Loudspeaker array multi-objective optimization method and system for sound field partition control

Abstract

The invention relates to a loudspeaker array multi-objective optimization method and system for sound field partition control, and the method comprises the steps: dividing a sound field region into a bright region and a dark region according to a hearing expectation; constructing a decision variable vector by taking the loudspeaker arrangement candidate points as decision variables; constructing a multi-objective optimization model of multiple objectives about decision variable vectors; solving the multi-objective optimization model by adopting a multi-objective genetic algorithm to obtain a Pareto optimal solution; constructing a linear weighted target planning model based on the expected values of the multiple targets, target function values of the multiple targets corresponding to the Pareto optimal solutions, and weight coefficients of negative deviation variables and positive deviation variables of the target function values relative to the expected values; and solving the linear weighted target planning model to obtain a Pareto optimal solution corresponding to the target function value when the linear weighted target planning model takes the minimum value, and obtaining the optimal position and number of the loudspeaker array. According to the invention, the optimal balance among multiple targets is realized, and the sound field partitioning effect is further improved.

Description

Technical Field

[0001] The present application relates to the technical field of sound field control, and in particular to a multi-objective optimization method and system for a loudspeaker array for sound field zoning control. Background Art

[0002] In recent years, with the development of science and technology and the improvement of living standards, car audio and video systems have attracted more and more attention. Passengers pay more and more attention to the function of independent sound zones in the acoustic environment of the cabin. Passengers in different seats expect to have different acoustic environments without interfering with each other. In order to achieve the above-mentioned personalized acoustic experience, it is necessary to solve the problem of speaker distribution in the car. This problem is also called the problem of regional sound field control in the car, that is, to reconstruct the target sound field in a specific desired area (bright area) while reducing the impact of the target sound field on other areas (dark areas).

[0003] Speaker array optimization aims to design the best distribution scheme by considering vehicle manufacturing cost, vehicle physical model, user experience and other aspects. Considering the manufacturing cost, automobile manufacturers always hope to use as few speakers as possible to provide as personalized independent sound zone functions as possible. At present, most speaker array optimization methods achieve the optimal design by designing a single objective function using the above indicators, and the independent sound zone effect needs to be improved. Summary of the invention

[0004] The embodiments of the present application provide a multi-objective optimization method and system for a speaker array with sound field zoning control, which achieves an optimal balance between multiple objectives and further improves the sound field zoning effect.

[0005] In a first aspect, a multi-objective optimization method for a loudspeaker array with sound field partition control is provided, comprising:

[0006] According to listening expectations, the sound field area is divided into bright area and dark area;

[0007] Taking the candidate points for speaker placement as decision variables, a decision variable vector is constructed according to the 0-1 type planning;

[0008] Constructing a multi-objective optimization model about the decision variable vector, wherein the multi-objectives include: a first objective based on the contrast of acoustic potential energy between bright and dark areas, a second objective based on the number of loudspeakers, and a third objective based on the normalized mean square error of the bright area and the spatial consistency of the loudspeaker signal in the bright area;

[0009] Using a multi-objective genetic algorithm to solve the multi-objective optimization model to obtain several Pareto optimal solutions;

[0010] Constructing a linear weighted objective programming model based on the expected values ​​of the multiple objectives, the objective function values ​​of the multiple objectives corresponding to each of the Pareto optimal solutions, and the weight coefficients of the negative deviation variables and the positive deviation variables of the objective function values ​​relative to the expected values;

[0011] The linear weighted objective programming model is solved to obtain the Pareto optimal solution corresponding to the objective function value when the linear weighted objective programming model takes the minimum value, and the optimal position and number of the loudspeaker array are obtained.

[0012] In some embodiments, the decision variable vector is denoted as X, which includes:

[0013] X=[x1,x2,…,x j ,…,x K ]

[0014] Among them, x j = 0 or 1, when x j = 0, no loudspeaker is set at the jth loudspeaker placement candidate point. j =1, the jth speaker arrangement candidate point sets the speaker, j=1, 2, ..., K, K is the total number of speaker arrangement candidate points in the sound field area.

[0015] In some embodiments, the sound field area is divided into four cabin areas symmetrical about the center of the roof according to the position of the cabin in the car, so that x j′ =x j′+L =x j′+2L =x j′+3L ;

[0016] Wherein, j′=1, 2, …, L, L is the total number of candidate points for speaker placement in each cockpit area, and K=4L.

[0017] In some embodiments, the multi-objective optimization model is as follows:

[0018]

[0019] Among them, F1(X) is the first goal based on the contrast of sound potential energy in bright area and dark area, F2(X) is the second goal based on the number of loudspeakers, and F3(X) is the third goal based on the normalized mean square error in bright area and the spatial consistency of loudspeaker signal in bright area.

