Sound field partition control method and device, and electronic equipment

Abstract

The application relates to a sound field partition control method and device and electronic equipment, the method is applied to a vehicle, comprising: determining a bright area and a dark area in the sound field of the vehicle, and obtaining a transfer function matrix corresponding to the bright area and a transfer function matrix corresponding to the dark area; based on the transfer function matrix corresponding to the bright area and the transfer function matrix corresponding to the dark area, a sound field partition control model is established; determining an optimal solution set corresponding to a multi-objective optimization problem, and based on the optimal solution set corresponding to the multi-objective optimization problem, determining the target order of the filter; based on the filter under the target order, determining a driving signal, and outputting the driving signal to the loudspeaker array of the vehicle to control the sound field of the vehicle. Since the target order of the filter is related to the multi-objective problem, the order of the filter is reduced, and the calculation resources and storage costs of the sound field partition control are reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of sound field, and particularly relates to a sound field partition control method and device and electronic equipment. BACKGROUND

[0002] In the related art, the sound field partition control is usually realized by using a pressure matching (PM) algorithm and an acoustic contrast control (ACC) algorithm in combination with a low-order finite impulse response (FIR) filter. After the FIR filter is designed, the PM algorithm and the ACC algorithm are used to generate a driving signal of a loudspeaker array through the FIR filter, so as to form a desired sound field in a specific area. However, in the above process, the order of the FIR filter is too high, and there are problems of high calculation resource and high storage cost. SUMMARY

[0003] The present application provides a sound field partition control method, device and electronic equipment to solve the problems of high calculation resource and high storage cost in the sound field partition control of a vehicle in the related art.

[0004] To achieve the above object, the present application provides a sound field partition control method applied to a vehicle, wherein the vehicle is provided with a loudspeaker array used to form a sound field of the vehicle, and the method comprises the following steps:

[0005] determining a bright zone area and a dark zone area in the sound field of the vehicle, and obtaining a transfer function matrix corresponding to the bright zone area and a transfer function matrix corresponding to the dark zone area;

[0006] establishing a sound field partition control model based on the transfer function matrix corresponding to the bright zone area and the transfer function matrix corresponding to the dark zone area, wherein the sound field partition control model comprises a multi-objective optimization problem;

[0007] determining an optimal solution set corresponding to the multi-objective optimization problem, and determining a target order of a filter based on the optimal solution set corresponding to the multi-objective optimization problem;

[0008] determining a driving signal based on the filter under the target order, and outputting the driving signal to the loudspeaker array of the vehicle to control the sound field of the vehicle.

[0009] According to the above-mentioned technical means, on the one hand, a zoning control model of the sound field is established by obtaining the transfer function matrix corresponding to the bright area and the transfer function matrix corresponding to the dark area. Since the zoning control model of the sound field established in this application takes into account multiple different objectives that need to be optimized, the accuracy of the zoning control of the sound field is improved; on the other hand, the target order of the filter is determined by the optimal solution set corresponding to the multi-objective optimization problem in the zoning control model of the sound field. Since the target order of the filter is related to the multi-objective optimization problem, the order of the filter is reduced while realizing the zoning control of the sound field. Furthermore, since the order of the filter is reduced, the computing resources of the zoning control of the sound field are reduced, and the storage cost is saved.

[0010] Furthermore, the multi-objective optimization problem includes a first objective function, a second objective function and a third objective function, and establishes a zoning control model of the sound field based on the transfer function matrix corresponding to the bright area and the transfer function matrix corresponding to the dark area, including: determining the first objective function, the second objective function and the third objective function based on the transfer function matrix corresponding to the bright area and the transfer function matrix corresponding to the dark area; wherein the first objective function and the second objective function are used to realize the zoning control of the sound field, and the third objective function is used to reduce the order of the filter; based on the first objective function, the second objective function and the third objective function, establish a zoning control model of the sound field.

[0011] According to the above technical means, a partition control model of the sound field is established through the first objective function, the second objective function and the third objective function, so that the partition control model of the sound field can realize the partition control of the sound field while reducing the order of the filter, thereby improving the flexibility of the partition control method of the sound field.

[0012] Furthermore, based on the transfer function matrix corresponding to the bright area and the transfer function matrix corresponding to the dark area, the first objective function, the second objective function and the third objective function are determined, including: obtaining the number of microphones in the bright area, the expected sound field in the bright area, the number of microphones in the dark area, the number of control frequency points and the weight vector of the control frequency points; determining the first objective function based on the diagonal matrix of the transfer function matrix corresponding to the bright area, the expected sound field in the bright area, the number of control frequency points and the number of microphones in the bright area; determining the second objective function based on the diagonal matrix of the transfer function matrix corresponding to the dark area, the number of control frequency points and the number of microphones in the dark area; and determining the third objective function based on the number of control frequency points and the weight vector of the control frequency points.

[0013] According to the above technical means, by acquiring the number of microphones of the bright zone area, the expected sound field of the bright zone area, the number of microphones of the dark zone area, the number of control frequency points, and the weight vector of the control frequency points, and combining the transfer function matrix corresponding to the bright zone area and the transfer function matrix corresponding to the dark zone area, the first target function, the second target function, and the third target function are determined, thereby improving the accuracy of the first target function, the second target function, and the third target function.

[0014] Further, the method further comprises: determining a single-objective problem corresponding to the multi-objective optimization problem, and converting the single-objective problem into an equality constraint optimization problem by using an alternating direction multiplier method; decomposing the equality constraint optimization problem into at least one sub-problem based on a Lagrange multiplier and a Lagrange variable, and iteratively solving the at least one sub-problem to obtain a solution set of the at least one sub-problem; and determining an optimal solution set corresponding to the multi-objective optimization problem, including: determining the optimal solution set corresponding to the multi-objective optimization problem based on the solution set of the at least one sub-problem.

[0015] According to the above technical means, by determining the equality constraint optimization problem corresponding to the multi-objective optimization problem, using the Lagrange multiplier and the Lagrange variable of the alternating direction multiplier method to iteratively solve the at least one sub-problem to obtain the solution set of the at least one sub-problem, and then determining the optimal solution set corresponding to the multi-objective optimization problem, the solving process of the multi-objective optimization problem is simplified, and the accuracy of the optimal solution set corresponding to the multi-objective optimization problem is improved.

[0016] Further, based on the optimal solution set corresponding to the multi-objective optimization problem, the target order of the filter is determined, including: projecting the optimal solution set corresponding to the multi-objective optimization problem into a related index to obtain a Pareto-like front corresponding to the optimal solution set; wherein the related index includes at least one of the following: a sound energy contrast between the bright zone area and the dark zone area, a sound field reconstruction normalization error of the bright zone area, and a mean value of at least one filter order; and determining the target order of the filter based on the Pareto-like front corresponding to the optimal solution set.

[0017] According to the above technical means, by projecting the optimal solution set corresponding to the multi-objective optimization problem into a related index to obtain a Pareto-like front corresponding to the optimal solution set, and then determining the target order of the filter according to the Pareto-like front corresponding to the optimal solution set, since different related indexes correspond to different measurement standards, the target order of the filter can be determined from multiple dimensions, thereby improving the accuracy of the target order of the filter.

[0018] Further, based on the Pareto-like front corresponding to the optimal solution set, the target order of the filter is determined, including: acquiring an actual demand, and determining an ideal solution from the optimal solution set based on the actual demand and the Pareto-like front corresponding to the optimal solution set; and determining the target order of the filter based on the ideal solution.

[0019] According to the above technical means, the ideal solution is determined from the optimal solution set through the Pareto-like frontier corresponding to the actual demand and the optimal solution set, and then the target order of the filter is determined according to the ideal solution, thereby improving the accuracy of the target order of the filter, and at the same time, the user can flexibly set the partition control effect of the sound field according to the actual demand, thereby improving the user experience.

[0020] Further, in the case where the related indicators include at least one filter order, the method further includes: obtaining at least one untruncated filter coefficient, determining the total energy corresponding to the at least one untruncated filter coefficient; obtaining a preset energy percentage, and performing truncation processing on the total energy of the at least one untruncated filter coefficient according to the preset energy percentage to obtain at least one truncated filter coefficient; and determining the at least one filter order based on the at least one truncated filter coefficient.

[0021] According to the above technical means, the total energy corresponding to the at least one untruncated filter coefficient is truncated according to the preset energy percentage to obtain at least one truncated filter coefficient, and then the at least one filter order is determined through the at least one truncated filter coefficient, thereby improving the accuracy of the at least one filter order.

[0022] Further, the vehicle is also provided with a microphone array, and the transfer function matrix corresponding to the bright zone region and the transfer function matrix corresponding to the dark zone region are obtained, including: obtaining the collection signal of the microphone array and the driving signal of the loudspeaker array; based on the driving signal of the loudspeaker array and the collection signal of the microphone array, the transfer function matrix corresponding to the bright zone region and the transfer function matrix corresponding to the dark zone region are obtained.

[0023] According to the above technical means, the transfer function matrix corresponding to the bright zone region and the transfer function matrix corresponding to the dark zone region are obtained through the driving signal of the loudspeaker array and the collection signal of the microphone array, thereby improving the accuracy of the transfer function matrix corresponding to the bright zone region and the transfer function matrix corresponding to the dark zone region.

[0024] A partition control device of a sound field is applied to a vehicle, and the vehicle is provided with a loudspeaker array for forming a sound field of the vehicle, and the device comprises:

[0025] A determination unit is configured to determine a bright zone region and a dark zone region in the sound field of the vehicle, and obtain a transfer function matrix corresponding to the bright zone region and a transfer function matrix corresponding to the dark zone region;

[0026] A building unit is configured to build a partition control model of the sound field based on the transfer function matrix corresponding to the bright zone region and the transfer function matrix corresponding to the dark zone region; wherein the partition control model of the sound field comprises a multi-objective optimization problem;

[0027] The determining unit is further configured to determine a set of optimal solutions corresponding to the multi-objective optimization problem, and determine the target order of the filter based on the set of optimal solutions corresponding to the multi-objective optimization problem; and determine the driving signal based on the filter with the target order, and output the driving signal to the loudspeaker array of the vehicle to perform the zoned control on the sound field of the vehicle.

[0028] An electronic device includes a processor and a memory storing a computer program executable on the processor, and the processor implements any of the above methods when executing the computer program.

