Sound source localization method, device and equipment based on microphone array

Abstract

The present application belongs to the field of sound source localization, and proposes a method, device and equipment for sound source localization based on a microphone array, the method comprising: acquiring a plurality of sound data groups corresponding to a plurality of predetermined time lengths by collecting a sound data group of a predetermined time length from a microphone array; determining the corresponding autocorrelation curves and cross-correlation curves in the plurality of sound data groups; selecting a sound data group according to the ratio of the maximum value of the autocorrelation curve to the maximum value of the cross-correlation curve; and localizing the sound source according to the maximum value of the autocorrelation curve and the maximum value of the cross-correlation curve in the selected sound data group. Complex number operations in the localization process can be avoided, the calculation process can be simplified, the localization efficiency can be improved, and the selection of the sound data group is conducive to ensuring the accuracy of the localization calculation.

Description

Technical Field

[0001] The present application relates to the field of sound source localization, and in particular to a method, device and equipment for sound source localization based on a microphone array. Background Art

[0002] The microphone arrays currently used for positioning usually include linear arrays and circular arrays. The microphones in linear arrays and circular arrays are usually equally spaced and symmetrically distributed. Based on the phase difference of different microphones, the direction of the sound source can be determined by finding the maximum value of the sound energy from all directions, the matrix eigenvector variation matrix, and the maximum likelihood value parameter solution.

[0003] However, due to the different frequencies of different sounds, the wavelengths and phases of different sounds will also be different to a certain extent, and the sound source positioning accuracy is not high. In order to improve the positioning accuracy, the sound source positioning calculation based on the frequency domain dimension can be used. However, the sound source positioning based on the frequency domain dimension requires the use of complex operations and complex matrix operations. The entire calculation process is relatively complicated, which is not conducive to improving the efficiency of sound source positioning. Summary of the invention

[0004] In view of this, the embodiments of the present application provide a sound source localization method, device and equipment based on a microphone array to solve the problem in the prior art that in order to improve the positioning accuracy, complex operations need to be used when performing sound source localization, and the calculation process is relatively complicated, which is not conducive to improving the efficiency of sound source localization.

[0005] A first aspect of an embodiment of the present application provides a sound source localization method based on a microphone array, the method comprising:

[0006] Acquiring a plurality of sound data groups corresponding to a plurality of predetermined time lengths by collecting a sound data group of a predetermined time length through a microphone array;

[0007] Determining corresponding autocorrelation curves and cross-correlation curves in a plurality of the sound data sets;

[0008] selecting a sound data set according to a ratio of a maximum value of the autocorrelation curve to a maximum value of the cross-correlation curve;

[0009] The sound source is localized based on the maximum value of the autocorrelation curve and the maximum value of the cross-correlation curve in the selected sound data set.

[0010] In combination with the first aspect, in a first possible implementation manner of the first aspect, selecting the sound data group according to the ratio of the maximum value of the autocorrelation curve to the maximum value of the cross-correlation curve includes:

[0011] Get the maximum value M of the autocorrelation curve Auto , and the maximum value M of the cross-correlation curve Cross;

[0012] According to the maximum value M of the autocorrelation curve Auto , and the maximum value M of the cross-correlation curve Cross , determine the energy ratio

[0013] A predetermined number of the energy ratio values ​​having larger values ​​are determined, and sound data groups corresponding to the determined energy ratio values ​​are selected.

[0014] In combination with the first aspect, in a second possible implementation manner of the first aspect, performing sound source localization according to a maximum value of an autocorrelation curve and a maximum value of a cross-correlation curve in the selected sound data group includes:

[0015] Get the maximum value M of the autocorrelation curve in the selected sound data set Auto and the maximum value M of the cross-correlation curve Cross Determine the sound delay between microphones;

[0016] The direction of the sound source is determined according to the sound delay.

[0017] In combination with the second possible implementation manner of the first aspect, in a third possible implementation manner of the first aspect, a maximum value M of the autocorrelation curve in the selected sound data group is obtained. Auto and the maximum value M of the cross-correlation curve Cross Determine the sound delay between microphones, including:

[0018] Get the maximum value M of the autocorrelation curve in the selected sound data set Auto The corresponding first time P Auto , and the maximum value M of the cross-correlation curve Cross The corresponding second time P Cross ;

[0019] The sound delay between the microphones is determined according to the difference between the first time and the second time.

[0020] In combination with the second possible implementation manner of the first aspect, in a fourth possible implementation manner of the first aspect, determining the direction of the sound source according to the sound delay includes:

[0021] According to the formula △t·V x =dcosθ determines the direction of the sound source, where Δt is the sound delay between microphones, V c is the speed of sound propagation, d is the distance between microphones, and θ is the direction angle of the sound source.