[0020] In some embodiments, the contrast ratio of the acoustic potential energy of the bright area and the dark area is denoted as AC i (X), and AC i (X) is:

[0021]

[0022] Among them, G B(f i ) is the transfer function matrix from the loudspeaker to the bright area microphone, with a dimension of M B ×K,M B is the number of microphones in the bright area, G B H (f i ) is G B (f i )'s conjugate transposed matrix;

[0023] G D (f i ) is the transfer function matrix from the loudspeaker to the dark area microphone, with a dimension of M D ×K,M D is the number of microphones in the dark area, G D H (f i ) is G D (f i ) is the conjugate transposed matrix; M B +M D =M, M is the total number of microphones in the sound field area;

[0024] q i is the driving signal vector of the speaker, with a dimension of K×1, K is the total number of candidate points for speaker placement in the sound field area, q i H Q i The conjugate transposed matrix of ;

[0025] q i =[x1q1(f i ),x2q2(f i ),…,x j q j (f i ),…,x K q K (f i )] T

[0026] Where j = 1, 2, ..., K, q j (f i ) is the driving signal of the jth speaker, i is the frequency point order, f i is the frequency of the i-th frequency point, i=1,2,…, f min is the lower limit of the speaker frequency, f max is the upper limit of the speaker frequency, f i+1 -f i =1Hz, f i =f min ,f min +1,fmin +2,…,f max ;

[0027] Based on the contrast of acoustic potential energy in bright and dark areas AC i The first objective F1(X) of (X) is:

[0028]

[0029] In some embodiments, the second target F2(X) based on the number of speakers is:

[0030] F2(X)=||X||1

[0031] Here, ||·||1 is the 1-norm.

[0032] In some embodiments, the normalized mean square error of the bright area is denoted by E r,i (X), and E r,i (X) is:

[0033]

[0034] Among them, P BT (f i ) is the target sound field matrix for bright area reconstruction, is the square of the 2-norm;

[0035] The spatial consistency of the loudspeaker signal in the bright area is denoted as ΔL i (X), and ΔL i (X) is:

[0036] ΔL i (X) = L i,B,max (X)-L i,B,min (X)

[0037]

[0038] Among them, L i,B (X) is the sound pressure level of the microphone in the bright area, P ref (f i ) is the reference sound pressure, L i,B,max (X) is the L of all microphones in the bright area i,B The maximum value of (X), L i,B,min (X) is the L of all microphones in the bright area i,B (X) minimum value;

[0039] The third objective F3(X) based on the normalized mean square error in the bright area and the spatial consistency of the loudspeaker signal in the bright area is:

[0040]

[0041] In some embodiments, the method further includes determining a transfer function matrix, and determining the transfer function matrix specifically includes:

[0042] Arrange speakers at all candidate speaker arrangement points, and arrange microphones in bright areas and dark areas;

[0043] Giving a test drive signal to each of the loudspeakers one by one, and obtaining the measured sound pressure collected by each microphone;

[0044] Based on the test drive signal and the measured sound pressure, the transfer function matrix G(f i ):

[0045]

[0046] Among them, p 0，mj (f i ) is the measured sound pressure of the speaker at the jth speaker arrangement candidate point collected by the mth microphone, m = 1, 2, ..., M, q 0，j (f i ) is the test driving signal of the jth speaker, is the transfer function from the jth loudspeaker to the mth microphone;

[0047] According to the microphones in the bright area and the dark area, the transfer function matrix G(f i ) to obtain the transfer function matrix G from the loudspeaker to the bright area microphone B (f i ) and the transfer function matrix G from the loudspeaker to the dark area microphone D (f i ).

[0048] In some embodiments, the method further includes optimizing the driving signal, and optimizing the driving signal specifically includes:

[0049] Construct the drive signal optimization model as follows:

[0050]

[0051] Solve the driving signal optimization model to obtain the driving signal vector q corresponding to the loudspeaker with the maximum eigenvalue i * .

[0052] In some embodiments, optimizing the driving signal further includes:

[0053] Q i * After multiplying by the coefficient c, the optimization model for the coefficient c is constructed as follows:

[0054]

[0055] in, is the square of the 2-norm, P BT (f i ) is the target sound field matrix for bright area reconstruction;

[0056] Solve the optimization model about coefficient c to obtain the optimal solution c of coefficient c * , q i * Multiply by c * After that, we get c * q i * , and as the driving signal vector q of the loudspeaker i .

[0057] In some embodiments, the linear weighted goal programming model is:

[0058]

[0059] Among them, F a (X)+s a -t a =b a , a=1,2,3, X is the decision variable vector, when a=1, F1(X) is the first target based on the contrast of the sound potential energy in the bright area and the dark area, when a=2, F2(X) is the second target based on the number of loudspeakers, when a=3, F3(X) is the third target based on the normalized mean square error in the bright area and the spatial consistency of the loudspeaker signal in the bright area; s a ,t a ≥0,s a ×t a =0, b a F a The expected value of (X), s a F a (X) is a positive deviation variable, u a For positive deviation variable s a The weight of a F a (X) is the negative deviation variable, v a is a negative deviation variable t a The weight, w a is the standardization constant.

