Method for determining the position of a sound source of a device based on multi-sensor acoustic array imaging

Abstract

The application provides a device sound source position determination method based on a multi-sensor acoustic array imaging, and belongs to the technical field of sound field imaging, and comprises the following steps: S1, acquiring microphone sensor array experimental data; S2, data preprocessing and calculating sample covariance matrix; S3, performing characteristic decomposition and determining the number of sound sources; S4, preliminarily judging the sound source parameter range to determine the iteration initial value, and calculating the power parameter initial value corresponding to each sound source; S5, estimating noise variance S6, calculating the characteristic value and characteristic vector theoretical value of the sound source subspace; S7, solving S+1 dimensional nonlinear equation set to obtain the estimation result. The application can take the positions and powers of multiple sound sources as parameters to be estimated, model the sound pressure signal covariance matrix, then perform characteristic decomposition on the covariance matrix to obtain the characteristic value and characteristic vector theoretical value.

Description

Technical Field

[0001] This invention relates to the field of acoustic field imaging technology, and in particular to a multi-source high-resolution acoustic array imaging method based on covariance matrix eigenvalue decomposition. Background Technology

[0002] Acoustic array imaging technology has wide applications in many fields, especially in modern acoustic imaging, monitoring, detection, and localization. By utilizing phased-array microphones and analyzing information such as the arrival time and direction of sound waves, precise location of sound sources and reconstruction of the sound field can be achieved. This technology serves multiple fields, including noise control, speech recognition, environmental monitoring, medical imaging, sonar detection, and intelligent transportation. For example, in the medical field, acoustic array technology can be used for ultrasound imaging and diagnosis; in industrial inspection, it can be applied to mechanical fault diagnosis. However, the development of acoustic array imaging technology still faces many challenges, among which imaging resolution and sound source intensity (power) estimation are the most prominent issues. The resolution of acoustic array imaging directly affects the accuracy of sound source localization results, which is crucial in practical applications. Too low a resolution may lead to blurred images, thus affecting the accurate identification and localization of sound sources. Especially in complex environments, such as those with multiple sound sources or high background noise, traditional acoustic array imaging methods often struggle to effectively distinguish signals from multiple sound sources, resulting in low resolution. Currently, the main methods to address the resolution problem in acoustic array imaging are increasing the array size, adding more microphones, or employing higher-precision signal processing techniques. However, these traditional methods often suffer from high computational complexity and, in some cases, still cannot effectively meet the demands for high-resolution imaging. Therefore, how to significantly improve the resolution of acoustic array imaging while maintaining low cost and small array size has become crucial for the development of acoustic imaging technology. On the other hand, traditional methods can only estimate the location of sound sources based on the imaging results, not their intensity. This makes it impossible to perform targeted processing during subsequent analysis of the imaging results. For example, in noise assessment and denoising, simply locating the noise source is insufficient for noise reduction; it is also necessary to analyze the intensity of different noise sources to focus on eliminating the primary challenge of strong noise sources. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention aims to provide a method for determining the location of sound sources in a device based on multi-sensor acoustic array imaging. This method uses the locations and power of multiple sound sources as parameters to be estimated and models the covariance matrix of the sound pressure signal. Then, it performs eigenvalue decomposition on the covariance matrix to obtain theoretical values ​​of eigenvalues ​​and eigenvectors. Conversely, it calculates the sample covariance matrix based on the sound pressure signal measured by the array and performs eigenvalue decomposition to obtain actual values ​​of eigenvalues ​​and eigenvectors. By matching the theoretical and actual values ​​of the eigenvalue decomposition, the problem of estimating the sound source location and power parameters can be transformed into a nonlinear problem with a different dimensionality. Furthermore, the sound source location and power are uniquely determined by these equations, leading to more accurate results.

[0004] Specifically, the present invention provides a method for determining the location of a device sound source based on multi-sensor acoustic array imaging, which includes the following steps:

[0005] S1. Acquire experimental data from the sensor array;

[0006] S2. Calculate the sample covariance matrix of the frequency domain signal obtained from the preprocessing of the experimental data.

[0007]

[0008] in, Let T be the sample covariance matrix, t be the sample number, and p be the sample sequence number. t Let H be the t-th sample signal, and let H be the transpose matrix.

[0009] S3. Perform eigenvalue decomposition and determine the number of sound sources: Perform eigenvalue decomposition on the sample covariance matrix to obtain M eigenvalues ​​and actual values ​​of eigenvectors. Then, sort the eigenvalues ​​in descending order and determine the number of sound sources S.