[0029] The beneficial effects of the present application are as follows:

[0030] (1) The target order of the filter is determined based on the set of optimal solutions corresponding to the multi-objective optimization problem in the determined zoned control model of the sound field. Since the target order of the filter is related to the multi-objective optimization problem, the order of the filter is reduced while the zoned control of the sound field is achieved.

[0031] (2) The zoned control model of the sound field of the present application considers different objectives, improves the accuracy of the zoned control of the sound field, and reduces the order of the filter, thereby reducing the computational resources and storage costs of the zoned control of the sound field.

[0032] (3) The zoned control model of the sound field is established by the first objective function, the second objective function, and the third objective function, so that the zoned control model of the sound field can achieve the zoned control of the sound field while reducing the order of the filter, thereby improving the flexibility of the zoned control method of the sound field.

[0033] (4) The Lagrange multipliers and the Lagrange variables of the alternating direction multiplier method are used to iteratively solve at least one sub-problem to obtain a solution set of the at least one sub-problem, and then determine the set of optimal solutions corresponding to the multi-objective optimization problem, thereby simplifying the solution process of the multi-objective optimization problem and improving the accuracy of the set of optimal solutions corresponding to the multi-objective optimization problem.

[0034] (5) The ideal solution is determined from the set of optimal solutions by the actual demand and the Pareto-like frontier corresponding to the set of optimal solutions, and then the target order of the filter is determined according to the ideal solution, thereby improving the accuracy of the target order of the filter, and allowing the user to flexibly set the zoned control effect of the sound field according to the actual demand, thereby improving the user experience. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 An implementation flowchart of a zoned control method of a sound field according to an embodiment of the present application Figure One ;

[0036] Figure 2A schematic diagram of a loudspeaker array provided for an embodiment of the present application;

[0037] Figure 3 A schematic diagram of a microphone array provided for an embodiment of the present application;

[0038] Figure 4 An implementation flowchart of a partition control method of a sound field provided for an embodiment of the present application Figure Two ;

[0039] Figure 5 A schematic diagram of a contrast of a bright zone region and a dark zone region provided for an embodiment of the present application;

[0040] Figure 6 A schematic diagram of a reconstruction normalization error of a bright zone sound field provided for an embodiment of the present application;

[0041] Figure 7 A schematic diagram of a filter order provided for an embodiment of the present application;

[0042] Figure 8 A component structure schematic diagram of a partition control system of a sound field provided for an embodiment of the present application;

[0043] Figure 9 A component structure schematic diagram of a partition control device of a sound field provided for an embodiment of the present application;

[0044] Figure 10 A hardware entity schematic diagram of an electronic device provided for an embodiment of the present application. DETAILED DESCRIPTION

[0045] Other advantages and effects of the present application can be easily understood by those skilled in the art from the above description of the embodiments of the present application. The present application can also be implemented or applied through other different specific embodiments, and the details in the present specification can be modified or changed based on different views 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 are not intended to limit the protection scope of the present application.

[0046] It should be noted that the diagrams provided in the following embodiments only schematically illustrate the basic concept of the present application, and only show the components related to the present application in the diagrams, but are not drawn according to the number, shape and size of the components in actual implementation. The type, number and proportion of the components in actual implementation can be arbitrarily changed, and the component layout type can also be more complex.

[0047] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but which can be understood as a description of all possible embodiments, or as a description of a different subset of all possible embodiments, and which can be combined, mutatis mutandis, with each other.

[0048] In the following description, the terms "first", "second", "third", etc. are merely used to distinguish similar objects, and do not represent a specific order or sequence for the objects. It can be understood that the "first", "second", "third", etc. can be interchanged in a specific order or sequence as allowed, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only, and is not intended to limit the present application.

[0050] The application of sound field partition control technology in the automotive intelligent cockpit audio system has attracted widespread attention. Passengers have individualized needs for the acoustic environment in the cockpit, and passengers in different seats expect to have different and non-interfering sound fields. Sound field partition control technology generates the driving signal of the loudspeaker array by designing a filter to form the desired sound field in a specific area, thereby controlling the sound field in the partition. Under the premise of meeting the partition control requirements of the sound field, the computing resources and storage costs of the sound field partition control system in the cockpit need to be reduced. The order represents the number of electronic element delays in the filter. The lower the order, the fewer the number of electronic elements in the filter, the lower the manufacturing cost, and the FIR filter can well reduce the complexity and cost of the sound field partition control system.

[0051] In the related art, the primary algorithms used for zoning sound fields are the PM algorithm and the ACC algorithm. Other algorithms are improvements upon the PM and ACC algorithms. The PM algorithm aims to accurately synthesize the desired sound field, setting the desired sound field in the dark zone to zero and reconstructing the desired sound field in both the bright and dark zones. The PM algorithm minimizes the error between the desired sound field and the reconstructed sound field to obtain a weight vector for the speaker array. The ACC algorithm aims to maximize the acoustic energy contrast between the bright and dark zones, optimizing the resulting weight vector for the speaker array to ensure that the sound signal propagates directly into the bright zone and minimizes sound leakage into the dark zone. It is worth noting that the drive signal for the speaker array is obtained by convolving an FIR filter with the input signal to be played. Using a higher-order FIR filter increases the computational and storage costs of the control system and also results in higher latency in the sound field zoning control system. However, when using the PM and ACC algorithms, the FIR filter order is too high, resulting in issues such as high computational resources and storage costs.

[0052] The embodiment of the present application provides a method for zoning control of a sound field. On the one hand, a zoning control model of the sound field is established by obtaining the transfer function matrix corresponding to the bright area and the transfer function matrix corresponding to the dark area. Since the zoning control model of the sound field established in the present application takes into account multiple different objectives that need to be optimized, the accuracy of the zoning control of the sound field is improved. On the other hand, the target order of the filter is determined by determining the optimal solution set corresponding to the multi-objective optimization problem in the zoning control model of the sound field. Since the target order of the filter is related to the multi-objective optimization problem, while achieving the zoning control of the sound field, the order of the filter is also reduced. Furthermore, since the order of the filter is reduced, the computing resources for the zoning control of the sound field are reduced, saving storage costs. The method provided in the embodiment of the present application can be executed by an electronic device, and the electronic device can be a vehicle computer in a vehicle.

[0053] Below, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the drawings in the embodiments of the present application.

[0054] Figure 1 A schematic diagram of the implementation process of a sound field partition control method provided in an embodiment of the present application Figure One ,like Figure 1 As shown, the method is applied to a vehicle, the vehicle is provided with a speaker array, and the speaker array is used to form a sound field of the vehicle. The method includes steps S101 to S104, wherein:

[0055] Step S101 : determining a bright area and a dark area in a sound field of a vehicle, and obtaining a transfer function matrix corresponding to the bright area and a transfer function matrix corresponding to the dark area.

[0056] Here, the sound field refers to the area within a medium where sound waves exist. A bright area refers to the area within the vehicle where a sound field is desired. This area can be any suitable area, such as the front passenger area or the front passenger seat. A dark area refers to the area within the vehicle where a sound field is undesirable. This area can be any suitable area, such as the rear passenger area or the front passenger seat.

[0057] A speaker array is an array of at least one speaker. The number of speakers in the speaker array can be any suitable size, for example, 10, 12, etc. In some embodiments, the speakers in the speaker array can be located in any suitable location, for example, on the vehicle door, on the roof, etc.

[0058] Figure 2 A schematic diagram of a speaker array provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the speaker array is divided into speakers at the door and speakers 22 at the roof, wherein the speakers at the door include 4 speakers, namely: a first speaker 211, a second speaker 212, a third speaker 213 and a fourth speaker 214, and the speakers 22 at the roof include 8 speakers.

[0059] In some embodiments, the speaker array generates a corresponding sound field upon receiving a driving signal. The area covered by this sound field is referred to as the vehicle's control area. The control area can be divided into areas where the sound field is desired and areas where the sound field is not desired, thereby defining bright and dark areas within the vehicle's sound field.

[0060] 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 transfer function matrix includes at least one speaker-to-microphone transfer function.

[0061] The method for obtaining the transfer function matrix corresponding to the bright zone region and the transfer function matrix corresponding to the dark zone region can include, but is not limited to, reading the transfer function matrix corresponding to the bright zone region and the transfer function matrix corresponding to the dark zone region from the data stored in the memory, calculating the transfer function matrix corresponding to the bright zone region and the transfer function matrix corresponding to the dark zone region through an acoustic model, and the like. For example, the memory can include, but is not limited to, a Read One Memory (ROM), a Random Access Memory (RAM), and the like. By reading the data in the memory, the transfer function matrix corresponding to the bright zone region and the transfer function matrix corresponding to the dark zone region can be obtained. For another example, the acoustic model is one of the most important parts in a speech recognition system. By establishing the geometric structure of the acoustic model, using methods such as Finite Element Analysis (FEA), Boundary Element Method (BEM), and Statistical Energy Analysis (SEA), the transfer function matrix corresponding to the bright zone region and the transfer function matrix corresponding to the dark zone region can be obtained. The geometric structure of the acoustic model can include, but is not limited to, a sound source, a sound transmission medium, a receiving point, and the like.

[0062] In some embodiments, the vehicle is further provided with a microphone array. The transfer function matrix corresponding to the bright zone region and the transfer function matrix corresponding to the dark zone region can be obtained by acquiring the collection signal of the microphone array and the driving signal of the loudspeaker array, and then based on the driving signal of the loudspeaker array and the collection signal of the microphone array.

[0063] In step S102, a partition control model of the sound field is established based on the transfer function matrix corresponding to the bright zone region and the transfer function matrix corresponding to the dark zone region. The partition control model of the sound field includes a multi-objective optimization problem.

[0064] Here, the partition control model of the sound field is a model for implementing the partition control of the sound field and evaluating the performance of the partition control of the sound field. The multi-objective optimization problem refers to a problem that needs to minimize or maximize two or more objective functions at the same time. In the multi-objective optimization problem, there is usually no single solution that can optimally satisfy all objective functions at the same time, but there is a series of solutions that provide different trade-offs between the objective functions.

[0065] In some embodiments, the multi-objective optimization problem can include at least one objective function. The objectives corresponding to different objective functions can be the same or different. The objectives can include, but are not limited to, implementing the partition control of the sound field, reducing the filter order, and the like.

