Random array sound vortex generation method and device, equipment and storage medium

Abstract

The embodiment of the invention discloses a random array sound vortex generation method, device and equipment and a storage medium, and belongs to the field of sound vortexes, and the method comprises the steps: building a sound field model from a random array to an observation plane, and determining the sound pressure distribution of ideal sound vortexes; expressing the sound pressure distribution of the ideal sound vortex by using a sparse representation basis, and generating a linear relationship between the sparse representation basis and a driving signal; establishing a relationship between the sparse coefficient and the sound pressure of the observation point by using the sensing matrix; minimizing the error between the actual sound pressure generated by the sparse coefficient through the sensing matrix and the sound pressure distribution as a target, and utilizing L1 norm penalty to make the sparse coefficient sparse to obtain an optimized sparse coefficient; and calculating according to the optimized sparse coefficient to obtain a driving signal vector. The geometric limitation of a traditional uniform circular array is broken through, and the flexibility and robustness of array design are improved. The array configuration and the number of units can be optimized for different scenes.

Description

Technical Field

[0001] This invention relates to the field of acoustic vortex technology, and in particular to a method, apparatus, device and storage medium for generating random array acoustic vortices. Background Technology

[0002] As classic information carriers, traditional forms of sound waves (such as plane waves and spherical waves) face limitations in terms of information transmission capacity and dimensionality. With the continuous growth in demand for high-speed, high-reliability acoustic communication, the development of novel acoustic wave manipulation technologies has become an urgent need. Acoustic vortices carrying orbital angular momentum (OAM) offer a completely new approach to acoustic communication. The phase of an acoustic vortex exhibits a helical distribution; this unique structure endows the vortex with physical properties such as a phase singularity at its center and orbital angular momentum, demonstrating great potential in fields such as complex sound field design, directional sound transmission, particle manipulation, biomedical systems, and precision manufacturing.

[0003] With the rapid development of technologies such as artificial acoustic metasurfaces and programmable transducer arrays, significant progress has been made in the field of acoustic vortex generation. Existing methods include phased arrays, acoustic resonances, and acoustic metamaterials, laying the foundation for the application of acoustic vortices in communication, manipulation, and sensing. Among these, phased array technology is one of the most flexible and controllable methods for generating acoustic vortices. However, current phased array technologies mainly employ techniques based on single-ring or multi-ring uniform circular arrays, requiring all transducers to be uniformly distributed on the same radius. If multiple rings are used, each ring has the same radius, and the spacing between rings is fixed. The phase allocation of a uniform circular array can only be achieved within a certain range. Discretization within the range, if the number of elements N on the ring is small, results in a coarse phase gradient, which limits the amplitude uniformity and wavefront quality of the vortex beam, thus restricting the array's degrees of freedom. Summary of the Invention

[0004] This invention provides a method, apparatus, device, and storage medium for generating random array acoustic vortices, in order to solve the technical problem of low array freedom in acoustic vortex generation in the prior art.

[0005] In a first aspect, embodiments of the present invention provide a method for generating random array acoustic vortices, comprising: Establish a sound field model from a random array to the observation plane to determine the sound pressure distribution of an ideal acoustic vortex; The sound pressure distribution of the ideal acoustic vortex is expressed using a sparse representation basis, and a linear relationship between the sparse representation basis and the driving signal is generated. The relationship between sparse coefficients and sound pressure at observation points is established using a sensing matrix generated from a sparse representation basis. With the goal of minimizing the error between the actual sound pressure and the sound pressure distribution generated by the sparse coefficients through the perception matrix, the L1 norm penalty is used to make the sparse coefficients spars, thus obtaining the optimized sparse coefficients. The driving signal vector is calculated based on the optimized sparse coefficients; The objective is to minimize the error between the actual sound pressure level and the sound pressure distribution generated by the sparse coefficients through the perception matrix. This is achieved by using L1 norm penalty to sparsify the sparse coefficients, resulting in optimized sparse coefficients, including: The fixed step size of gradient descent is calculated using the perception matrix; Pre-set the maximum number of iterations and convergence tolerance, and initialize the sparse coefficient vector; Substitute the initialized sparse coefficient vector into the error function between the actual sound pressure and the sound pressure distribution generated by the perception matrix to obtain the corresponding computational gradient. The predicted solution after gradient descent is calculated based on the corresponding computational gradient, fixed step size, and current sparsity coefficients. Elements in the predicted solution are pruned using the L1 norm and a fixed step size; The current sparse coefficients are updated based on the pruned elements, and the error function between the actual sound pressure and the sound pressure distribution generated by the perception matrix is ​​substituted to obtain the corresponding computational gradient. Return to the steps of calculating the predicted solution after gradient descent based on the corresponding calculated gradient, fixed step size and current sparse coefficients, until the rate of change of the updated sparse coefficients is less than the convergence tolerance, or the maximum number of iterations is reached; The final sparse coefficients are used as the optimized sparse coefficients.

