Optimization method and device for multi-target collaborative three-dimensional microphone array for sound field measurement

Abstract

The present invention discloses a method and device for optimizing a three-dimensional microphone array for multi-target collaboration in sound field measurement, and relates to the technical field of microphone array signal measurement. The method comprises: generating an initial array element group of a microphone array through an improved Sine chaotic mapping; constructing a hybrid fitness function to calculate the fitness of the microphone array to the sound source positioning and sound field reconstruction algorithm; and obtaining an optimized three-dimensional microphone array for multi-target collaboration in sound field measurement according to the hybrid fitness function using the whale elite optimization algorithm. The present invention can solve the problem that the initial array element group cannot be evenly distributed in the entire space, improve the fitness of the microphone array for the sound source positioning and sound field reconstruction algorithm, and realize the optimal distribution of the three-dimensional microphone array elements and the excitation weights of the microphone array elements. Through the fusion of the above three parts, a three-dimensional microphone array with high fitness for beamforming and acoustic holography algorithms is finally optimized, which can effectively improve the output response characteristics of the microphone array.

Description

Technical Field

[0001] The present invention relates to the technical field of microphone array signal measurement, and in particular to a method and device for optimizing a multi-target collaborative three-dimensional microphone array for sound field measurement. Background Art

[0002] A microphone array is composed of multiple acoustic sensor units arranged in a certain structure. The sound source distribution is determined by performing correlation analysis on the measured sound signals. The structure of the microphone array affects the spatial resolution of sound source identification and the accuracy of sound field reconstruction. Therefore, it is necessary to analyze the factors that affect the performance of the microphone array in order to find the most reasonable microphone array structure. The geometric parameters of the microphone array mainly include the microphone array aperture size, microphone spacing, microphone spatial position, and the number of microphones. Among them, the larger the microphone array aperture, the smaller the sound source frequency that can be measured; the smaller the microphone array aperture, the lower the spatial resolution of sound source identification; the microphone spacing determines the range of sound source frequencies that the microphone array can identify; and the microphone spatial position determines that the microphone array has different main lobe widths and sidelobe levels.

[0003] Sound source localization and sound field reconstruction are two major research hotspots in acoustics. They have been widely used in noise identification in recent years. Sound source localization uses a positioning algorithm to process measured acoustic signals to determine the direction and distance of the sound source relative to the microphone. It is primarily used for ship and vehicle detection, locating major noise sources in machinery, target selection and interference suppression in communication equipment or speech recognition processing, and condition monitoring of mechanical systems. The most commonly used method is the beamforming algorithm. Sound field reconstruction uses a reconstruction algorithm to accurately obtain the sound source amplitude and spatial sound pressure distribution. By analyzing the noise distribution in different spatial regions and clarifying the sound propagation path, it can provide a reference for noise assessment and noise reduction. The most commonly used method is the acoustic holography algorithm.

[0004] Among beamforming methods, this one boasts superior high-frequency spatial resolution. Beamforming calculates the sound source location based on the phase of the sound signal. The larger the element spacing, the narrower the beamwidth, and the higher the positioning accuracy. However, if the element spacing is too small, the beamwidth becomes too wide, resulting in insufficient positioning accuracy. Excessive element spacing increases inter-element delay, leading to larger phase differences and the generation of grating lobes, which can cause positioning ambiguity.

[0005] Acoustic holography, which has superior low-frequency spatial resolution, reversely calculates the sound source location based on the sound signal amplitude. As the spacing between array elements increases, the reconstructed position deviation also increases. Therefore, by varying the array element spacing and element excitation weights, the accuracy of the sound source localization results from the beamforming algorithm and the acoustic holography algorithm becomes competitive.

[0006] In traditional microphone array methods, various optimization methods have been proposed that can only solve one of the two major problems: the positioning accuracy of the microphone array beamforming algorithm or the sound field reconstruction accuracy of the acoustic holography algorithm. Faced with the different requirements of the measurement microphone array for simultaneously solving sound source localization and sound field reconstruction, traditional optimization methods cannot effectively solve the problem. Summary of the Invention

[0007] The present invention addresses the problem that in traditional microphone array methods, various optimization methods have been proposed that can only solve one of the two major problems, namely, the positioning accuracy of the microphone array beamforming algorithm or the sound field reconstruction accuracy of the acoustic holography algorithm. In the face of the different requirements of the measurement microphone array for simultaneously solving the sound source positioning and sound field reconstruction, the traditional optimization methods cannot effectively solve the problem, and thus the present invention is proposed.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0009] In one aspect, the present invention provides a method for optimizing a multi-objective collaborative three-dimensional microphone array for sound field measurement. The method is implemented by an electronic device and includes:

[0010] S1. Obtain the geometric parameters of the traditional microphone array element group and generate the initial generation of microphone array element group through the improved Sine chaotic mapping.

[0011] S2. Construct a hybrid fitness function.

[0012] S3. Based on the initial array element group of the microphone array, the hybrid fitness function and the whale elite optimization algorithm, an optimized three-dimensional microphone array for multi-target collaboration in sound field measurement is obtained.

[0013] Optionally, the step of obtaining geometric parameters of a conventional microphone array element group in S1 and generating an initial microphone array element group through an improved Sine chaotic mapping includes:

[0014] S11. Acquire geometric parameters of a conventional array element group of a microphone array; wherein the geometric parameters include the number of array elements, the initial size of the conventional array element group, the microphone array aperture, the lower limit of the array element spacing constraint threshold, and the upper limit of the array element spacing constraint threshold.

[0015] S12. Setting the control parameters and initial parameters of the improved Sine chaotic map.

[0016] S13. Generate sparse initial microphone array positions according to the geometric parameters and the improved Sine chaotic map to obtain the initial generation of microphone array element groups.

[0017] Optionally, the construction of a hybrid fitness function in S2 includes:

[0018] S21. Construct the peak sidelobe level PSLL as the optimization target to evaluate the suitability of the microphone array for the beamforming algorithm in sound source localization.

[0019] S22. Construct the peak signal-to-noise ratio (PSNR) as the optimization target to evaluate the applicability of the acoustic holography algorithm in the microphone array for sound field reconstruction.

[0020] S23. Construct a mixing function according to the sidelobe level and the peak signal-to-noise ratio, and then construct a mixing fitness function.

[0021] Optionally, the mixing function is as shown in the following formula (1):

[0022]

[0023] Among them, α1, α2∈(0,1), w represents the iterative sequence value of the improved Sine chaotic map, and d represents the initial parameter of the improved Sine chaotic map.

[0024] Optionally, a hybrid fitness function is shown in the following formula (2):

[0025]

[0026] Among them, fitness represents the mixing function, BW -3dB Indicates the main lobe width, BW thr represents the main lobe width constraint threshold, d ij Represents the distance between any two array elements, i, j = 1, 2, 3...N P , N P is the size of the traditional microphone array element group; d min Indicates the lower limit of the element spacing constraint threshold; d max Indicates the upper limit of the element spacing constraint.