[0010] S4. Use beamforming methods to initially determine the range of sound source parameters to determine the initial values ​​for iteration, and calculate the initial power parameter values ​​for each sound source:

[0011]

[0012] Where G(r)=(g(r1),…,g(r) S )) is a matrix composed of the transfer functions of S sound sources, g(r)=(g1(r),...,g M (r)) Τ Let r represent the transfer function from the monopole sound source located at r to the array of M microphones;

[0013] S5. Estimating noise variance

[0014] S6. Calculate the theoretical values ​​of the sound source subspace eigenvalues ​​and eigenvectors, specifically:

[0015] S61. Calculate the S×S dimension matrix Z based on the sound source location and initial power parameter values ​​from step S4:

[0016]

[0017] in, I represents the power covariance matrix of the sound source. S Represents the identity matrix with dimension S;

[0018] S62. Perform eigenvalue decomposition on matrix Z and arrange the eigenvalues ​​in descending order to obtain S eigenvalues ​​λ1,…,λ2. S With the corresponding theoretical value of the eigenvector Each feature vector has a dimension of S×1;

[0019] S7. Solving the S+1 dimension nonlinear equations yields the calculated results of the sound source location and power parameters: The nonlinear equations consist of the theoretical and actual values ​​of eigenvalues ​​and eigenvectors, respectively, where λ1,…,λ S and These represent the theoretical values ​​of the eigenvalues ​​and eigenvectors obtained in step S62, respectively. and These represent the actual values ​​of the eigenvalues ​​and eigenvectors obtained in step S3, respectively; select a set of eigenvalues ​​and eigenvectors. The calculation is as follows:

[0020] S71. Determine a nonlinear equation based on a selected set of eigenvalues:

[0021]

[0022] Wherein, the subscript x represents the xth eigenvalue;

[0023] S72. Determine based on selected feature vectors and The relationship between them yields S equations:

[0024]

[0025] Among them, 0 S Let S be a zero vector of dimension S×1. Treat each element of this equation as an equation, and determine S equations:

[0026]

[0027] S73. Solve the S+1 dimension nonlinear equations in steps S71 and S72 to obtain the estimation results of the sound source location and power parameters.

[0028] Preferably, the sensor mentioned in step S1 is an acoustic sensor.

[0029] Preferably, the experimental data in step S1 includes time-domain sound pressure signal values, sampling frequency, position parameters of the sensor array, and the number of sensors.

[0030] Preferably, step S2 specifically includes the following sub-steps:

[0031] S21. Determine the number of time-domain signal samples contained in each snapshot based on the time-domain signal length obtained in step S1, and then obtain the number of snapshots under the frequency domain signal.

[0032] S22, S22, Based on the length of each snapshot in step S21, select a window function and use discrete Fourier transform to obtain the frequency domain signal p of the sample. t , t=1…T;

[0033] S23. Calculate the sample covariance matrix based on the frequency domain signal obtained in step S22.

[0034] Preferably, the specific steps for determining the number of sound sources in step S3 are as follows:

[0035] S31. Arrange the M eigenvalues ​​after eigenvalue decomposition in descending order as follows:

[0036] S32. Determine the signal-to-noise ratio threshold δ;

[0037] S33, satisfy the eigenvalues All λ i If we consider them as sound sources, then the number of sound sources S is the total number of characteristic values ​​that satisfy this equation.

[0038] Preferably, step S4 specifically includes the following sub-steps:

[0039] S41. Perform a discrete search in the potential region where sound sources may exist, and use a beamformer |B(r) 2 The S maxima of | determine the location of the sound source:

[0040]

[0041] Where g(r)=(g1(r),...,g M (r)) Τ The transfer function from the monopole sound source at position r to the array of M microphones is:

[0042]

[0043] By executing a traditional beamforming algorithm, initial values ​​for the positions of S sound sources are obtained.

[0044] S42. Based on the initial values ​​of the S sound source locations, calculate the initial value of the sound source power using the least squares method, and then convert the initial values ​​of all sound source locations obtained in step S41 into the initial values ​​of the S sound source locations. The initial values ​​of the power parameters for each sound source are obtained by substituting them into the following formula:

[0045]

[0046] Preferably, the sampling time in step S1 is 30s or 60s, and the sampling frequency is greater than or equal to twice the frequency of the highest sound source signal, i.e., f. sample ≥2f source , where f sample f is the sampling frequency. source This is the highest frequency of the sound source signal.