[0006] Secondly, embodiments of the present invention also provide a random array acoustic vortex generation device, comprising: A module is established to build a sound field model from a random array to the observation plane and determine the sound pressure distribution of an ideal acoustic vortex; The expression module is used to express the sound pressure distribution of the ideal acoustic vortex using a sparse representation basis, and to generate a linear relationship between the sparse representation basis and the driving signal. The relation establishment module is used to establish the relationship between sparse coefficients and sound pressure at observation points using a perception matrix generated from a sparse representation basis. The sparse module is used to minimize the error between the actual sound pressure and the sound pressure distribution generated by the sparse coefficients through the perception matrix. It uses L1 norm penalty to make the sparse coefficients spars, thus obtaining optimized sparse coefficients. The calculation module is used to calculate the driving signal vector based on the optimized sparse coefficients; The sparse module includes: The step size calculation unit is used to calculate the fixed step size of gradient descent using the perception matrix; An initialization unit is used to pre-set the maximum number of iterations and the convergence tolerance, and to initialize the sparse coefficient vector; The substitution unit is used to substitute the initialized sparse coefficient vector into the error function between the actual sound pressure and the sound pressure distribution generated by the perception matrix, and to obtain the corresponding computational gradient. The prediction solution calculation unit is used to calculate the predicted solution after gradient descent based on the corresponding calculation gradient, fixed step size and current sparsity coefficients. Pruning cells are used to trim elements in the predicted solution using the L1 norm and a fixed step size; The update unit is used to update the current sparse coefficients based on the pruned elements, and substitute the error function between the actual sound pressure and the sound pressure distribution generated by the perception matrix to obtain the corresponding calculation gradient. The return unit is used to return the steps of the predicted solution after gradient descent calculated based on the corresponding calculated gradient, fixed step size and current sparse coefficients, until the rate of change of the updated sparse coefficients is less than the convergence tolerance, or the maximum number of iterations is reached. As a unit, it is used to take the final sparse coefficient as the optimized sparse coefficient.

[0007] Thirdly, embodiments of the present invention also provide an apparatus for generating random array acoustic vortices, comprising: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the random array acoustic vortex generation method provided in the above embodiments.

[0008] Fourthly, embodiments of the present invention also provide a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the random array acoustic vortex generation method provided in the above embodiments.

[0009] The random array acoustic vortex generation method, apparatus, device, and storage medium provided in this invention establish a sound field model from the random array to the observation plane to determine the sound pressure distribution of the ideal acoustic vortex; express the sound pressure distribution of the ideal acoustic vortex using a sparse representation basis, and generate a linear relationship between the sparse representation basis and the driving signal; establish the relationship between sparse coefficients and the sound pressure at the observation point using a perception matrix generated by the sparse representation basis; aiming to minimize the error between the actual sound pressure generated by the sparse coefficients through the perception matrix and the sound pressure distribution, use L1 norm penalty to promote sparsification of the sparse coefficients to obtain optimized sparse coefficients; calculate the driving signal vector based on the optimized sparse coefficients. By expressing the sound pressure of the ideal acoustic vortex using a sparse representation basis, introducing a spherical harmonic domain sparse prior, and minimizing the error between the actual sound pressure and the ideal sound pressure distribution, sparse optimization is calculated, and the driving signal for each array element in the array is obtained based on the sparse optimization. This method overcomes the geometric limitations of traditional uniform circular arrays, improving the flexibility and robustness of array design. Array configuration and unit quantity can be optimized for different scenarios. Attached Figure Description

[0010] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart illustrating the random array acoustic vortex generation method provided in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the random array acoustic vortex generation model in the random array acoustic vortex generation method provided in Embodiment 1 of the present invention; Figure 3 This is a flowchart illustrating the random array acoustic vortex generation method provided in Embodiment 2 of the present invention; Figure 4 This is a schematic diagram of three array configurations, A, B, and C, in the simulation verification of the random array acoustic vortex generation method provided in Embodiment 2 of the present invention. Figure 5 This is a schematic diagram of the structure of the random array acoustic vortex generation device provided in Embodiment 3 of the present invention; Figure 6 This is a schematic diagram of the device provided in Embodiment 4 of the present invention. Detailed Implementation

[0011] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0012] Example 1 Figure 1This is a flowchart of a method for generating acoustic vortices based on sparse optimization in Embodiment 1 of the present invention. This embodiment achieves high-quality acoustic vortex generation through sparse optimization technology. The method can be executed by a random array acoustic vortex generation device based on sparse optimization, and specifically includes the following steps: Step 110: Establish a sound field model from the random array to the observation plane and determine the sound pressure distribution of the ideal sound vortex.