[0027] Optionally, in S3, based on the initial array element group of the microphone array, the hybrid fitness function, and the whale elite optimization algorithm, an optimized three-dimensional microphone array for multi-target collaboration in sound field measurement is obtained, including:

[0028] S31. Based on the initial generation of microphone array elements, an excellent element file with the lowest value of the hybrid fitness function is established, and the leading element of the microphone array is selected to generate a microphone array deep search group element group.

[0029] S32. Update the positions and element excitation weights of the traditional group of microphone array elements through the whale elite optimization algorithm to generate a new traditional group of microphone array elements.

[0030] S33. Update the excellent element file according to the new microphone array traditional element group, select new microphone array leading elements, and generate a new microphone array deep search element group.

[0031] S34. Determine whether the array element group size of the new microphone array deep search group array element group reaches the preset maximum array element group size. If so, adjust the number of individuals in the new microphone array traditional group array element group and the new microphone array deep search group array element group, and execute step S35; if not, execute step S35.

[0032] S35. Update the excellent array element file according to the adjusted traditional array element group of the microphone array, and update the deep search array element group.

[0033] S36. Determine whether the preset number of iterations has been reached. If so, obtain the optimal array element group positions and array element excitation weights to obtain an optimized three-dimensional microphone array for multi-target collaboration in sound field measurement. If not, go back to step S32.

[0034] Optionally, in S31, based on the initial array element group of the microphone array, an excellent array element file with the lowest value of the hybrid fitness function is established, and a leading array element of the microphone array is selected to generate an array element group of the microphone array deep search group, including:

[0035] S311. Calculate the mixed fitness function value of each individual in the traditional group of microphone array elements, and sort the individuals from small to large according to the mixed fitness function value.

[0036] S312, establish scale N A The excellent array element archives are selected and the individuals with the lowest contemporary mixed fitness function values ​​are archived.

[0037] S313. Select a leading element of the microphone array according to the deep prey group solution, and generate a deep search group element group near the leading element of the microphone array.

[0038] Optionally, in S32, updating the positions and element excitation weights of the traditional group of microphone array elements by using the whale elite optimization algorithm to generate a new traditional group of microphone array elements includes:

[0039] S321. Iteratively optimize the positions and element excitation weights of the traditional group of array elements of the microphone array by surrounding the prey, advanced spiral contraction surrounding, and advanced searching for the prey to obtain an updated traditional group of array elements of the microphone array.

[0040] S322. Check whether there are any individuals in the updated microphone array traditional group of array elements that exceed the microphone array aperture. If so, randomly generate a new individual in the microphone array aperture to replace the exceeded individual, thereby generating a new microphone array traditional group of array elements.

[0041] Optionally, adjusting the number of elements in the new microphone array traditional group and the new microphone array deep search group in S34 includes:

[0042] According to the deep prey group plan, the number of individuals in the new microphone array traditional group element group and the new microphone array deep search group element group is increased or decreased by N0 units, and the size of the total element group is kept unchanged. The total element group is composed of the new microphone array traditional group element group and the new microphone array deep search group element group.

[0043] On the other hand, the present invention provides a device for optimizing a multi-objective collaborative three-dimensional microphone array for sound field measurement, which is used to implement a method for optimizing a multi-objective collaborative three-dimensional microphone array for sound field measurement. The device includes:

[0044] The acquisition module is used to obtain the geometric parameters of the traditional array element group of the microphone array and generate the initial generation array element group of the microphone array through the improved Sine chaotic mapping.

[0045] Function construction module, used to construct hybrid fitness function.

[0046] The output module is used to obtain an optimized three-dimensional microphone array for multi-target collaboration in sound field measurement based on the initial array element group of the microphone array, the hybrid fitness function and the whale elite optimization algorithm.

[0047] Optionally, the acquisition module is further configured to:

[0048] S11. Acquire geometric parameters of a conventional array element group of a microphone array; wherein the geometric parameters include the number of array elements, the initial size of the conventional array element group, the microphone array aperture, the lower limit of the array element spacing constraint threshold, and the upper limit of the array element spacing constraint threshold.

[0049] S12. Setting the control parameters and initial parameters of the improved Sine chaotic map.

[0050] S13. Generate sparse initial microphone array positions according to the geometric parameters and the improved Sine chaotic map to obtain the initial generation of microphone array element groups.

[0051] Optionally, the function construction module is further used to:

[0052] S21. Construct the peak sidelobe level PSLL as the optimization target to evaluate the suitability of the microphone array for the beamforming algorithm in sound source localization.

[0053] S22. Construct the peak signal-to-noise ratio (PSNR) as the optimization target to evaluate the applicability of the acoustic holography algorithm in the microphone array for sound field reconstruction.

[0054] S23. Construct a mixing function according to the sidelobe level and the peak signal-to-noise ratio, and then construct a mixing fitness function.

[0055] Optionally, the mixing function is as shown in the following formula (1):

[0056]

[0057] Among them, α1, α2∈(0,1), w represents the iterative sequence value of the improved Sine chaotic map, and d represents the initial parameter of the improved Sine chaotic map.

[0058] Optionally, a hybrid fitness function is shown in the following formula (2):

[0059]

[0060] Among them, fitness represents the mixing function, BW -3dB Indicates the main lobe width, BW thr represents the main lobe width constraint threshold, d ij Represents the distance between any two array elements, i, j = 1, 2, 3...N P , N P is the size of the traditional microphone array element group; d min Indicates the lower limit of the element spacing constraint threshold; d max Indicates the upper limit of the element spacing constraint.

[0061] Optionally, the output module is further configured to:

[0062] S31. Based on the initial generation of microphone array elements, an excellent element file with the lowest value of the hybrid fitness function is established, and the leading element of the microphone array is selected to generate a microphone array deep search group element group.

[0063] S32. Update the positions and element excitation weights of the traditional group of microphone array elements through the whale elite optimization algorithm to generate a new traditional group of microphone array elements.

[0064] S33. Update the excellent element file according to the new microphone array traditional element group, select new microphone array leading elements, and generate a new microphone array deep search element group.

[0065] S34. Determine whether the array element group size of the new microphone array deep search group array element group reaches the preset maximum array element group size. If so, adjust the number of individuals in the new microphone array traditional group array element group and the new microphone array deep search group array element group, and execute step S35; if not, execute step S35.

[0066] S35. Update the excellent array element file according to the adjusted traditional array element group of the microphone array, and update the deep search array element group.

[0067] S36. Determine whether the preset number of iterations has been reached. If so, obtain the optimal array element group positions and array element excitation weights to obtain an optimized three-dimensional microphone array for multi-target collaboration in sound field measurement. If not, go back to step S32.

[0068] Optionally, the output module is further configured to:

[0069] S311. Calculate the mixed fitness function value of each individual in the traditional group of microphone array elements, and sort the individuals from small to large according to the mixed fitness function value.

[0070] S312, establish scale N A The excellent array element archives are selected and the individuals with the lowest contemporary mixed fitness function values ​​are archived.