[0047] Preferably, in step S21, if the length of the time-domain signal is N, then the number of snapshots T and the length L of each snapshot satisfy the following relationship:

[0048]

[0049] Preferably, the noise variance estimation method in step S5 is as follows:

[0050]

[0051] Preferably, in step S7, the largest set of eigenvalues ​​and eigenvectors is selected. Make an estimate.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0053] (1) This invention provides a high-resolution acoustic array imaging method for multiple sound sources based on covariance matrix eigenvalue decomposition, which has significant advantages over traditional methods. Compared with traditional algorithm research, this invention achieves higher resolution sound field imaging results for multiple sound sources and can accurately determine the location of sound sources.

[0054] (2) The method of the present invention can estimate the power and accurate location of each sound source, while traditional methods can only estimate the approximate location of the sound source. The theoretical research and experimental results of the embodiments of the present invention show that the present invention can achieve more accurate localization and power parameter estimation of multiple sound sources, exhibiting higher imaging resolution, and thus providing methodological support for the identification and analysis of sound sources.

[0055] (3) The method of the present invention calculates the sample covariance matrix based on the sound pressure signal measured by the array and performs eigenvalue decomposition to obtain the actual values ​​of eigenvalues ​​and eigenvectors. Then, the theoretical values ​​and actual values ​​of the eigenvalue decomposition are matched and calculated. The problem of estimating the sound source location and power parameters can be transformed into a nonlinear problem of solving the dimension. The sound source location and power are uniquely determined by these equations, which can make the results more accurate and can be widely applied to multiple scenarios. Attached Figure Description

[0056] Figure 1 This is a flowchart of the multi-source high-resolution acoustic array imaging method based on covariance matrix eigenvalue decomposition according to the present invention;

[0057] Figure 2 This is a schematic diagram of the acoustic experimental scenario in step S1 of an embodiment of the present invention;

[0058] Figure 3 This is a schematic diagram of the sensor coordinate parameters in the array during step S1 in an embodiment of the present invention;

[0059] Figure 4 This is a scatter plot of the feature values ​​arranged in descending order after the feature decomposition is performed in step S3 of the present invention.

[0060] Figures 5a-5c The images are obtained using traditional beamforming, minimum variance distortion-free estimation, and multi-signal estimation methods, respectively.

[0061] Figure 5d This is an imaging diagram of the sound field reconstruction of the estimation results in a specific embodiment S of the present invention. Detailed Implementation

[0062] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings.

[0063] A specific embodiment of the present invention provides a method for determining the location of a sound source in a device based on multi-sensor acoustic array imaging, used to detect the location and intensity of a source in a sound source output device, i.e., a method for determining the location of a sound source output device based on multi-sensor acoustic array imaging, such as... Figure 1 As shown, it includes the following steps:

[0064] S1. Acquire experimental data from the acoustic sensor array. In this specific application, the acoustic sensor is a microphone sensor. The experimental data includes the time-domain sound pressure signal value, sampling frequency, position parameters of the microphone array, and the number of microphone sensors.

[0065] The time-domain sound pressure level signal value measured by the microphone sensor array. The sampling time is typically 30s or 60s; the array's sampling frequency should be set to no less than twice the frequency of the highest sound source signal under study, f. sample ≥2fsource The position parameters of the microphone array and the number of microphone sensors are used for parameter calculation in subsequent algorithms.

[0066] S2. Calculate the sample covariance matrix of the frequency domain signal obtained from the preprocessing of the experimental data. The specific steps are as follows:

[0067] S21. Based on the time-domain signal length obtained in step S1, determine the number of time-domain signal samples contained in each snapshot, and then obtain the number of snapshots in the frequency domain signal. The snapshots in this invention are samples of the frequency domain signal, and the data of each snapshot is obtained by transforming a segment of time-domain signal data. Specifically, based on the time-domain sound pressure signal obtained in step S1, frequency parameters are set, and a discrete Fourier transform is performed on the signal of each microphone sensor to obtain the sound pressure frequency domain signal at the corresponding frequency.

[0068] Let the length of the time-domain signal be N. Then the number of snapshots T and the length L of each snapshot satisfy the following relationship:

[0069]

[0070] S22. Based on the length of each snapshot in step S21, select a window function and use discrete Fourier transform to obtain the frequency domain signal p. t , t=1…T, where p t This represents the sound field signal at the t-th snapshot.