[0060] In a second aspect, a multi-objective optimization system for a loudspeaker array with sound field partition control is provided, comprising:

[0061] The first module is used to: divide the sound field area into a bright area and a dark area according to listening expectations;

[0062] The second module is used to: construct a decision variable vector according to 0-1 type planning using the speaker arrangement candidate points as decision variables;

[0063] The third module is used to: construct a multi-objective optimization model about the decision variable vector, wherein the multi-objectives include: a first objective based on the contrast of acoustic potential energy between bright and dark areas, a second objective based on the number of loudspeakers, and a third objective based on the normalized mean square error of the bright area and the spatial consistency of the loudspeaker signal in the bright area;

[0064] A fourth module is used to: solve the multi-objective optimization model using a multi-objective genetic algorithm to obtain a plurality of Pareto optimal solutions;

[0065] A fifth module is used to construct a linear weighted objective programming model based on the expected values ​​of the multiple objectives, the objective function values ​​of the multiple objectives corresponding to each of the Pareto optimal solutions, and the weight coefficients of the negative deviation variables and the positive deviation variables of the objective function values ​​relative to the expected values;

[0066] The sixth module is used to solve the linear weighted objective programming model to obtain the Pareto optimal solution corresponding to the objective function value when the linear weighted objective programming model takes the minimum value, and obtain the optimal position and number of the speaker array.

[0067] The beneficial effects of the technical solution provided by this application include:

[0068] The multi-objective optimization method provided in the embodiment of the present application adopts a multi-objective optimization strategy, comprehensively considering the contrast of the acoustic potential energy in the bright area and the dark area, the normalized mean square error in the bright area, the spatial consistency in the bright area, and the minimization of the number of speakers, so as to effectively balance the sound field quality and cost, and is particularly suitable for the speaker layout in the limited space in the car. At the same time, the linear weighted target programming is used to accurately select the speaker position, achieve the best balance between multiple objectives, and further improve the sound field zoning effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0070] Figure 1 A flow chart of a multi-objective optimization method for a loudspeaker array for sound field partition control provided in an embodiment of the present application;

[0071] Figure 2 A distribution diagram of the speaker array and microphone array provided in the embodiment of the present application;

[0072] Figure 3 A top view of a speaker array provided in an embodiment of the present application;

[0073] Figure 4 A side view of a speaker array provided in an embodiment of the present application;

[0074] Figure 5 The optimal speaker array diagram obtained by the multi-objective optimization method provided in the embodiment of the present application;

[0075] Figure 6 The optimal loudspeaker array diagram obtained by the single-objective optimization method provided in the comparative example of the present application;

[0076] Figure 7 A result diagram of the bright area spatial consistency provided in the embodiment of the present application;

[0077] Figure 8 A normalized mean square error result diagram of the bright area provided in the embodiment of the present application;

[0078] Fig. 9 This is a result diagram of the contrast between the acoustic potential energy in the bright and dark areas provided in the embodiment of the present application. DETAILED DESCRIPTION

[0079] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0080] See also Figure 1 As shown, the embodiment of the present application provides a multi-objective optimization method for a loudspeaker array with sound field partition control, which includes the following steps:

[0081] 10: Divide the sound field into bright and dark areas based on listening expectations.

[0082] For step 10, when dividing the sound field area, the area where a specific sound field is desired to be generated is defined as a bright area, and the other areas are defined as dark areas.

[0083] For example, as an example, the interior of a car can be divided into four independent sound zones according to the position of the cabin, and these four cabin areas are symmetrical about the center of the roof.

[0084] Generally speaking, microphones are evenly arranged in the space where the passenger's head is located in each cabin area as control points, and speakers are arranged on the roof and doors, so candidate points for speaker arrangement can be set on the roof and doors.

[0085] After the speaker array emits sound according to the input driving signal, the microphone array located at the passenger's head collects the signal. According to the speaker driving signal and the sound signal collected by the microphone, the transfer function matrix from the speaker array to the microphone array can be obtained, which is related to the signal frequency.

[0086] Determining the transfer function matrix specifically includes the following steps:

[0087] 101: Arrange speakers at all speaker arrangement candidate points, and arrange microphones in bright areas and dark areas.

[0088] 102: Give a test drive signal to each of the loudspeakers one by one, and obtain the measured sound pressure collected by each microphone.

[0089] 103: Based on the test drive signal and the measured sound pressure, obtain the transfer function matrix G (f i ):

[0090]

[0091] Among them, p 0，mj (f i ) is the measured sound pressure of the speaker at the jth speaker arrangement candidate point collected by the mth microphone, m = 1, 2, ..., M, M is the total number of microphones in the sound field area, q 0，j (f i ) is the test drive signal of the jth speaker, j=1, 2, ..., K, K is the total number of candidate points for speaker arrangement in the sound field area, is the transfer function from the jth loudspeaker to the mth microphone, i is the frequency point order, f i is the frequency of the i-th frequency point, i=1,2,…, f min is the lower limit of the speaker frequency, f max is the upper limit of the speaker frequency, f i+1 -f i =1Hz, f i =f min ,f min +1,f min +2,…,f max , the frequency of each frequency point increases in increments of 1 Hz.

[0092] Through step 103, different frequencies f can be obtained. i The transfer function matrix G(f i ).

