field is not desired to be generated. The dark zone area can be any suitable area, for example, the rear row area, the passenger's area, etc.

[0059] A speaker array is an array composed of at least one speaker. The number of speakers in the speaker array can be of any suitable size, for example, 10, 12, etc. In some embodiments, the speakers in the speaker array can be located at any suitable position, for example, at the door, on the roof, etc.

[0060] Figure 2 A schematic diagram of a speaker array provided by an embodiment of the present application is as Figure 2 shown. The speaker array is divided into speakers at the door and speakers 22 on the roof. Among them, the speakers at the door include 4 speakers, namely: the first speaker 211, the second speaker 212, the third speaker 213, and the fourth speaker 214. The speakers 22 on the roof include 8 speakers.

[0061] In some embodiments, after receiving a driving signal, the speaker array generates a corresponding sound field, and the range covered by the sound field is called the control area of the vehicle. The control area can be divided into an area where a sound field is desired to be generated and an area where a sound field is not desired to be generated, so as to determine a bright area and a dark area in the sound field of the vehicle.

[0062] The transfer function matrix corresponding to the bright area is used to characterize the propagation characteristics of the audio signal from the sound source to the receiving point in the bright area. The transfer function matrix corresponding to the dark area is used to characterize the propagation characteristics of the audio signal from the sound source to the receiving point in the dark area. In some embodiments, the number of transfer function matrices corresponding to the bright area is at least one, and the temperature and humidity corresponding to the transfer function matrix of each bright area are different. The number of transfer function matrices corresponding to the dark area is at least one, and the temperature and humidity corresponding to the transfer function matrix of each dark area are different. The number of transfer function matrices corresponding to the bright area and the number of transfer function matrices corresponding to the dark area can be the same or different.

[0063] The methods for obtaining the transfer function matrix corresponding to the bright area region and the transfer function matrix corresponding to the dark area region may include, but are not limited to: reading the transfer function matrix corresponding to the bright area region and the transfer function matrix corresponding to the dark area region from the data stored in the memory, calculating the transfer function matrix corresponding to the bright area region and the transfer function matrix corresponding to the dark area region through an acoustic model, etc. For example, the memory may include, but is not limited to: non-volatile memory (Read One Momory, ROM), volatile memory (Random Access Memory, RAM), etc. By reading the data in the memory, the transfer function matrix corresponding to the bright area region and the transfer function matrix corresponding to the dark area region can be obtained. Again, for example, the acoustic model is one of the most important parts of the speech recognition system. By establishing the geometric structure of the acoustic model and using methods such as Finite Element Analysis (FEA), Boundary Element Method (BEM), Statistical Energy Analysis (SEA), etc., the transfer function matrix corresponding to the bright area region and the transfer function matrix corresponding to the dark area region can be obtained. The geometric structure of the acoustic model may include, but is not limited to: sound source, sound transmission medium, receiving point, etc.

[0064] In some embodiments, temperature parameters and humidity parameters can be obtained, and then based on the temperature parameters and humidity parameters, at least one transfer function matrix corresponding to the bright area region and at least one transfer function matrix corresponding to the dark area region can be obtained, so that the temperature and humidity corresponding to the transfer function matrix of each bright area region are different, and the temperature and humidity corresponding to the transfer function matrix of each dark area region are different. Among them, the temperature parameters include a temperature range and a temperature acquisition interval, and the humidity parameters include a humidity range and a humidity acquisition interval.

[0065] Step S102, based on the transfer function matrix corresponding to the bright area region and the transfer function matrix corresponding to the dark area region, determine the error sample corresponding to the bright area region and the error sample corresponding to the dark area region.

[0066] Here, the error sample corresponding to the bright area region is used to correct the correlation matrix corresponding to the bright area region. The error sample corresponding to the dark area region is used to correct the correlation matrix corresponding to the dark area region. In some embodiments, the number of error samples corresponding to the bright area region may be the same as or different from the number of transfer function matrices corresponding to the bright area region. The number of error samples corresponding to the dark area region may be the same as or different from the number of transfer function matrices corresponding to the dark area region.

[0067] In some embodiments, error samples corresponding to the bright region and error samples corresponding to the dark region can be determined based on the transfer function matrix corresponding to the bright region and the transfer function matrix corresponding to the dark region under different temperatures and different humidities.

[0068] In some embodiments, the mathematical expectation of the transfer function matrix corresponding to the bright region can be determined, and based on the mathematical expectation of the transfer function matrix corresponding to the bright region and the transfer function matrix corresponding to the bright region, error samples corresponding to the bright region can be determined. The mathematical expectation of the transfer function matrix corresponding to the dark region can be determined, and based on the mathematical expectation of the transfer function matrix corresponding to the dark region and the transfer function matrix corresponding to the dark region, error samples corresponding to the dark region can be determined.

[0069] In some embodiments, an ideal transfer function matrix for the bright region can be determined from the transfer function matrix corresponding to the bright region, and an ideal transfer function matrix for the dark region can be determined from the transfer function matrix corresponding to the dark region. Then, based on the first deviation information between the ideal transfer function matrix for the bright region and the transfer function matrix corresponding to the bright region, an error matrix corresponding to the bright region can be determined. Based on the second deviation information between the ideal transfer function matrix for the dark region and the transfer function matrix corresponding to the dark region, an error matrix corresponding to the dark region can be determined. Finally, based on the error matrix corresponding to the bright region and the error matrix corresponding to the dark region, error samples corresponding to the bright region and error samples corresponding to the dark region can be determined.

[0070] Step S103: Based on the error samples corresponding to the bright region and the error samples corresponding to the dark region, correct the correlation matrix corresponding to the bright region and the correlation matrix corresponding to the dark region to obtain a target bright region correlation matrix and a target dark region correlation matrix.

[0071] Here, the correlation matrix corresponding to the bright region and the correlation matrix corresponding to the dark region can be used to establish a sound field partition control model. However, the robustness of the sound field partition control model directly established through the correlation matrix corresponding to the bright region and the correlation matrix corresponding to the dark region is poor. Therefore, it is necessary to correct the correlation matrix corresponding to the bright region and the correlation matrix corresponding to the dark region through the error samples corresponding to the bright region and the error samples corresponding to the dark region to obtain a target bright region correlation matrix and a target dark region correlation matrix, so as to establish a sound field partition control model based on the target bright region correlation matrix and the target dark region correlation matrix.

[0072] In some embodiments, there is a corresponding relationship between the elements of the error samples corresponding to the bright area region and the elements of the correlation matrix corresponding to the bright area region. First, the target elements of the error samples corresponding to the bright area region corresponding to each element in the correlation matrix corresponding to the bright area region can be determined, and then each element in the correlation matrix corresponding to the bright area region is corrected based on the target elements of the error samples. Each element in the corrected correlation matrix corresponding to the bright area region is added to the target bright area correlation matrix, so as to realize the correction of the correlation matrix corresponding to the bright area region based on the error samples corresponding to the bright area region, and obtain the target bright area correlation matrix.

[0073] In some embodiments, there is a corresponding relationship between the elements of the error samples corresponding to the dark area region and the elements of the correlation matrix corresponding to the dark area region. First, the target elements of the error samples corresponding to the dark area region corresponding to each element in the correlation matrix corresponding to the dark area region can be determined, and then each element in the correlation matrix corresponding to the dark area region is corrected based on the target elements of the error samples. Each element in the corrected correlation matrix corresponding to the dark area region is added to the target dark area correlation matrix, so as to realize the correction of the correlation matrix corresponding to the dark area region based on the error samples corresponding to the dark area region, and obtain the target dark area correlation matrix.

[0074] In some embodiments, based on the error samples corresponding to the bright area region and the error samples corresponding to the dark area region, the intermediate parameters corresponding to the bright area region and the intermediate parameters corresponding to the dark area region can be determined. Then, based on the intermediate parameters corresponding to the bright area region and the intermediate parameters corresponding to the dark area region, the parameter sets corresponding to the bright area region and the parameter sets corresponding to the dark area region can be determined. Finally, based on the parameter sets corresponding to the bright area region and the parameter sets corresponding to the dark area region, the correlation matrix corresponding to the bright area region and the correlation matrix corresponding to the dark area region are corrected. Among them, the parameter set includes the mean value, the covariance matrix, and the mixing coefficient.