[0013] The sound pressure distribution of an ideal acoustic vortex refers to a sound pressure field on the observation plane that satisfies a specific spiral phase structure and has a circular amplitude distribution. For example, establishing the sound field model from the random array to the observation plane may include: randomly arranging a predetermined number of point sound sources within a predetermined circle on the array plane; obtaining the spherical coordinates of each point sound source; setting the observation plane based on the array plane; defining an observation circle on the observation plane; and uniformly setting a predetermined number of observation points within the observation circle.

[0014] Figure 2 This is a schematic diagram of the random array acoustic vortex generation model in the random array acoustic vortex generation method provided in Embodiment 1 of the present invention. See [link / reference]. Figure 2 The left side represents the transmitting array, with its geometric center located at the origin of the coordinate system. The positions (coordinates) of each array element are randomly distributed within a region. The right side represents the observation plane, parallel to the transmitting array. Several points uniformly distributed on a circular ring on the observation plane serve as observation points. In this embodiment, this can be expressed mathematically. For example, it can be set as follows: In the XOY plane, Ne array elements are randomly distributed within a circle centered at the origin and with radius R. Each array element is much smaller than the wavelength and can be considered a point sound source. The position of the nth sound source... . The distance from the array element to the origin. Defined as the angle between the position vector and the positive z-axis, which is equal to 90 degrees; The azimuth angle is the angle between the projection of the array element's position onto the XOY plane and the positive X-axis. In the observation plane ( Take an observation circle within the circle, and uniformly distribute M observation points therein. The spatial vector of the i-th observation point is... .

[0015] Accordingly, determining the sound pressure distribution of the ideal acoustic vortex may include: establishing a synthetic pressure expression model for the observation point based on the positional relationship between the point sound source and the observation point; and determining the distribution of the ideal acoustic vortex field on the observation circle based on the synthetic pressure expression model for the observation point.

[0016] Correspondingly, its mathematical expression is: the synthesized sound field of the acoustic array at the observation point is: , in It is the driving signal for the nth array element, representing the amplitude and initial phase of the array element. It is the wave number. , It is the speed of sound. The sound pressure level of the observed circle is: , It can be represented in vector form: ,in, It consists of Ne array unit drive signals. .matrix It is a transfer matrix, represented in the form of: , , To pass the element in the i-th row and j-th column of the matrix.

[0017] At the observation point, the sound pressure distribution of the ideal acoustic vortex is as follows: , in It is a Bessel function of the first kind. For the topological charge number, the ideal acoustic vortex at the observation circle is: .

[0018] Step 120: The sound pressure distribution of the acoustic vortex is expressed using a sparse representation basis, and a linear relationship between the sparse representation basis and the driving signal is generated.

[0019] By leveraging the inherent sparsity of acoustic vortices in the spherical harmonic domain, the solution for the driving signal is transformed into a sparse optimization problem. For a loudspeaker array, the sound pressure generated at a point in space can be expressed as a linear superposition of the driving signals of all array elements. Through spherical harmonic expansion, the sound pressure distribution of the target acoustic vortex field on the observation circle can be correlated with the coefficients of a set of spherical harmonic basis functions.

[0020] For example, it may include: selecting a sparse representation basis based on the observation point synthetic pressure expression model, wherein the sparse representation basis is a basis function that effectively represents the target sound field; and establishing a linear transformation relationship between the driving signal and the sparse representation basis using the driving matrix.

[0021] Optionally, the drive signal can be expanded using spherical harmonics as follows: , in, , For spherical harmonic order, For topological load number, It is the truncation order. It is the Legendre function. These are the spherical harmonic expansion coefficients that need to be solved, representing the global sparse weight distribution of the element driving signals in the spherical harmonic domain. Spherical harmonic basis functions. middle and The order of follows these rules for generation: Beginning, for each order , Enumerate in sequence until the truncation order. To utilize sparsity for numerical solutions, a spherical harmonic basis matrix is ​​constructed. , , The nth row of this matrix consists of the spherical harmonic basis function values ​​of the nth element. All coefficients within the truncation order are... Stacked into vectors , , The solution for the driving signal vector can be obtained by solving for the spherical harmonic coefficient vector. A linear transformation relationship between the driving signal and the sparse representation basis is established using the driving matrix; the driving signal vector of the entire array is then expressed as the product of the driving matrix and the spherical harmonic coefficient vector.

[0022] Correspondingly, the driving signal vector of the entire array This can be expressed as: .

[0023] Step 130: Establish the relationship between sparse coefficients and sound pressure at observation points using the perception matrix generated by the sparse representation basis.

[0024] In this embodiment, the perception matrix integrates the physics of sound field propagation and the driving mode of the selected basis, and can be constructed by the product of the transfer matrix and the driving matrix, thereby establishing a linear mapping relationship between the sparse coefficient vector and the sound pressure at the observation point. For example, each column in the perception matrix corresponds to the response of the sparse representation basis.

[0025] Using the aforementioned sensing matrix, the sound pressure equation can be transformed into a product of the sensing matrix and the sparse coefficient vector, so that the solution of the driving signal vector can be obtained by solving the spherical harmonic coefficient vector.