[0093] 104: According to the microphones in the bright area and the dark area, the transfer function matrix G (fi ) to obtain the transfer function matrix G from the loudspeaker to the bright area microphone B (f i ) and the transfer function matrix G from the loudspeaker to the dark area microphone D (f i ).

[0094] The sound pressure at the M microphones is a linear transformation of the driving signal in the frequency domain, and the corresponding linear transformation matrix is ​​the transfer function matrix G(f i ), from which we can get:

[0095]

[0096] Among them, P B (f i ) is the sound pressure received by the microphone in the bright area, P D (f i ) is the sound pressure received by the microphone in the dark area.

[0097] 20: Using the candidate speaker placement points as decision variables, construct a decision variable vector according to the 0-1 type planning.

[0098] Speaker array optimization aims to select the best arrangement from a set of speaker arrangement candidate points, which can usually be constructed as a combinatorial optimization problem. The control variables of the optimization model can be described by 0-1 type variables. 1 means that the speaker is arranged at the candidate point, and 0 means that the speaker is not arranged at the point.

[0099] Therefore, the decision variable vector is recorded as X, which includes:

[0100] X=[x1,x2,…,x j ,…,x K ]

[0101] Among them, x j = 0 or 1, when x j = 0, no loudspeaker is set at the jth loudspeaker placement candidate point. j =1, the jth speaker arrangement candidate point sets the speaker, j=1, 2, ..., K, K is the total number of speaker arrangement candidate points in the sound field area.

[0102] According to the common speaker positions in current vehicles, the door speakers are considered and the geometric dimensions of the vehicle are abstracted into a rectangular parallelepiped. For the aesthetics of the vehicle, the speakers are considered to be symmetrical about the center point of the roof. The candidate points are shown in Figure 2 A diagram showing the locations of the speaker and microphone array in a car.

[0103] The sound field area is divided into four cabin areas symmetrical about the center of the roof according to the position of the cabin in the car, so that x j′ =x j′+L =x j′+2L =x j′+3L ;

[0104] Wherein, j′=1, 2, …, L, L is the total number of candidate points for speaker placement in each cockpit area, and K=4L.

[0105] 30: Construct a multi-objective optimization model about the decision variable vector, wherein the multi-objectives include: a first objective based on the contrast of sound potential energy in the bright area and the dark area, a second objective based on the number of loudspeakers, and a third objective based on the normalized mean square error of the bright area and the spatial consistency of the loudspeaker signal in the bright area.

[0106] In step 30, the multi-objective optimization model is as follows:

[0107]

[0108] Among them, F1(X) is the first goal based on the contrast of sound potential energy in bright area and dark area, F2(X) is the second goal based on the number of loudspeakers, and F3(X) is the third goal based on the normalized mean square error in bright area and the spatial consistency of loudspeaker signal in bright area.

[0109] (1) Build the first goal

[0110] The contrast of the acoustic potential energy between the bright and dark areas is denoted as AC i (X), and AC i (X) is:

[0111]

[0112] The AC i (X) The higher the index value, the higher the contrast between the sound potential energy in the bright area and the dark area. In the optimization design of the speaker array, it is generally hoped to obtain a better acoustic experience in the bright area by improving the contrast.

[0113] Among them, G B (f i ) is the transfer function matrix from the loudspeaker to the bright area microphone, with a dimension of M B ×K,M B is the number of microphones in the bright area, G B H (f i ) is G B (f i )'s conjugate transposed matrix;

[0114] G D (f i) is the transfer function matrix from the loudspeaker to the dark area microphone, with a dimension of M D ×K,M D is the number of microphones in the dark area, G D H (f i ) is G D (f i ) is the conjugate transposed matrix; M B +M D =M, M is the total number of microphones in the sound field area;

[0115] q i is the driving signal vector of the speaker, with a dimension of K×1, K is the total number of candidate points for speaker placement in the sound field area, q i H Q i The conjugate transposed matrix of ;

[0116] q i =[x1q1(f i ),x2q2(f i ),…,x j q j (f i ),…,x K q K (f i )] T

[0117] Where j = 1, 2, ..., K, q j (f i ) is the driving signal of the jth speaker, i is the frequency point order, f i is the frequency of the i-th frequency point, i=1,2,…, f min is the lower limit of the speaker frequency, f max is the upper limit of the speaker frequency, f i+1 -f i =1Hz, f i =f min ,f min +1,f min +2,…,f max ; From the above q i As can be seen from the expression, it is q i Related to the decision variable vector X.

[0118] Based on the contrast of acoustic potential energy in bright and dark areas AC i The first objective F1(X) of (X) is:

[0119]

[0120] (2) Constructing the Second Target

[0121] The second goal is based on the number of speakers. In the optimization of speaker array layout, it is always hoped to reduce the use of speakers to reduce costs.

[0122] Thus, the second target F2(X) based on the number of speakers is:

[0123] F2(X)=||X||1

[0124] Here, ||·||1 is the 1-norm.

[0125] The number of loudspeakers essentially refers to the number of non-zero elements in the decision variable vector X, so the 1-norm is used as an indicator to describe it.