[0071] S23. Calculate the sample covariance matrix based on the frequency domain signal obtained in step S22.

[0072]

[0073] in, Let T be the sample covariance matrix, t be the sample number, and p be the sample sequence number. t Let H be the t-th sample signal, and H be the transpose matrix.

[0074] S3. Perform eigenvalue decomposition and determine the number of sound sources: Perform eigenvalue decomposition on the sample covariance matrix to obtain M eigenvalues ​​and actual values ​​of eigenvectors. Then, sort the eigenvalues ​​in descending order to determine the number of sound sources S.

[0075] The specific steps are as follows:

[0076] S31. Arrange the M eigenvalues ​​after eigenvalue decomposition in descending order as follows:

[0077] S32. Determine the signal-to-noise ratio threshold δ.

[0078] S33, for eigenvalues ​​satisfying All λ iIf we consider it as a sound source, then the number of sound sources S is the number of eigenvalues ​​that satisfy this equation.

[0079] Since the number S of sound sources in space is finite, this is called the "peak model," which produces S large eigenvalues ​​after eigenvalue decomposition. S feature vectors are generated accordingly. This forms a sound source subspace S and MS smaller eigenvalues. MS feature vectors are generated accordingly. Forming a noise subspace S ⊥ The feature vectors in both the sound source subspace and the noise subspace have a dimension of M×1. Therefore, the number of sound sources can be determined by arranging the feature values ​​obtained from the decomposition in descending order.

[0080] S4. Initially determine the range of sound source parameters to determine the initial values ​​for iteration, and calculate the initial values ​​of the power parameters corresponding to each sound source:

[0081]

[0082] Where G(r)=(g(r1),…,g(r) S )) is a matrix composed of the transfer functions of S sound sources, g(r)=(g1(r),...,g M (r)) Τ Let r represent the transfer function from the monopole sound source located at r to the array of M microphones.

[0083] For a sound field space with S sound sources, using an array of M microphones for measurement, the corresponding frequency domain sound pressure signal can be expressed as:

[0084] p t =G(r)A t +n t

[0085] Where G(r) is an M×S dimension matrix, where the elements g ms Let A represent the sound field transfer function from the s-th dipole sound source to the m-th microphone. t =(A 1t ,...,A St ) T Let n represent the amplitudes of S incoherent sound sources. t This represents ambient noise. The sound source transfer function is:

[0086]

[0087] Where, r s Indicates the location of the sound source, r′ m The position of the sensor is indicated by i, where i is the imaginary unit and k is the wave number.

[0088] Step S4 specifically includes the following sub-steps:

[0089] S41. Perform a discrete search in the potential region where sound sources may exist, and use a beamformer |B(r) 2 |S maxima are used to determine the location of the sound source:

[0090]

[0091] Where g(r)=(g1(r),...,g M (r)) Τ The transfer function from the monopole sound source at position r to the array of M microphones is:

[0092]

[0093] By executing a traditional beamforming algorithm, initial values ​​for the positions of S sound sources are obtained.

[0094] S42. Based on the initial value of the sound source location, the initial value of the sound source power is further estimated using the least squares method. This process is repeated for all the location results obtained in step S41. Substituting these values ​​into the calculation, we obtain the initial power parameter values ​​for each sound source:

[0095]

[0096] S5. Estimating noise variance The noise variance estimation method is as follows:

[0097]

[0098] S6. Calculate the theoretical values ​​of the sound source subspace eigenvalues ​​and eigenvectors, specifically:

[0099] S61. Calculate the S×S dimension matrix Z based on the sound source location and initial power parameter values ​​from step S4:

[0100]

[0101] in, I represents the power covariance matrix of the sound source. S This represents the identity matrix with dimension S.

[0102] S62. Perform eigenvalue decomposition on matrix Z and arrange the eigenvalues ​​in descending order to obtain S eigenvalues ​​λ1,…,λ2. S With the corresponding theoretical value of the eigenvector Each feature vector has a dimension of S×1.

