Clustering thought-based sound source rapid evaluation and positioning method

Abstract

The invention discloses a sound source rapid evaluation and positioning method based on a clustering thought, belongs to the technical field of sound target signal detection and parameter estimation, and solves the problem that a sound source cannot be rapidly evaluated and positioned under special conditions. According to sound source signals, sound source position constraint and abnormal point elimination are carried out through trigger statistics; secondly, time delay statistics is carried out on the constrained sound source signals, and data set division is carried out according to time delay; thirdly, time delay differences triggered between different sound detection modules are calculated, and abnormal data points are eliminated in combination with the distance between the modules; and finally, performing grid division according to the geometrical relationship of the arrangement of the sound detection modules, traversing any node in the same data set based on the idea of ray tracing, completing straight line connection between any two points, counting the number of times that all straight lines pass through different grids, and further performing clustering analysis on the number of times that the grids pass through to determine the position of a sound source. According to the invention, the sound source positioning speed and accuracy can be improved.

Description

Technical Field

[0001] The invention belongs to the technical field of sound target signal detection and parameter estimation, and in particular relates to a sound source rapid assessment and positioning method based on clustering idea. Background Art

[0002] At present, the sound source localization methods mainly include traditional methods and deep learning intelligent methods. The time delay estimation method based on the time difference of arrival (TDOA) is a traditional sound source localization method commonly used at home and abroad. This method has a simple structure and fast calculation speed and is often used to estimate the three-dimensional coordinates of the sound source. In the process of solving the sound source position, the existing methods have the following shortcomings:

[0003] In embedded applications, direct calculation methods are often used to simplify calculations, but their anti-interference ability and positioning accuracy are not high.

[0004] Newton iteration method is also often used for position estimation, but its positioning accuracy depends on the initial value and the operation time is long;

[0005] The spherical interpolation method has fast calculation speed and relatively high positioning accuracy and has been widely used. However, its anti-noise performance is weak and it is not suitable for some occasions where high positioning accuracy is required. Summary of the invention

[0006] The main purpose of the present invention is to overcome the deficiencies in the prior art and solve the technical problem that the traditional TDOA method is difficult to quickly and accurately identify and estimate the sound source signal. The present invention provides a sound source rapid assessment and positioning method based on the clustering idea, which uses time delay sorting, data set division, and grid clustering analysis based on the clustering idea to complete the rapid assessment and positioning of the parameter estimation of the sound source signal.

[0007] The present invention is implemented by the following technical scheme: a method for rapid assessment and positioning of sound sources based on clustering idea, comprising the following steps:

[0008] S1, collecting sound signals through the sound detection module, and triggering time sorting through the sound detection module;

[0009] S2, for the acoustic detection module data obtained in step S1, divide it into several data sets in units of time period T according to the size of the acquisition time, and count the number of times the acoustic detection module is triggered in each data set;

[0010] S3, select the time domain T with the largest total number of times the acoustic detection module is triggered in step S2. a and adjacent time domain T a-1 、T a+1The acoustic detection module data in the adjacent time domains has a time difference of less than 10ms, and the time domain T a-1 , T a , T a+1 Merge into a new sound source signal dataset;

[0011] S4, repeating the above steps S2 to S3 until the sound source signal data of the required time length is obtained to eliminate the influence of abnormal signals;

[0012] S5, let "0" be the unknown sound source center, and the delay difference P between adjacent sensor nodes ki for:

[0013]

[0014] In formula (1), P i is the triggered acoustic sensor node, t k is the trigger delay of acquisition, i=1, 2, ..., Q, k=1, 2, ..., Q;

[0015] S6, dividing and clustering the delay differences between adjacent sensor nodes determined in step S5 according to the time range to obtain a data set D:

[0016]

[0017] In formula (2), v is the speed of sound propagation in the medium, ε is the error of the acoustic signal trigger delay; R is the distance position of the triggered sensor;

[0018] S7, the data set D obtained in step S6 is arranged in descending order according to the delay size, and the data set is divided according to the value of the error ε, and the divided data set D' is obtained as follows:

[0019] D′=[A(x1,y1),B(x2,y2),C(x3,y3),D(x4,y4),E(x5,y5)]; Formula (3)

[0020] The size of the divided data set D' in the X and Y directions is divided into grids of Δx and Δy respectively;

[0021] S8. Connect any two points A, B, C, D, E in step S7 to obtain a straight line y between points i and j. ij (i≠j) is:

[0022]

[0023] S9, take the coordinate point of the grid Y axis in step S7 as the reference point, traverse all the straight lines of formula (4) in step S8, and obtain the straight line y ij (i≠j) Coordinates that intersect the X axis for:

[0024]

[0025] According to formula (5), the grid position through which the corresponding straight line passes is determined as:

[0026]

[0027] S10, traverse the grids that each straight line obtained by equation (6) passes through, and cluster analysis and count the number of times each grid passes through the straight line, and select the coordinates of the grid with the maximum number of times, which is the estimated position of the sound source

[0028] S11, use the shortest delay node to perform weighted average processing on all nodes, and adjust the estimated position of the sound source in step S10 to improve the accuracy of sound source estimation; extract the minimum delay t1 of D' in formula (3), and select the corresponding reference point A(x1, y1), and the position difference between OA can be obtained as:

[0029]

[0030] S12: The estimated position of the sound source in step S10 according to formula (7) Adjust and estimate twice, we can get:

[0031]

[0032] In formula (8), l represents the number of points contained in the data set D', r x 、r y Indicates the amount of adjustment of the sound source position;

[0033] S13, select each data set in formula (3) as a reference point in turn, repeat the above steps S11 to S12, and perform mean processing on the obtained sound source position to complete the rapid evaluation and positioning of the sound source based on the clustering idea.

[0034] Furthermore, in step S1, the sound detection module includes a high threshold and a low threshold, the initial value of the high threshold is 120dB, and the initial value of the low threshold is 90dB. When the decibel value of the sound signal exceeds the high threshold or is lower than the low threshold, it is triggered and outputs a low-level signal.

[0035] Furthermore, in step S4, the required time length is 18 seconds.

[0036] Furthermore, in step S6, v is the speed of sound propagation in the air, v=340 m / s; the error ε of the acoustic signal trigger delay is determined according to the distance at which the sensors are arranged.

[0037] Furthermore, in step S7, the values ​​of the grid sizes Δx and Δy are determined according to the distance resolution of the sampling nodes.

[0038] Furthermore, in step S12, the sound source position adjustment amount r x 、r y for:

[0039]

[0040] The beneficial effects of the present invention are:

[0041] 1. The present invention can realize the location constraint of the sound source and the elimination of abnormal points by triggering statistics through the detection module for the sound source signal;

[0042] 2. The present invention aims at constraining the sound source signal and can divide the sound source data set by delay sorting;

[0043] 3. The present invention completes grid division based on the geometric relationship of the detection module layout, and traverses the data set nodes based on the idea of ​​ray tracing to achieve accurate cluster analysis to determine the location of the sound source

[0044] In summary, the present invention can improve the speed and accuracy of sound source localization and provide technical guarantee for the detection and identification requirements of subsequent comprehensive test systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a flowchart of the present invention;

[0046] Figure 2 A schematic diagram of dividing the sound source signal into three data sets in Example 1;

[0047] Figure 3 This is a schematic diagram of acoustic sensor data obtained after traversing the data set nodes in Example 1;

[0048] Figure 4 A schematic diagram of first time delay statistics extracted by the acoustic sensor in the data set of Example 1;

[0049] Figure 5 Schematic diagram of a statistical data set of acoustic sensor trigger times obtained by dividing by T=180s in Example 2;

[0050] Figure 6 Schematic diagram of a statistical data set of acoustic sensor trigger times obtained by dividing by T=20s in Example 2;

[0051] Figure 7 Schematic diagram of valid data in three acoustic sensor triggering times statistical data sets obtained by dividing by T=600ms in Example 3;