[0066] In some embodiments, the partition control model of the sound field can be established by elements in the transfer function matrix corresponding to the bright zone region and the transfer function matrix corresponding to the dark zone region. For example, a diagonal matrix of the transfer function matrix corresponding to the bright zone region and a diagonal matrix of the transfer function matrix corresponding to the dark zone region can be read, and then the partition control model of the sound field can be established by the diagonal matrix of the transfer function matrix corresponding to the bright zone region and the diagonal matrix of the transfer function matrix corresponding to the dark zone region.

[0067] In some embodiments, the multi-objective optimization problem includes a first objective function, a second objective function, and a third objective function. The first objective function, the second objective function, and the third objective function can be determined based on the transfer function matrix corresponding to the bright zone region and the transfer function matrix corresponding to the dark zone region, and then the partition control model of the sound field can be established based on the first objective function, the second objective function, and the third objective function. The first objective function and the second objective function are used to achieve the partition control of the sound field, and the third objective function is used to reduce the order of the filter.

[0068] In step S103, the optimal solution set corresponding to the multi-objective optimization problem is determined, and the target order of the filter is determined based on the optimal solution set corresponding to the multi-objective optimization problem.

[0069] Here, the optimal solution set includes at least one optimal solution. In some embodiments, the optimal solution set corresponding to the multi-objective optimization problem can be an effective solution of the partition control model of the sound field. The effective solution refers to a solution that can meet the requirements of the partition control model of the sound field.

[0070] The method for determining the optimal solution set corresponding to the multi-objective optimization problem can include but is not limited to: determining the optimal solution set corresponding to the multi-objective optimization problem by an Elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II) with an elitist strategy, determining the optimal solution set corresponding to the multi-objective optimization problem by a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, etc. For example, the NSGA-II is a genetic algorithm based on the Pareto optimal concept, which improves the operation speed and robustness of the algorithm through non-dominated sorting, crowding degree, and elitist strategy, and can be used to determine the optimal solution set corresponding to the multi-objective optimization problem. For another example, the MOPSO is an optimization method that extends the Particle Swarm Optimization (PSO) optimization method and is specifically used to solve multi-objective optimization problems. The MOPSO determines the optimal solution set corresponding to the multi-objective optimization problem by simulating the foraging behavior of bird or fish groups and combining the special requirements of multi-objective optimization.

[0071] In some embodiments, a single-objective problem corresponding to the multi-objective optimization problem can be determined first, and the single-objective problem corresponding to the multi-objective optimization problem can be converted into an equality constraint optimization problem by using an Alternating Direction Method of Multipliers (ADMM). Then, the equality constraint optimization problem can be decomposed into at least one sub-problem based on a Lagrange multiplier and a Lagrange variable, and the at least one sub-problem can be solved iteratively to obtain a solution set of the at least one sub-problem. Finally, an optimal solution set corresponding to the multi-objective optimization problem can be determined based on the solution set of the at least one sub-problem.

[0072] A filter is a frequency-selective device that allows certain frequency components of a signal to pass through while greatly attenuating other frequency components. The filter can include, but is not limited to, a FIR filter, an Infinite Impulse Response (IIR) filter, and the like. The FIR filter, also known as a non-recursive filter, is the most basic element in a digital signal processing system, which can have a strict linear phase-frequency characteristic while ensuring any amplitude-frequency characteristic. The IIR filter, also known as a recursive filter, has a feedback loop in structure, with historical output participating in feedback, which can achieve better filtering effect. The target order of the filter can be any suitable size, for example, 2022, 2053, and the like.

[0073] In some embodiments, different optimal solutions have corresponding filter orders, and the filter orders corresponding to different optimal solutions can be the same or different. After determining the optimal solution set corresponding to the multi-objective optimization problem, at least one optimal solution in the optimal solution set can be determined to correspond to a filter order, and at least one filter order can be obtained. The filter order with the smallest size is selected from the at least one filter order, and the filter order is used as the target order of the filter.

[0074] In some embodiments, the optimal solution set corresponding to the multi-objective optimization problem can be projected into relevant indicators to obtain a Pareto-like frontier corresponding to the optimal solution set, and then the target order of the filter can be determined based on the Pareto-like frontier corresponding to the optimal solution set. The relevant indicators include at least one of the following: an acoustic energy contrast between the bright region and the dark region, a sound field reconstruction normalized error of the bright region, and a mean value of the at least one filter order.

[0075] In step S104, a driving signal is determined based on the filter with the target order, and the driving signal is output to a loudspeaker array of the vehicle to control the sound field of the vehicle.

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

[0077] In some embodiments, the signal type of the signal output by the filter is a digital signal, and the speaker can only process signals of which the signal type is an analog signal. After determining the target order of the filter, the processed digital signal can be obtained by filtering the signal through the filter with the target order according to the zoned control requirements of the sound field in the bright zone region and the dark zone region, so as to remove the unnecessary frequency components or adjust the phase and amplitude of the signal. Then, the processed digital signal is converted into an analog signal through a digital-to-analog converter (also known as a D / A converter), and the analog signal is used as the driving signal.

[0078] In some embodiments, the filter can be connected to the input end of the speaker through an audio line, and then the driving signal can be output to the speaker array of the vehicle through the audio line to perform zoned control of the sound field of the vehicle. The audio line can include, but is not limited to, an RCA (Radio Corporation of America) connector, a Cannon X Series (Latch Rubber, XLR) connector, etc. The RCA connector uses a coaxial transmission signal, with the central axis used for signal transmission and the contact layer on the outer edge used for grounding. The XLR connector is used to transmit various audio signals in the audio system, and the balanced input and output terminals are generally connected using the XLR connector.

[0079] In the embodiments of the present application, on the one hand, the zoned control model of the sound field is established by the obtained transfer function matrix corresponding to the bright zone region and the transfer function matrix corresponding to the dark zone region. Since the zoned control model of the sound field of the present application takes into account multiple different targets to be optimized, the accuracy of the zoned control of the sound field is improved. On the other hand, the target order of the filter is determined by the optimal solution set corresponding to the multi-objective optimization problem in the determined zoned control model of the sound field. Since the target order of the filter is related to the multi-objective optimization problem, the order of the filter is reduced while achieving zoned control of the sound field. Further, since the order of the filter is reduced, the computing resources for zoned control of the sound field are reduced, and the storage cost is saved.

[0080] In some embodiments, the vehicle is also provided with a microphone array, and the "obtaining the transfer function matrix corresponding to the bright zone region and the transfer function matrix corresponding to the dark zone region" in step S101 includes steps S113 and S114, wherein:

[0081] Step S113, obtaining the collection signal of the microphone array and the driving signal of the loudspeaker array.

[0082] Here, 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 can be provided in the vehicle. The microphones in the microphone array can be located at any suitable position, for example, at the vehicle door, at the vehicle roof, etc.

[0083] Figure 3 A schematic diagram of a microphone array provided by an embodiment of the present application is shown in FIG. 3. As shown in FIG. 3, two microphone arrays are provided at the roof of the vehicle, wherein the first microphone array 31 is located at the front row area of the vehicle and includes 12 microphones, and the second microphone array 32 is located at the rear row area of the vehicle and includes 12 microphones. Figure 3

[0084] The collection signal of the microphone array refers to the sound signal captured by the microphone array. In some embodiments, after the loudspeaker array sounds according to the input driving signal, the sound emitted by the loudspeaker array can be collected by the microphone array to obtain the collection signal of the microphone array.

[0085] In some embodiments, the collection signal of the microphone array and the driving signal of the loudspeaker array can be obtained by reading the parameter information of the microphone array and the loudspeaker array.

[0086] Step S114, obtaining the transfer function matrix corresponding to the bright zone area and the transfer function matrix corresponding to the dark zone area based on the driving signal of the loudspeaker array and the collection signal of the microphone array.

[0087] Here, the cross power spectral density between the driving signal of the loudspeaker array and the collection signal of the microphone array can be determined, and then the transfer function matrix corresponding to the bright zone area and the transfer function matrix corresponding to the dark zone area can be obtained according to the cross power spectral density. 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 Transformation (FFT) method can be used to calculate the cross power spectral density of two signals.

[0088] ​In some embodiments, a white noise input signal can be given to the loudspeaker array, a self-power spectral density corresponding to the white noise input signal is obtained, and then a transfer function matrix corresponding to the bright zone region and a transfer function matrix corresponding to the dark zone region are obtained according to the cross-power spectral density between the driving signal of the loudspeaker array and the collected signal of the microphone array and the self-power spectral density corresponding to the white noise input signal.

[0089] In some embodiments, the transfer function matrix G i (f) can be determined by the following formula (1-1), that is, refer to formula (1-1):

[0090]

[0091] In formula (1-1), is the transfer function from the lth loudspeaker to the mth microphone in the ith control region, is the sound signal collected by the mth microphone in the ith control region and the cross-power spectral density of the lth loudspeaker excitation signal x l (t), x l (f) (l = 1,..., L) is the self-power spectral density corresponding to the white noise input signal x l (t) (l = 1,..., L) given to the loudspeaker array, G 1 (f) and G 2 (f) are the transfer function matrix of the front row region and the transfer function matrix of the back row region respectively, M is the number of microphones, and L is the number of loudspeakers.

[0092] In some embodiments, the transfer function matrix G i (f) obtained according to formula (1-1) satisfies wherein, means the complex field, and M x L represents the matrix dimension; represents a set of M x L-dimensional complex matrices.

[0093] In some embodiments, the front row region can be regarded as the bright zone region, and the back row region can be regarded as the dark zone region, so the transfer function matrix of the front row region is the transfer function matrix corresponding to the bright zone region, and the transfer function matrix of the back row region is the transfer function matrix corresponding to the dark zone region. Correspondingly, G B (f) = G 1 (f), G D (f) = G 2 (f). Wherein, G B (f) is the transfer function matrix corresponding to the bright zone region, and G D (f) is the transfer function matrix corresponding to the dark zone region.

[0094] In the embodiments of the present application, the transfer function matrix corresponding to the bright zone region and the transfer function matrix corresponding to the dark zone region are obtained through the driving signal of the loudspeaker array and the collection signal of the microphone array, and the accuracy of the transfer function matrix corresponding to the bright zone region and the transfer function matrix corresponding to the dark zone region is improved.