[0126] (3) Constructing the third goal

[0127] For the error percentage of the bright area sound field, in order to overcome the difference in index comparison caused by the error order of magnitude, the normalized bright area sound field error ratio is used, that is, the normalized mean square error of the bright area is recorded as E r,i (X), and E r,i (X) is:

[0128]

[0129] Among them, P BT (f i ) is the target sound field matrix for bright area reconstruction, is the square of the 2-norm;

[0130] In order to describe the spatial consistency of the loudspeaker signal in the bright area, ΔL is introduced. i (X), the smaller the index is, the more concentrated the sound pressure distribution is, that is, the spatial consistency of the loudspeaker signal in the bright area is recorded as ΔL i (X), and ΔL i (X) is:

[0131] ΔL i (X) = L i,B,max (X)-L i,B,min (X)

[0132]

[0133] Among them, L i,B (X) is the sound pressure level of the microphone in the bright area, P ref (f i ) is the reference sound pressure, L i,B,max (X) is the L of all microphones in the bright area i,B The maximum value of (X), L i,B,min (X) is the L of all microphones in the bright areai,B (X) minimum value;

[0134] Therefore, the third objective F3(X) based on the normalized mean square error in the bright area and the spatial consistency of the loudspeaker signal in the bright area is:

[0135]

[0136] 40: Using a multi-objective genetic algorithm to solve the multi-objective optimization model to obtain several Pareto optimal solutions.

[0137] The multi-objective genetic algorithm (NSGA-II algorithm) is an evolutionary algorithm specifically used to solve multi-objective optimization problems. Its main idea is to effectively search and maintain the non-dominated solution set in the optimization problem by combining the basic principles of genetic algorithms and non-dominated sorting techniques.

[0138] For multi-objective optimization models:

[0139]

[0140] Any two solutions X Δ1 and X Δ2 , if the following two conditions are met:

[0141] (1) Non-Domination: For all a=1, 2, 3, there is F a (X Δ1 )≤F a (X Δ2 ).

[0142] (1) Improvement: There is at least one objective function F a′ (X), so that F a (X Δ1 )<F a (X Δ2 ), a′=1,2,3.

[0143] Then solve X Δ1 As a Pareto optimal solution, we can obtain a set of Pareto optimal solutions, also called Pareto frontier.

[0144] 50: Construct a linear weighted objective programming model based on the expected values ​​of the multiple objectives, the objective function values ​​of the multiple objectives corresponding to each of the Pareto optimal solutions, and the weight coefficients of the negative deviation variables and the positive deviation variables of the objective function values ​​relative to the expected values.

[0145] For the three optimization objectives F a (X), give each optimization target an expected value b a, thus, a linear weighted target programming model can be obtained, and the linear weighted target programming model is:

[0146]

[0147] Among them, F a (X)+s a -t a =b a , a=1,2,3, X is the decision variable vector, when a=1, F1(X) is the first target based on the contrast of the sound potential energy in the bright area and the dark area, when a=2, F2(X) is the second target based on the number of loudspeakers, when a=3, F3(X) is the third target based on the normalized mean square error in the bright area and the spatial consistency of the loudspeaker signal in the bright area; s a ,t a ≥0,s a ×t a =0, b a F a The expected value of (X), s a F a (X) is a positive deviation variable, u a For positive deviation variable s a The weight of a F a (X) is the negative deviation variable, v a is a negative deviation variable t a The weight, w a is the standardization constant.

[0148] According to F a (X)+s a -t a =b a ,a=1,2,3,F a (X) is the corresponding objective function value calculated from the Pareto optimal solution.

[0149] 60: Solving the linear weighted objective programming model to obtain a Pareto optimal solution corresponding to the objective function value when the linear weighted objective programming model takes a minimum value, and obtaining the optimal position and number of the speaker array.

[0150] According to the above linear weighted goal programming model, the minimum value is solved, and F can be obtained. a The Pareto optimal solution corresponding to (X) is obtained, and then the decision variable vector X is obtained. According to the value of each element of the decision variable vector X (0 or 1), the optimal position and number of the speaker array can be obtained.

[0151] In order to improve the sound field partition control effect, the method provided in the present application also includes optimizing the driving signal, and the optimization of the driving signal specifically includes the following steps:

[0152] 301: Construct a drive signal optimization model as follows:

[0153]

[0154] 302: Solve the driving signal optimization model to obtain the driving signal vector q corresponding to the loudspeaker with the maximum eigenvalue i * .

[0155] It is understandable that when performing step 302, due to q i The elements of are related to the decision variable vector X, that is, X = [x1, x2, ..., x j ,…,x K ], x j =0 or 1. If an element is 0, then q i The elements at the corresponding positions in are also 0. Therefore, when solving the above optimization model, when a row or a column is 0, the row or column can be temporarily removed to obtain the maximum eigenvalue. After completion, the driving signal vector is supplemented with 0 to obtain q i * .

[0156] This application optimizes the driving signal by constructing a driving signal optimization model to use the optimized driving signal q i * , which can further improve the sound potential energy contrast between the bright area and the dark area and the spatial consistency of the speaker signal in the bright area, and enhance the passengers' experience of enjoying independent sound zones.