[0026] For example, the sound pressure equation It can be converted into the following form: , in Using the above expression, generating acoustic vortices close to the theoretical value can be transformed into finding the least squares error. , .

[0027] Step 140: With the goal of minimizing the error between the actual sound pressure and the sound pressure distribution generated by the sparse coefficients through the perception matrix, the L1 norm penalty is used to make the sparse coefficients sparsified, thus obtaining the optimized sparse coefficients.

[0028] Compared to traditional ridge regression, i.e., L2 norm regularization, which tends to produce dense solutions and introduce redundant components, this embodiment uses L1 norm regularization. The geometry of L1 norm regularization has sharp corners on the coordinate axes, where some coefficients are zero. This geometric characteristic naturally promotes sparsity, keeping only coefficients important for reconstructing the target acoustic vortex field non-zero. The optimization objective is: , in, For regular expression parameters, the first term This is the data fidelity item, ensuring that the reconstructed sound field is as close as possible to the ideal target sound field. The second item... This is the L1 regularization term. Using the L1 regularization term penalizes the sum of the absolute values ​​of the coefficients c, causing the mathematical model to compress many unimportant coefficients to zero, thus obtaining a sparse solution. Regularization parameter. Used to control the strength of sparsity. Too small, the error is small but not sparse; Too large a number, too sparse, but the error may increase.

[0029] To efficiently solve the L1 regularization optimization problem described above, this embodiment proposes an iterative approach combining gradient descent steps with a smooth data fidelity term with an approximation operator for handling non-smooth L1 norms. This approach balances convergence guarantees with computational efficiency. The specific steps are as follows: Perception Matrix Target acoustic vortex vector As the original input, and input the regularization parameters. Maximum number of iterations K and tolerance By obtaining intermediate parameters, the corresponding sparse coefficient vector is obtained.

[0030] Step 150: Calculate the driving signal vector based on the optimized sparse coefficients.

[0031] After solving for the sparse coefficients, a coordinate point in the coefficient space can be obtained. To enable the array to produce sound, this coordinate point needs to be mapped back to the spatial domain using a driving matrix. For example, the driving signal vector can be calculated as follows: The abstract sparse coefficient space can be transformed into a concrete speaker driver space. For example, the driving matrix can be a driving matrix. The matrix defines how each sparse basis function is mapped to the output signal of each speaker. The optimal sparse coefficient vector obtained from the above steps can be represented as the decomposition coefficients of the target sound field (such as an acoustic vortex) in the base space. Some of its components are zero, with only a few being non-zero. Using this method, a driving signal vector can be obtained, which can then be used to generate an ideal acoustic vortex.

[0032] This embodiment establishes a sound field model from a random array to the observation plane to determine the sound pressure distribution of an ideal acoustic vortex. The sound pressure distribution of the ideal acoustic vortex is expressed using a sparse representation basis, and a linear relationship between the sparse representation basis and the driving signal is generated. The relationship between sparse coefficients and the sound pressure at the observation point is established using a perception matrix generated by the sparse representation basis. With the goal of minimizing the error between the actual sound pressure generated by the sparse coefficients through the perception matrix and the sound pressure distribution, L1 norm penalty is used to sparsify the sparse coefficients, resulting in optimized sparse coefficients. The driving signal vector is calculated based on the optimized sparse coefficients. By expressing the sound pressure of the ideal acoustic vortex using a sparse representation basis, introducing a spherical harmonic domain sparse prior, and minimizing the error between the actual sound pressure and the ideal sound pressure distribution, sparse optimization is calculated. The driving signal for each array element in the array is obtained based on the sparse optimization. This approach breaks through the geometric limitations of traditional uniform circular arrays, improving the flexibility and robustness of array design. Array configuration and the number of elements can be optimized for different scenarios.

[0033] Example 2 Figure 3 This is a flowchart illustrating the random array acoustic vortex generation method provided in Embodiment 2 of the present invention. Based on the above embodiments, this embodiment aims to minimize the error between the actual sound pressure generated by the sparse coefficients through the perception matrix and the sound pressure distribution. It utilizes L1 norm penalty to sparsify the sparse coefficients, obtaining optimized sparse coefficients. Specifically, the optimization involves: calculating a fixed step size for gradient descent using the perception matrix; pre-setting the maximum number of iterations and convergence tolerance, and initializing the sparse coefficient vector; substituting the initialized sparse coefficient vector into the error function between the actual sound pressure generated by the perception matrix and the sound pressure distribution to obtain the corresponding calculated gradient; and then... The predicted solution after gradient descent is calculated based on the corresponding computational gradient, fixed step size, and current sparse coefficients. Elements in the predicted solution are pruned using the L1 norm and fixed step size. The current sparse coefficients are updated based on the pruned elements, and the error function between the actual sound pressure and the sound pressure distribution generated by the perception matrix is ​​substituted to obtain the corresponding computational gradient. The process of calculating the predicted solution after gradient descent based on the corresponding computational gradient, fixed step size, and current sparse coefficients is repeated until the rate of change of the updated sparse coefficients is less than the convergence tolerance or the maximum number of iterations is reached. The final sparse coefficients are used as the optimized sparse coefficients.