[0103] S7. Solving the S+1 dimension nonlinear equation system yields the estimation results: The nonlinear equation system consists of the theoretical and actual values ​​of eigenvalues ​​and eigenvectors, respectively, where λ1,…,λ S and These represent the theoretical values ​​of the eigenvalues ​​and eigenvectors obtained in step S62, respectively. and These represent the actual values ​​of the eigenvalues ​​and eigenvectors obtained in step S3, respectively; select the set of eigenvalues ​​and eigenvectors with the largest values. The estimation is as follows:

[0104] S71. Determine a nonlinear equation based on a selected set of eigenvalues:

[0105]

[0106] S72. Determine based on selected feature vectors and The relationship between them yields S equations:

[0107]

[0108] Among them, 0 S Let S be a zero vector of dimension S×1. Treat each element of this equation as an equation, and determine S equations:

[0109]

[0110] S73. Solve the S+1 dimension nonlinear equations in steps S71 and S72 to obtain the estimation results of the sound source location and power parameters. Specific Implementation

[0112] The method of the present invention will be further described in detail below with the example of an acoustic array imaging experiment consisting of multiple sound wave generators. Figure 1 Figure 5 is a schematic diagram of a multi-source high-resolution acoustic array imaging method based on covariance matrix eigenvalue decomposition. The imaging results are shown in Figure 5. Figures 5a-5c The imaging results obtained by traditional methods, Figure 5d The image obtained by the feature decomposition method of the present invention is shown. Although the specific embodiment of the present invention uses multiple sound wave generators, the present invention is still applicable to other detectable devices. Specific embodiments are as follows:

[0113] Step 1: Acquire experimental data from the sound wave generator array. In this embodiment, the sampling time is 60 seconds, the sampling frequency is 25.6 kHz, and the number of microphone sensors is 60. The acoustic scene is illustrated below. Figure 2 As shown, the array positions are distributed as follows: Figure 3 As shown.

[0114] Step 2: Data Preprocessing. Based on the experimental signal samples obtained in Step S1, the time-domain signal length N = 983040 is obtained. The length of each snapshot L = 4096 and the number of snapshots T = 239 are determined. The Hanning window is selected as the window function, and the frequency is selected as 800Hz. The sound pressure frequency domain signal is obtained by performing a Discrete Fourier Transform (DFT) on the time-domain signal.

[0115] Step 3: Perform eigenvalue decomposition on the sample covariance matrix to obtain eigenvalues ​​and eigenvectors. The eigenvalue decomposition results are as follows: Figure 4 As shown.

[0116] Step 4: Determine the number of sound sources. Selecting a threshold of δ = 3, the calculated number of sound sources is S = 3.

[0117] Step 5: Use conventional beamforming to initially determine the range of sound source parameters to determine the initial values ​​for iteration. The results of conventional beamforming are as follows: Figure 5a As shown.

[0118] Step 6: Estimate the noise variance, obtained from the noise variance estimation formula.

[0119] Step 7: Calculate the theoretical values ​​of the sound source subspace eigenvalues ​​and eigenvectors.

[0120] Step 8: Solve the S+1 dimension nonlinear equation system to obtain the estimation results. Table 1 shows the estimation results using the eigenvalue decomposition method of this invention.

[0121] Figure 5d It is an image formed by reconstructing the sound field from the estimated results. Figures 5a-5c The imaging results obtained using traditional beamforming, minimum variance distortionless estimation, and multiple signal estimation (MUSIC) show that the method proposed in this invention has higher resolution at this frequency compared to traditional methods.

[0122] Table 1 shows the results estimated using the eigenvalue decomposition method of the present invention. It can be seen that the method proposed in this invention can simultaneously and accurately estimate the location and intensity of the sound source.

[0123] Table 1. Results of sound source parameter estimation

[0124]

[0125] This embodiment provides a multi-source high-resolution acoustic array imaging method based on covariance matrix eigenvalue decomposition, which has significant advantages over traditional acoustic imaging methods. For example... Figure 5aAs shown, traditional beamforming methods can only provide the approximate location range of noise sources, i.e., the imaging resolution is low. Compared with traditional methods, this invention achieves high-resolution imaging of sound sources. In addition, this invention can also provide estimates of the number and intensity parameters of sound sources, thus providing strong data support for further analysis of noise sources and the design of noise reduction structures.