[0095] In some embodiments, the multi-objective optimization problem includes a first objective function, a second objective function and a third objective function, and step S102 includes step S121 and step S122, wherein:

[0096] Step S121, determining the first objective function, the second objective function and the third objective function based on the transfer function matrix corresponding to the bright zone region and the transfer function matrix corresponding to the dark zone region; wherein the first objective function and the second objective function are used to realize the partition control of the sound field, and the third objective function is used to reduce the order of the filter.

[0097] Here, the first objective function is used to measure the difference between the reconstructed sound field of the bright zone region and the expected sound field of the bright zone region, and the second objective function is used to measure the quality of the dark zone reconstructed sound field, so the first objective function and the second objective function can be used to realize the partition control of the sound field. The third objective function is used to measure the flatness of the loudspeaker output signal, that is, the uniformity of the signal in the frequency spectrum. In some embodiments, the smaller the value of the third objective function, the more uniform the distribution of the output signal in the frequency spectrum, and the better the flatness. At the same time, the flatness of the signal is positively correlated with the energy concentration degree of its corresponding time domain signal, and the more concentrated the energy is, the smaller the order of the filter is, so the third objective function is used to reduce the order of the filter.

[0098] In some embodiments, the transfer function matrix corresponding to the bright zone region and the transfer function matrix corresponding to the dark zone region include at least one transfer function, and the first objective function, the second objective function and the third objective function can be determined by extracting the at least one transfer function.

[0099] In some embodiments, a first correspondence relationship between the transfer function matrix and the objective function can be established, and then the first objective function, the second objective function and the third objective function corresponding to the transfer function matrix corresponding to the bright zone region and the transfer function matrix corresponding to the dark zone region can be determined through the first correspondence relationship.

[0100] In some embodiments, the target parameter is acquired, and then the diagonal matrix of the transfer function matrix corresponding to the bright region area and the diagonal matrix of the transfer function matrix corresponding to the dark region area are read, and the first target function, the second target function and the third target function are determined according to the target parameter and the diagonal matrix of the transfer function matrix corresponding to the bright region area and the diagonal matrix of the transfer function matrix corresponding to the dark region area. The target parameter can include but is not limited to the number of microphones in the bright region area, the expected sound field in the bright region area, the number of microphones in the dark region area, the number of control frequency points and the weight vector of the control frequency points.

[0101] In step S122, the partition control model of the sound field is established based on the first target function, the second target function and the third target function.

[0102] Here, the first target function, the second target function and the third target function can be used as the objective function in the multi-objective optimization problem, and the partition control model of the sound field is further established according to the multi-objective optimization problem including the first target function, the second target function and the third target function.

[0103] In some embodiments, the partition control model of the sound field can be established by deep learning or machine learning method based on the first target function, the second target function and the third target function.

[0104] In the embodiments of the present application, the partition control model of the sound field is established by the first target function, the second target function and the third target function, so that the partition control model of the sound field can realize the partition control of the sound field while reducing the order of the filter and improving the flexibility of the partition control method of the sound field.

[0105] In some embodiments, step S121 includes steps S1211 to S1214, wherein:

[0106] In step S1211, the number of microphones in the bright region area, the expected sound field in the bright region area, the number of microphones in the dark region area, the number of control frequency points and the weight vector of the control frequency points are acquired.

[0107] Here, the number of microphones in the bright region area can be any suitable size, for example, 12, 10, etc. The number of microphones in the dark region area can be any suitable size, for example, 12, 10, etc. In some embodiments, the number of microphones in the bright region area and the number of microphones in the dark region area can be the same or different. In some embodiments, after the bright region area and the dark region area are determined, the number of microphones in the bright region area and the number of microphones in the dark region area can be acquired by reading the parameter information of the microphones.

[0108] The expected sound field of the bright zone region refers to a sound field expected to be formed in the bright zone region. The expected sound field of the bright zone region can include, but is not limited to, navigation sound, music sound, broadcast sound, etc. In some embodiments, after the bright zone region is determined, the parameters of the sound field can be set through a selection operation, and then the expected sound field of the bright zone region is obtained.

[0109] The control frequency point refers to a value selected in the control frequency range. The number of control frequency points can be any suitable size, for example, 1000, 1200, etc. The control frequency range is a pre-set frequency range, and the control frequency range can be any suitable range, for example, 200Hz (Hertz)~1200Hz, 500Hz~1800Hz, etc. In some embodiments, after the control frequency range is obtained, the control frequency interval can be obtained, and then the control frequency points can be determined according to the control frequency range and the control frequency interval. The control frequency interval refers to the interval between the control frequency points. The control frequency interval can be any suitable size, for example, 1Hz, 2Hz, etc. Exemplarily, the control frequency range is 200Hz~1200Hz, and the control frequency interval is 1Hz, then the control frequency points can include, but are not limited to, 200Hz, 201Hz, 1200Hz, etc. Correspondingly, the number of control frequency points is 1001. In some embodiments, the control frequency range can be obtained by reading the parameter information of the loudspeaker, and then the number of control frequency points is obtained.

[0110] The weight vector of the control frequency point refers to the importance of the control frequency point in the multi-objective optimization problem. In some embodiments, the weight vector is positively correlated with the importance of the control frequency point in the multi-objective optimization problem. The greater the modulus of the weight vector, the more important the control frequency point. In some embodiments, the weight vector of the control frequency point can refer to the weight vector of the loudspeaker at all control frequency points. In some embodiments, the weight vector of the control frequency point can be obtained by reading the parameter information of the loudspeaker.

[0111] In step S1212, a first objective function is determined based on the diagonal matrix of the transfer function matrix corresponding to the bright zone region, the expected sound field of the bright zone region, the number of control frequency points, and the number of microphones of the bright zone region.

[0112] Here, the diagonal matrix of the transfer function matrix corresponding to the bright zone region is a matrix formed by the transfer functions on the diagonal line of the transfer function matrix corresponding to the bright zone region. In some embodiments, the first objective function can be the sum of the square errors of the reconstructed sound field of the bright zone region and the expected sound field of the bright zone region.

[0113] In some embodiments, the first objective function A can be determined by the following formula (1-2), that is, see formula (1-2):

[0114] A = ||p B -p BT || 2 (1-2);

[0115] In formula (1-2), p B is the reconstructed sound field of the bright zone region, wherein, M B is the number of microphones in the bright zone (also referred to as the total number of control points in the bright zone), and J is the number of control frequency points, is a complex domain, M B ·J is a constant (a positive integer), represents an M B ·J-dimensional complex vector, is the loudspeaker array weight vector of all frequency points, p BT is the expected sound field of the bright zone region, {f j |j = 1,..., J} is a control frequency bin, and H B is a diagonal matrix of the transfer function matrix of all control frequency points in the bright zone region, more specifically, H B = diag{G B (f1),..., G B (f J )}.

[0116] In some embodiments, the expected sound field of the bright zone region is usually set as a unit impulse signal.

[0117] In some embodiments, the control frequency bin is a set composed of all control frequency points. For example, the control frequency range is 200 Hz to 1200 Hz, and the control frequency interval is 1 Hz; then, the control frequency bin can be {200 Hz, 201 Hz,..., 1200 Hz}.

[0118] In step S1213, a second objective function is determined based on a diagonal matrix of a transfer function matrix corresponding to the dark zone region, the number of control frequency points, and the number of microphones in the dark zone region.

[0119] Here, the diagonal matrix of the transfer function matrix corresponding to the dark zone region is a matrix formed by the transfer functions on the diagonal lines of the transfer function matrix corresponding to the dark zone region. In some embodiments, the second objective function can be the square sum of the reconstructed sound field of the dark zone region.

[0120] In some embodiments, the second objective function B can be determined by formula (1-3) as follows:

[0121] B = ||p D || 2 (1-3);

[0122] In formula (1-3), p D is the reconstructed sound field in the dark area, where M D is the number of dark area microphones (also known as the total number of dark area control points), H D is the diagonal matrix of the transfer function matrix of all frequency control points in the dark region. More specifically, H D =diag{G D (f1),...,G D (f J )}.

[0123] Step S1214: determining a third objective function based on the number of control frequency points and the weight vectors of the control frequency points.

[0124] Here, the third objective function may be a sum term of the absolute values ​​of the differences between adjacent frequency points of the speaker array weight vector.

[0125] In some embodiments, the third objective function C may be determined by the following formula (1-4), that is, see formula (1-4):

[0126] C=||Fq||1 (1-4);

[0127] In formula (1-4), Among them, q l =(q l (f1),...,q l (f J )) is the weight vector of the l-th loudspeaker at all control frequency points, I L is the L-dimensional identity matrix, is the Kronecker product (also known as Kronecker product), is the difference matrix, is a real number field, D is a (J-1)×J dimensional matrix in the real number field, D ij is the element in the i-th row and j-th column of matrix D,

[0128] In some embodiments, after determining the first objective function, the second objective function, and the third objective function, the multi-objective optimization problem D may be determined by the following formula (1-5), that is, see formula (1-5):

[0129] D=min q {A, B, C} (1-5);

[0130] In formula (1-5), is the loudspeaker array weight vector of all frequency points, A is the first objective function, B is the second objective function, and C is the third objective function.

[0131] In the embodiments of the present application, by the microphone number of the bright zone region, the expected sound field of the bright zone region, the microphone number of the dark zone region, the number of control frequency points, and the weight vector of the control frequency points, in combination with the transfer function matrix corresponding to the bright zone region and the transfer function matrix corresponding to the dark zone region, the first target function, the second target function, and the third target function are determined respectively, thereby improving the accuracy of the first target function, the second target function, and the third target function.

[0132] In some embodiments, the method further comprises steps S105 and S106, and the "determining an optimal solution set corresponding to the multi-objective optimization problem" in step S103 comprises step S103a, wherein:

[0133] In step S105, a single-objective problem corresponding to the multi-objective optimization problem is determined, and the single-objective problem is converted into an equality constraint optimization problem by using the alternating direction multiplier method.

[0134] Here, the weight method can be used to assign weights to the objective functions in the multi-objective optimization problem, and the objective functions with assigned weights are summed, and then the single-objective problem corresponding to the multi-objective optimization problem can be determined.

[0135] In some embodiments, the single-objective problem E can be determined by the following formula (1-6), i.e., referring to formula (1-6):

[0136] E = min q (δ1A+δ2B+δ3C) (1-6);

[0137] In formula (1-6), for any δ = (δ1, δ2, δ3) ∈ Λ, where the simplex Λ is Λ = {δ | δ k > 0 (k = 1, 2, 3), δ1+ δ2+ δ3= 1}, A is the first target function, B is the second target function, C is the third target function, is the loudspeaker array weight vector of all frequency points.