[0034] See Figure 3 The random array acoustic vortex generation method includes: Step 210: Establish a sound field model from the random array to the observation plane, determine the sound pressure distribution of the ideal sound vortex, express the sound pressure distribution of the ideal sound vortex using a sparse representation basis, and generate a linear relationship between the sparse representation basis and the driving signal.

[0035] Step 220: Establish the relationship between sparse coefficients and sound pressure at observation points using the perception matrix generated by the sparse representation basis.

[0036] Step 230: Calculate the fixed step size of gradient descent using the perceptron matrix, pre-set the maximum number of iterations and convergence tolerance, and initialize the sparse coefficient vector.

[0037] In this embodiment, a fixed step size for gradient descent can be calculated using the perceptron matrix. For example, this is equivalent to calculating a constant that controls the drasticness of gradient changes using the perceptron matrix A. This constant essentially sets the maximum step size, which can be used as the upper limit of the step size, and a fixed step size is obtained based on this setting. The maximum number of iterations and convergence tolerance can be manually set to determine the termination condition for iterative optimization. The sparse coefficient vector is initialized, for example: At this point, the error between the initial sound pressure and the target sound pressure is the largest.

[0038] Step 240: Substitute the initialized sparse coefficient vector into the error function between the actual sound pressure and the sound pressure distribution generated by the perception matrix to obtain the corresponding calculation gradient. Calculate the predicted solution after gradient descent based on the corresponding calculation gradient, fixed step size, and current sparse coefficients.

[0039] For example, the initial sparse coefficient vector can be substituted into the error function between the actual sound pressure and the sound pressure distribution generated by the perception matrix, i.e., the aforementioned... The corresponding computational gradient is then obtained, and this gradient is used to calculate the predicted solution after the descent step. This is equivalent to determining the position the coefficient vector should reach at the current moment, ignoring sparsity constraints and based solely on the error gradient direction, after taking a step. For example, this can be calculated as follows: ,in, Let be the current coefficient at step k. For the objective function f(x) in gradient at, The learning rate is used to control the distance moved in each step. This is the updated temporary variable.

[0040] Step 250: Prune the elements in the predicted solution using the L1 norm and a fixed step size; update the current sparse coefficients based on the pruned elements, and substitute them into the error function between the actual sound pressure and the sound pressure distribution generated by the perception matrix.

[0041] For example, it may include: determining whether an element in the predicted solution is less than a threshold formed by the L1 norm and a fixed step size, setting the element to 0 to remove the component of the predicted solution represented by the element; otherwise, subtracting the threshold formed by the L1 norm and the fixed step size from the element. This expression is used in the Soft Thresholding Operator. `L` is the regularization parameter, and `O` is a constant representing the gradient of the objective function. For example, it can be used to correct predicted values ​​after gradient descent. The threshold for soft thresholding operations... It is usually set as follows: If L is very large, for example, if the loudspeaker array is very dense and the sound field coupling is very strong, it means that the gradient is very steep. If it is not divided by L, it is easy to cause oscillation and divergence, and it is easy to skip the optimal solution. Determining the degree of sparsity means sacrificing a certain amount of sound pressure accuracy in exchange for sparsity of coefficients. Together with L, they determine the pruning intensity. If the calculated predicted solution is less than the threshold... If the calculated predicted solution is greater than the threshold, it is considered to be set to zero for sparsity. If the calculated predicted solution is greater than the threshold, it is retained, but the intensity is reduced. .

[0042] Step 260: Return to the step of calculating the predicted solution after gradient descent based on the corresponding calculated gradient, fixed step size and current sparse coefficients, until the rate of change of the updated sparse coefficients is less than the convergence tolerance, or the maximum number of iterations is reached; use the final obtained sparse coefficients as the optimized sparse coefficients.

[0043] Repeat the above iterations to calculate the predicted solution after gradient descent, updating the sparse coefficients until the rate of change of the updated sparse coefficients is less than the convergence tolerance or the iteration termination condition of the maximum number of iterations. The sparse coefficients obtained in the last step are then used as the optimized sparse coefficients.

[0044] Step 270: Calculate the driving signal vector based on the optimized sparse coefficients.