[0075] Step S104, establish a sound field partition control model based on the target bright area correlation matrix and the target dark area correlation matrix, and adjust the driving signals of the speaker array based on the sound field partition control model to perform partition control on the sound field of the vehicle.

[0076] Here, the sound field partition control model is used to adjust the driving signals of the speaker array. The driving signal is a signal used to drive the speakers in the speaker array to output sound. The driving signal can be used to control the vibration of the speakers, the output volume of the speakers, etc.

[0077] In some embodiments, each speaker in the speaker array can be controlled by a separate driving signal. By controlling the phase and amplitude of the driving signals of different speakers, partition control of the sound field can be achieved.

[0078] In some embodiments, when establishing a sound field zoning control model based on the target bright area correlation matrix and the target dark area correlation matrix, the target bright area correlation matrix and the target dark area correlation matrix can be used as performance parameters of the sound field zoning control model to reflect the performance of the sound field zoning control model.

[0079] In some embodiments, the sound field partition control model is supported by a sound field partition algorithm method. The sound field partition algorithm may include, but is not limited to, a PM algorithm and an ACC algorithm. The PM algorithm is committed to accurately synthesizing the desired sound field, setting the desired sound field in the dark area to 0, and reconstructing the desired sound field in the bright area and the dark area. The PM algorithm obtains the weight vector of the speaker array by minimizing the error between the desired sound field and the reconstructed sound field. The ACC algorithm is committed to maximizing the acoustic energy contrast between the bright area and the dark area, and optimizing the obtained weight vector of the speaker array so that the sound signal is propagated directionally to the bright area, and the sound signal is prevented from leaking to the dark area as much as possible.

[0080] In some embodiments, the excitation signal of the speaker array, the number of microphones corresponding to the bright area, and the number of microphones corresponding to the dark area can be obtained, and then a sound field zoning control model can be established based on the target bright area correlation matrix, the target dark area correlation matrix, the excitation signal of the speaker array, the number of microphones corresponding to the bright area, and the number of microphones corresponding to the dark area.

[0081] In an embodiment of the present application, a transfer function matrix corresponding to at least one bright area and a transfer function matrix corresponding to at least one dark area with different temperatures and humidities are obtained from the bright area and the dark area determined in the sound field of the vehicle, and then error samples corresponding to the bright area and error samples corresponding to the dark area are determined according to the transfer function matrix corresponding to the bright area and the transfer function matrix corresponding to the dark area, so as to correct the correlation matrix corresponding to the bright area and the correlation matrix corresponding to the dark area according to the error samples corresponding to the bright area and the error samples corresponding to the dark area, and finally establish a sound field partition control model to adjust the driving signal of the speaker array, so as to control the sound field of the vehicle. By performing zoning control, on the one hand, the transfer function matrix of different temperatures and humidities is measured, and the zoning control of the sound field can be realized respectively under different temperatures and humidities, thereby improving the flexibility of the sound field zoning control method; on the other hand, since the sound field zoning control model of the present application is established based on the correlation matrix corresponding to the corrected bright area and the correlation matrix corresponding to the dark area, while improving the robustness of the sound field zoning control model, it also improves the accuracy of the sound field zoning control; on another hand, compared with the prior art, the model of the present application utilizes actual error data, and there is no need to manually set the regularization parameters before the algorithm runs, thereby improving the stability of the sound field zoning method.

[0082] In some embodiments, "obtaining the transfer function matrix corresponding to at least one bright region and the transfer function matrix corresponding to at least one dark region" in step S101 includes step S111 and step S112, where:

[0083] Step S111, obtaining temperature parameters and humidity parameters; wherein, the temperature parameters include a temperature range and a temperature acquisition interval, and the humidity parameters include a humidity range and a humidity acquisition interval.

[0084] Here, the temperature parameters may include, but are not limited to: temperature range, temperature acquisition interval, etc. The humidity parameters may include, but are not limited to: humidity range, humidity acquisition interval, etc. The humidity range may be any suitable range, for example, 0°C (Celsius) to 40°C, 10°C to 35°C, etc. The temperature acquisition interval may be any suitable size, for example, 1°C, 0.5°C, etc. The humidity range may be any suitable range, for example, 20% to 60%, 10% to 80%. The humidity acquisition interval may be any suitable size, for example, 1%, 0.5%, etc.

[0085] The methods for obtaining the temperature parameters and humidity parameters may include, but are not limited to: reading the temperature parameters and humidity parameters from a memory, reading the temperature parameters and humidity parameters from the cloud, etc. For example, the memory may include, but is not limited to: ROM, RAM, etc. By reading the data in the memory, the transfer function matrix corresponding to the bright region and the transfer function matrix corresponding to the dark region can be obtained. The temperature parameters and humidity parameters can be read from the data stored in the memory. Again, for example, a large amount of data can be stored in the cloud, and the temperature parameters and humidity parameters can be read from the data stored in the cloud.

[0086] Step S112, based on the temperature parameters and humidity parameters, obtaining the transfer function matrix corresponding to at least one bright region and the transfer function matrix corresponding to at least one dark region.

[0087] Here, the vehicle is also provided with a microphone array, and the microphone array is an array composed of at least one microphone. The number of microphones in the microphone array can be any suitable size, for example, 12, 16, etc. In some embodiments, at least one microphone array may be provided in the vehicle. The microphones in the microphone array can be located at any suitable position, for example, at the door, on the roof, etc.

[0088] Figure 3 As shown in the schematic diagram of a microphone array provided by an embodiment of the present application, as Figure 3 shown, two microphone arrays are provided on the roof of the vehicle. Among them, the first microphone array 31 is located in the front row area of the vehicle and includes 12 microphones, and the second microphone array 32 is located in the rear row area of the vehicle and includes 12 microphones.

[0089] In some embodiments, based on temperature parameters and humidity parameters, by acquiring the acquisition signals of the microphone array and the drive signals of the speaker array at different temperatures and different humidities, and then based on the drive signals of the speaker array and the acquisition signals of the microphone array, a transfer function matrix corresponding to at least one bright region and a transfer function matrix corresponding to at least one dark region can be obtained; wherein, the temperature and humidity corresponding to the transfer function matrix of each bright region are different, and the temperature and humidity corresponding to the transfer function matrix of each dark region are different.

[0090] In some embodiments, the acquisition signal of the microphone array refers to the sound signal captured by the microphone array. After the speaker array emits sound according to the input drive signal, the sound emitted by the speaker array can be acquired by the microphone array to obtain the acquisition signal of the microphone array.

[0091] In some embodiments, the cross-power spectral density between the drive signal of the speaker array and the acquisition signal of the microphone array can be determined at different temperatures and different humidities, and then based on the cross-power spectral density, a transfer function matrix corresponding to at least one bright region and a transfer function matrix corresponding to at least one dark region can be obtained. The cross-power spectral density is a method for describing the statistical correlation degree between two different signals in the frequency domain. In some embodiments, the cross-power spectral density can be expressed by the cross-correlation function of two signals. In some embodiments, the fast Fourier transform (FFT) method can be used to calculate the cross-power spectral density of two signals.

[0092] In some embodiments, at different temperatures and different humidities, a white noise input signal can be used as the drive signal of the speaker array, and then the auto-power spectral density corresponding to the white noise input signal can be obtained. Further, based on the cross-power spectral density between the white noise input signal and the acquisition signal of the microphone array and the auto-power spectral density corresponding to the white noise input signal, a transfer function matrix corresponding to at least one bright region and a transfer function matrix corresponding to at least one dark region can be obtained.