[0022] In combination with the first aspect, in a fifth possible implementation manner of the first aspect, before determining the corresponding autocorrelation curves and cross-correlation curves in the plurality of sound data groups, the method further includes:

[0023] Determining energy corresponding to the sound data group;

[0024] If the energy corresponding to the collected sound data group is greater than a preset energy threshold, then determining the corresponding autocorrelation curves and cross-correlation curves in the plurality of sound data groups is performed.

[0025] In combination with the first aspect, in a sixth possible implementation manner of the first aspect, collecting a sound data group of a predetermined length through a microphone array includes:

[0026] The acquisition frequency of the sound data in the sound data group is determined according to the distance between the microphones in the microphone array, and the sound data group is acquired according to the determined acquisition frequency, wherein the propagation distance of the sound signal within the acquisition interval corresponding to the acquisition frequency is less than the distance between the microphones.

[0027] A second aspect of an embodiment of the present application provides a sound source localization device based on a microphone array, the device comprising:

[0028] A sound data group acquisition unit, used to collect a sound data group of a predetermined time length through a microphone array, and acquire a plurality of sound data groups corresponding to a plurality of predetermined time lengths;

[0029] A curve determination unit, used to determine the corresponding autocorrelation curves and cross-correlation curves in the plurality of sound data groups;

[0030] a sound data group selection unit, configured to select a sound data group according to a ratio of a maximum value of the autocorrelation curve to a maximum value of the cross-correlation curve;

[0031] The sound source localization unit is used to perform sound source localization according to the maximum value of the autocorrelation curve and the maximum value of the cross-correlation curve in the selected sound data group.

[0032] A third aspect of an embodiment of the present application provides a sound source localization device based on a microphone array, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method described in any one of the first aspects are implemented.

[0033] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in any one of the first aspects are implemented.

[0034] Compared with the prior art, the embodiments of the present application have the following beneficial effects: the present application collects multiple sound data groups corresponding to multiple predetermined time lengths through a microphone array, and determines the required sound data group based on the ratio of the maximum value of the autocorrelation curve of the sound data group to the maximum value in the cross-correlation curve, and locates the sound source based on the maximum value of the autocorrelation curve and the maximum value of the cross-correlation curve in the selected sound data group, which can avoid complex number operations in the positioning process, simplify the calculation process, and improve the positioning efficiency. The sound data group is selected by the ratio of the maximum values, which is conducive to ensuring the accuracy of the positioning calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0036] Figure 1 It is a schematic diagram of an implementation flow of a sound source localization method based on a microphone array provided in an embodiment of the present application;

[0037] Figure 2 This is a schematic diagram of a process structure for obtaining a sound data group through a microphone array provided in an embodiment of the present application;

[0038] Figure 3 is a schematic diagram of an autocorrelation-cross-correlation curve provided in an embodiment of the present application;

[0039] Figure 4 It is a structural schematic diagram of calculating the sound source angle provided by an embodiment of the present application;

[0040] Figure 5 is a schematic diagram of a sound source localization device based on a microphone array provided in an embodiment of the present application;

[0041] Figure 6 It is a schematic diagram of a sound source localization device based on a microphone array provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0043] To illustrate the technical solution described in this application, the following will be described through specific embodiments.

[0044] When using a microphone array for sound source localization, it usually includes methods such as finding the sound source direction based on the maximum sound energy, matrix vector variation matrix, or maximum likelihood parameter solution, and determining the sound source direction through the phase difference of the microphones. Then, since the frequencies of different sounds may vary, the wavelengths of different sounds will also be different. Different frequencies of sounds will also have different phase differences, so there will be errors in the accuracy of the determined sound source direction. Although sound source localization calculation in the frequency domain dimension can effectively improve the sound source localization accuracy, however, sound source localization in the frequency domain dimension requires complex complex number calculations, consumes a large amount of computing resources, and has low localization efficiency.

[0045] To overcome the above defects, an embodiment of this application proposes a sound source localization method based on a microphone array. It collects multiple sound data groups corresponding to multiple predetermined time lengths through the microphone array, and filters the sound data groups by the ratio of the maximum value of the autocorrelation curve and the maximum value of the cross-correlation curve in the sound data groups, and performs sound source localization based on the maximum value of the autocorrelation curve and the maximum value of the cross-correlation curve in the filtered sound data groups, thereby simplifying the localization calculation and improving the localization efficiency on the basis of ensuring the localization accuracy.