[0052] Figure 8This is a schematic diagram of the first trigger delay of three acoustic sensors in Example 3;

[0053] Fig. 9 Schematic diagram of valid data in the statistical data set of four groups of acoustic sensor trigger times divided by T=600ms;

[0054] Fig.10 This is a schematic diagram of the first trigger delay of four groups of acoustic sensors;

[0055] Fig.11 Schematic diagram of sound source localization for grid cluster analysis;

[0056] In each figure: Figure 2 , Figure 5 and Figure 6 The horizontal axis is the name of the data set, and the vertical axis is the number of times the acoustic sensor is triggered; Figure 3 , Figure 4 , Figure 7 , Figure 8 , Fig. 9 and Fig.10 The horizontal axis is the acoustic sensor trigger time in milliseconds, and the vertical axis is the acoustic sensor serial number. DETAILED DESCRIPTION

[0057] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0058] Example 1

[0059] like Figure 1 A method for quickly evaluating and locating a sound source based on clustering idea is shown, comprising the following steps:

[0060] S1, collect sound signals through the sound detection module, and sort the trigger time through the sound detection module; the sound detection module includes a high threshold and a low threshold, the initial value of the high threshold is 120dB, the initial value of the low threshold is 90dB, and the decibel value of the sound signal exceeds the high threshold or is lower than the low threshold. It is triggered and outputs a low-level signal;

[0061] S2, for the acoustic detection module data obtained in step S1, divide it into several data sets in units of time period T according to the size of the acquisition time, and count the number of times the acoustic detection module is triggered in each data set;

[0062] S3, select the time domain T with the largest total number of times the acoustic detection module is triggered in step S2. a and adjacent time domain T a-1 , T a+1 The acoustic detection module data in the adjacent time domains has a time difference of less than 10ms, and the time domain T a-1 , T a , T a+1Merge into a new sound source signal data set, in this embodiment 1, a=11;

[0063] S4, repeating the above steps S2 to S3 until the sound source signal data with the required time length of 18s is obtained to eliminate the influence of abnormal signals;

[0064] S5, let "0" be the unknown sound source center, and the delay difference P between adjacent sensor nodes ki for:

[0065]

[0066] In formula (1), P i is the triggered acoustic sensor node, t k is the trigger delay of acquisition, i=1, 2, ..., Q, k=1, 2, ..., Q;

[0067] S6, dividing and clustering the delay differences between adjacent sensor nodes determined in step S5 according to the time range to obtain a data set D:

[0068]

[0069] In formula (2), v is the speed of sound propagation in the medium. In the first embodiment, v is the speed of sound propagation in the air, v=340 m / s; ε is the error of the acoustic signal trigger delay, and the error ε of the acoustic signal trigger delay is determined according to the distance at which the sensor is arranged; R is the distance position at which the triggered sensor is located;

[0070] S7, the data set D obtained in step S6 is arranged in descending order according to the delay size, and the data set is divided according to the value of the error ε, and the divided data set D' is obtained as follows:

[0071] D′=[A(x1,y1),B(x2,y2),C(x3,y3),D(x4,y4),E(x5,y5)]; Formula (3)

[0072] The size of the divided data set D' in the X and Y directions is divided into grids of Δx and Δy respectively, and the values ​​of the grid sizes Δx and Δy are determined according to the distance resolution of the sampling nodes;

[0073] S8. Connect any two points A, B, C, D, E in step S7 to obtain a straight line y between points i and j. ij (i≠j) is:

[0074]

[0075] S9, take the coordinate point of the grid Y axis in step S7 as the reference point, traverse all the straight lines of formula (4) in step S8, and obtain the straight line y ij(i≠j) Coordinates that intersect the X axis for:

[0076]

[0077] According to formula (5), the grid position through which the corresponding straight line passes is determined as:

[0078]

[0079] S10, traverse the grids that each straight line obtained by equation (6) passes through, and cluster analysis and count the number of times each grid passes through the straight line, and select the coordinates of the grid with the maximum number of times, which is the estimated position of the sound source