[0093] In some embodiments, the transfer function matrix of the speaker array from different temperatures and humidities to the microphone array in different regions can be determined by the following formula (1-1) where f represents frequency, and t and h represent temperature and humidity respectively. For the convenience of writing, the following transfer function matrix will omit the subscripts t and h, that is, see formula (1-1):

[0094]

[0095] In formula (1-1), i = 1,..., M, M is the number of control regions. l = 1, …, L, where L is the number of speakers. k = 1, …, K i , K i is the number of microphones in the i-th control region. is the transfer function from the l-th speaker to the k-th control point in the i-th control region. is the white noise input signal q l (t) of the l-th speaker and the acquisition signal of the k-th microphone The cross-power spectral density between them. U ll (f) is the auto-power spectral density of the white noise input signal q l (t) of the l-th speaker. f represents the frequency domain and t represents the time domain.

[0096] In some embodiments, it can be through Z B (f) represents the transfer function matrix corresponding to the bright region, and through Z D (f) represents the transfer function matrix corresponding to the dark region.

[0097] In the embodiments of the present application, by obtaining the temperature parameter and humidity parameter to obtain at least one transfer function matrix corresponding to the bright region and at least one transfer function matrix corresponding to the dark region, the transfer function matrices under different temperatures and different humidities can be obtained, and the zonal control of the sound field can be realized respectively under different temperatures and humidities, improving the flexibility of the sound field zonal control method.

[0098] In some embodiments, "correcting the correlation matrix corresponding to the bright region and the correlation matrix corresponding to the dark region based on the error samples corresponding to the bright region and the error samples corresponding to the dark region" in step S103 includes steps S31 to S33, where:

[0099] Step S31, based on the error samples corresponding to the bright region and the error samples corresponding to the dark region, determine the intermediate parameter corresponding to the bright region and the intermediate parameter corresponding to the dark region.

[0100] Here, the intermediate parameter corresponding to the bright region and the intermediate parameter corresponding to the dark region are used to determine the parameter set corresponding to the bright region and the parameter set corresponding to the dark region.

[0101] In some embodiments, γ ij is actually a posterior probability formula, and the intermediate parameter γ can be determined by the following formula (1-2) ij , that is, refer to formula (1-2):

[0102]

[0103] In formula (1-2), i = 1, 2, …, I, j = 1, 2, …, J, and π j is the j-th mixing coefficient, μ j is the mean of the j-th Gaussian distribution, and ∑ j is the covariance matrix of the j-th Gaussian distribution.

[0104] Step S32: Based on the intermediate parameters corresponding to the bright region and the intermediate parameters corresponding to the dark region, determine the parameter sets corresponding to the bright region and the parameter sets corresponding to the dark region; where the parameter sets include the mean, the covariance matrix, and the mixing coefficient.

[0105] Here, the parameter sets include at least one of, but are not limited to, the mean, the covariance matrix, the mixing coefficient, etc.

[0106] In some embodiments, the parameter sets can be determined by the following formula (1-3), that is, see formula (1-3):

[0107]

[0108] In formula (1-3), i = 1, 2, …, I, j = 1, 2, …, J, and π j is the j-th mixing coefficient, μ j is the mean of the j-th Gaussian distribution, Σ j is the covariance matrix of the j-th Gaussian distribution, γ ij is the intermediate parameter, and J is the number of Gaussian distributions in the mixture Gaussian distribution.

[0109] Step S33: Based on the parameter sets corresponding to the bright region and the parameter sets corresponding to the dark region, correct the correlation matrix corresponding to the bright region and the correlation matrix corresponding to the dark region.

[0110] Here, the parameter set corresponding to the bright region can be used as the compensation parameter for the correlation matrix corresponding to the bright region to correct the correlation matrix corresponding to the bright region. The parameter set corresponding to the dark region can be used as the compensation parameter for the correlation matrix corresponding to the dark region to correct the correlation matrix corresponding to the dark region.

[0111] In some embodiments, the expectation-maximization algorithm can be used to determine the probability density function corresponding to the bright region and the probability density function corresponding to the dark region based on the parameter sets corresponding to the bright region and the parameter sets corresponding to the dark region, and then correct the correlation matrix corresponding to the bright region and the correlation matrix corresponding to the dark region based on the probability density function corresponding to the bright region and the probability density function corresponding to the dark region.

[0112] In the embodiments of the present application, the intermediate parameters corresponding to the bright area region and the intermediate parameters corresponding to the dark area region are determined through the error samples corresponding to the bright area region and the error samples corresponding to the dark area region, and then the parameter set corresponding to the bright area region and the parameter set corresponding to the dark area region are determined, so as to correct the correlation matrix corresponding to the bright area region and the correlation matrix corresponding to the dark area region according to the parameter set corresponding to the bright area region and the parameter set corresponding to the dark area region, improving the accuracy of the target bright area correlation matrix and the target dark area correlation matrix.

[0113] In some embodiments, step S33 includes step S331 and step S332, where:

[0114] Step S331, using the Expectation-Maximization (EM) algorithm, based on the parameter set corresponding to the bright area region and the parameter set corresponding to the dark area region, determine the probability density function corresponding to the bright area region and the probability density function corresponding to the dark area region.

[0115] Here, the Expectation-Maximization (EM) algorithm is an iterative optimization algorithm used to find the maximum likelihood or maximum a posteriori estimation of parameters in a statistical model. The probability density function is a concept in probability theory and statistics used to describe the probability distribution of the values taken by a continuous random variable.

[0116] In some embodiments, the EM algorithm can be used. Input the error samples corresponding to the bright area region, the error samples corresponding to the dark area region, and the number of Gaussian distributions in the mixture Gaussian distribution. Through the K-means algorithm, obtain the initial mean and covariance matrix and intermediate parameters of the mixture Gaussian model, and finally output the probability density function corresponding to the bright area region and the probability density function corresponding to the dark area region. The K-means algorithm is a classic clustering algorithm whose goal is to divide the given data set into K clusters such that the sum of the squared distances between each data point and its assigned cluster center is minimized.

[0117] In some embodiments, the probability density function p(Δy|Θ) can be determined through the parameter set corresponding to the bright area region and the error samples corresponding to the bright area region, where Δy is the error sample corresponding to the bright area region, i = 1, 2, …, I, j = 1, 2, …, J, π j is the j-th mixing coefficient, μ j is the mean of the j-th Gaussian distribution, Σ j is the covariance matrix of the j-th Gaussian distribution, γ ij is the intermediate parameter, J is the number of Gaussian distributions in the mixture Gaussian distribution, and Θ is the parameter set corresponding to the bright area region.

[0118] Step S332: Based on the probability density function corresponding to the bright area region and the probability density function corresponding to the dark area region, correct the correlation matrix corresponding to the bright area region and the correlation matrix corresponding to the dark area region.

[0119] Here, the probability density function corresponding to the bright area region can be substituted into the correlation matrix corresponding to the bright area region to correct the correlation matrix corresponding to the bright area region. The probability density function corresponding to the dark area region is substituted into the correlation matrix corresponding to the dark area region to correct the correlation matrix corresponding to the dark area region.

[0120] In some embodiments, the correlation matrix R corresponding to the bright area region can be determined by the following formula (1-4) B and the correlation matrix R corresponding to the dark area region D , that is, see formula (1-4):

[0121]

[0122] In formula (1-4), K B is the number of microphones in the bright area region, K D is the number of microphones in the dark area region, Z B is the transfer function matrix corresponding to the bright area region, Z D is the transfer function matrix corresponding to the dark area region, is the transpose of the transfer function matrix corresponding to the bright area region, is the transpose of the transfer function matrix corresponding to the dark area region.

[0123] In the embodiments of the present application, the probability density function corresponding to the bright area region and the probability density function corresponding to the dark area region are determined by the expectation-maximization algorithm, and then the correlation matrix corresponding to the bright area region and the correlation matrix corresponding to the dark area region are corrected according to the determined probability density function corresponding to the bright area region and the probability density function corresponding to the dark area region, improving the accuracy of the target correlation matrix of the bright area and the target correlation matrix of the dark area.

[0124] In some embodiments, step S332 includes step S3321 and step S3322, where:

[0125] Step S3321: Based on the probability density function corresponding to the bright area region and the probability density function corresponding to the dark area region, determine the mathematical expectation corresponding to the error samples in the bright area region and the mathematical expectation corresponding to the error samples in the dark area region.