[0046] Figure 1 The following is a schematic flowchart of the implementation of a sound source localization method based on a microphone array provided by an embodiment of this application. The method includes:

[0047] In S101, sound data groups corresponding to multiple predetermined time lengths are collected through the microphone array to obtain multiple sound data groups corresponding to multiple predetermined time lengths.

[0048] In the sound data groups in the embodiments of this application, it includes the sound data collected by the microphones in the microphone array within the time period of the same predetermined time length. Each microphone collects a segment of sound data, and multiple microphones in the microphone array collect multiple segments of sound data, and the multiple segments of sound data constitute the sound data group.

[0049] For example, the microphone array includes N microphones, and the predetermined time length is t seconds. Within this predetermined time length t seconds, the sound data collected by each microphone is S(k), where 0 < k < N. Then, the sound data group can be obtained from the N pieces of sound data S(k).

[0050] In a possible implementation manner of this application, the process structure for obtaining the sound data group through the microphone array can be as Figure 2 shown:

[0051] First, an analog audio signal is acquired through a microphone array. The microphones in the microphone array may be omnidirectional microphones. The analog audio signals acquired by the microphone array may be amplified by an amplifier. The amplifier may adjust the gain value of the amplifier according to actual needs. The analog audio signal amplified by the amplifier is further filtered by a filter to filter out the noise signal included in the acquired analog audio signal, and then the acquired and amplified analog audio signal is converted into digital audio data by an analog / digital converter.

[0052] In the implementation of the present application, when the microphone array is used to collect audio data, the collection frequency of the microphone array may be related to the shortest distance between the microphones in the microphone array. The sound propagation distance corresponding to the interval duration of the collection operation of the collection frequency is smaller than the shortest distance between the microphones in the microphone array.

[0053] For example, assuming that the sampling frequency of the microphone array is 16kHz, the minimum fineness recorded by the system per unit time is 1 / (16k) sampling points. The time interval between each two adjacent sampling points is 0.0000625 seconds. Since the propagation speed of sound in the air is about 343 meters per second (the propagation speed corresponding to different environmental conditions such as temperature may be different, and the corresponding sound propagation speed can be determined based on environmental parameters), the sound propagation distance within the time interval between sampling points is 0.0000625*343=0.0214 meters. If the distance between the microphones is less than 2.14 centimeters, the minimum time difference cannot be recorded. Therefore, the spacing between the microphones in the microphone array needs to be greater than 2.14 centimeters, for example, the spacing distance can be set to 3 centimeters. Alternatively, after determining the spacing distance between the microphones in the microphone array, the microphone array acquisition frequency can be increased, or the sampling frequency can be increased by interpolation to ensure the calculation accuracy of the sound source.

[0054] In S102, corresponding autocorrelation curves and cross-correlation curves in the plurality of sound data groups are determined.

[0055] In order to improve calculation efficiency and calculation accuracy, before determining the corresponding autocorrelation curve and cross-correlation curve in the sound data set, the energy corresponding to the sound data set may also be determined. The sound data set is filtered according to a preset energy threshold.

[0056] When the energy corresponding to the sound data group is less than or equal to the preset energy threshold, the calculation of the autocorrelation curve and the cross-correlation curve corresponding to the sound data group may not be triggered. When the energy corresponding to the sound data group is greater than the preset energy threshold, the calculation of the autocorrelation curve and the cross-correlation curve of the sound data group is triggered. In this way, more effective calculation data can be obtained, and the accuracy of the calculated sound source direction can be improved while reducing the amount of calculation.

[0057] In a possible implementation of the present application, before calculating the autocorrelation curve and the cross-correlation curve according to the sound data set, filtering can also be performed through a bandpass filter. The range of the bandpass filter is related to the distance between the microphones in the microphone array. The bandpass range of the bandpass filter is determined according to the effective bandwidth determined by the distance between the microphones in the microphone array.

[0058] The autocorrelation curve in the sound data group in the embodiment of the present application may refer to the correlation of the sound data collected by the microphones in the microphone array. The cross-correlation curve in the sound data group may refer to the correlation between the sound data collected by the microphones in the microphone array. The corresponding autocorrelation curve or cross-correlation curve in the sound group may be determined according to a pre-set autocorrelation calculation function or cross-correlation calculation function.

[0059] In S103, a sound data group is selected according to the ratio of the maximum value of the autocorrelation curve to the maximum value of the cross-correlation curve.