[0045] This embodiment aims to minimize the error between the actual sound pressure level and the sound pressure distribution generated by the sparse coefficients through the perception matrix. It utilizes L1 norm penalty to sparsify the sparse coefficients, resulting in optimized sparse coefficients. Specifically, the optimization involves: calculating a fixed step size for gradient descent using the perception matrix; pre-setting the maximum number of iterations and convergence tolerance, and initializing the sparse coefficient vector; substituting the initialized sparse coefficient vector into the error function between the actual sound pressure level and the sound pressure distribution generated by the perception matrix to obtain the corresponding computational gradient; calculating the predicted solution after gradient descent based on the corresponding computational gradient, the fixed step size, and the current sparse coefficients; pruning elements in the predicted solution using the L1 norm and the fixed step size; updating the current sparse coefficients based on the pruned elements and substituting them into the error function between the actual sound pressure level and the sound pressure distribution generated by the perception matrix to obtain the corresponding computational gradient; returning to the step of calculating the predicted solution after gradient descent based on the corresponding computational gradient, the fixed step size, and the current sparse coefficients, until the rate of change of the updated sparse coefficients is less than the convergence tolerance or the maximum number of iterations is reached; and using the final obtained sparse coefficients as the optimized sparse coefficients. Using the above method, under the constraint of the physically real perception matrix, a sparse coefficient vector that can accurately reproduce complex acoustic vortices can be calculated through L1 regularized gradient descent, thus achieving efficient and low-cost sound field control.

[0046] The performance of the method in this embodiment can be verified by numerical simulation. The specific core experimental parameters are all derived from the requirements of actual engineering scenarios. Table 1 is the core experimental parameter table. Please refer to Table 1 for details.

[0047] Table 1

[0048] Two core metrics are used to evaluate the quality of acoustic vortex generation: orthogonality G and acoustic field error E. Figure 4 This is a schematic diagram of three array configurations (A, B, and C) in the simulation verification of the random array acoustic vortex generation method provided in Embodiment 2 of the present invention. (See attached diagram.) Figure 4 Acoustic vortices with topological charge numbers m=-2,-1,1,2 were generated respectively, and 20 independent simulations were performed for each configuration. The average values ​​of modal purity G and acoustic field error E were calculated. Table 2 is the table of modal purity G, and Table 3 is the table of acoustic field error E. Please refer to Tables 2 and 3.

[0049] Table 2

[0050] Table 3

[0051] The experimental data above show that all three random arrays can generate high-quality acoustic eddies with clear spiral phase structures. The number of phase transitions is linearly related to the absolute value of m. The mode purity G is greater than 0.99 and the sound field error E is less than 0.02. Among them, array C has the lowest sound field error because the unit distribution is more concentrated. The performance of arrays A and B verifies the core advantage of this embodiment in maintaining high performance despite overcoming the constraints of circular structures.

[0052] Example 3 Figure 5 This is a schematic diagram of the structure of the random array acoustic vortex generation device provided in Embodiment 3 of the present invention. See also... Figure 5 The random array acoustic vortex generating device includes: Module 310 is established to build a sound field model from a random array to the observation plane and determine the sound pressure distribution of an ideal acoustic vortex. The expression module 320 is used to express the sound pressure distribution of the ideal acoustic vortex using a sparse representation basis, and to generate a linear relationship between the sparse representation basis and the driving signal. The relation establishment module 330 is used to establish the relationship between sparse coefficients and sound pressure at observation points using the perception matrix generated by the sparse representation basis. The sparse module 340 is used to minimize the error between the actual sound pressure and the sound pressure distribution generated by the sparse coefficients through the perception matrix. It uses the L1 norm penalty to make the sparse coefficients spars, thus obtaining optimized sparse coefficients. The calculation module 350 is used to calculate the driving signal vector based on the optimized sparse coefficients.

[0053] The random array acoustic vortex generation device provided in this embodiment determines the sound pressure distribution of an ideal acoustic vortex by establishing a sound field model from the random array to the observation plane. The sound pressure distribution of the ideal acoustic vortex is expressed using a sparse representation basis, and a linear relationship between the sparse representation basis and the driving signal is generated. A perception matrix generated using the sparse representation basis is used to establish the relationship between sparse coefficients and the sound pressure at the observation point. With the goal of minimizing the error between the actual sound pressure generated by the sparse coefficients through the perception matrix and the sound pressure distribution, L1 norm penalty is used to promote sparsification of the sparse coefficients, resulting in optimized sparse coefficients. The driving signal vector is calculated based on the optimized sparse coefficients. The sound pressure of the ideal acoustic vortex is expressed using a sparse representation basis, a spherical harmonic domain sparse prior is introduced, and the sparse optimization is calculated by minimizing the error between the actual sound pressure and the ideal sound pressure distribution. The driving signal for each array element in the array is obtained based on the sparse optimization. This overcomes the geometric limitations of traditional uniform circular arrays, improving the flexibility and robustness of array design. The array configuration and number of elements can be optimized for different scenarios.