[0126] Here, the probability density function corresponding to the bright region can be decomposed, and then, based on the probability density function corresponding to the decomposed bright region, the mathematical expectation of the error samples in the bright region can be determined. The probability density function corresponding to the dark region is decomposed, and then, based on the probability density function corresponding to the decomposed dark region, the mathematical expectation of the error samples in the dark region is determined.

[0127] In some embodiments, R can be determined by the following formula (1-5) D,p The element in the m-th row and n-th column of, that is, see formula (1-5):

[0128]

[0129] In formula (1-5), z ij represents the element in the i-th row and j-th column of the transfer function under ideal conditions, represents the conjugate complex number of the number z ij A ij represents the random variable of the real part of the i-th row and j-th column of the transfer function error matrix, and B ij represents the random variable of the imaginary part of the i-th row and j-th column of the transfer function error matrix.

[0130] Step S3322: Based on the mathematical expectation of the error samples in the bright region and the mathematical expectation of the error samples in the dark region, correct the correlation matrix corresponding to the bright region and the correlation matrix corresponding to the dark region.

[0131] Here, the mathematical expectation of the error samples in the bright region can be substituted into the correlation matrix corresponding to the bright region to correct the correlation matrix corresponding to the bright region. The mathematical expectation of the error samples in the dark region is substituted into the correlation matrix corresponding to the dark region to correct the correlation matrix corresponding to the dark region.

[0132] In some embodiments, the following formula (1-6) can be obtained through the probability density function corresponding to the dark region, and then, by substituting the following formula (1-6) into the above formula (1-5), the mathematical expectation of the error samples in the dark region can be obtained, that is, see formula (1-6):

[0133]

[0134] In formula (1-6), j = 1, 2,..., J D , Δy D is the error sample in the dark region, is the transpose of the error sample in the dark region, π D,j is the j-th mixing coefficient in the dark region, μ D,j is the mean of the j-th Gaussian distribution in the dark region, ΣD,j is the covariance matrix of the Gaussian distribution of the j-th dark area region, is the transpose of the covariance matrix of the Gaussian distribution of the j-th dark area region.

[0135] In the embodiment of the present application, by using the probability density function corresponding to the bright area region and the probability density function corresponding to the dark area region, the mathematical expectation corresponding to the error sample of the bright area region and the mathematical expectation corresponding to the error sample of the dark area region are determined. Furthermore, based on the mathematical expectation corresponding to the error sample of the bright area region and the mathematical expectation corresponding to the error sample of the dark area region, the correlation matrix corresponding to the bright area region and the correlation matrix corresponding to the dark area region are corrected, thereby improving the accuracy of the target bright area correlation matrix and the target dark area correlation matrix.

[0136] In some embodiments, the vehicle is further provided with a microphone array, and the microphone array includes at least one microphone. The "establishing an acoustic field partition control model based on the target bright area correlation matrix and the target dark area correlation matrix" in step S104 includes step S141 and step S142, where:

[0137] Step S141, obtaining the excitation signal of the speaker array, the number of microphones corresponding to the bright area region, and the number of microphones corresponding to the dark area region.

[0138] Here, the microphone array is an array composed of at least one microphone. The number of microphones in the microphone array can be of any suitable size, for example, 12, 16, etc. In some embodiments, at least one microphone array can be provided in the vehicle. The microphones in the microphone array can be located at any suitable position, for example, at the door, on the roof, etc.

[0139] Figure 3 is a schematic diagram of a microphone array provided by an embodiment of the present application, as Figure 3 shown, two microphone arrays are provided on the roof of the vehicle. Among them, the first microphone array 31 is located in the front row area of the vehicle and includes 12 microphones, and the second microphone array 32 is located in the rear row area of the vehicle and includes 12 microphones.

[0140] The number of microphones in the bright area region can be of any suitable size, for example, 12, 10, etc. The number of microphones in the dark area region can be of any suitable size, for example, 12, 10, etc. In some embodiments, the number of microphones in the bright area region and the number of microphones in the dark area region can be the same or different. In some embodiments, after determining the bright area region and the bright area region, the number of microphones in the bright area region and the number of microphones in the bright area region can be obtained by reading the parameter information of the microphones.

[0141] In some implementations, the excitation signal of the speaker array, the number of microphones corresponding to the bright area, and the number of microphones corresponding to the dark area may be obtained by reading parameter information of the microphone array and parameter information of the speaker array.

[0142] Step S142, establishing a sound field partition control model based on the target bright area correlation matrix, the target dark area correlation matrix, the excitation signal of the speaker array, the number of microphones corresponding to the bright area, and the number of microphones corresponding to the dark area.

[0143] Here, the target bright area correlation matrix, the target dark area correlation matrix, the excitation signal of the speaker array, the number of microphones corresponding to the bright area, and the number of microphones corresponding to the dark area can be used as model parameters to establish a sound field partition control model.

[0144] In some embodiments, the sound field partition control model is supported by a sound field partition algorithm method. The sound field partition algorithm may include but is not limited to: PM algorithm, ACC algorithm. The sound field partition control model can be established using the sound field partition algorithm based on the target bright area correlation matrix, the target dark area correlation matrix, the excitation signal of the speaker array, the number of microphones corresponding to the bright area, and the number of microphones corresponding to the dark area.

[0145] In some embodiments, the performance of the sound field zoning control model can be measured by the sound energy contrast between the bright area and the dark area and the extreme difference of the reconstructed sound field in the bright area. The sound energy contrast between the bright area and the dark area is the ratio between the sound energy of the bright area and the sound energy of the dark area. The sound energy contrast between the bright area and the dark area can be any suitable size, for example, 30, 21, etc. The extreme difference of the reconstructed sound field in the bright area is used to describe the uniform distribution of the sound pressure at each position point in the bright area.

[0146] In some embodiments, the greater the acoustic energy contrast between the bright area and the dark area, the better the zoning effect of characterizing the sound field. The smaller the range of the reconstructed sound field in the bright area, the better the zoning effect of characterizing the sound field.

[0147] In some embodiments, the acoustic energy contrast AC(f) between the bright area and the dark area can be determined by the following formula (1-7), that is, see formula (1-7):

[0148]

[0149] In formula (1-7), K B and K D are the number of microphones in the bright area and the number of microphones in the dark area, respectively. B (f) is the transfer function matrix of the bright area, Z D(f) is the transfer function matrix of the dark area, and w(f) is the optimal solution corresponding to any frequency.

[0150] In some embodiments, the range difference ΔL(f) of the reconstructed sound field in the bright area may be determined by the following formula (1-8), that is, see formula (1-8):

[0151] ΔL(f)=L B,max (f)-L B,min (f) (1-8);

[0152] In formula (1-8), L B,max (f) and L B,min (f) are the maximum and minimum sound pressure levels of each control point in the bright area at the frequency point f, respectively. B (f)) i The calculation formula is () i represents the i-th element of the vector, p ref =20μPa is the reference sound pressure.

[0153] In some implementations, the sound field partition control model M may be determined by the following formula (1-9), that is, see formula (1-9):

[0154]

[0155] In formula (1-9), K B ,K D are the number of microphones in the bright area and the dark area respectively, q is the speaker excitation signal, C LD is the consistency constraint matrix, which aims to reduce the ΔL value in the bright area. α>0 is the weight coefficient, which is manually adjusted according to the actual situation and can be any appropriate number. ( ) H Indicates transpose. C LD is defined as follows: ( ) i represents the i-th element of a vector, ( ) j Represents the j-th element of a vector.

[0156] In the implementation mode of the present application, a sound field partitioning control model is established by acquiring the target bright area correlation matrix, the target dark area correlation matrix, the excitation signal of the speaker array, the number of microphones corresponding to the bright area, and the number of microphones corresponding to the dark area. By utilizing the actual error data, there is no need to manually set the regularization parameters before the algorithm runs, thereby improving the stability of the sound field partitioning method.

[0157] In some embodiments, step S102 includes steps S121 to S124, wherein:

[0158] Step S121: Determine the bright-region ideal transfer function matrix from the transfer function matrix corresponding to the bright-region area, and determine the dark-region ideal transfer function matrix from the transfer function matrix corresponding to the dark-region area.