[0071] S313. Select a leading element of the microphone array according to the deep prey group solution, and generate a deep search group element group near the leading element of the microphone array.

[0072] Optionally, the output module is further configured to:

[0073] S321. Iteratively optimize the positions and element excitation weights of the traditional group of array elements of the microphone array by surrounding the prey, advanced spiral contraction surrounding, and advanced searching for the prey to obtain an updated traditional group of array elements of the microphone array.

[0074] S322. Check whether there are any individuals in the updated microphone array traditional group of array elements that exceed the microphone array aperture. If so, randomly generate a new individual in the microphone array aperture to replace the exceeded individual, thereby generating a new microphone array traditional group of array elements.

[0075] Optionally, the output module is further configured to:

[0076] According to the deep prey group plan, the number of individuals in the new microphone array traditional group element group and the new microphone array deep search group element group is increased or decreased by N0 units, and the size of the total element group is kept unchanged. The total element group is composed of the new microphone array traditional group element group and the new microphone array deep search group element group.

[0077] On the one hand, an electronic device is provided, comprising a processor and a memory, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the above-mentioned multi-objective collaborative three-dimensional microphone array optimization method for sound field measurement.

[0078] On the one hand, a computer-readable storage medium is provided, wherein the storage medium stores at least one instruction, and the at least one instruction is loaded and executed by a processor to implement the above-mentioned multi-objective collaborative three-dimensional microphone array optimization method for sound field measurement.

[0079] Compared with the prior art, the above technical solution has at least the following beneficial effects:

[0080] The above scheme proposes a hybrid fitness function to optimize the microphone array. The optimized microphone array can simultaneously meet the two major functional requirements of sound source localization and sound field reconstruction, providing a reference scheme for the design of multifunctional microphone arrays.

[0081] The present invention introduces an improved Sine chaotic mapping to randomly generate array element positions of a three-dimensional sparse microphone array, so that the initial generation of array element groups is evenly distributed in space, has more obvious chaotic characteristics, and accelerates the subsequent optimization convergence speed; introduces a whale elite optimization algorithm, and during the optimization process of array element positions and array element excitation weights, weakens the influence of the previous generation population, expands the array element search range, improves development and detection capabilities, and gets rid of local optimality in the later stage. Through faster local convergence speed, higher convergence accuracy and lower computational complexity, the array element positions and array element excitation weights are iteratively calculated, thereby obtaining a three-dimensional sparse microphone array with higher applicability to beamforming and acoustic holography algorithms.

[0082] This invention is applicable to the optimization of three-dimensional microphone arrays. Traditional optimization methods primarily target two-dimensional array designs, which struggle to accurately capture the temporal, frequency, and spatial distribution of complex sound fields. By optimizing the array element positions and excitation weights, this invention achieves a high-performance three-dimensional microphone array with a wide dynamic response range, addressing the difficulty of accurately measuring the random spatial distribution of noise sources. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0084] Figure 11. It is a flow chart of a method for optimizing a multi-objective collaborative three-dimensional microphone array for sound field measurement provided by an embodiment of the present invention;

[0085] Figure 2 Schematic diagram of the structure of a three-dimensional microphone array optimization method for multi-objective collaboration in sound field measurement provided by an embodiment of the present invention;

[0086] Figure 3 This is a diagram showing an arrangement of an optimized three-dimensional microphone array for measuring multiple sound source signals provided by an embodiment of the present invention;

[0087] Figure 4 This is a block diagram of a multi-objective collaborative three-dimensional microphone array optimization device for sound field measurement provided by an embodiment of the present invention;

[0088] Figure 5 It is a structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0089] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0090] like Figure 1 As shown, the embodiment of the present invention provides a method for optimizing a multi-objective collaborative three-dimensional microphone array for sound field measurement, which can be implemented by an electronic device. Figure 1 The flowchart of the method for optimizing a multi-objective collaborative three-dimensional microphone array for sound field measurement is shown. The processing flow of the method may include the following steps:

[0091] S1. Obtain the geometric parameters of the traditional microphone array element group and generate the initial generation of microphone array element group through the improved Sine chaotic mapping.

[0092] Optionally, the above step S1 may include the following steps S11-S13:

[0093] S11. Obtain geometric parameters of a conventional array element group of a microphone array.

[0094] The geometric parameters may include the number of array elements N, the initial size of the traditional array element group, the microphone array aperture, the lower limit of the array element spacing constraint threshold, and the upper limit of the array element spacing constraint threshold.

[0095] In a feasible embodiment, the process of the present invention is as follows Figure 2 As shown, among them, Figure 3 As shown in the figure, the three-dimensional microphone array consists of two orthogonally distributed planar microphone arrays, each of which has 28 electret free-field microphones. The multi-channel synchronous acquisition of the microphone array is completed using a data acquisition system based on the NI-PXIe bus, with a sampling frequency of 44100 Hz.

[0096] The improved Sine chaotic map is used to generate sparse initial microphone array positions as the first generation of microphone array elements, so that the array element positions are evenly distributed in space.

[0097] S12. Setting the control parameters and initial parameters of the improved Sine chaotic map.

[0098] S13. Generate sparse initial microphone array positions according to the geometric parameters and the improved Sine chaotic map to obtain the initial generation of microphone array element groups.

[0099] In a feasible implementation, according to the number of array elements N, the initial size N of the traditional array element group of the microphone array P0 , microphone array aperture D, array element spacing constraint threshold lower limit d min , upper limit of the element spacing constraint threshold d max , set the control parameter μ and initial parameters d(1)=rand, e(1)=rand of the improved Sine chaotic map, where rand is a random real number generated in [0,1).

[0100] The improved Sine chaotic map is used to generate the initial position of the microphone array, that is, the initial position X of the traditional group of microphone array elements traditional ={x1,x2,…,x NP0}

[0101] S2. Construct a hybrid fitness function.

[0102] Optionally, the above step S2 may include the following steps S21-S23:

[0103] S21. Construct the peak sidelobe level PSLL as the optimization target to evaluate the suitability of the microphone array for the beamforming algorithm in sound source localization.

[0104] S22. Construct the peak signal-to-noise ratio (PSNR) as the optimization target to evaluate the applicability of the acoustic holography algorithm in the microphone array for sound field reconstruction.

[0105] S23. Construct a mixing function according to the sidelobe level and the peak signal-to-noise ratio, and then construct a mixing fitness function.

[0106] In a feasible implementation, facing the different requirements of sound source localization and sound field reconstruction for the measurement microphone array, multi-objective optimization of the microphone array element position and array element excitation weight is performed to achieve the best effect of the beamforming algorithm and acoustic holography algorithm, and a hybrid fitness function of sidelobe level and peak signal-to-noise ratio is constructed.