[0054] Based on the above embodiments, the sparse module includes: The step size calculation unit is used to calculate the fixed step size of gradient descent using the perception matrix; An initialization unit is used to pre-set the maximum number of iterations and the convergence tolerance, and to initialize the sparse coefficient vector; The substitution unit is used to substitute the initialized sparse coefficient vector into the error function between the actual sound pressure and the sound pressure distribution generated by the perception matrix, and to obtain the corresponding computational gradient. The prediction solution calculation unit is used to calculate the predicted solution after gradient descent based on the corresponding calculation gradient, fixed step size and current sparsity coefficients. Pruning cells are used to trim elements in the predicted solution using the L1 norm and a fixed step size; The update unit is used to update the current sparse coefficients based on the pruned elements, and substitute the error function between the actual sound pressure and the sound pressure distribution generated by the perception matrix to obtain the corresponding calculation gradient. The return unit is used to return the steps of the predicted solution after gradient descent calculated based on the corresponding calculated gradient, fixed step size and current sparse coefficients, until the rate of change of the updated sparse coefficients is less than the convergence tolerance, or the maximum number of iterations is reached. As a unit, it is used to take the final sparse coefficient as the optimized sparse coefficient.

[0055] Based on the above embodiments, the construction unit is used for: If an element in the predicted solution is less than the threshold L1 norm and the threshold formed by the fixed step size, the element is set to 0 to remove the component of the predicted solution represented by the element. Otherwise, subtract the threshold formed by the L1 norm and the fixed step size from the element.

[0056] Based on the above embodiments, the establishment module includes: The random arrangement unit is used to randomly arrange a preset number of point sound sources within a preset circle on the array plane. The spherical coordinate acquisition unit is used to acquire the spherical coordinates of each point sound source; The uniform setting unit is used to set the observation plane based on the array plane, set the observation circle on the observation plane, and uniformly set a preset number of observation points in the observation circle.

[0057] Based on the above embodiments, the establishment module further includes: The model building unit is used to build a synthetic pressure expression model for the observation point based on the positional relationship between the point sound source and the observation point. The distribution determination unit is used to determine the distribution of the ideal acoustic vortex field on the observation circle based on the synthetic pressure expression model of the observation point.

[0058] Based on the above embodiments, the relationship establishment module includes: The selection unit is used to select sparse representation bases based on the observation point synthetic pressure expression model, wherein the sparse representation bases are basis functions that effectively represent the target sound field; Establishment unit, used to establish the linear transformation relationship between the driving signal and the sparse representation basis using the driving matrix.

[0059] Based on the above embodiments, the computing module includes: The calculation unit is used to calculate the driving signal vector using the driving matrix and optimized sparse coefficients.

[0060] The random array acoustic vortex generation device provided in the embodiments of the present invention can execute the random array acoustic vortex generation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0061] Example 4 Figure 6 This is a schematic diagram of the structure of a device for generating random array acoustic vortices provided in Embodiment 4 of the present invention. Figure 6 A block diagram of an exemplary device 12 suitable for implementing embodiments of the present invention is shown. Figure 6 The device 12 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0062] like Figure 6 As shown, device 12 is represented as a general-purpose computing device. Components of device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and a bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0063] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0064] Device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by device 12, including volatile and non-volatile media, removable and non-removable media.

[0065] System memory 28 may include computer system readable media in the form of volatile memory, such as RAM 30 and / or cache 32. Device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 6 Not shown; usually referred to as a "hard drive"). Although Figure 6 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0066] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of the present invention.

[0067] Device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with device 12, and / or with any device that enables device 12 to communicate with one or more other computing devices (e.g., network interface card, modem, etc.). This communication can be performed through I / O interface 22. Furthermore, device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. Figure 6 As shown, network adapter 20 communicates with other modules of device 12 via bus 18. It should be understood that, although... Figure 6 As not shown, other hardware and / or software modules can be used in conjunction with device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0068] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the random array acoustic vortex generation method provided in the embodiments of the present invention.

[0069] Example 5 Embodiment 5 of the present invention also provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the random array acoustic vortex generation method as described in any of the above embodiments.