[0159] Here, the bright-region ideal transfer function matrix refers to the transfer function matrix expected to be achieved in the bright-region area. The dark-region ideal transfer function matrix refers to the transfer function matrix expected to be achieved in the dark-region area.

[0160] In some embodiments, a correspondence relationship can be established between the transfer function matrix and the ideal transfer function matrix. Then, based on this correspondence relationship, the bright-region ideal transfer function matrix can be determined from the transfer function matrix corresponding to the bright-region area, and the dark-region ideal transfer function matrix can be determined from the transfer function matrix corresponding to the dark-region area.

[0161] In some embodiments, the transfer function matrix at a common temperature and common humidity can be selected from the transfer function matrices corresponding to the bright-region area at different temperatures and different humidities, and this transfer function matrix at the common temperature and common humidity can be used as the bright-region ideal transfer function matrix. Exemplarily, the bright-region ideal transfer function matrix can be the transfer function matrix corresponding to the bright-region area at a temperature of 25°C and a humidity of 60%.

[0162] In some embodiments, the transfer function matrix at a common temperature and common humidity can be selected from the transfer function matrices corresponding to the dark-region area at different temperatures and different humidities, and this transfer function matrix at the common temperature and common humidity can be used as the dark-region ideal transfer function matrix.

[0163] Step S122: Based on the first deviation information between the bright-region ideal transfer function matrix and the transfer function matrix corresponding to the bright-region area, determine the error matrix corresponding to the bright-region area.

[0164] Here, the first deviation information refers to the deviation information between each element in the bright-region ideal transfer function matrix and each element in the transfer function matrix corresponding to the bright-region area. In some embodiments, the number of the first deviation information is the same as the number of the transfer function matrix corresponding to the bright-region area.

[0165] In some embodiments, after determining the bright-region ideal transfer function matrix, traverse the transfer function matrices corresponding to the bright-region area at different temperatures and different humidities according to this bright-region ideal transfer function matrix to obtain at least one first deviation information, and then use the at least one first deviation information as the error matrix corresponding to the bright-region area.

[0166] Step S123: Based on the second deviation information between the dark-region ideal transfer function matrix and the transfer function matrix corresponding to the dark-region area, determine the error matrix corresponding to the dark-region area.

[0167] Here, the second deviation information refers to the deviation information between each element of the ideal transfer function matrix of the dark area and each element of the transfer function matrix corresponding to the dark area region. In some embodiments, the number of the second deviation information is the same as the number of the transfer function matrices corresponding to the dark area region.

[0168] In some embodiments, after determining the ideal transfer function matrix of the dark area, traverse the transfer function matrices corresponding to the dark area regions at different temperatures and different humidities according to the ideal transfer function matrix of the dark area to obtain at least one piece of second deviation information, and then use the at least one piece of second deviation information as the error matrix corresponding to the dark area region.

[0169] Step S124, based on the error matrix corresponding to the bright area region and the error matrix corresponding to the dark area region, determine the error samples corresponding to the bright area region and the error samples corresponding to the dark area region.

[0170] Here, the error matrix corresponding to the bright area region can be used as the error sample corresponding to the bright area region, and the error matrix corresponding to the dark area region can be used as the error sample corresponding to the dark area region.

[0171] In some embodiments, the error matrix corresponding to the bright area region and the error matrix corresponding to the dark area region can be vectorized respectively to obtain the error vector corresponding to the bright area region and the error vector corresponding to the dark area region, and based on the error vector corresponding to the bright area region and the error vector corresponding to the dark area region, determine the error samples corresponding to the bright area region and the error samples corresponding to the dark area region.

[0172] In the embodiments of the present application, the error matrix corresponding to the bright area region is determined by the first deviation information between the ideal transfer function matrix of the bright area and the transfer function matrix corresponding to the bright area region, and the error matrix corresponding to the dark area region is determined by the second deviation information between the ideal transfer function matrix of the dark area and the transfer function matrix corresponding to the dark area region, so as to determine the error samples corresponding to the bright area region and the error samples corresponding to the dark area region according to the error matrix corresponding to the bright area region and the error matrix corresponding to the dark area region, improving the accuracy of the error samples corresponding to the bright area region and the error samples corresponding to the dark area region.

[0173] In some embodiments, step S124 includes step S1241 and step S1242, where:

[0174] Step S1241, vectorize the error matrix corresponding to the bright area region and the error matrix corresponding to the dark area region respectively to obtain the error vector corresponding to the bright area region and the error vector corresponding to the dark area region.

[0175] Here, vectorization refers to converting a matrix into a vector form. The error vector corresponding to the bright area region can be in any suitable form, for example, a row vector, a column vector, etc. The error vector corresponding to the dark area region can be in any suitable form, for example, a row vector, a column vector, etc. In some embodiments, the form of the error vector corresponding to the bright area region and the form of the error vector corresponding to the dark area region can be the same or different.

[0176] Methods for vectorizing the error matrix may include but are not limited to: flattening, column vectorization, row vectorization, etc. Flattening refers to the process of arranging all elements of the matrix in row or column order into a unit vector. Column vectorization refers to the process of stacking each column of the matrix to form a vector. Row vectorization refers to the process of concatenating each row of the matrix to form a vector.

[0177] Step S1242, based on the error vector corresponding to the bright area region and the error vector corresponding to the dark area region, determine the error sample corresponding to the bright area region and the error sample corresponding to the dark area region.

[0178] Here, the error vector corresponding to the bright area region can be added to a set, and this set is used as the error sample corresponding to the bright area region. The error vector corresponding to the dark area region can be added to a set, and this set is used as the error sample corresponding to the dark area region.

[0179] In the embodiments of the present application, by vectorizing the error matrix corresponding to the bright area region and the error matrix corresponding to the dark area region to obtain the error vector corresponding to the bright area region and the error vector corresponding to the dark area region, so as to determine the error sample corresponding to the bright area region and the error sample corresponding to the dark area region, the accuracy of the error sample corresponding to the bright area region and the error sample corresponding to the dark area region is improved.

[0180] The following describes the application of the sound field zoning control method provided by the embodiments of the present application in an actual scenario, taking a vehicle equipped with a microphone array and a speaker array as an example.

[0181] The application of sound field zoning control technology in the automotive intelligent cockpit audio system has received extensive attention. Passengers have personalized requirements for the acoustic environment in the cockpit, and passengers in different seats expect to have different and non-interfering sound fields. The goal of sound field zoning control is to solve the problem of forming different sound fields in different regions of the space and provide a private sound environment for listeners. When performing sound field zoning control, the PM algorithm and the ACC algorithm are usually used to achieve sound field zoning control. When using the above algorithms, it is necessary to control the robustness of the algorithms. The robustness of an algorithm refers to the degree of loss of various indicators of the algorithm compared to the ideal situation when the actual data has errors, that is, the sensitivity of the algorithm to errors. Controlling the robustness of the algorithm can reduce the sensitivity of the algorithm to errors, thereby improving the stability of the algorithm.

[0182] In the related art, a diagonal matrix and a regularization parameter are usually introduced, and the robustness control of the algorithm is realized through a regularization method. Among them, the regularization parameter is designed to adjust the robustness of the algorithm. However, the regularization parameter is a hyperparameter and needs to be manually set before the algorithm runs. If the regularization parameter is too large, the solution of the algorithm will be inaccurate, resulting in poor indicators obtained by the algorithm. If the regularization parameter is too small, the robustness of the algorithm will be poor, resulting in a large loss of the indicators obtained by the algorithm. It is difficult to set a suitable regularization parameter in the actual application process, and there are problems such as low accuracy and poor flexibility.