[0157] Furthermore, after completing step 302, the drive signal is optimized, further comprising the following steps:

[0158] 303: q i * After multiplying by the coefficient c, the optimization model for the coefficient c is constructed as follows:

[0159]

[0160] in, is the square of the 2-norm, P BT (f i ) is the target sound field matrix for bright area reconstruction;

[0161] 304: Solve the optimization model for coefficient c to obtain the optimal solution c for coefficient c * , q i * Multiply by c * After that, we get c * qi * , and as the driving signal vector q of the loudspeaker i .

[0162] By constructing an optimization model for coefficient c, the optimal solution c of coefficient c is obtained. * , to apply the optimized drive signal c * q i * , which can further improve the normalized mean square error of the bright area and enhance the passengers' experience of enjoying independent sound zones.

[0163] It can be seen that the multi-objective optimization method provided in the embodiment of the present application, first of all, adopts a multi-objective optimization strategy, comprehensively considering the sound potential energy contrast between the bright and dark areas, the normalized mean square error of the bright area, the spatial consistency of the bright area and the minimization of the number of speakers, so as to effectively balance the sound field quality and cost, and is particularly suitable for the speaker layout in the limited space in the car. Secondly, by optimizing the driving signal, the present application significantly improves the sound field partition control effect, improves the sound potential energy contrast between the bright and dark areas and the spatial consistency of the bright area, improves the normalized mean square error of the bright area, and enhances the passengers' enjoyment of independent sound zone experience. Finally, the linear weighted target planning is used to accurately select the speaker position, achieve the best balance between multiple objectives, and further improve the sound field partition effect.

[0164] In order to verify the effect of the multi-objective optimization method of the present application in the optimization of the loudspeaker array layout, the algorithm is compared with the single-objective optimization algorithm.

[0165] Table 1 Microphone location information

[0166]

[0167] Table 1 takes the left front area as an example, with a length of 1:0.1:1.2, which means that microphones are placed every 0.1m in the longitudinal range of 1 to 1.2m; microphones are placed every 0.3m in the lateral range of 0.2m to 0.8m; and microphones are placed every 0.2m in the height range of 0.8m to 1m. Therefore, there are 18 microphones in the left front area. The placement of the right front area, left rear area, and right rear area is similar.

[0168] Table 2. Location information of candidate loudspeaker placement points

[0169]

[0170] Table 2 is the location information of the candidate points for speaker layout. The specific layout of the speaker array and microphone array is as follows: the microphone array in each area is composed of 18 microphones, and the speaker array in each roof area and door area is composed of 9 speakers respectively. The specific situation of the controlled frequency points is as follows: the control frequency range is 0Hz~900Hz, and the control frequency interval is 1Hz. At the same time, consider that the user needs to set the right front area (co-pilot) as a bright area and other areas as dark areas. See Table 1 for the microphone installation layout coordinates, and Table 2 for the speaker layout candidate point location information. For the speaker array and microphone array distribution, see Figure 2 . Figure 3 A top view of the speaker array. Figure 4 A side view of the speaker array.

[0171] In this specific case, the results of the multi-objective optimization method of this application are compared as follows:

[0172] Figure 5 is the optimal speaker array diagram obtained by the multi-objective optimization method of this application. For comparison, Figure 6 The optimal loudspeaker array diagram of the single-objective optimization method is listed.

[0173] Figure 7 This is a diagram of the spatial consistency results in the bright area. In the figure, the average consistency index of the single-objective optimization method is 5.7506dB, and the average consistency index of the multi-objective optimization method is 3.9322dB. This figure shows that the present application has better consistency.

[0174] Figure 8 This is the normalized mean square error result diagram for the bright area. In this diagram, the average value of the target sound field restoration error index of the single-objective optimization method is 0.9930, and the average value of the target sound field restoration error index of the multi-objective optimization method is 0.9945.

[0175] Fig. 9 Shown is the result graph of the contrast between the sound potential energy of the bright area and the dark area. In the range of 0Hz to 900Hz, the average value of the contrast between the sound potential energy of the bright area and the dark area of ​​the single-objective optimization method is 13.1011dB, and the average value of the contrast between the sound potential energy of the bright area and the dark area of ​​the present application is 25.8820dB. The above results show that the bright area consistency and the contrast between the sound potential energy of the bright area and the dark area of ​​the present application method are better than the single-objective optimization method when the error loss of the target sound field restoration is not large.

[0176] In summary, the present application maintains a low normalized mean square error in the bright area, while improving the sound potential energy contrast between the bright area and the dark area and the spatial consistency of the bright area. Therefore, the method of the present application has good speaker array layout optimization performance.