[0070] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0071] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0072] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0073] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or device. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0074] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for generating acoustic vortices in a random array, characterized in that, include: Establish a sound field model from a random array to the observation plane to determine the sound pressure distribution of an ideal acoustic vortex; The sound pressure distribution of the ideal acoustic vortex is expressed using a sparse representation basis, and a linear relationship between the sparse representation basis and the driving signal is generated. The relationship between sparse coefficients and sound pressure at observation points is established using a sensing matrix generated from a sparse representation basis. With the goal of minimizing the error between the actual sound pressure and the sound pressure distribution generated by the sparse coefficients through the perception matrix, the L1 norm penalty is used to make the sparse coefficients spars, thus obtaining the optimized sparse coefficients. The driving signal vector is calculated based on the optimized sparse coefficients; The objective is to minimize the error between the actual sound pressure level and the sound pressure distribution generated by the sparse coefficients through the perception matrix. This is achieved by using L1 norm penalty to sparsify the sparse coefficients, resulting in optimized sparse coefficients, including: The fixed step size of gradient descent is calculated using the perception matrix; Pre-set the maximum number of iterations and convergence tolerance, and initialize the sparse coefficient vector; Substitute the initialized sparse coefficient vector into the error function between the actual sound pressure and the sound pressure distribution generated by the perception matrix to obtain the corresponding computational gradient. The predicted solution after gradient descent is calculated based on the corresponding computational gradient, fixed step size, and current sparsity coefficients. Elements in the predicted solution are pruned using the L1 norm and a fixed step size; The current sparse coefficients are updated based on the pruned elements, and the error function between the actual sound pressure and the sound pressure distribution generated by the perception matrix is ​​substituted to obtain the corresponding computational gradient. Return to the steps of calculating the predicted solution after gradient descent based on the corresponding calculated gradient, fixed step size and current sparse coefficients, until the rate of change of the updated sparse coefficients is less than the convergence tolerance, or the maximum number of iterations is reached; The final sparse coefficients are used as the optimized sparse coefficients.

2. The method according to claim 1, characterized in that, The step of pruning elements in the predicted solution using the L1 norm and a fixed step size includes: If an element in the predicted solution is less than the threshold L1 norm and the threshold formed by the fixed step size, the element is set to 0 to remove the component of the predicted solution represented by the element. Otherwise, subtract the threshold formed by the L1 norm and the fixed step size from the element.

3. The method according to claim 1, characterized in that, The establishment of the acoustic field model from the random array to the observation plane includes: On the array plane, within a preset circle, a preset number of point sound sources are randomly arranged; Obtain the spherical coordinates of each point sound source; The observation plane is set based on the array plane. An observation circle is set on the observation plane, and a preset number of observation points are evenly set in the observation circle.

4. The method according to claim 3, characterized in that, Determining the sound pressure distribution of an ideal acoustic vortex includes: Based on the positional relationship between the point sound source and the observation point, a synthetic pressure expression model for the observation point is established; The distribution of the ideal acoustic vortex field on the observation circle is determined based on the synthetic pressure expression model of the observation point.

5. The method according to claim 1, characterized in that, The step of expressing the sound pressure distribution of the ideal acoustic vortex using a sparse representation basis and generating a linear relationship between the sparse representation basis and the driving signal includes: Based on the observation point synthetic pressure expression model, a sparse representation basis is selected, which is a basis function that effectively represents the target sound field; The linear transformation relationship between the driving signal and the sparse representation basis is established using the driving matrix.

6. The method according to claim 5, characterized in that, The step of calculating the driving signal vector based on the optimized sparse coefficients includes: The driving signal vector is calculated using the driving matrix and optimized sparse coefficients.

7. A random array acoustic vortex generation device, characterized in that, include: A module is established to build a sound field model from a random array to the observation plane and determine the sound pressure distribution of an ideal acoustic vortex; The expression module is used to express the sound pressure distribution of the ideal acoustic vortex using a sparse representation basis, and to generate a linear relationship between the sparse representation basis and the driving signal. The relation establishment module is used to establish the relationship between sparse coefficients and sound pressure at observation points using a perception matrix generated from a sparse representation basis. The sparse module is used to minimize the error between the actual sound pressure and the sound pressure distribution generated by the sparse coefficients through the perception matrix. It uses L1 norm penalty to make the sparse coefficients spars, thus obtaining optimized sparse coefficients. The calculation module is used to calculate the driving signal vector based on the optimized sparse coefficients; The sparse module includes: The step size calculation unit is used to calculate the fixed step size of gradient descent using the perception matrix; An initialization unit is used to pre-set the maximum number of iterations and the convergence tolerance, and to initialize the sparse coefficient vector; The substitution unit is used to substitute the initialized sparse coefficient vector into the error function between the actual sound pressure and the sound pressure distribution generated by the perception matrix, and to obtain the corresponding computational gradient. The prediction solution calculation unit is used to calculate the predicted solution after gradient descent based on the corresponding calculation gradient, fixed step size and current sparsity coefficients. The trimming cell is used to trim elements in the predicted solution using the L1 norm and a fixed step size; The update unit is used to update the current sparse coefficients based on the pruned elements, and substitute the error function between the actual sound pressure and the sound pressure distribution generated by the perception matrix to obtain the corresponding calculation gradient. The return unit is used to return the steps of the predicted solution after gradient descent calculated based on the corresponding calculated gradient, fixed step size and current sparse coefficients, until the rate of change of the updated sparse coefficients is less than the convergence tolerance, or the maximum number of iterations is reached. As a unit, it is used to take the final sparse coefficient as the optimized sparse coefficient.

8. A device for generating random array acoustic vortices, characterized in that, include: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the random array acoustic vortex generation method as described in any one of claims 1-6.

9. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the random array acoustic vortex generation method as described in any one of claims 1-6.

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