[0107] Specifically, to evaluate the suitability of microphone arrays for beamforming algorithms in sound source localization, we first generate a microphone array response function. If the microphone array is focused on any point x in space, the response function of the microphone array can be written as follows (1):

[0108]

[0109] Where x s The sound source position, the sound source coordinates are (x, y, z), the microphone array center coordinates are (x0, y0, z0), and the m-channel microphone coordinates are (x m ,y m ,z m ), r0 and r m are the distances from the sound source to the center of the microphone array and the m-channel microphone, respectively. r is the distance from the sound source to any point in space, w m is the corresponding weighting factor for the m-channel microphone, which can be used to adjust the loudness of the microphone array, k is the wave number, j is the complex unit, and M is the number of channels. The loudness of the microphone array focused on any point relative to the source point x0 is given by the following formula (2):

[0110]

[0111] The ratio of the sidelobe level to the mainlobe level (i.e., peak sidelobe level PSLL = max(dB(x))) is used as the optimization target. The smaller the PSLL value, the higher the suitability of the microphone array for the beamforming algorithm.

[0112] Furthermore, in order to evaluate the applicability of the microphone array to the acoustic holography algorithm in sound field reconstruction, the peak signal-to-noise ratio (PSNR) is used as the optimization target. The larger the PSNR value, the smaller the absolute error of reconstruction, indicating that the microphone array has a higher applicability to the acoustic holography algorithm. Its mathematical expression is as follows (3):

[0113]

[0114] Among them, MAX I is the maximum value of the sound field in the area, F rec is the reconstructed sound field, RMSE(F,F rec ) is the root mean square error between the original sound field and the reconstructed sound field.

[0115] In order to optimize the suitability of the microphone array for both beamforming and acoustic holography algorithms, a hybrid function of the sidelobe level and the peak signal-to-noise ratio is used as the optimization target, as shown in the following equation (4):

[0116]

[0117] Wherein, α1 and α2 are the sidelobe level and peak signal-to-noise ratio weights, α1, α2∈(0,1), w represents the iterative sequence value of the improved Sine chaotic map, and d represents the initial parameter of the improved Sine chaotic map.

[0118] The lower the fitness value, the better the microphone array performance. Affects the main lobe width BW -3dB The main factor is the microphone array aperture. Taking into account the reasonable setting of the microphone array aperture and the array element spacing, the hybrid fitness function is constructed as shown in the following formula (5):

[0119]

[0120] Among them, fitness represents the mixing function, BW -3dB Indicates the main lobe width, BW thr represents the main lobe width constraint threshold, d ij Represents the distance between any two array elements, i, j = 1, 2, 3...N P , N P is the size of the traditional microphone array element group; d min Indicates the lower limit of the element spacing constraint threshold; d max Indicates the upper limit of the element spacing constraint.

[0121] S3. Based on the initial array element group of the microphone array, the hybrid fitness function and the whale elite optimization algorithm, an optimized three-dimensional microphone array for multi-target collaboration in sound field measurement is obtained.

[0122] Optionally, the above step S3 may include the following steps S31-S36:

[0123] S31. Based on the initial generation of microphone array elements, an excellent element file with the lowest value of the hybrid fitness function is established, and the leading element of the microphone array is selected to generate a microphone array deep search group element group.

[0124] Optionally, the above step S31 may include the following steps S311-S313:

[0125] S311. Calculate the mixed fitness function value of each individual in the traditional group of microphone array elements, and sort the individuals from small to large according to the mixed fitness function value.

[0126] In a feasible implementation, WEOA (Whale Elite Optimization Algorithm) divides the search element group into two parts, one of which is the traditional group of microphone array elements X. traditional , through iterative optimization of three parts: surrounding the prey, advanced spiral contraction surrounding and advanced search for prey, and the other part is the microphone array deep search group element group X deep , will be iteratively optimized based on the deep prey group method.

[0127] Calculate the first generation array element group X of the traditional group traditional ={x1,x2,…,x NP}’s fitness function fitness and sorts the values ​​from small to large.

[0128] S312, establish scale N A The excellent array element archives are selected and the individuals with the lowest contemporary mixed fitness function values ​​are archived.

[0129] In a feasible implementation, an archive is created to record the best individuals in history, which is used to update the position of new individuals. The archive size is N A According to the traditional group of first generation array element X traditional The mixed fitness function fitness is from small to large, and the individual with the lowest value of the contemporary mixed fitness function is selected for archiving.

[0130] S313. Select a leading element of the microphone array according to the deep prey group solution, and generate a deep search group element group near the leading element of the microphone array.

[0131] In a feasible implementation, the leader element X is selected according to the deep prey group scheme. leader , and generate a deep search group array group X near the leading array deep To speed up local search. Set the deep search group array element group X deep The number is N F Among them, N F +N P =N.

[0132] Using the deep prey group adjustment principle, the traditional array element group X traditional and deep search group X deep The size of the array element group increases or decreases by N0 units based on its array element fitness value, and the total array element group remains unchanged. traditional Too small to ensure diversity of array elements, X deep The upper limit is set to NF max , and the initial X deep is NF0.

[0133] According to the first generation deep search array element group X deep The fitness function fitness updates the archive records and records the best individual X of the first generation * (1).

[0134] S32. Update the positions and element excitation weights of the traditional group of microphone array elements through the whale elite optimization algorithm to generate a new traditional group of microphone array elements.

[0135] Optionally, the above step S32 may include the following steps S321-S322:

[0136] S321. Iteratively optimize the positions and element excitation weights of the traditional group of array elements of the microphone array by surrounding the prey, advanced spiral contraction surrounding, and advanced searching for the prey to obtain an updated traditional group of array elements of the microphone array.

[0137] S322. Check whether there are any individuals in the updated microphone array traditional group of array elements that exceed the microphone array aperture. If so, randomly generate a new individual in the microphone array aperture to replace the exceeded individual, thereby generating a new microphone array traditional group of array elements.

[0138] In a feasible implementation, the traditional array element group X is controlled by three parts: surrounding the prey, advanced spiral contraction and surrounding, and advanced search for the prey. traditional Perform iterative optimization of position and excitation weight. traditional After the position update is completed, check whether there is an individual outside the aperture. If there is an individual outside the aperture, a new individual is randomly generated within the aperture to replace it.

[0139] Furthermore, calculate the traditional array element group X of this generation traditional The fitness function fitness is then sorted and the archive records is updated.

[0140] Furthermore, a new leader element X is selected leader , and generate a deep search array element group X deep , according to the deep search array element group X deep The fitness function fitness is used to update the archive records again; the traditional array element group X is updated according to the deep prey group array element group size adjustment strategy. traditional and deep search group X deep The number of; record the best individual X of this generation * (t).

[0141] S33. Update the excellent element file according to the new microphone array traditional element group, select new microphone array leading elements, and generate a new microphone array deep search element group.

[0142] S34. Determine whether the array element group size of the new microphone array deep search group array element group reaches the preset maximum array element group size. If so, adjust the number of individuals in the new microphone array traditional group array element group and the new microphone array deep search group array element group, and execute step S35; if not, execute step S35.