[0126] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for determining the location of a sound source in a device based on multi-sensor acoustic array imaging, characterized in that: It includes the following steps: S1. Obtain experimental data from the sensor array; S2. Preprocess the experimental data to obtain the frequency domain signal and calculate the sample covariance matrix. : in, Represents the sample covariance matrix. Let t be the number of samples, and t be the sample sequence. For the first Each sample signal, where H refers to the conjugate transpose matrix; S3. Perform eigenvalue decomposition and determine the number of sound sources: Perform eigenvalue decomposition on the sample covariance matrix to obtain... The actual values ​​of the eigenvalues ​​and eigenvectors are then used to sort the eigenvalues ​​in descending order and determine the number of sound sources. The specific steps for determining the number of sound sources in step S3 are as follows: S31. After feature decomposition The eigenvalues ​​are arranged in descending order as follows: ; S32. Determine the signal-to-noise ratio threshold. ; S33, satisfy the eigenvalues All If considered as a sound source, then the number of sound sources To meet The total number of eigenvalues; S4. Use beamforming methods to initially determine the range of sound source parameters to determine the initial values ​​for iteration, and calculate the initial power parameter values ​​for each sound source: in, It is a matrix composed of the transfer functions of S sound sources. Indicates that it is located at From the monopole sound source at that location The transfer function of a microphone array; Step S4 specifically includes the following sub-steps: S41. Perform discrete searches in potential areas where sound sources may exist, and use beamformers. The maximum value determines the location of the sound source: in, Indicates that it is located at From the monopole sound source at that location Transfer function of a microphone array: By executing a traditional beamforming algorithm, we obtain Initial values ​​for the location of each sound source ; S42, based on The initial values ​​of the sound source locations are obtained in step S41. The initial values ​​of the sound source power are calculated using the least squares method. Substituting the values ​​into the following formula, we can obtain the initial values ​​of the power parameters for each sound source: ； S5. Estimating noise variance : ； S6. Calculate the theoretical values ​​of the sound source subspace eigenvalues ​​and eigenvectors, specifically: S61. Calculate based on the sound source location and initial power parameter values ​​from step S4. Dimensional matrix : in, Represents the power covariance matrix of the sound source. express An identity matrix of dimension 1; S62, Regarding the matrix Perform eigenvalue decomposition and sort the eigenvalues ​​in descending order to obtain eigenvalues With the corresponding theoretical value of the eigenvector Each feature vector has a dimension of 1. ; S7, Solve The nonlinear equations yield the calculated results of the sound source location and power parameters: the nonlinear equations consist of theoretical and actual values ​​of eigenvalues ​​and eigenvectors, respectively. and These represent the theoretical values ​​of the eigenvalues ​​and eigenvectors obtained in step S62, respectively. and These represent the actual values ​​of the eigenvalues ​​and eigenvectors obtained in step S3, respectively; select a set of eigenvalues ​​and eigenvectors. The calculation is as follows: S71. Determine a nonlinear equation based on a selected set of eigenvalues: ； Wherein, the subscript x represents the xth eigenvalue; S72. Determine based on selected feature vectors and The relationship between them yields S equations: in, Representing a dimension as The zero vector will Each element is considered as an equation, which determines... Equations: ； S73, Solving steps S71 and S72 The location and power parameters of the sound source are obtained from the system of nonlinear equations.

2. The method for determining the location of a device sound source based on multi-sensor acoustic array imaging according to claim 1, characterized in that: The sensor mentioned in step S1 is an acoustic sensor.

3. The method for determining the location of a device sound source based on multi-sensor acoustic array imaging according to claim 2, characterized in that: The experimental data in step S1 includes time-domain sound pressure signal values, sampling frequency, position parameters of the sensor array, and the number of sensors.

4. The method for determining the location of a device sound source based on multi-sensor acoustic array imaging according to claim 1, characterized in that: Step S2 specifically includes the following sub-steps: S21. Determine the number of time-domain signal samples contained in each snapshot based on the length of the time-domain sound pressure signal value obtained in step S1, and then obtain the number of snapshots under the frequency domain signal. S22. Based on the length of each snapshot in step S21, select a window function and use discrete Fourier transform to obtain the frequency domain signal of the sample. , ; S23. Calculate the sample covariance matrix based on the frequency domain signal obtained in step S22. .

5. The method for determining the location of a device sound source based on multi-sensor acoustic array imaging according to claim 1, characterized in that: In step S1, the sampling time is 30 s or 60 s, and the sampling frequency is greater than or equal to twice the frequency of the highest sound source signal. ,in, Sampling frequency, This is the highest frequency of the sound source signal.

6. The method for determining the location of a device sound source based on multi-sensor acoustic array imaging according to claim 1, characterized in that: In step S21, the length of the time-domain signal is Then the number of snapshots With the length of each snapshot Satisfying Relationship: 。 7. The method for determining the location of a sound source in a device based on multi-sensor acoustic array imaging according to claim 1, characterized in that: In step S7, select the largest set of eigenvalues ​​and eigenvectors. .

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