[0183] The embodiment of the present application provides a method for zonal control of a sound field. By obtaining the transfer function matrix corresponding to at least one bright zone area and the transfer function matrix corresponding to at least one dark zone area with different temperatures and humidities in the sound field of a vehicle, and then determining the error samples corresponding to the bright zone area and the error samples corresponding to the dark zone area according to the transfer function matrix corresponding to the bright zone area and the transfer function matrix corresponding to the dark zone area, so as to correct the correlation matrix corresponding to the bright zone area and the correlation matrix corresponding to the dark zone area according to the error samples corresponding to the bright zone area and the error samples corresponding to the dark zone area. Finally, a sound field zonal control model is established to adjust the driving signal of the speaker array to perform zonal control on the sound field of the vehicle. On the one hand, by measuring the transfer function matrix at different temperatures and humidities, zonal control of the sound field can be realized respectively at different temperatures and humidities, improving the flexibility of the sound field zonal control method; on the other hand, since the sound field zonal control model of the present application is established according to the corrected correlation matrix corresponding to the bright zone area and the correlation matrix corresponding to the dark zone area, while improving the robustness of the sound field zonal control model, it also improves the accuracy of the sound field zonal control; on the other hand, compared with the prior art, the model of the present application uses actual error data and does not need to manually set the regularization parameter before the algorithm runs, improving the stability of the sound field zonal method.

[0184] Figure 4 Schematic implementation process of a method for zonal control of a sound field provided by an embodiment of the present application Figure 2 As Figure 4 shown, the method includes steps S201 to S210, where:

[0185] Step S201, determine a bright zone area and a dark zone area in the sound field of the vehicle, and obtain the transfer function matrix corresponding to at least one bright zone area and the transfer function matrix corresponding to at least one dark zone area under different temperatures and humidities;

[0186] Step S202: Determine the bright-region ideal transfer function from the transfer function matrix corresponding to the bright-region area, and determine the dark-region ideal transfer function from the transfer function matrix corresponding to the dark-region area;

[0187] Step S203: Determine the error matrix corresponding to the bright-region area based on the first deviation information between the bright-region ideal transfer function and the transfer function matrix corresponding to the bright-region area, and determine the error matrix corresponding to the dark-region area based on the second deviation information between the dark-region ideal transfer function and the transfer function matrix corresponding to the dark-region area;

[0188] Step S204: Respectively perform vectorization processing on the error matrix corresponding to the bright-region area and the error matrix corresponding to the dark-region area to obtain the error vector corresponding to the bright-region area and the error vector corresponding to the dark-region area;

[0189] Step S205: Determine the error sample corresponding to the bright-region area and the error sample corresponding to the dark-region area based on the error vector corresponding to the bright-region area and the error vector corresponding to the dark-region area;

[0190] Step S206: Determine the intermediate parameter corresponding to the bright-region area and the intermediate parameter corresponding to the dark-region area based on the error sample corresponding to the bright-region area and the error sample corresponding to the dark-region area;

[0191] Step S207: Determine the parameter set corresponding to the bright-region area and the parameter set corresponding to the dark-region area based on the intermediate parameter corresponding to the bright-region area and the intermediate parameter corresponding to the dark-region area;

[0192] Step S208: Using the expectation-maximization algorithm, determine the probability density function corresponding to the bright-region area and the probability density function corresponding to the dark-region area based on the parameter set corresponding to the bright-region area and the parameter set corresponding to the dark-region area;

[0193] Step S209: Based on the probability density function corresponding to the bright-region area and the probability density function corresponding to the dark-region area, correct the correlation matrix corresponding to the bright-region area and the correlation matrix corresponding to the dark-region area to obtain the target bright-region correlation matrix and the target dark-region correlation matrix;

[0194] Step S210: Establish a sound field partition control model based on the target bright-region correlation matrix and the target dark-region correlation matrix, and adjust the driving signal of the speaker array based on the sound field partition control model to perform partition control on the sound field of the vehicle.

[0195] Figure 5 The figure is a schematic diagram of a sound field partition control system provided by an embodiment of the present application. As Figure 5 shown, the sound field partition control system includes a microphone array 51, an algorithm integration unit 52, a power amplifier 53, and a speaker array 54, where:

[0196] The microphone array 51 collects sound and transmits it to the algorithm integration unit 52. The algorithm integration unit 52 first generates a transfer function 521 based on the sound collected by the microphone array 51, then generates a bright area transfer function 522 and a dark area transfer function 523 based on the transfer function 521, and then obtains the transfer function under ideal conditions, and generates an error transfer function 524 based on the bright area transfer function 522 and the dark area transfer function 523. Then, the probability density functions 525 of the bright and dark areas are generated through the EM algorithm. After taking the expectation of the probability density functions of the bright and dark areas, a corrected correlation matrix 526 is obtained. Finally, a partition control model of the sound field is established based on the corrected correlation matrix 526, and a speaker signal 527 is output to the power amplifier 53, so that the speaker array 54 generates an output signal according to the speaker signal 527 transmitted by the power amplifier 53.

[0197] In some embodiments, to test the robustness effect of the sound field partition control model established by the sound field partition control method provided in this application, this application is compared with the results of the regularization robustness control method in the prior art in the low-frequency range of 0 - 300 Hz. At each frequency point, under different transfer function error magnitudes, the contrast ratio and the extreme difference are compared with the regularization robustness control method in the prior art. The comparison results are as follows:

[0198] Figure 6 Schematic diagram of the sound energy contrast ratio between the bright area and the dark area provided by the embodiment of this application Figure 1 , as Figure 6 shown, the curves of the sound energy contrast ratios of each bright area and dark area are respectively: without robustness control and without error 61, without robustness control and with error 62, regularization method and with error 63, the method of this application and with error 64. At 0 - 300 Hz, the maximum value of the sound energy contrast ratio of the frequency domain level of the bright area and the dark area obtained by solving the regularization-based robustness control algorithm is 26.5 dB, while the maximum value of the sound energy contrast ratio of the bright area and the dark area obtained by the sound field partition control method provided by the embodiment of this application is 31.3 dB, which is better than the regularization method at all 0 - 300 Hz frequency points.

[0199] Figure 7 Schematic diagram of the extreme difference of the bright area provided by the embodiment of this application Figure 1 , as Figure 7As shown, the range curves of each bright area region are as follows: without robustness control and without error 71, without robustness control and with error 72, regularization method and with error 73, and the method of this application and with error 74. When at 0 - 300 Hz, the minimum value of the range of the bright area region obtained by solving the robustness control algorithm based on regularization in the frequency domain is 2.1 dB, while the minimum value of the range of the bright area region obtained by the sound field partition control method provided in the embodiment of this application is 1.4 dB, and the consistency at other frequency points is significantly better than that of the regularization method.

[0200] Figure 8 It is a schematic diagram of the sound energy contrast between the bright area region and the dark area region provided by the embodiment of this application Figure 2 , such as Figure 8 As shown, the curves of the sound energy contrast of each bright area region and dark area region are as follows: without robustness control 81, regularization method 82, and the method of this application 83. When the F-norm ‖ΔZ‖ of the transfer function error matrix ΔZ F Within the range from 0.25 - 0.75, the AC value of the ACC-LD method without robustness control shows an obvious downward trend, that is, it is more sensitive to errors. While the regularization method with robustness control and the method of this patent both have no obvious change trend, that is, they are less sensitive to errors. However, the overall AC of the method of this patent is higher than that of the traditional regularization method.

[0201] Figure 9 It is a schematic diagram of the range of the bright area region provided by the embodiment of this application Figure 2 , such as Figure 9 As shown, the curves of the range of each bright area region are as follows: without robustness control 91, regularization method 92, and the method of this application 93. When the F-norm ‖ΔZ‖ of the transfer function error matrix ΔZ F Within the range from 0.25 - 0.75, the ΔL value of the ACC-LD method without robustness control shows an obvious upward trend, that is, it is more sensitive to errors. While the regularization method with robustness control and the method of this patent both have no obvious change trend, that is, they are less sensitive to errors. However, the overall ΔL of the method of this patent is lower than that of the traditional regularization method.

[0202] In summary, when in the low-frequency range of 0 Hz - 300 Hz, with the main driving area as the bright area region and the remaining areas as the dark area regions, the sound energy contrast between the bright area region and the dark area region and the range of the bright area region provided by the sound field partition control method in the embodiment of this application are both greatly improved compared with the traditional regularization method.