[0143] Optionally, adjusting the number of elements in the new microphone array traditional group and the new microphone array deep search group in S34 includes:

[0144] According to the deep prey group plan, the number of individuals in the new microphone array traditional group element group and the new microphone array deep search group element group is increased or decreased by N0 units, and the size of the total element group is kept unchanged. The total element group is composed of the new microphone array traditional group element group and the new microphone array deep search group element group.

[0145] S35. Update the excellent array element file according to the adjusted traditional array element group of the microphone array, and update the deep search array element group.

[0146] S36. Determine whether the preset number of iterations has been reached. If so, obtain the optimal array element group positions and array element excitation weights to obtain an optimized three-dimensional microphone array for multi-target collaboration in sound field measurement. If not, go back to step S32.

[0147] In a feasible implementation, the microphone array element group is iterated cyclically to finally obtain the optimal element group position distribution and element excitation weight, and the final element group X is output. f And the final optimization result P f .

[0148] Specifically, set the maximum number of iterations T max Repeat the iteration to optimize the array group position and array element excitation weight, and record the optimal individual X in each generation. * (t), finally, the optimal array element group position distribution and array element excitation weight are obtained, and the final array element group X is output. f And the final optimization result P f , and a sparse sensor three-dimensional microphone array with high adaptability to beamforming and acoustic holography algorithms is obtained.

[0149] The present invention first determines the microphone that can meet the sound pressure level and frequency response range according to the sound pressure level requirements of the sound signal; then builds a three-dimensional sparse microphone array, connects it to the NI-PXIe multi-channel data acquisition device via BNC cables, and realizes data acquisition and storage of multi-channel sound signals through LabVIEW software.

[0150] Then, a sparse microphone array is generated by improving the Sine chaotic map as the first-generation array element group of the microphone array, a hybrid fitness function is constructed, and the individual fitness function of the traditional array element group of the microphone array is calculated; an archive of excellent array elements with the lowest fitness function value is established, the leading array element is selected, and the deep search group array element group of the microphone array is generated and updated; the traditional group array element group is updated by the whale elite optimization algorithm to generate the next generation array element group; the deep search group array element group is updated according to the new leading array element, the number of traditional group array element group and deep search group array element group is adjusted, and the archive records the best individuals of this generation of array element group; the maximum number of iterations is set to iteratively update the array element group position to obtain the final optimal microphone array array element group, and the optimized microphone array position and each microphone excitation weight are obtained.

[0151] Based on the solved sound pressure matrix P, a reconstructed cloud map is drawn to determine the location of the noise source. Alternatively, a MathScript program module can be added to the LabVIEW acquisition software to perform real-time calculations on the collected data, achieving dynamic location of the sound source.

[0152] In this example, a three-dimensional microphone array is constructed using two planar orthogonal sparse microphone arrays to measure sound signals from two sound sources in space. The microphone array has an aperture of 0.5 μm and is equipped with 28 electret free-field microphones. Multi-channel synchronous data acquisition is performed using an NI-PXIe bus-based data acquisition system with a sampling frequency of 44,100 Hz.

[0153] S1. Use the improved Sine chaotic map to generate the sparse initial microphone array positions as the first generation of microphone array elements.

[0154] Specifically, according to the number of array elements N, the initial size of the traditional array element group N of the microphone array P0 , microphone array aperture D, array element spacing constraint threshold lower limit d min , upper limit of the element spacing constraint threshold d max , set the control parameter μ and initial parameters d(1)=rand, e(1)=rand of the improved Sine chaotic map, where rand is a random real number generated in [0,1).

[0155] The improved Sine chaotic map is used to generate the initial position of the microphone array, that is, the initial position X of the traditional array element group traditional ={x1,x2,…,xNP}. Among them, the improved Sine chaotic mapping formula is:

[0156]

[0157] Among them, μ and w are the control parameter and iterative sequence value of the improved one-dimensional Sine chaotic map, respectively.

[0158] S2. Construct a mixed fitness function, calculate the individual fitness functions of the array element group, and sort the function values ​​from small to large.

[0159] Specifically, to evaluate the suitability of microphone arrays for beamforming algorithms in sound source localization, we first generate a microphone array response function. If the microphone array is focused on any point x in space, the response function of the microphone array can be written as:

[0160]

[0161] Where x s The sound source position, the sound source coordinates are (x, y, z), the microphone array center coordinates are (x0, y0, z0), and the m-channel microphone coordinates are (x m ,y m ,z m ), r0 and r m are the distances from the sound source to the center of the microphone array and the m-channel microphone, respectively. r is the distance from the sound source to any point in space, w m is the corresponding weighting factor of the m-channel microphone, which can be used to adjust the loudness of the microphone array, k is the wave number, j is the complex unit, and M is the number of channels. Relative to the source point x0, the loudness of the microphone array focused on any point is:

[0162]

[0163] The ratio of the sidelobe level to the mainlobe level (i.e., peak sidelobe level PSLL = max(dB(x))) is used as the optimization target. The smaller the PSLL value, the higher the suitability of the microphone array for the beamforming algorithm.

[0164] In order to evaluate the applicability of microphone arrays to acoustic holography algorithms in sound field reconstruction, the peak signal-to-noise ratio (PSNR) is used as the optimization target. The larger the PSNR value, the smaller the absolute error of reconstruction, indicating that the microphone array has a higher applicability to acoustic holography algorithms. Its mathematical expression is:

[0165]

[0166] Among them, MAX I is the maximum value of the sound field in the area, F recis the reconstructed sound field, RMSE(F,F rec ) is the root mean square error between the original sound field and the reconstructed sound field.

[0167] In order to optimize the suitability of the microphone array for both beamforming and acoustic holography algorithms, a hybrid fitness function of sidelobe level and peak signal-to-noise ratio is constructed:

[0168]

[0169] The lower the fitness value, the better the microphone array performance. Among them, α1 and α2 are the sidelobe level and peak signal-to-noise ratio weights, α1, α2∈(0,1). Affects the main lobe width BW -3dB The main factor is the microphone array aperture. Taking into account the reasonable setting of the microphone array aperture and the array element spacing, the hybrid fitness function is constructed as follows:

[0170]

[0171] Among them, BW thr represents the main lobe width constraint threshold; d ij Represents the distance between any two array elements; d min Indicates the lower limit of the element spacing constraint threshold; d max Indicates the upper limit of the element spacing constraint threshold; i, j = 1, 2, 3...N P (N P is the traditional array element group size).

[0172] S3. Create records of the best array elements with the lowest fitness function value and select the leading whale X leader , generate the microphone array deep search group element group X deep And update the file.

[0173] Specifically, the Whale Elite Optimization Algorithm divides the search element group into two parts. One part is the traditional group element group X of the microphone array. traditional , through iterative optimization of three parts: surrounding the prey, advanced spiral contraction surrounding and advanced search for prey, and the other part is the microphone array deep search group element group X deep , will be iteratively optimized based on the deep prey group method.

[0174] Calculate the first generation array element group X of the traditional group traditional ={x1,x2,…,x NP0}’s fitness function fitness and sorts the values ​​from small to large.