[0138] In some embodiments, δ should take all values on Λ, but δ is an infinite set, and it is actually impossible to obtain a complete optimal solution set Therefore, a limited subset Λ + of Λ is selected by using the average sampling method, and δ can be uniformly taken on the simplex Λ with a preset interval, thereby obtaining the limited subset Λ + of Λ. The preset interval can be any suitable size, for example, 0.02, 0.04, etc. Exemplarily, δ is uniformly taken on the simplex Λ with an interval of 0.02, and the taken set Λ + is Λ + = {δ | δk = 0.02t (k = 1, 2, 3; t = 1,.., 50), δ1+δ2+δ3=1}.

[0139] ADMM is an algorithm for solving optimization problems, especially those with separable structure. The core of the ADMM algorithm is the augmented Lagrangian method of the original dual algorithm.

[0140] In step S106, the equality constrained optimization problem is decomposed into at least one sub-problem based on the Lagrange multiplier and the Lagrange variable, and the at least one sub-problem is solved iteratively to obtain a solution set of the at least one sub-problem;

[0141] Here, the single-objective problem in the above formula (1-6) can be quickly calculated by ADMM to obtain the optimal solution set of the multi-objective optimization problem

[0142] In some embodiments, ADMM decomposes the single-objective problem into simpler sub-problems for solving by introducing Lagrange multipliers and Lagrange variables. This decomposition helps to reduce the complexity of the single-objective problem and makes each sub-problem easier to solve. At the same time, the introduction of Lagrange multipliers helps to handle the constraints to ensure that the optimal solution of the constraints is gradually achieved each time. At the same time, since the sub-problems obtained by ADMM have analytical solutions, they can be summarized for solving, thereby speeding up the solving process of the single-objective problem.

[0143] In some embodiments, auxiliary variables can be introduced to convert the single-objective problem in the above formula (1-6) into a constrained optimization problem. The main purpose of introducing auxiliary variables is to decompose the single-objective problem into two simpler parts, making the variables separable. This decomposition helps to reduce the complexity of the single-objective problem and makes each sub-problem easier to solve.

[0144] In some embodiments, the constrained optimization problem H can be determined by the following formula (1-7), i.e., see formula (1-7):

[0145] H = min q (δ1A+δ2B+δ3||z||1)

[0146] s.t z = Fq (1-7) ;

[0147] In formula (1-7), z = Fq is an auxiliary variable, is the loudspeaker array weight vector of all frequency points, and s.t represents the constraint condition of the constrained optimization problem H as z = Fq.

[0148] In some embodiments, for the constraint optimization problem in formula (1-7), a Lagrangian function of the equation constraint optimization problem can be introduced, and the augmented Lagrangian function can be determined by formula (1-8) as follows That is, referring to formula (1-8):

[0149]

[0150] In formula (1-8), z=Fq is an auxiliary variable, is a loudspeaker array weight vector of all frequency points, μ is a Lagrange multiplier, and ρ is a penalty coefficient, where ρ>0.

[0151] In some embodiments, the augmented Lagrangian function can be determined according to formula (1-8) as follows The constraint optimization problem in formula (1-7) is solved iteratively by ADMM, and the iterative update formula of the constraint optimization problem in formula (1-7) can be determined by formula (1-9) as follows, that is, referring to formula (1-9):

[0152]

[0153] In formula (1-9), τ takes 1.5 as an iteration step, ρ takes 1 as a penalty coefficient, the iteration initial value is (q 0 , z 0 , μ 0 ), and the iteration termination condition is: err<0.01, where err=||q k+1 -q k || 2 +||z k+1 -z k || 2 +||μ k+1 -μ k || 2 .

[0154] In step S103a, the optimal solution set corresponding to the multi-objective optimization problem is determined based on the solution set of the at least one sub-problem.

[0155] Here, since the sub-problem obtained by ADMM has an analytical solution, the solution set of the at least one sub-problem can be spliced to determine the optimal solution set corresponding to the multi-objective optimization problem, thereby accelerating the solution process of the entire problem.

[0156] In an embodiment of the present application, by determining the equality constraint optimization problem corresponding to the multi-objective optimization problem, using the Lagrange multipliers and Lagrange variables of the alternating direction multiplier method, at least one sub-problem is iteratively solved to obtain a solution set of at least one sub-problem, and then the optimal solution set corresponding to the multi-objective optimization problem is determined, which simplifies the solution process of the multi-objective optimization problem and improves the accuracy of the optimal solution set corresponding to the multi-objective optimization problem.

[0157] In some embodiments, the step of “determining the target order of the filter based on the optimal solution set corresponding to the multi-objective optimization problem” in step S103 includes steps S103b and S103c, wherein:

[0158] Step S103b, projecting the optimal solution set corresponding to the multi-objective optimization problem into relevant indicators to obtain a Pareto-like front corresponding to the optimal solution set; wherein the relevant indicators include at least one of the following: the acoustic energy contrast between the bright area and the dark area, the normalized error of the sound field reconstruction in the bright area, and the mean of at least one filter order.

[0159] Here, projecting the optimal solution set corresponding to the multi-objective optimization problem into the relevant indicators refers to the process of substituting the optimal solution set corresponding to the multi-objective optimization problem into the definition formula of each relevant indicator. In some embodiments, the optimal solution set corresponding to the multi-objective optimization problem is Among them, q # (δ)=(q(f1),...,q(f J )). When q # (δ) is obtained, then q(f i )(i=1,...,J), and then q(f i ) into the definition formula of each relevant indicator to complete the projection.

[0160] A quasi-Pareto frontier (also known as a quasi-Pareto frontier) is a set of optimal solutions that achieve the best trade-off between multiple objectives. This quasi-Pareto frontier allows for a more intuitive assessment of the performance of acoustic field partitioning and the effectiveness of filter order reduction.

[0161] When sound waves propagate through a medium, they cause particles in the medium to reciprocate near their equilibrium positions, generating kinetic energy. They also cause the medium to compress and expand, creating deformation potential energy. The sum of these two energies is the acoustic energy imparted to the medium by the acoustic vibrations. The acoustic energy can be any suitable value, for example, 100 joules or 500 joules.

[0162] The sound energy contrast between the bright zone region and the dark zone region refers to the ratio between the sound energy of the bright zone region and the sound energy of the dark zone region. The sound energy contrast between the bright zone region and the dark zone region can be any suitable size, for example, 30, 21, etc. In some embodiments, the greater the sound energy contrast between the bright zone region and the dark zone region, the better the partitioning effect of the sound field is represented.

[0163] In some embodiments, the sound energy contrast AC(f) can be determined by the following formula (1-10), i.e., see formula (1-10):

[0164]

[0165] In formula (1-10), M B is the number of microphones of the bright zone region, M D is the number of microphones of the dark zone region, and q(f) is the optimal solution corresponding to any frequency.

[0166] In some embodiments, when calculating the sound energy contrast between the bright zone region and the dark zone region, only the points in the control frequency bin, i.e., the control frequency points, can be calculated. After calculating the sound energy contrast between the bright zone region and the dark zone region of multiple control frequency points, the average of the sound energy contrast between the multiple bright zone regions and the dark zone region in the control frequency range is obtained, so that the partitioning effect of the sound field can be better measured. Exemplarily, q(f i )(i = 1,..., J) can be substituted into the contrast definition formula AC(f) to obtain the contrast index AC(f i )(i = 1,..., J) corresponding to the frequency f i , and then the arithmetic mean of {AC(f i )(i = 1,..., J)} can be obtained to obtain the average of the bright-dark zone sound energy contrast in the control frequency range.

[0167] The sound field reconstruction normalized error of the bright zone region refers to the difference between the reconstructed sound field of the bright zone region and the sound field of the bright zone region before reconstruction. The sound field reconstruction normalized error of the bright zone region can be any suitable size, for example, 0.1, 0.03, etc. In some embodiments, the greater the sound field reconstruction normalized error of the bright zone region, the smaller the difference between the reconstructed sound field of the bright zone region and the sound field of the bright zone region before reconstruction is represented, and the higher the sound quality of the reconstructed sound field of the bright zone region is.

[0168] In some embodiments, the sound field reconstruction normalized error E r (f) of the bright zone region can be determined by the following formula (1-11), i.e., see formula (1-11):

[0169]

[0170] In formula (1-11), p BT (f) represents the expected sound field of the bright zone region at the control frequency point f BT = (p BT (f1),..., p BT (f J )), p BT (f i ) represents the expected sound field of the bright zone region at the control frequency point f i

[0171] In some embodiments, when calculating the sound field reconstruction normalized error of the bright zone region, only the points in the control frequency bin, i.e., the control frequency points, can be calculated. After calculating the sound field reconstruction normalized error of the bright zone region at multiple control frequency points, the average of the sound field reconstruction normalized error of the multiple bright zone regions in the control frequency range is calculated, so that the sound quality of the reconstructed sound field of the bright zone region can be better measured.

[0172] The filter order can be any suitable size, for example, 1450, 2560, etc. In some embodiments, the smaller the filter order, the faster the partition control of the sound field can be represented, and correspondingly, the smaller the computing resources required to implement the partition control of the sound field.

[0173] In some embodiments, different optimal solutions in the optimal solution set corresponding to the multi-objective optimization problem can correspond to different filter orders, so that at least one filter order can be obtained after determining the optimal solution set corresponding to the multi-objective optimization problem. The arithmetic mean of the at least one filter order can be obtained.

[0174] In some embodiments, at least one filter coefficient before truncation can be obtained first, the total energy corresponding to the at least one filter coefficient before truncation is determined, further, a preset energy percentage is obtained, the total energy of the at least one filter coefficient before truncation is truncated according to the preset energy percentage to obtain at least one filter coefficient after truncation, and finally, the at least one filter order is determined based on the at least one filter coefficient after truncation.

[0175] In step S103c, the target order of the filter is determined based on the Pareto-like front corresponding to the optimal solution set.