[0203] Based on the above embodiments, the embodiment of this application further provides a sound field partition control device. This sound field partition control device is applied to a vehicle. The vehicle is provided with a speaker array, and the speaker array is used to form the sound field of the vehicle.Figure 10 This is a schematic diagram of the composition structure of a sound field zoning control device provided by an embodiment of the present application. As Figure 10 shown, the sound field zoning control device 1000 includes a determination unit 1001, a correction unit 1002, and an adjustment unit 1003, where:

[0204] The determination unit 1001 is configured to determine a bright area region and a dark area region in the sound field of the vehicle, and obtain a transfer function matrix corresponding to at least one bright area region and a transfer function matrix corresponding to at least one dark area region; wherein, the temperature and humidity corresponding to the transfer function matrix of each bright area region are different, and the temperature and humidity corresponding to the transfer function matrix of each dark area region are different; based on the transfer function matrix corresponding to the bright area region and the transfer function matrix corresponding to the dark area region, determine an error sample corresponding to the bright area region and an error sample corresponding to the dark area region;

[0205] The correction unit 1002 is configured to correct the correlation matrix corresponding to the bright area region and the correlation matrix corresponding to the dark area region based on the error sample corresponding to the bright area region and the error sample corresponding to the dark area region, to obtain a target bright area correlation matrix and a target dark area correlation matrix;

[0206] The adjustment unit 1003 is configured to establish a sound field zoning control model based on the target bright area correlation matrix and the target dark area correlation matrix, and adjust the driving signal of the speaker array based on the sound field zoning control model to perform zoning control on the sound field of the vehicle.

[0207] In some embodiments, the correction unit 1002 is further configured to determine an intermediate parameter corresponding to the bright area region and an intermediate parameter corresponding to the dark area region based on the error sample corresponding to the bright area region and the error sample corresponding to the dark area region; based on the intermediate parameter corresponding to the bright area region and the intermediate parameter corresponding to the dark area region, determine a parameter set corresponding to the bright area region and a parameter set corresponding to the dark area region; wherein, the parameter set includes a mean value, a covariance matrix, and a mixing coefficient; based on the parameter set corresponding to the bright area region and the parameter set corresponding to the dark area region, correct the correlation matrix corresponding to the bright area region and the correlation matrix corresponding to the dark area region.

[0208] In some embodiments, the correction unit 1002 is further configured to use the expectation-maximization algorithm to determine a probability density function corresponding to the bright area region and a probability density function corresponding to the dark area region based on the parameter set corresponding to the bright area region and the parameter set corresponding to the dark area region; based on the probability density function corresponding to the bright area region and the probability density function corresponding to the dark area region, correct the correlation matrix corresponding to the bright area region and the correlation matrix corresponding to the dark area region.

[0209] In some embodiments, the correction unit 1002 is further configured to determine the mathematical expectation of the error samples corresponding to the bright area region and the mathematical expectation of the error samples corresponding to the dark area region based on the probability density function corresponding to the bright area region and the probability density function corresponding to the dark area region; and correct the correlation matrix corresponding to the bright area region and the correlation matrix corresponding to the dark area region based on the mathematical expectation of the error samples corresponding to the bright area region and the mathematical expectation of the error samples corresponding to the dark area region.

[0210] In some embodiments, the vehicle is further provided with a microphone array, where the microphone array includes at least one microphone, and the adjustment unit 1003 is further configured to obtain the excitation signal of the speaker array, the number of microphones corresponding to the bright area region, and the number of microphones corresponding to the dark area region; and establish a sound field partition control model based on the target bright area correlation matrix, the target dark area correlation matrix, the excitation signal of the speaker array, the number of microphones corresponding to the bright area region, and the number of microphones corresponding to the dark area region.

[0211] In some embodiments, the determination unit 1001 is further configured to determine the ideal transfer function matrix of the bright area region from the transfer function matrix corresponding to the bright area region, and determine the ideal transfer function matrix of the dark area region from the transfer function matrix corresponding to the dark area region; determine the error matrix corresponding to the bright area region based on the first deviation information between the ideal transfer function matrix of the bright area region and the transfer function matrix corresponding to the bright area region; determine the error matrix corresponding to the dark area region based on the second deviation information between the ideal transfer function matrix of the dark area region and the transfer function matrix corresponding to the dark area region; and determine the error samples corresponding to the bright area region and the error samples corresponding to the dark area region based on the error matrix corresponding to the bright area region and the error matrix corresponding to the dark area region.

[0212] In some embodiments, the determination unit 1001 is further configured to perform vectorization processing on the error matrix corresponding to the bright area region and the error matrix corresponding to the dark area region respectively to obtain the error vector corresponding to the bright area region and the error vector corresponding to the dark area region; and determine the error samples corresponding to the bright area region and the error samples corresponding to the dark area region based on the error vector corresponding to the bright area region and the error vector corresponding to the dark area region.

[0213] In some embodiments, the determination unit 1001 is further configured to obtain a temperature parameter and a humidity parameter; where the temperature parameter includes a temperature range and a temperature acquisition interval, and the humidity parameter includes a humidity range and a humidity acquisition interval; and obtain the transfer function matrix corresponding to at least one bright area region and the transfer function matrix corresponding to at least one dark area region based on the temperature parameter and the humidity parameter.

[0214] The description of the above device embodiments is similar to that of the above method embodiments and has similar beneficial effects to those of the method embodiments. For the technical details not disclosed in the device embodiments of the present application, please refer to the description of the method embodiments of the present application for understanding.

[0215] It should be noted that in the embodiments of the present application, if the above method is implemented in the form of software function modules and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of the present application, in essence or the part that contributes to the related technology, can be embodied in the form of a software product. The software product is stored in a storage medium and includes several instructions for causing an electronic device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories, magnetic disks, or optical discs that can store program codes. In this way, the embodiments of the present application are not limited to any specific combination of hardware and software.

[0216] The present application also provides an electronic device, including a memory and a processor. The memory stores a computer program that can run on the processor, and when the processor executes the computer program, the above method is implemented.

[0217] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above method is implemented. The computer-readable storage medium can be transient or non-transient.

[0218] The present application also provides a computer program product, including a computer program or instruction. When the computer program or instruction is executed by a processor, some or all of the steps in the above method are implemented. The computer program product can be specifically implemented in a manner of hardware, software, or a combination thereof. The computer program product can be specifically implemented in a manner of hardware, software, or a combination thereof. In an optional embodiment, the computer program product is specifically embodied as a computer storage medium. In another optional embodiment, the computer program product is specifically embodied as a software product, such as a Software Development Kit (SDK), etc.

[0219] It should be noted that Figure 11 is a schematic diagram of the hardware entity of an electronic device provided by the embodiments of the present application. As Figure 11 shown, the hardware entity of the electronic device 1100 includes: a processor 1101, a communication interface 1102, and a memory 1103, where:

[0220] The processor 1101 generally controls the overall operation of the electronic device 1100.

[0221] The communication interface 1102 enables the electronic device 1100 to communicate with other terminals or servers via a network.

[0222] The memory 1103 is configured to store instructions and applications executable by the processor 1101, and can also cache data to be processed or already processed by the processor 1101 and each module in the electronic device 1100 (such as image data, audio data, voice communication data, and video communication data), and can be implemented by flash memory (FLASH) or RAM. Data transmission can be carried out between the processor 1101, the communication interface 1102, and the memory 1103 via the bus 1104.

[0223] Here, the electronic device can be a car machine in a vehicle.

[0224] It should be noted here that the descriptions of the above storage medium and device embodiments are similar to those of the above method embodiments and have beneficial effects similar to those of the method embodiments. For the technical details not disclosed in the storage medium and device embodiments of the present application, please refer to the descriptions of the method embodiments of the present application for understanding.

[0225] It should be understood that the "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures, or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, the appearances of "in one embodiment" or "in an embodiment" throughout the specification do not necessarily refer to the same embodiment. In addition, these specific features, structures, or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above steps / processes do not mean the order of execution is prior or subsequent. The order of execution of each step / process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application. The serial numbers of the embodiments of the present application are only for description and do not represent the superiority or inferiority of the embodiments.

[0226] It should be noted that in this article, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, the element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.

[0227] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the above units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed with each other can be through some interfaces. The indirect coupling or communication connection of devices or units can be electrical, mechanical, or other forms.