[0175] Create an archive to record the best individuals in history, which is used to update the position of new individuals. The archive size is N A According to the traditional group of first generation array element X traditionalThe mixed fitness function fitness is from small to large, and the individual with the lowest value of the contemporary mixed fitness function is selected for archiving.

[0176] Select the leader X according to the deep prey group plan leader , and generate a deep search group array group X near the leading array deep To speed up local search. Set the deep search group array element group X deep The number is N F Among them, N F +N P = N. The specific method is:

[0177] (a) Method for updating the position of deep prey groups.

[0178] First, the best individuals of the current era are selected as leaders, expressed as:

[0179] X leader (t) = X * (t) (12)

[0180] Among them, X * (t) is the position of the current best array element.

[0181] Then, several deep search group cells are generated near the leader cell to speed up the local search:

[0182] X p (t)X leader (t)+k(m-0.5)(X leader (t)-X records (t)) (13)

[0183] Where p = 1, 2, ..., NF, m is a random number between 0 and 1, and k is a constant.

[0184] (b) The size adjustment strategy of the deep prey group array.

[0185] In order to ensure the availability of the proposed deep search group, an adjustment principle is used. In this way, the traditional array element group X traditional ={x1,x2,…,x NP} and deep search array element group X deep ={x1,x2,…,x NF}'s array element group size increases or decreases by N0 units based on its member fitness values, and the total array element group remains unchanged. traditional Too small to ensure diversity of array elements, X deep The upper limit is set to NF max , and the initial X deep is NF0.

[0186] According to the first generation deep search array element group X deep The fitness function fitness updates the archive records and records the best individual X of the first generation * (1).

[0187] S4. Update the positions and excitation weights of the traditional microphone array elements through the whale elite optimization algorithm to generate the next generation of elements. Update the deep search element group of the microphone array according to the new microphone array leader element, adjust the number of traditional microphone array elements and deep search element groups, and record the best individuals of this generation of elements.

[0188] Specifically, the traditional array element group X is controlled by three parts: basic surrounding and encircling the prey, advanced spiral contraction and encirclement, and advanced search for the prey. traditional Perform iterative optimization of position and incentive weights. The specific method is as follows:

[0189] (a) Surrounding the prey:

[0190] Assume that the current position of the leader is X * (t), the position of the individual element is X(t), and the next position of the individual element X(t) under the influence of the leader element is X(t+1), then:

[0191] X k (t+1)=X * (t)-A·D, p<0.5 and |A|<1 (14)

[0192] D=C·X * (t)-X k (t),k=1,2,…,NP (15)

[0193] Where: t is the current iteration number; X k (t) is the position vector of the individual array element; D is the distance between the individual array element and the prey; · is the element-by-element multiplication; A and C are coefficient vectors used to control the movement of the array element, and they are:

[0194] C=2r,A=a(2r-1) (16)

[0195] a=2-2t / T max (t=1,2,…,T max ) (17)

[0196] Among them, r is a random number between 0 and 1. Obviously, a gradually decreases from 2 to 0 during the search process.

[0197] (b) Advanced spiral contraction and encirclement:

[0198] Xk (t + 1)= w·X * (t)= D′e bl′ cos(2πl′) (18)

[0199] D′ = X * (t)-X t (t), k = 1, 2, …, NP (19)

[0200] w = w1 - (w1 - w2)×(t / T max ) 1 / t (20)

[0201] l′ = 2i / NP (i = 1, 2, …, NP) (21)

[0202] To better utilize this mechanism, the parameter l is replaced by l′ to ensure that individuals with better fitness values correspond to smaller l′, and vice versa. In addition, the non - linear variable w decreases from its maximum value w1 to a minimum of 0 ≤ w2 < w1 ≤ 1. Therefore, as the optimization process progresses, the influence of the previous generation is weakened, aiming to expand the search range and get rid of local optima in the later stage.

[0203] (c) Advanced search for prey:

[0204] X k (t + 1)= X records (t)′ - A·D records ′, p < 0.5 and |A| ≥ 1 (22) <​​​​​​​​​​​​​​​​​​​​​​​​​​

[0210] According to step S3, select the individual with the smallest new fitness function as the leader element X leader , and generate a deep search array element group X deep , calculate the deep search array element group X deep The fitness function fitness is used to update the archive records again; the traditional array element group X is updated according to the deep prey group array element group size adjustment strategy. traditional and deep search group X deep The number of; record the best individual X of this generation * (t).

[0211] S5. Repeat S4 according to the maximum number of iterations, iteratively update the microphone array element group to obtain the optimal individual position and excitation weight of the array element group, and obtain the optimized microphone array.

[0212] Specifically, set the maximum number of iterations T max , repeat iteration S4, optimize the array group position and array element excitation weight, and record the optimal individual X in each generation * (t), finally, the optimal array element group position distribution and array element excitation weight are obtained, and the final array element group X is output. f And the final optimization result P f , and a sparse sensor three-dimensional microphone array with high adaptability to beamforming and acoustic holography algorithms is obtained.

[0213] In an embodiment of the present invention, a hybrid fitness function is proposed to optimize the microphone array. The optimized microphone array can simultaneously meet the two functional requirements of sound source localization and sound field reconstruction, providing a reference solution for the design of multifunctional microphone arrays.

[0214] The present invention introduces an improved Sine chaotic mapping to randomly generate array element positions of a three-dimensional sparse microphone array, so that the initial generation of array element groups is evenly distributed in space, has more obvious chaotic characteristics, and accelerates the subsequent optimization convergence speed; introduces a whale elite optimization algorithm, and during the optimization process of array element positions and array element excitation weights, weakens the influence of the previous generation population, expands the array element search range, improves development and detection capabilities, and gets rid of local optimality in the later stage. Through faster local convergence speed, higher convergence accuracy and lower computational complexity, the array element positions and array element excitation weights are iteratively calculated, thereby obtaining a three-dimensional sparse microphone array with higher applicability to beamforming and acoustic holography algorithms.

[0215] This invention is applicable to the optimization of three-dimensional microphone arrays. Traditional optimization methods primarily target two-dimensional array designs, which struggle to accurately capture the temporal, frequency, and spatial distribution of complex sound fields. By optimizing the array element positions and excitation weights, this invention achieves a high-performance three-dimensional microphone array with a wide dynamic response range, addressing the difficulty of accurately measuring the random spatial distribution of noise sources.

[0216] like Figure 4 As shown, an embodiment of the present invention provides a device 400 for optimizing a multi-objective collaborative three-dimensional microphone array for sound field measurement. The device 400 is used to implement a method for optimizing a multi-objective collaborative three-dimensional microphone array for sound field measurement. The device 400 includes:

[0217] The acquisition module 410 is used to acquire geometric parameters of a conventional array element group of a microphone array and generate an initial generation array element group of the microphone array through an improved Sine chaotic mapping.

[0218] The function construction module 420 is used to construct a hybrid fitness function.