[0060] After determining the corresponding autocorrelation curve and cross-correlation curve in the sound data set, the following can be obtained: Figure 3 The schematic diagram of the autocorrelation-cross-correlation curve is shown in Figure 2. Figure 3 As shown, there is a certain time delay between the autocorrelation curve and the cross-correlation curve. The maximum value M of the autocorrelation curve corresponding to the sound data group can be determined based on the autocorrelation curve. Auto According to the cross-correlation curve, the maximum value M of the cross-correlation curve corresponding to the sound data group can be determined Cross .

[0061] In a possible implementation, the energy ratio R corresponding to the sound data group can be determined according to the ratio of the maximum value of the cross-correlation curve to the maximum value of the autocorrelation curve. That is:

[0062] When the microphone array performs data collection, M sound data groups can be obtained by collection and filtering. The sound data groups can be sorted from large to small according to the energy ratios corresponding to the sound data groups, and a predetermined number of sound data groups with larger energy ratios can be selected. For example, Q sound data values ​​with larger energy ratios can be selected. The sound source localization is calculated based on the selected sound data groups.

[0063] Since the larger the energy ratio is, the more accurate the delay difference of the signal is, therefore, when the sound source localization calculation is performed according to the selected sound data group, the accuracy of the sound source localization calculation can be effectively improved.

[0064] In S104, sound source localization is performed based on the maximum value of the autocorrelation curve and the maximum value of the cross-correlation curve in the selected sound data set.

[0065] After further selecting a sound data group for sound source localization calculation based on the energy ratio between the maximum value of the autocorrelation curve and the maximum value of the cross-correlation curve in the sound data group, the sound delay between the corresponding microphones can be determined based on the time point corresponding to the maximum value of the autocorrelation curve in the selected sound data group and the time point corresponding to the maximum value of the cross-correlation curve in the sound data group.

[0066] For example, the time point corresponding to the maximum value of the autocorrelation curve is P Auto The time point corresponding to the maximum value of the cross-correlation curve is P Cross , the sound delay time △t of the microphone can be expressed as: △t=P Auto -P Cross .

[0067] like Figure 4 As shown, assume that the angle between the sound and the line connecting the microphone used for calculation is θ, the distance between the microphones used for calculation is d, and the sound propagation speed is V c The time delay between the sound source and the microphone can be expressed as: △t·V c =dcosθ. That is, the sound source angle can be expressed as: Therefore, the direction of the sound source can be effectively determined. In addition, no complex operations are required during the calculation process, and the calculation efficiency is high. By screening the sound data group, the accuracy of the sound source localization calculation can be improved.

[0068] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0069] Figure 5 A schematic diagram of a sound source localization device based on a microphone array provided in an embodiment of the present application, the device comprising:

[0070] The sound data group acquisition unit 501 is used to collect sound data groups of a predetermined time length through a microphone array to acquire multiple sound data groups corresponding to multiple predetermined time lengths;

[0071] A curve determination unit 502, used to determine the corresponding autocorrelation curves and cross-correlation curves in the plurality of sound data groups;

[0072] A sound data group selection unit 503, configured to select a sound data group according to a ratio of a maximum value of the autocorrelation curve to a maximum value of the cross-correlation curve;

[0073] The sound source localization unit 504 is used to perform sound source localization according to the maximum value of the autocorrelation curve and the maximum value of the cross-correlation curve in the selected sound data group.

[0074] Figure 5 The sound source localization device based on microphone array shown in FIG. Figure 1 The microphone array-based sound source localization method shown corresponds to the above.

[0075] Figure 6 is a schematic diagram of a sound source localization device based on a microphone array provided in an embodiment of the present application. Figure 6 As shown, the microphone array-based sound source localization device 6 of this embodiment includes: a processor 60, a memory 61, and a computer program 62 stored in the memory 61 and executable on the processor 60, such as a microphone array-based sound source localization program. When the processor 60 executes the computer program 62, the steps in the above-mentioned various microphone array-based sound source localization method embodiments are implemented. Alternatively, when the processor 60 executes the computer program 62, the functions of the modules / units in the above-mentioned various device embodiments are implemented.

[0076] Exemplarily, the computer program 62 may be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 62 in the microphone array-based sound source localization device 6.

[0077] The microphone array-based sound source localization device may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art will appreciate that Figure 6 The present invention is merely an example of a microphone array-based sound source localization device 6 and does not constitute a limitation on the microphone array-based sound source localization device 6. The present invention may include more or fewer components than those shown in the figure, or a combination of certain components, or different components. For example, the microphone array-based sound source localization device may also include input and output devices, network access devices, buses, etc.