[0228] The units described above as separate components may or may not be physically separated. The components shown as units may or may not be physical units; they can be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0229] In addition, each functional unit in the embodiments of the present application can be all integrated in a processing unit, or each unit can be separately used as a unit, or two or more units can be integrated in one unit; the above integrated unit can be implemented in the form of hardware, or in the form of hardware plus software functional units.

[0230] Those of ordinary skill in the art can understand that all or part of the steps for implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above method embodiments; and the foregoing storage medium includes: removable storage devices, read-only memories, magnetic disks, or optical disks and other various media that can store program codes.

[0231] Alternatively, if the above integrated unit of the present application is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the related technology can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the various embodiments of the present application. And the foregoing storage medium includes: removable storage devices, ROMs, magnetic disks, or optical disks and other various media that can store program codes.

[0232] As described above, it is only the implementation mode of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application.

Claims

1. A method for controlling a sound field by partitioning, characterized in that: Applied in a vehicle, the vehicle is provided with a speaker array, the speaker array is used to form a sound field of the vehicle, the method comprises: Determine a bright area and a dark area in the sound field of the vehicle, and obtain a transfer function matrix corresponding to at least one of the bright areas and a transfer function matrix corresponding to at least one of the dark areas; wherein the temperature and humidity corresponding to the transfer function matrix of each of the bright areas are different, and the temperature and humidity corresponding to the transfer function matrix of each of the dark areas are different; Determine error samples corresponding to the bright area and error samples corresponding to the dark area based on a transfer function matrix corresponding to the bright area and a transfer function matrix corresponding to the dark area; Based on the error samples corresponding to the bright area and the error samples corresponding to the dark area, the correlation matrix corresponding to the bright area and the correlation matrix corresponding to the dark area are corrected to obtain a target bright area correlation matrix and a target dark area correlation matrix; A sound field partition control model is established based on the target bright area correlation matrix and the target dark area correlation matrix, and a driving signal of a speaker array is adjusted based on the sound field partition control model to perform partition control on the sound field of the vehicle.

2. The partition control method according to claim 1, characterized in that: The correcting the correlation matrix corresponding to the bright area and the correlation matrix corresponding to the dark area based on the error samples corresponding to the bright area and the error samples corresponding to the dark area includes: Determine an intermediate parameter corresponding to the bright area and an intermediate parameter corresponding to the dark area based on the error sample corresponding to the bright area and the error sample corresponding to the dark area; Based on the intermediate parameters corresponding to the bright area and the intermediate parameters corresponding to the dark area, determine a parameter set corresponding to the bright area and a parameter set corresponding to the dark area; wherein the parameter set includes a mean, a covariance matrix, and a mixing coefficient; Based on the parameter set corresponding to the bright area and the parameter set corresponding to the dark area, the correlation matrix corresponding to the bright area and the correlation matrix corresponding to the dark area are modified.

3. The partition control method according to claim 2, characterized in that: The modifying of the correlation matrix corresponding to the bright area and the correlation matrix corresponding to the dark area based on the parameter set corresponding to the bright area and the parameter set corresponding to the dark area comprises: Determine a probability density function corresponding to the bright area and a probability density function corresponding to the dark area by using a maximum expectation algorithm based on a parameter set corresponding to the bright area and a parameter set corresponding to the dark area; Based on the probability density function corresponding to the bright area and the probability density function corresponding to the dark area, the correlation matrix corresponding to the bright area and the correlation matrix corresponding to the dark area are modified.

4. The partition control method according to claim 3, characterized in that: The modifying of the correlation matrix corresponding to the bright area and the correlation matrix corresponding to the dark area based on the probability density function corresponding to the bright area and the probability density function corresponding to the dark area comprises: Determine, based on the probability density function corresponding to the bright area and the probability density function corresponding to the dark area, a mathematical expectation corresponding to the error samples of the bright area and a mathematical expectation corresponding to the error samples of the dark area; Based on the mathematical expectation corresponding to the error samples of the bright area and the mathematical expectation corresponding to the error samples of the dark area, the correlation matrix corresponding to the bright area and the correlation matrix corresponding to the dark area are corrected.

5. The partition control method according to claim 1, characterized in that: The vehicle is further provided with a microphone array, wherein the microphone array includes at least one microphone, and the sound field partition control model is established based on the target bright area correlation matrix and the target dark area correlation matrix, including: Obtaining an excitation signal of a speaker array, the number of microphones corresponding to the bright area, and the number of microphones corresponding to the dark area; The sound field partition control model is established based on the target bright area correlation matrix, the target dark area correlation matrix, the excitation signal of the speaker array, the number of microphones corresponding to the bright area, and the number of microphones corresponding to the dark area.

6. The partition control method according to claim 1, characterized in that: The determining, based on the transfer function matrix corresponding to the bright area and the transfer function matrix corresponding to the dark area, the error samples corresponding to the bright area and the error samples corresponding to the dark area, comprises: Determine an ideal transfer function matrix for a bright area from the transfer function matrix corresponding to the bright area, and determine an ideal transfer function matrix for a dark area from the transfer function matrix corresponding to the dark area; Determine an error matrix corresponding to the bright area based on first deviation information between the bright area ideal transfer function matrix and the transfer function matrix corresponding to the bright area; Determine an error matrix corresponding to the dark area based on second deviation information between the dark area ideal transfer function matrix and the transfer function matrix corresponding to the dark area; Based on the error matrix corresponding to the bright area and the error matrix corresponding to the dark area, an error sample corresponding to the bright area and an error sample corresponding to the dark area are determined.

7. The partition control method according to claim 6, characterized in that: The determining, based on the error matrix corresponding to the bright area and the error matrix corresponding to the dark area, the error samples corresponding to the bright area and the error samples corresponding to the dark area, comprises: Performing vectorization processing on the error matrix corresponding to the bright area and the error matrix corresponding to the dark area respectively, to obtain an error vector corresponding to the bright area and an error vector corresponding to the dark area; Based on the error vector corresponding to the bright area and the error vector corresponding to the dark area, an error sample corresponding to the bright area and an error sample corresponding to the dark area are determined.

8. The partition control method according to any one of claims 1 to 7, characterized in that: The obtaining of a transfer function matrix corresponding to at least one of the bright areas and a transfer function matrix corresponding to at least one of the dark areas comprises: Acquire temperature parameters and humidity parameters; wherein the temperature parameters include a temperature range and a temperature acquisition interval, and the humidity parameters include a humidity range and a humidity acquisition interval; Based on the temperature parameter and the humidity parameter, a transfer function matrix corresponding to at least one of the bright areas and a transfer function matrix corresponding to at least one of the dark areas are obtained.

9. A sound field partition control device, characterized in that: Applied in a vehicle, the vehicle is provided with a speaker array, the speaker array is used to form a sound field of the vehicle, and the device comprises: A determination unit is used to determine a bright area and a dark area in the sound field of the vehicle, and obtain a transfer function matrix corresponding to at least one of the bright areas and a transfer function matrix corresponding to at least one of the dark areas; wherein the temperature and humidity corresponding to each transfer function matrix of the bright area are different, and the temperature and humidity corresponding to each transfer function matrix of the dark area are different; based on the transfer function matrix corresponding to the bright area and the transfer function matrix corresponding to the dark area, determine an error sample corresponding to the bright area and an error sample corresponding to the dark area; a correction unit, configured to correct the correlation matrix corresponding to the bright area and the correlation matrix corresponding to the dark area based on the error samples corresponding to the bright area and the error samples corresponding to the dark area, so as to obtain a target bright area correlation matrix and a target dark area correlation matrix; An adjustment unit is used to establish a sound field partition control model based on the target bright area correlation matrix and the target dark area correlation matrix, and adjust the driving signal of the speaker array based on the sound field partition control model to perform partition control on the sound field of the vehicle.

10. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program executable on the processor, and the processor implements the method according to any one of claims 1 to 8 when executing the computer program.

11. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the method described in any one of claims 1 to 8 is implemented.

12. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps in the method according to any one of claims 1 to 8 are implemented.

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