[0219] The output module 430 is used to obtain an optimized three-dimensional microphone array for multi-target collaboration in sound field measurement based on the initial array element group of the microphone array, the hybrid fitness function and the whale elite optimization algorithm.

[0220] Optionally, the acquisition module 410 is further configured to:

[0221] S11. Acquire geometric parameters of a conventional array element group of a microphone array; wherein the geometric parameters include the number of array elements, the size of the conventional array element group, the microphone array aperture, the lower limit of the array element spacing constraint threshold, and the upper limit of the array element spacing constraint threshold.

[0222] S12. Setting the control parameters and initial parameters of the improved Sine chaotic map.

[0223] S13. Generate sparse initial microphone array positions according to the geometric parameters and the improved Sine chaotic map to obtain the initial generation of microphone array element groups.

[0224] Optionally, the function construction module 420 is further configured to:

[0225] S21. Construct the peak sidelobe level PSLL as the optimization target to evaluate the suitability of the microphone array for the beamforming algorithm in sound source localization.

[0226] S22. Construct the peak signal-to-noise ratio (PSNR) as the optimization target to evaluate the applicability of the acoustic holography algorithm in the microphone array for sound field reconstruction.

[0227] S23. Construct a mixing function according to the sidelobe level and the peak signal-to-noise ratio, and then construct a mixing fitness function.

[0228] Optionally, the mixing function is as shown in the following formula (1):

[0229]

[0230] Among them, α1, α2∈(0,1), w represents the iterative sequence value of the improved Sine chaotic map, and d represents the initial parameter of the improved Sine chaotic map.

[0231] Optionally, a hybrid fitness function is shown in the following formula (2):

[0232]

[0233] Among them, fitness represents the mixing function, BW -3dB Indicates the main lobe width, BW thr represents the main lobe width constraint threshold, d ij Represents the distance between any two array elements, i, j = 1, 2, 3...N P , N P is the size of the traditional microphone array element group; d min Indicates the lower limit of the element spacing constraint threshold; d max Indicates the upper limit of the element spacing constraint.

[0234] Optionally, the output module 430 is further configured to:

[0235] S31. Based on the initial generation of microphone array elements, an excellent element file with the lowest value of the hybrid fitness function is established, and the leading element of the microphone array is selected to generate a microphone array deep search group element group.

[0236] S32. Update the positions and element excitation weights of the traditional group of microphone array elements through the whale elite optimization algorithm to generate a new traditional group of microphone array elements.

[0237] S33. Update the excellent element file according to the new microphone array traditional element group, select new microphone array leading elements, and generate a new microphone array deep search element group.

[0238] S34. Determine whether the array element group size of the new microphone array deep search group array element group reaches the preset maximum array element group size. If so, adjust the number of individuals in the new microphone array traditional group array element group and the new microphone array deep search group array element group, and execute step S35; if not, execute step S35.

[0239] S35. Update the excellent array element file according to the adjusted traditional array element group of the microphone array, and update the deep search array element group.

[0240] S36. Determine whether the preset number of iterations has been reached. If so, obtain the optimal array element group positions and array element excitation weights to obtain an optimized three-dimensional microphone array for multi-target collaboration in sound field measurement. If not, go back to step S32.

[0241] Optionally, the output module 430 is further configured to:

[0242] S311. Calculate the mixed fitness function value of each individual in the traditional group of microphone array elements, and sort the individuals from small to large according to the mixed fitness function value.

[0243] S312, establish scale N A The excellent array element archives are selected and the individuals with the lowest contemporary mixed fitness function values ​​are archived.

[0244] S313. Select a leading element of the microphone array according to the deep prey group solution, and generate a deep search group element group near the leading element of the microphone array.

[0245] Optionally, the output module is further configured to:

[0246] S321. Iteratively optimize the positions and element excitation weights of the traditional group of array elements of the microphone array by surrounding the prey, advanced spiral contraction surrounding, and advanced searching for the prey to obtain an updated traditional group of array elements of the microphone array.

[0247] S322. Check whether there are any individuals in the updated microphone array traditional group of array elements that exceed the microphone array aperture. If so, randomly generate a new individual in the microphone array aperture to replace the exceeded individual, thereby generating a new microphone array traditional group of array elements.

[0248] Optionally, the output module 430 is further configured to:

[0249] According to the deep prey group plan, the number of individuals in the new microphone array traditional group element group and the new microphone array deep search group element group is increased or decreased by N0 units, and the size of the total element group is kept unchanged. The total element group is composed of the new microphone array traditional group element group and the new microphone array deep search group element group.

[0250] In an embodiment of the present invention, a hybrid fitness function is proposed to optimize the microphone array. The optimized microphone array can simultaneously meet the two functional requirements of sound source localization and sound field reconstruction, providing a reference solution for the design of multifunctional microphone arrays.

[0251] The present invention introduces an improved Sine chaotic mapping to randomly generate array element positions of a three-dimensional sparse microphone array, so that the initial generation of array element groups is evenly distributed in space, has more obvious chaotic characteristics, and accelerates the subsequent optimization convergence speed; introduces a whale elite optimization algorithm, and during the optimization process of array element positions and array element excitation weights, weakens the influence of the previous generation population, expands the array element search range, improves development and detection capabilities, and gets rid of local optimality in the later stage. Through faster local convergence speed, higher convergence accuracy and lower computational complexity, the array element positions and array element excitation weights are iteratively calculated, thereby obtaining a three-dimensional sparse microphone array with higher applicability to beamforming and acoustic holography algorithms.

[0252] This invention is applicable to the optimization of three-dimensional microphone arrays. Traditional optimization methods primarily target two-dimensional array designs, which struggle to accurately capture the temporal, frequency, and spatial distribution of complex sound fields. By optimizing the array element positions and excitation weights, this invention achieves a high-performance three-dimensional microphone array with a wide dynamic response range, addressing the difficulty of accurately measuring the random spatial distribution of noise sources.

[0253] Figure 5 1 is a schematic diagram of the structure of an electronic device 500 provided in an embodiment of the present invention. The electronic device 500 may vary significantly due to different configurations or performances, and may include one or more processors (central processing units, CPUs) 501 and one or more memories 502. The memories 502 store at least one instruction, which is loaded and executed by the processor 501 to implement the following multi-objective collaborative three-dimensional microphone array optimization method for sound field measurement:

[0254] S1. Obtain the geometric parameters of the traditional microphone array element group and generate the initial generation of microphone array element group through the improved Sine chaotic mapping.

[0255] S2. Construct a hybrid fitness function.

[0256] S3. Based on the initial array element group of the microphone array, the hybrid fitness function and the whale elite optimization algorithm, an optimized three-dimensional microphone array for multi-target collaboration in sound field measurement is obtained.

[0257] In an exemplary embodiment, a computer-readable storage medium is also provided, such as a memory device including instructions. The instructions are executable by a processor in a terminal to implement the multi-objective collaborative three-dimensional microphone array optimization method for sound field measurement. For example, the computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, or optical data storage device.