[0177] Based on the above method, the embodiment of the present application further provides a speaker array multi-objective optimization system for sound field partition control, which includes:

[0178] The first module is used to: divide the sound field area into a bright area and a dark area according to listening expectations;

[0179] The second module is used to: construct a decision variable vector according to 0-1 type planning using the speaker arrangement candidate points as decision variables;

[0180] The third module is used to: construct a multi-objective optimization model about the decision variable vector, wherein the multi-objectives include: a first objective based on the contrast of acoustic potential energy between bright and dark areas, a second objective based on the number of loudspeakers, and a third objective based on the normalized mean square error of the bright area and the spatial consistency of the loudspeaker signal in the bright area;

[0181] A fourth module is used to: solve the multi-objective optimization model using a multi-objective genetic algorithm to obtain a plurality of Pareto optimal solutions;

[0182] A fifth module is used to construct a linear weighted objective programming model based on the expected values ​​of the multiple objectives, the objective function values ​​of the multiple objectives corresponding to each of the Pareto optimal solutions, and the weight coefficients of the negative deviation variables and the positive deviation variables of the objective function values ​​relative to the expected values;

[0183] The sixth module is used to solve the linear weighted objective programming model to obtain the Pareto optimal solution corresponding to the objective function value when the linear weighted objective programming model takes the minimum value, and obtain the optimal position and number of the speaker array.

[0184] In the description of the present application, it should be noted that the terms "upper", "lower", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. Unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances.

[0185] It should be noted that, in this application, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0186] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features applied for herein.

Claims

1. A multi-objective optimization method for a loudspeaker array with sound field partition control, characterized in that: It includes: According to listening expectations, the sound field area is divided into bright area and dark area; Taking the candidate points for speaker placement as decision variables, a decision variable vector is constructed according to the 0-1 type planning; Constructing a multi-objective optimization model about the decision variable vector, wherein the multi-objectives include: a first objective based on the contrast of acoustic potential energy between bright and dark areas, a second objective based on the number of loudspeakers, and a third objective based on the normalized mean square error of the bright area and the spatial consistency of the loudspeaker signal in the bright area; Using a multi-objective genetic algorithm to solve the multi-objective optimization model to obtain several Pareto optimal solutions; Constructing a linear weighted objective programming model based on the expected values ​​of the multiple objectives, the objective function values ​​of the multiple objectives corresponding to each of the Pareto optimal solutions, and the weight coefficients of the negative deviation variables and the positive deviation variables of the objective function values ​​relative to the expected values; The linear weighted objective programming model is solved to obtain the Pareto optimal solution corresponding to the objective function value when the linear weighted objective programming model takes the minimum value, and the optimal position and number of the loudspeaker array are obtained.

2. The multi-objective optimization method for a loudspeaker array with sound field partition control according to claim 1, characterized in that: The decision variable vector is denoted as X, which includes: Among them, x j = 0 or 1, when x j = 0, no loudspeaker is set at the jth loudspeaker placement candidate point. j =1, the jth speaker arrangement candidate point sets the speaker, j=1, 2, ..., K, K is the total number of speaker arrangement candidate points in the sound field area.

3. The multi-objective optimization method for a loudspeaker array with sound field zoning control according to claim 2, characterized in that: The sound field area is divided into four cabin areas symmetrical about the center of the roof according to the position of the cabin in the car, so that x j′ =x j′+L =x j′+2L =x j′+3L ; Wherein, j′=1, 2, …, L, L is the total number of candidate points for speaker placement in each cockpit area, and K=4L.

4. The multi-objective optimization method for a loudspeaker array with sound field zoning control according to claim 2, characterized in that: The multi-objective optimization model is as follows: Among them, F1(X) is the first goal based on the contrast of sound potential energy in bright area and dark area, F2(X) is the second goal based on the number of loudspeakers, and F3(X) is the third goal based on the normalized mean square error in bright area and the spatial consistency of loudspeaker signal in bright area.

5. The multi-objective optimization method for a loudspeaker array with sound field zoning control according to claim 4, characterized in that: The contrast of the acoustic potential energy between the bright and dark areas is denoted as AC i (X), and AC i (X) is: Among them, G B (f i ) is the transfer function matrix from the loudspeaker to the bright area microphone, with a dimension of M B ×K,M B is the number of microphones in the bright area, G B H (f i ) is G B (f i )'s conjugate transposed matrix; G D (f i ) is the transfer function matrix from the loudspeaker to the dark area microphone, with a dimension of M D ×K,M D is the number of microphones in the dark area, G D H (f i ) is G D (f i ) is the conjugate transposed matrix; M B +M D =M, M is the total number of microphones in the sound field area; q i is the driving signal vector of the speaker, with a dimension of K×1, K is the total number of candidate points for speaker placement in the sound field area, q i H for q i The conjugate transposed matrix of ; q i =[x1q1(f i ),x2q2(f i ),…,x j q j (f i ),…,x K q K (f i )] T Where j = 1, 2, ..., K, q j (f i ) is the driving signal of the jth speaker, i is the frequency point order, f i is the frequency of the ith frequency point, f min is the lower limit of the speaker frequency, f max is the upper limit of the speaker frequency, f i+1 -f i =1Hz, f i =f min ,f min +1,f min +2,…,f max ; Based on the contrast of acoustic potential energy in bright and dark areas AC i The first objective F1(X) of (X) is:

6. The multi-objective optimization method for a loudspeaker array with sound field zoning control according to claim 4, characterized in that: The second target F2(X) based on the number of speakers is: F2(X)=||X||1 Here, ||·||1 is the 1-norm.