[0176] Here, after obtaining the Pareto-like front corresponding to the optimal solution set, the optimal solutions in the optimal solution set corresponding to the multi-objective optimization problem can be sorted under different relevant indicators to obtain the optimal solution set with a certain order under multiple different relevant indicators, and then the filter order corresponding to the optimal solution with the highest priority in the order can be selected as the target order of the filter.​

[0177] In some embodiments, a second correspondence between the quasi-Pareto front and the filter order can be established, after determining the quasi-Pareto front corresponding to the optimal solution set, the filter order corresponding to the quasi-Pareto front can be determined according to the second correspondence, and the filter order is taken as the target order of the filter.

[0178] In the embodiments of the present application, the quasi-Pareto front corresponding to the optimal solution set is obtained by projecting the optimal solution set corresponding to the multi-objective optimization problem into the relevant indicators, and then the target order of the filter is determined according to the quasi-Pareto front corresponding to the optimal solution set. Since different relevant indicators correspond to different measurement standards, the target order of the filter can be determined from multiple dimensions, thereby improving the accuracy of the target order of the filter.

[0179] In some embodiments, step S103c includes step S103c1 and step S103c2, wherein:

[0180] In step S103c1, the actual demand is obtained, and the ideal solution is determined from the optimal solution set based on the actual demand and the quasi-Pareto front corresponding to the optimal solution set.

[0181] Here, the actual demand refers to the target focused on by the zone control of the sound field. The actual demand can include but is not limited to: pursuing the zone effect of the sound field, pursuing the sound quality of the reconstructed sound field, pursuing faster implementation of the zone control of the sound field, etc. For example, when passengers are listening to songs, they often pursue the experience of listening to songs more, and the actual demand at this time can be to pursue the sound quality of the reconstructed sound field.

[0182] In some embodiments, different actual demands have corresponding relevant indicators, after determining the actual demand, the corresponding quasi-Pareto front corresponding to the optimal solution set under the relevant indicator corresponding to the actual demand can be obtained, and then the first solution in order in the quasi-Pareto front can be selected as the ideal solution.

[0183] In some embodiments, the ideal solutions corresponding to different actual demands can be the same or different.

[0184] In step S103c2, the target order of the filter is determined based on the ideal solution.

[0185] Here, after determining the ideal solution, the filter order corresponding to the ideal solution can be obtained, and then the target order of the filter is determined according to the filter order corresponding to the ideal solution. In some embodiments, the target order of the filter can be determined by the filter order corresponding to the ideal solution, the weighted / logarithmic / indexed filter order corresponding to the ideal solution. For example, the filter order corresponding to the ideal solution is taken as the target order of the filter.

[0186] In some embodiments, the ideal solution q * The filter coefficients are obtained by inverse Fourier transform to the time domain, so as to obtain a filter with a lower order. The ideal solution q * After being determined, the corresponding filter order K l and the filter coefficients are also determined, and the filter coefficients are:

[0187] In the embodiments of the present application, the ideal solution is determined from the optimal solution set through the actual demand and the Pareto-like frontier corresponding to the optimal solution set, and then the target order of the filter is determined according to the ideal solution, which improves the accuracy of the target order of the filter, and at the same time, the user can flexibly set the partition control effect of the sound field according to the actual demand, thereby improving the user experience.

[0188] In some embodiments, in the case where the relevant indicators include the mean of at least one filter order, the method further includes steps S107 to S109, wherein:

[0189] In step S107, at least one filter coefficient before being truncated is obtained, and the total energy corresponding to the at least one filter coefficient before being truncated is determined.

[0190] Here, the order of the filter is usually equal to the number of filter coefficients minus one. After the sound field partition control algorithm obtains the speaker driving signal in the frequency domain, it needs to be converted to the time domain to obtain the filter coefficient vector before being truncated.

[0191] In some embodiments, q l is the weight vector of the lth speaker in the frequency domain obtained by the sound field partition control algorithm, the control frequency range of which is [f L , f H ], the control frequency interval is Δf, and the sampling frequency of the control system is f s . In order to quickly calculate the filter coefficients before being truncated, zero padding operation can be performed on q l to obtain wherein, The zero padding operation specifically refers to padding 0 at the remaining control frequency points within the frequency range to form a coexisting vector q l .

[0192] In some embodiments, the coexisting vector q may be determined by the following formula (1-12):

[0193]

[0194] In formula (1-12), [f L , f H ] is the control frequency range, f s is the sampling frequency of the control system.

[0195] In some embodiments, the symbiotic vectors in the above formulas (1-12) can be Perform fast Fourier transform to obtain the filter coefficient vector before truncation The field of real numbers.

[0196] Step S108 : obtaining a preset energy percentage, and performing truncation processing on the total energy of at least one filter coefficient before truncation according to the preset energy percentage to obtain at least one filter coefficient after truncation.

[0197] Here, since the filter coefficients before truncation may contain some low-amplitude coefficients, the filter coefficients with larger values ​​in the middle portion can be truncated, thereby reducing computational and storage overhead while retaining the main characteristics of the filter. The preset energy percentage can be any suitable value, for example, 99%, 98%, etc.

[0198] In some embodiments, the untruncated filter coefficient vector F can be determined by the following formula (1-13): l The total energy || F l || 2 , that is, see formula (1-13):

[0199]

[0200] In formula (1-13), The field of real numbers.

[0201] In some embodiments, when truncating the total energy of at least one filter coefficient before truncation, the interval Medium energy accounts for F l Total energy||F l || 2 The ratio C l (K), determine the preset energy percentage, where Refers to the set of natural numbers.

[0202] In some embodiments, the interval can be determined by the following formula (1-14): Medium energy accounts for F l Total energy||F l || 2 The ratio C l (K), that is, see formula (1-14):

[0203]

[0204] In formula (1-14), ||F l || 2 is the total energy of the untruncated filter coefficient vector F l . is a real number field.

[0205] In step S109, at least one filter order is determined based on the at least one truncated filter coefficient.

[0206] Here, since the at least one truncated filter coefficient can retain the main characteristics of the filter, the final filter can still accurately realize the partition control of the sound field under the premise of reducing the order.

[0207] In some embodiments, the filter order K l may be determined by formula (1-15) as follows:

[0208]

[0209] In formula (1-15), C l (K) is the ratio of the energy in the interval segment to the total energy ||F l . l || 2 At the same time, the interval segment contains 2K+1 positive integers, that is, the number of filter coefficients is 2K+1, and since the filter order is usually equal to the number of filter coefficients minus one, the filter order K l here is 2K.

[0210] In the embodiments of the present application, the total energy corresponding to the at least one untruncated filter coefficient is truncated according to a preset energy percentage to obtain at least one truncated filter coefficient, and then at least one filter order is determined through the at least one truncated filter coefficient, thereby improving the accuracy of the at least one filter order.

[0211] In some embodiments, the "determining a bright area and a dark area in the sound field of the vehicle" in step S101 includes steps S111 and S112, wherein:

[0212] In step S111, based on the division requirement of the sound field, the area where the sound field is expected to be generated and the area where the sound field is not expected to be generated are determined.

[0213] Here, the division requirement of the sound field represents the division of the sound field. The division requirement of the sound field can include, but is not limited to, the front row area having sound, the co-driver area being quiet, and the like.

[0214] In some embodiments, based on the division requirement of the sound field, the area in the vehicle where sound is expected to occur and the area in the vehicle where sound is not expected to occur can be determined, and then the area in the vehicle where sound is expected to occur is taken as the area where the sound field is expected to be generated, and the area in the vehicle where sound is not expected to occur is taken as the area where the sound field is not expected to be generated.

[0215] In some embodiments, the control area that can be covered by the sound field of the vehicle can be presented in the center screen of the vehicle, and then the division requirement of the sound field can be determined through preset operations. The preset operations can include, but are not limited to, single-click, double-click, circle selection, and the like.

[0216] In step S112, the area where the sound field is expected to be generated is taken as the bright area, and the area where the sound field is not expected to be generated is taken as the dark area.

[0217] Here, the user can select the control area as the bright area or the dark area according to the bright-dark area self-defined display screen, take the area where the sound field is expected to be generated as the bright area, and take the area where the sound field is not expected to be generated as the dark area.

[0218] In the embodiments of the present application, by taking the area where the sound field is expected to be generated as the bright area and the area where the sound field is not expected to be generated as the dark area according to the division requirement of the sound field, the bright area and the dark area can be accurately divided according to the user's demand, and the accuracy of the bright area and the dark area is improved.

[0219] The application of the sound field partition control method provided in the embodiments of the present application in actual scenarios is described below, taking a vehicle provided with a microphone array and a loudspeaker array as an example.

[0220] The application of the sound field partition control technology in the intelligent cabin audio system of an automobile has attracted widespread attention. Passengers have individualized needs for the acoustic environment in the cabin, and passengers in different seats expect to have different and non-interfering sound fields. The sound field partition control technology generates a driving signal for the loudspeaker array by designing a filter to form a desired sound field in a specific area, thereby controlling the partition of the sound field. Under the premise of meeting the partition control requirement of the sound field, the computing resources and storage costs of the sound field partition control system in the cabin need to be reduced. The order represents the number of electronic element delays in the filter, and the lower the order, the fewer the number of electronic elements in the filter, and the lower the manufacturing cost. The FIR filter can well reduce the complexity and cost of the sound field partition control system.

[0221] In the related art, when the partition control of the sound field is performed, the main algorithms used are PM algorithm and ACC algorithm, and other algorithms are improved on the basis of the PM algorithm and the ACC algorithm. Among them, the PM algorithm is committed to accurately synthesizing the required sound field, setting the expected sound field of the dark area region to 0, and reconstructing the expected sound field of the bright area region and the dark area region. The PM algorithm obtains the weight vector of the loudspeaker array by minimizing the error between the expected sound field and the reconstructed sound field. The ACC algorithm is committed to maximizing the sound energy contrast of the bright area region and the dark area region, and optimizing the obtained weight vector of the loudspeaker array, so that the sound signal is directionally propagated to the bright area region and the sound signal is avoided to leak to the dark area region as much as possible. It is worth noting that the driving signal of the loudspeaker array is obtained by convolution operation of the FIR filter and the input signal to be played, and the use of a high-order FIR filter will increase the calculation and storage cost of the control system, and also cause the sound field partition control system to have a high delay. However, in the process of using the PM algorithm and the ACC algorithm, the order of the FIR filter is too high, and there are problems of too many calculation resources and high storage cost.