[0258] Those skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware, or by a program to instruct the relevant hardware, and the program may be stored in a computer-readable storage medium, which may be a read-only memory, a disk, or an optical disk, etc.

[0259] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for optimizing a multi-objective collaborative three-dimensional microphone array for sound field measurement, characterized in that: The method comprises: S1. Obtain the geometric parameters of the traditional microphone array element group and generate the initial generation of microphone array element group through the improved Sine chaotic mapping; S2, construct hybrid fitness function; S3. Obtaining an optimized three-dimensional microphone array for multi-target collaboration in sound field measurement based on the initial-generation microphone array element group, the hybrid fitness function, and the whale elite optimization algorithm; The construction of the hybrid fitness function in S2 includes: S21. Construct the peak sidelobe level (PSLL) as the optimization target to evaluate the suitability of the microphone array for the beamforming algorithm in sound source localization. S22. Construct the peak signal-to-noise ratio (PSNR) as the optimization target to evaluate the applicability of the acoustic holography algorithm in the microphone array for sound field reconstruction. S23, constructing a mixing function according to the sidelobe level and the peak signal-to-noise ratio, and then constructing a mixing fitness function; The mixing function is shown in the following formula (1): Where, α1, α2∈(0,1), w represents the iterative sequence value of the improved Sine chaotic map, and d represents the initial parameter of the improved Sine chaotic map; The hybrid fitness function is shown in the following formula (2): Among them, fitness represents the mixing function, BW -3dB Indicates the main lobe width, BW thr represents the main lobe width constraint threshold, d ij Represents the distance between any two array elements, i, j = 1, 2, 3...N P , N P is the size of the traditional microphone array element group; d min Indicates the lower limit of the element spacing constraint threshold; d max Indicates the upper limit of the element spacing constraint.

2. The method according to claim 1, characterized in that The step S1 of obtaining geometric parameters of a conventional microphone array element group and generating an initial microphone array element group through an improved Sine chaotic mapping includes: S11. Acquire geometric parameters of a conventional array element group of a microphone array; wherein the geometric parameters include the number of array elements, the initial size of the conventional array element group, the microphone array aperture, the lower limit of the array element spacing constraint threshold, and the upper limit of the array element spacing constraint threshold; S12, setting control parameters and initial parameters of the improved Sine chaotic map; S13. Generate sparse initial microphone array positions according to the geometric parameters and the improved Sine chaotic map to obtain a first-generation array element group of the microphone array.

3. The method according to claim 1, characterized in that The step S3, based on the first-generation array element group of the microphone array, the hybrid fitness function, and the whale elite optimization algorithm, obtains an optimized three-dimensional microphone array for multi-target collaboration in sound field measurement, including: S31. Based on the initial group of microphone array elements, establish an excellent element file with the lowest value of the hybrid fitness function, select a leading element of the microphone array, and generate a deep search group of microphone array elements; S32. Updating the positions and element excitation weights of the traditional group of microphone array elements by using the whale elite optimization algorithm to generate a new traditional group of microphone array elements; S33, updating the excellent array element file according to the new microphone array traditional array element group, and selecting new microphone array leading array elements to generate a new microphone array deep search array element group; S34, determining whether the element group size of the new microphone array deep search group element group reaches a preset maximum element group size; if so, adjusting the number of elements in the new microphone array traditional group element group and the new microphone array deep search group element group, and executing step S35; if not, executing step S35; S35. Update the excellent array element file according to the adjusted traditional array element group of the microphone array, and update the deep search array element group; S36. Determine whether the preset number of iterations has been reached. If so, obtain the optimal array element group positions and array element excitation weights to obtain an optimized three-dimensional microphone array for multi-target collaboration in sound field measurement. If not, go back to step S32.

4. The method according to claim 3, characterized in that The step S31 of establishing an excellent element file with the lowest value of a hybrid fitness function based on the initial generation element group of the microphone array, selecting a leading element of the microphone array, and generating an element group of a deep search group of the microphone array includes: S311, calculating the mixed fitness function value of each individual in the traditional group of array elements of the microphone array, and sorting the individuals in ascending order according to the mixed fitness function value; S312, establish scale N A The excellent array element archives are selected and archived with the individuals with the lowest contemporary mixed fitness function values; S313 . Select a leading element of the microphone array according to the deep prey group solution, and generate a deep search group element group near the leading element of the microphone array.

5. The method according to claim 3, characterized in that The step S32 of updating the positions and element excitation weights of the traditional microphone array element group by using the whale elite optimization algorithm to generate a new traditional microphone array element group includes: S321, iteratively optimizing the positions and element excitation weights of the traditional group of array elements of the microphone array by surrounding the prey, advanced spiral contraction surrounding, and advanced search for the prey, to obtain an updated traditional group of array elements of the microphone array; S322: Detect whether there is an individual in the updated microphone array traditional group of array elements that exceeds the microphone array aperture; if so, randomly generate a new individual in the microphone array aperture to replace the excess individual, thereby generating a new microphone array traditional group of array elements.

6. The method according to claim 3, characterized in that The adjusting of the number of elements in the new microphone array traditional group and the new microphone array deep search group in S34 includes: According to the deep prey group scheme, the number of individuals in the new microphone array traditional group element group and the new microphone array deep search group element group is increased or decreased by N0 units, and the size of the total element group is kept unchanged. The total element group is composed of the new microphone array traditional group element group and the new microphone array deep search group element group.

7. A multi-target collaborative three-dimensional microphone array optimization device for sound field measurement, characterized in that: The device comprises: The acquisition module is used to obtain the geometric parameters of the traditional array element group of the microphone array and generate the initial generation array element group of the microphone array through the improved Sine chaotic mapping; Function construction module, used to construct hybrid fitness function; An output module is used to obtain an optimized three-dimensional microphone array for multi-target collaboration of sound field measurement based on the initial array element group of the microphone array, the hybrid fitness function and the whale elite optimization algorithm; The constructing of the hybrid fitness function includes: S21. Construct the peak sidelobe level (PSLL) as the optimization target to evaluate the suitability of the microphone array for the beamforming algorithm in sound source localization. S22. Construct the peak signal-to-noise ratio (PSNR) as the optimization target to evaluate the applicability of the acoustic holography algorithm in the microphone array for sound field reconstruction. S23, constructing a mixing function according to the sidelobe level and the peak signal-to-noise ratio, and then constructing a mixing fitness function; The mixing function is shown in the following formula (1): Where, α1, α2∈(0,1), w represents the iterative sequence value of the improved Sine chaotic map, and d represents the initial parameter of the improved Sine chaotic map; The hybrid fitness function is shown in the following formula (2): Among them, fitness represents the mixing function, BW -3dB Indicates the main lobe width, BW thr represents the main lobe width constraint threshold, d ij Represents the distance between any two array elements, i, j = 1, 2, 3...N P , N P is the size of the traditional microphone array element group; d min Indicates the lower limit of the element spacing constraint threshold; d max Indicates the upper limit of the element spacing constraint.

Images