7. The multi-objective optimization method for a loudspeaker array with sound field zoning control according to claim 5, characterized in that: The normalized mean square error of the bright area is recorded as E r,i (X), and E r,i (X) is: Among them, P BT (f i ) is the target sound field matrix for bright area reconstruction, is the square of the 2-norm; The spatial consistency of the loudspeaker signal in the bright area is denoted as ΔL i (X), and ΔL i (X) is: ΔL i (X)=L i,B,max (X)-L i,B,min (X) Among them, L i,B (X) is the sound pressure level of the microphone in the bright area, P ref (f i ) is the reference sound pressure, L i,B,max (X) is the L of all microphones in the bright area i,B The maximum value of (X), L i,B,min (X) is the L of all microphones in the bright area i,B (X) minimum value; The third objective F3(X) based on the normalized mean square error in the bright area and the spatial consistency of the loudspeaker signal in the bright area is:

8. The multi-objective optimization method for a loudspeaker array with sound field zoning control according to claim 5, characterized in that: The method further includes determining a transfer function matrix, wherein determining the transfer function matrix specifically includes: Arrange speakers at all candidate speaker arrangement points, and arrange microphones in bright areas and dark areas; Giving a test drive signal to each of the loudspeakers one by one, and obtaining the measured sound pressure collected by each microphone; Based on the test drive signal and the measured sound pressure, the transfer function matrix G(f i ): Among them, p 0，mj (f i ) is the measured sound pressure of the speaker at the jth speaker arrangement candidate point collected by the mth microphone, m = 1, 2, ..., M, q 0，j (f i ) is the test driving signal of the jth speaker, is the transfer function from the jth loudspeaker to the mth microphone; According to the microphones in the bright area and the dark area, the transfer function matrix G(f i ) to obtain the transfer function matrix G from the loudspeaker to the bright area microphone B (f i ) and the transfer function matrix G from the loudspeaker to the dark area microphone D (f i ).

9. The multi-objective optimization method for a loudspeaker array with sound field zoning control according to claim 5, characterized in that: The method further includes optimizing the driving signal, and the optimization of the driving signal specifically includes: Construct the drive signal optimization model as follows: Solve the driving signal optimization model to obtain the driving signal vector q corresponding to the loudspeaker with the maximum eigenvalue i * .

10. The multi-objective optimization method for a loudspeaker array with sound field zoning control according to claim 9, characterized in that: Optimizing the drive signal also includes: Q i * After multiplying by the coefficient c, the optimization model for the coefficient c is constructed as follows: in, is the square of the 2-norm, P BT (f i ) is the target sound field matrix for bright area reconstruction; Solve the optimization model about coefficient c to obtain the optimal solution c of coefficient c * , q i * Multiply by c * After that, we get c * q i * , and as the driving signal vector q of the loudspeaker i .

11. The multi-objective optimization method for a loudspeaker array with sound field zoning control according to claim 1, characterized in that: The linear weighted goal programming model is: Among them, F a (X)+s a -t a =b a , a=1,2,3, X is the decision variable vector, when a=1, F1(X) is the first target based on the contrast of the sound potential energy in the bright area and the dark area, when a=2, F2(X) is the second target based on the number of loudspeakers, when a=3, F3(X) is the third target based on the normalized mean square error in the bright area and the spatial consistency of the loudspeaker signal in the bright area; s a ,t a ≥0,s a ×t a =0, b a F a The expected value of (X), s a F a (X) is a positive deviation variable, u a For positive deviation variable s a The weight of a F a (X) is the negative deviation variable, v a is a negative deviation variable t a The weight, w a is the standardization constant.

12. A multi-objective optimization system for a loudspeaker array with sound field partition control, characterized in that: It includes: The first module is used to: divide the sound field area into a bright area and a dark area according to listening expectations; The second module is used to: construct a decision variable vector according to 0-1 type planning using the speaker arrangement candidate points as decision variables; The third module is used to: construct a multi-objective optimization model about the decision variable vector, wherein the multi-objectives include: a first objective based on the contrast of acoustic potential energy between bright and dark areas, a second objective based on the number of loudspeakers, and a third objective based on the normalized mean square error of the bright area and the spatial consistency of the loudspeaker signal in the bright area; A fourth module is used to: solve the multi-objective optimization model using a multi-objective genetic algorithm to obtain a plurality of Pareto optimal solutions; A fifth module is used to construct a linear weighted objective programming model based on the expected values ​​of the multiple objectives, the objective function values ​​of the multiple objectives corresponding to each of the Pareto optimal solutions, and the weight coefficients of the negative deviation variables and the positive deviation variables of the objective function values ​​relative to the expected values; The sixth module is used to solve the linear weighted objective programming model to obtain the Pareto optimal solution corresponding to the objective function value when the linear weighted objective programming model takes the minimum value, and obtain the optimal position and number of the speaker array.

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