[0222] The embodiment of the present application provides a sound field partition control method, on the one hand, by obtaining the transfer function matrix corresponding to the bright area region and the transfer function matrix corresponding to the dark area region, a sound field partition control model is established, since the sound field partition control model of the present application considers multiple different targets to be optimized, the accuracy of the sound field partition control is improved; on the other hand, the target order of the filter is determined by the optimal solution set corresponding to the multi-objective optimization problem in the determined sound field partition control model, since the target order of the filter is related to the multi-objective optimization problem, the order of the filter is reduced while the sound field partition control is realized, and further, since the order of the filter is reduced, the calculation resources of the sound field partition control are reduced, and the storage cost is saved.

[0223] Figure 4 The implementation process of the sound field partition control method provided by the embodiment of the present application is shown Figure Two As shown in Figure 4 The method comprises steps S201 to S208, wherein:

[0224] In step S201, the acquisition signal of the microphone array and the driving signal of the loudspeaker array are obtained, and the transfer function matrix corresponding to the bright area region and the transfer function matrix corresponding to the dark area region are measured based on the driving signal of the loudspeaker array and the acquisition signal of the microphone array;

[0225] In step S202, the first objective function, the second objective function and the third objective function are determined based on the transfer function matrix corresponding to the bright area region and the transfer function matrix corresponding to the dark area region;

[0226] Step S203, based on the first objective function, the second objective function and the third objective function, a partition control model of the sound field including a multi-objective optimization problem is established;

[0227] Step S204, a single objective problem corresponding to the multi-objective optimization problem is determined, and an alternating direction multiplier method is used to convert the single objective problem into an equality constraint optimization problem;

[0228] Step S205, based on the Lagrange multiplier and the Lagrange variable, the equality constraint optimization problem is decomposed into at least one sub-problem, and the at least one sub-problem is iteratively solved to determine a set of optimal solutions corresponding to the multi-objective optimization problem;

[0229] Step S206, the set of optimal solutions corresponding to the multi-objective optimization problem is projected into the relevant indicators to obtain a Pareto-like front corresponding to the set of optimal solutions;

[0230] Step S207, an actual demand is obtained, and based on the actual demand and the Pareto-like front corresponding to the set of optimal solutions, an ideal solution is determined from the set of optimal solutions;

[0231] Step S208, based on the ideal solution, the target order of the filter is determined through inverse Fourier transform, and based on the filter under the target order, a driving signal is determined and output to the loudspeaker array of the vehicle to control the sound field of the vehicle.

[0232] In some embodiments, to verify the partition control performance and filter order reduction effect of the sound field partition control method provided in the present application, the sound field partition control method of the present application is compared with the PM algorithm. The specific arrangement of the loudspeaker array and the microphone array is as follows: there are two microphone arrays, which are arranged in the front row area of the vehicle and the rear row area of the vehicle respectively, and each microphone array is composed of 12 microphones (for reference Figure 3 ), and there is one microphone array, which is composed of 12 loudspeakers (for reference Figure 2 ). The specific situation of the frequency control point is as follows: the control frequency range is 200Hz-1200Hz, the control frequency interval is 1Hz, and the sampling frequency is 48kHz. At the same time, it is assumed that the front row area is set as the bright area region and the rear row area is set as the dark area region. In this specific case, the comparison between the sound field partition control method provided in the present application and the PM algorithm is as follows:

[0233] Figure 5 A schematic diagram of the contrast ratio of the bright area region and the dark area region provided in the embodiments of the present application is shown in Figure 5As shown in the figure, 51 is the contrast curve of the bright area and the dark area of the sound field partition control method provided by the application, 52 is the contrast curve of the bright area and the dark area of the PM algorithm, and 53 is the contrast expected value of the bright area and the dark area. In the range of 200Hz-1200Hz, the average value of the contrast of the bright area and the dark area of the PM algorithm is 21.8dB (decibel), the average value of the contrast of the bright area and the dark area of the sound field partition control method provided by the application is 23.9dB, and the contrast of the bright area and the dark area of the sound field partition control method provided by the application is better than that of the PM algorithm.

[0234] Figure 6 A schematic diagram of the reconstruction normalization error of the bright area sound field provided by the embodiment of the application is shown in the figure, Figure 6 As shown in the figure, 61 is the reconstruction normalization error curve of the bright area sound field of the sound field partition control method provided by the application, 62 is the reconstruction normalization error curve of the bright area sound field of the PM algorithm, and 63 is the reconstruction normalization error expected value of the bright area sound field. In the range of 200Hz-1200Hz, the average value of the reconstruction normalization error of the bright area sound field of the PM algorithm is 0.023, the average value of the reconstruction normalization error of the bright area sound field of the sound field partition control method provided by the application is 0.061, and the reconstruction normalization error result of the bright area sound field of the sound field partition control method provided by the application is slightly worse than that of the PM algorithm.

[0235] Figure 7 A schematic diagram of the filter order provided by the embodiment of the application is shown in the figure, Figure 7 As shown in the figure, 71 is the filter order of the sound field partition control method provided by the application, 72 is the filter order of the PM algorithm, the average value of the filter order of the sound field partition control method provided by the application is 3313, the average value of the filter order of the PM algorithm is 2021, and compared with the PM algorithm, the average value of the filter order of the sound field partition control method provided by the application is reduced by 1292.

[0236] In summary, the sound field partition control method provided by the application has good sound field partition control performance, and at the same time, the order of the filter is greatly reduced; compared with the PM algorithm, the sound field partition control method provided by the application sacrifices the reconstruction normalization error of the bright area sound field, improves the contrast of the bright area and the dark area, and reduces the filter order.

[0237] Figure 8 A schematic diagram of the composition structure of the sound field partition control system provided by the embodiment of the application is shown in the figure, Figure 8As shown, the sound field partition control system 80 includes a measurement unit 81, a user interface 82, a control integration unit 83, and a signal processing unit 84, wherein:

[0238] The measurement unit 81, composed of a loudspeaker array and a microphone array, is used to receive the driving signal of the loudspeaker array and the collection signal of the microphone array as input, and obtain the transfer function matrix corresponding to the bright zone area and the transfer function matrix corresponding to the dark zone area after processing, and input the transfer function matrix corresponding to the bright zone area and the transfer function matrix corresponding to the dark zone area to the control integration unit 83;

[0239] The user interface 82 is used to provide the division requirement of the sound field to the control integration unit 83, for example, setting the front row area as a region producing a specific sound field (i.e. the bright zone area), and setting the back row area as a quiet area (i.e. the dark zone area);

[0240] The control integration unit 83 is used to simultaneously receive the division requirement of the sound field provided by the user interface 82 and the transfer function matrix corresponding to the bright zone area and the transfer function matrix corresponding to the dark zone area provided by the measurement unit 81, and then output the transfer function matrix corresponding to the bright zone area and the transfer function matrix corresponding to the dark zone area meeting the division requirement of the sound field to the signal processing unit 84;

[0241] The signal processing unit 84 is used to output the driving signal of the loudspeaker array under the division of the bright zone area and the dark zone area to the loudspeaker array according to the information provided by the control integration unit 83.

[0242] Based on the above embodiment, the embodiment of the present application further provides a sound field partition control device, which is applied to a vehicle, the vehicle is provided with a loudspeaker array, the loudspeaker array is used to form a sound field of the vehicle, Figure 9 The composition structure diagram of the sound field partition control device provided by the embodiment of the present application is shown as Figure 9 As shown, the sound field partition control device 900 includes a determination unit 901 and an establishment unit 902, wherein:

[0243] The determination unit 901 is used to determine the bright zone area and the dark zone area in the sound field of the vehicle, and obtain the transfer function matrix corresponding to the bright zone area and the transfer function matrix corresponding to the dark zone area;

[0244] The establishment unit 902 is used to establish a sound field partition control model based on the transfer function matrix corresponding to the bright zone area and the transfer function matrix corresponding to the dark zone area; wherein the sound field partition control model includes a multi-objective optimization problem;

[0245] The determining unit 901 is further configured to determine a set of optimal solutions corresponding to the multi-objective optimization problem, and determine a target order of the filter based on the set of optimal solutions corresponding to the multi-objective optimization problem; and determine a driving signal based on the filter with the target order, and output the driving signal to a loudspeaker array of the vehicle to perform zoned control on a sound field of the vehicle.

[0246] In some embodiments, the multi-objective optimization problem includes a first objective function, a second objective function, and a third objective function, and the establishing unit 902 is further configured to determine the first objective function, the second objective function, and the third objective function based on the transfer function matrix corresponding to the bright zone region and the transfer function matrix corresponding to the dark zone region; the first objective function and the second objective function are used to implement zoned control of the sound field, and the third objective function is used to reduce the order of the filter; and the establishing unit 902 is further configured to establish a zoned control model of the sound field based on the first objective function, the second objective function, and the third objective function.

[0247] In some embodiments, the establishing unit 902 is further configured to obtain a number of microphones of the bright zone region, an expected sound field of the bright zone region, a number of microphones of the dark zone region, a number of control frequency points, and a weight vector of the control frequency points; determine the first objective function based on a diagonal matrix of the transfer function matrix corresponding to the bright zone region, the expected sound field of the bright zone region, the number of control frequency points, and the number of microphones of the bright zone region; determine the second objective function based on a diagonal matrix of the transfer function matrix corresponding to the dark zone region, the number of control frequency points, and the number of microphones of the dark zone region; and determine the third objective function based on the number of control frequency points and the weight vector of the control frequency points.

[0248] In some embodiments, the determining unit 901 is further configured to determine a single-objective problem corresponding to the multi-objective optimization problem, and convert the single-objective problem into an equality-constrained optimization problem by using an alternating direction multiplier method; decompose the equality-constrained optimization problem into at least one sub-problem based on a Lagrange multiplier and a Lagrange variable, and iteratively solve the at least one sub-problem to obtain a solution set of the at least one sub-problem; and determine a set of optimal solutions corresponding to the multi-objective optimization problem based on the solution set of the at least one sub-problem.

[0249] In some embodiments, the determining unit 901 is further configured to project the set of optimal solutions corresponding to the multi-objective optimization problem into a relevant index to obtain a Pareto-like frontier corresponding to the set of optimal solutions; the relevant index includes at least one of the following: a sound energy contrast between the bright zone region and the dark zone region, a sound field reconstruction normalized error of the bright zone region, and a mean value of at least one filter order; and determine the target order of the filter based on the Pareto-like frontier corresponding to the set of optimal solutions.