[0078] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0079] The memory 61 may be an internal storage unit of the microphone array-based sound source localization device 6, such as a hard disk or memory of the microphone array-based sound source localization device 6. The memory 61 may also be an external storage device of the microphone array-based sound source localization device 6, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the microphone array-based sound source localization device 6. Further, the memory 61 may also include both the internal storage unit of the microphone array-based sound source localization device 6 and an external storage device. The memory 61 is used to store the computer program and other programs and data required by the microphone array-based sound source localization device. The memory 61 may also be used to temporarily store data that has been output or is to be output.

[0080] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0081] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0082] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0083] In the embodiments provided in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0084] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0085] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0086] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process in the above-mentioned embodiment method, and can also be completed by hardware related to computer program instructions. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0087] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A sound source localization method based on a microphone array, characterized in that: The method comprises: Acquiring a plurality of sound data groups corresponding to a plurality of predetermined time lengths by collecting a sound data group of a predetermined time length through a microphone array; Determining corresponding autocorrelation curves and cross-correlation curves in a plurality of the sound data sets; selecting a sound data set according to a ratio of a maximum value of the autocorrelation curve to a maximum value of the cross-correlation curve; Performing sound source localization according to the maximum value of the autocorrelation curve and the maximum value of the cross-correlation curve in the selected sound data group; The step of selecting the sound data group according to the ratio of the maximum value of the autocorrelation curve to the maximum value of the cross-correlation curve comprises: Get the maximum value M of the autocorrelation curve Auto , and the maximum value M of the cross-correlation curve Cross ; According to the maximum value M of the autocorrelation curve Auto , and the maximum value M of the cross-correlation curve Cross , determine the energy ratio The energy ratios are sorted to determine a predetermined number of energy ratios with larger values, and the sound data groups corresponding to the determined energy ratios are selected.

2. The method according to claim 1, characterized in that: The method comprises: performing sound source localization according to the maximum value of the autocorrelation curve and the maximum value of the cross-correlation curve in the selected sound data group, comprising: Get the maximum value M of the autocorrelation curve in the selected sound data set Auto and the maximum value M of the cross-correlation curve Cross Determine the sound delay between microphones; The direction of the sound source is determined based on the sound delay.

3. The method according to claim 2, characterized in that Get the maximum value M of the autocorrelation curve in the selected sound data set Auto and the maximum value M of the cross-correlation curve Cross Determine the sound delay between microphones, including: Get the maximum value M of the autocorrelation curve in the selected sound data set Auto The corresponding first time P Auto , and the maximum value M of the cross-correlation curve Cross The corresponding second time P Cross ; The sound delay between the microphones is determined according to the difference between the first time and the second time.

4. The method according to claim 2, characterized in that: Determining the direction of the sound source according to the sound delay includes: According to the formula Δt·V c =d cosθ determines the direction of the sound source, where Δt is the sound delay between microphones, V c is the speed of sound propagation, d is the distance between microphones, and θ is the direction angle of the sound source.

5. The method according to claim 1, characterized in that Before determining the corresponding autocorrelation curves and cross-correlation curves in the plurality of sound data groups, the method further comprises: Determining energy corresponding to the sound data group; If the energy corresponding to the collected sound data group is greater than a preset energy threshold, then determining the corresponding autocorrelation curves and cross-correlation curves in the plurality of sound data groups is performed.

6. The method according to claim 1, characterized in that The sound data set of a predetermined length is collected through the microphone array, including: The acquisition frequency of the sound data in the sound data group is determined according to the distance between the microphones in the microphone array, and the sound data group is acquired according to the determined acquisition frequency, wherein the propagation distance of the sound signal within the acquisition interval corresponding to the acquisition frequency is less than the distance between the microphones.

7. A sound source localization device based on a microphone array, characterized in that: The device comprises: A sound data group acquisition unit, used to collect a sound data group of a predetermined time length through a microphone array, and acquire a plurality of sound data groups corresponding to a plurality of predetermined time lengths; A curve determination unit, used to determine the corresponding autocorrelation curves and cross-correlation curves in the plurality of sound data groups; a sound data group selection unit, configured to select a sound data group according to a ratio of a maximum value of the autocorrelation curve to a maximum value of the cross-correlation curve; A sound source localization unit, used for performing sound source localization according to the maximum value of the autocorrelation curve and the maximum value of the cross-correlation curve in the selected sound data group; The sound data group selection unit is specifically used to obtain the maximum value M of the autocorrelation curve. Auto , and the maximum value M of the cross-correlation curve Cross ; According to the maximum value M of the autocorrelation curve Auto , and the maximum value M of the cross-correlation curve Cross , determine the energy ratio The energy ratios are sorted to determine a predetermined number of energy ratios with larger values, and the sound data groups corresponding to the determined energy ratios are selected.

8. A sound source localization device based on a microphone array, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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