[0250] In some embodiments, the determining unit 901 is further configured to obtain an actual demand, and determine an ideal solution from the set of optimal solutions based on the actual demand and the class-Pareto frontier corresponding to the set of optimal solutions; and determine the target order of the filter based on the ideal solution.

[0251] In some embodiments, the determining unit 901 is further configured to obtain at least one filter coefficient before truncation, determine total energy corresponding to the at least one filter coefficient before truncation; determine at least one filter order based on the at least one filter coefficient after truncation; and the sound field partition control device 900 further comprises a processing unit configured to obtain a preset energy percentage, and perform truncation processing on the total energy of the at least one filter coefficient before truncation according to the preset energy percentage to obtain the at least one filter coefficient after truncation.

[0252] In some embodiments, the vehicle is further provided with a microphone array, and the determining unit 901 is further configured to obtain a collection signal of the microphone array and a driving signal of the loudspeaker array; and obtain a transfer function matrix corresponding to the bright zone region and a transfer function matrix corresponding to the dark zone region based on the driving signal of the loudspeaker array and the collection signal of the microphone array.

[0253] The above device embodiments are similar to the descriptions of the above method embodiments, and have similar beneficial effects to the method embodiments. For 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.

[0254] It should be noted that, in the embodiments of the present application, if the above method is realized in the form of a software function module and sold or used as an independent product, it can also be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of 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 embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory, a magnetic disk or an optical disk, and various storage media that can store program codes. Thus, the embodiments of the present application are not limited to any specific hardware and software combination.

[0255] The present application also provides an electronic device, comprising a memory and a processor, the memory stores a computer program capable of running on the processor, and the processor executes the computer program to realize the above method.

[0256] The present application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the above method. The computer readable storage medium can be transitory or non-transitory.

[0257] The application further provides a computer program product comprising computer programs or instructions, which, when executed by a processor, implement some or all of the steps of the above method. The computer program product can be implemented in particular by hardware, software or a combination thereof. The computer program product can be implemented in particular by hardware, software or a combination thereof. In an optional embodiment, the computer program product is embodied as a computer storage medium, and in another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (SDK) or the like.

[0258] It should be noted that, Figure 10 A hardware entity diagram of an electronic device provided by an embodiment of the application is shown in FIG. 10, which includes a processor 1001, a communication interface 1002 and a memory 1003. Figure 10 The hardware entity of the electronic device 1000 includes a processor 1001, a communication interface 1002 and a memory 1003, wherein:

[0259] The processor 1001 generally controls the overall operation of the electronic device 1000.

[0260] The communication interface 1002 can enable the electronic device 1000 to communicate with other terminals or servers through a network.

[0261] The memory 1003 is configured to store instructions and applications executable by the processor 1001, and can also cache data to be processed by the processor 1001 and modules in the electronic device 1000 (for example, image data, audio data, voice communication data and video communication data) that have been processed or are to be processed, which can be implemented by FLASH or RAM. The processor 1001, the communication interface 1002 and the memory 1003 can transmit data through a bus 1004.

[0262] Here, the electronic device can be a car machine in a vehicle.

[0263] It should be noted that: the above description of the storage medium and the device embodiment is similar to the description of the above method embodiment, and has similar beneficial effects to the method embodiment. For technical details not disclosed in the storage medium and device embodiments of the application, please refer to the description of the method embodiments of the application.

[0264] It should be understood that every feature, structure, or characteristic described in relation to one embodiment is applicable to at least one other embodiment. Therefore, the appearance of the phrase "in one embodiment" or "in an embodiment" in various places throughout the specification is not necessarily referring to the same embodiment. Furthermore, the particular features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that the sequence of steps / processes in various embodiments of the present application does not mean the order of execution, the execution order of the steps / processes should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The sequence number of the above embodiments of the present application is only for description, not representing the advantages and disadvantages of the embodiments.

[0265] It should be noted that the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without more limitations, an element defined by the phrase "comprising a... " does not exclude the existence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0266] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The above-described device embodiments are only illustrative, for example, the division of the above units is only a logical function division, and actual implementation can have another division manner, such as: 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 or direct coupling or communication connection between the displayed or discussed components can be through some interface, indirect coupling or communication connection between devices or units, which can be electrical, mechanical or other forms.

[0267] The units described above as separate components can or can not be physically separate, and the components shown as units can or can not be physical units; they can be located in one place or distributed on multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0268] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be realized in the form of hardware or hardware plus software functional unit.

[0269] Those skilled in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by program instruction related hardware, and the foregoing program can be stored in a computer readable storage medium, and the program executes the steps of the above-mentioned method embodiments when executed; and the foregoing storage medium includes various storage media that can store program codes, such as mobile storage devices, read-only memories, magnetic discs or optical discs.

[0270] Alternatively, the integrated units of the present application can be stored in a computer readable storage medium if they are realized in the form of software function modules and sold or used as independent products. Based on such understanding, the technical solutions of the present application or the parts that make contributions to the related art can be embodied in the form of software products, and the computer software products are stored in a storage medium, including a plurality of 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 embodiments of the method of the present application. The foregoing storage medium includes various storage media that can store program codes, such as mobile storage devices, ROMs, magnetic discs or optical discs.

[0271] The above is only an embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present 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, and the method includes: Determining a bright region and a dark region in the sound field of the vehicle, and obtaining a transfer function matrix corresponding to the bright region and a transfer function matrix corresponding to the dark region; Based on the transfer function matrix corresponding to the bright area and the transfer function matrix corresponding to the dark area, a partitioned control model of the sound field is established; wherein the partitioned control model of the sound field includes a multi-objective optimization problem; Determining an optimal solution set corresponding to the multi-objective optimization problem, and determining a target order of the filter based on the optimal solution set corresponding to the multi-objective optimization problem; A driving signal is determined based on the filter at the target order, and the driving signal is output to a speaker array of the vehicle to perform zone-by-zone control on the sound field of the vehicle.

2. The partition control method according to claim 1, characterized in that: The multi-objective optimization problem includes a first objective function, a second objective function, and a third objective function. The establishment of a partitioned control model of the sound field based on the transfer function matrix corresponding to the bright area and the transfer function matrix corresponding to the dark area includes: Determining the first objective function, the second objective function, and the third objective function based on the transfer function matrix corresponding to the bright area and the transfer function matrix corresponding to the dark area; wherein the first objective function and the second objective function are used to achieve zoning control of the sound field, and the third objective function is used to reduce the order of the filter; A partition control model of the sound field is established based on the first objective function, the second objective function, and the third objective function.

3. The partition control method according to claim 2, characterized in that: The determining the first objective function, the second objective function, and the third objective function based on the transfer function matrix corresponding to the bright area and the transfer function matrix corresponding to the dark area includes: Obtaining the number of microphones in the bright area, the desired sound field in the bright area, the number of microphones in the dark area, the number of control frequency points, and weight vectors of the control frequency points; determining the first objective function based on a diagonal matrix of a transfer function matrix corresponding to the bright area, a desired sound field of the bright area, the number of control frequency points, and the number of microphones in the bright area; determining the second objective function based on a diagonal matrix of a transfer function matrix corresponding to the dark area, the number of control frequency points, and the number of microphones in the dark area; The third objective function is determined based on the number of the control frequency points and the weight vectors of the control frequency points.

4. The partition control method according to claim 1, characterized in that: The method further comprises: Determining a single-objective problem corresponding to the multi-objective optimization problem, and converting the single-objective problem into an equality-constrained optimization problem using an alternating direction multiplier method; Decomposing the equality-constrained optimization problem into at least one subproblem based on Lagrange multipliers and Lagrange variables, and iteratively solving the at least one subproblem to obtain a solution set of the at least one subproblem; Determining the optimal solution set corresponding to the multi-objective optimization problem includes: Based on the solution set of the at least one sub-problem, an optimal solution set corresponding to the multi-objective optimization problem is determined.

5. The partition control method according to claim 1, characterized in that: The determining of the target order of the filter based on the optimal solution set corresponding to the multi-objective optimization problem includes: Projecting the optimal solution set corresponding to the multi-objective optimization problem onto relevant indicators to obtain a quasi-Pareto front corresponding to the optimal solution set; wherein the relevant indicators include at least one of the following: an acoustic energy contrast between the bright area and the dark area, a normalized error of sound field reconstruction in the bright area, and a mean value of at least one filter order; The target order of the filter is determined based on the Pareto-like front corresponding to the optimal solution set.

6. The partition control method according to claim 5, characterized in that: The determining the target order of the filter based on the Pareto-like front corresponding to the optimal solution set includes: obtaining actual demand, and determining an ideal solution from the optimal solution set based on a Pareto-like front corresponding to the actual demand and the optimal solution set; Based on the ideal solution, a target order of the filter is determined.

7. The partition control method according to claim 5, characterized in that: In the case where the relevant indicator comprises a mean value of the at least one filter order, the method further comprises: Obtaining at least one filter coefficient before truncation, and determining a total energy corresponding to the at least one filter coefficient before truncation; Obtaining a preset energy percentage, and truncating the total energy of the at least one untruncated filter coefficient according to the preset energy percentage to obtain at least one truncated filter coefficient; At least one filter order is determined based on the at least one truncated filter coefficient.

8. The partition control method according to any one of claims 1 to 7, wherein the vehicle is further provided with a microphone array, and obtaining the transfer function matrix corresponding to the bright area and the transfer function matrix corresponding to the dark area comprises: Acquiring a collection signal from a microphone array and a driving signal from the speaker array; Based on the driving signal of the speaker array and the collection signal of the microphone array, a transfer function matrix corresponding to the bright area and a transfer function matrix corresponding to the dark area 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 includes: a determination unit, configured to determine a bright region and a dark region in the sound field of the vehicle, and obtain a transfer function matrix corresponding to the bright region and a transfer function matrix corresponding to the dark region; An establishing unit is used to establish a partitioned control model of the sound field based on the transfer function matrix corresponding to the bright area and the transfer function matrix corresponding to the dark area; wherein the partitioned control model of the sound field includes a multi-objective optimization problem; The determination unit is further configured to determine an optimal solution set corresponding to the multi-objective optimization problem, and determine a target order of a filter based on the optimal solution set corresponding to the multi-objective optimization problem; determine a driving signal based on the filter at the target order and output the driving signal to a speaker array of the vehicle to perform zoned control of the vehicle's sound field.

10. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

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