[0080] S11, use the shortest delay node to perform weighted average processing on all nodes, and adjust the estimated position of the sound source in step S10 to improve the accuracy of sound source estimation; extract the minimum delay t1 of D' in formula (3), and select the corresponding reference point A(x1, y1), and the position difference between OA can be obtained as:

[0081]

[0082] S12: The estimated position of the sound source in step S10 according to formula (7) Adjust and estimate twice, we can get:

[0083]

[0084] In formula (8), l represents the number of points contained in the data set D', r x 、r y Indicates the amount of adjustment of the sound source position;

[0085] Sound source position adjustment amount r x 、r y for:

[0086]

[0087] S13, select each data set in formula (3) as a reference point in turn, repeat the above steps S11 to S12, and perform mean processing on the obtained sound source position to complete the rapid evaluation and positioning of the sound source based on the clustering idea.

[0088] In this embodiment 1, the sound source signal is divided into T 10 , T 11 , T 12 Three datasets, such as Figure 2 shown. Figure 3 is the result of traversing the dataset nodes. Figure 4 To extract the first delay from the data set, grid cluster analysis is performed. Figure 3and Figure 4 The time period with the most concentrated acoustic signal triggering can be obtained, providing a data source for clustering diversity.

[0089] It can be seen that the present invention can realize grid division according to the geometric relationship of the detection module layout, traverse the data set nodes based on the idea of ​​ray tracing, and accurately perform cluster analysis to determine the location of the sound source.

[0090] Example 2

[0091] like Figure 1 A method for quickly evaluating and locating a sound source based on clustering idea is shown, comprising the following steps:

[0092] S1, collect sound signals through the sound detection module, and sort the trigger time through the sound detection module; the sound detection module includes a high threshold and a low threshold, the initial value of the high threshold is 120dB, the initial value of the low threshold is 90dB, and the decibel value of the sound signal exceeds the high threshold or is lower than the low threshold. It is triggered and outputs a low-level signal;

[0093] S2. For the acoustic detection module data obtained in step S1, according to the size of the acquisition time, divide it into several data sets in units of time period T1=180s, and count the number of times the acoustic detection module is triggered in each data set, such as Figure 5 As shown;

[0094] S3, select the time domain T with the largest total number of times the acoustic detection module is triggered in step S2. a and adjacent time domain T a-1 、T a+1 The acoustic detection module data in the time domain T a-1 、T a 、T a+1 Merge into a new sound source signal data set, in this embodiment 2, a=5;

[0095] S4, repeat the above steps S2-S3, divide several data sets into units of time period T2=20s, select the acoustic detection module data in the time domain T6 with the largest total number of acoustic detection module triggers and the adjacent time domains T5 and T7, and merge the time domains T5-T7 into a new sound source signal data set, and count the number of times the acoustic detection module is triggered in each data set, such as Figure 6 As shown; until the sound source signal data with the required time length of 18s is obtained, the influence of abnormal signals is eliminated;

[0096] Subsequent steps S5 to S13 are the same as those in Example 1 and are not described in detail here, so as to complete the rapid assessment and positioning of the sound source based on the clustering concept.

[0097] This embodiment 2 is used to verify the effectiveness of the present invention in selecting valid sound source signals and eliminating abnormal sound source signals, and can obtain a valid sound source signal data set after eliminating abnormal signals.

[0098] Example 3

[0099] like Figure 1 A method for quickly evaluating and locating a sound source based on clustering idea is shown, comprising the following steps:

[0100] S1. In this embodiment 3, three acoustic sensors are arranged at 35 meters, 40 meters and 80 meters away from the sound source, respectively. Sound signals are collected by the acoustic detection module, and the trigger time is sorted by the acoustic detection module; the acoustic detection module includes a high threshold and a low threshold, the initial value of the high threshold is 120dB, and the initial value of the low threshold is 90dB. When the decibel value of the sound signal exceeds the high threshold or is lower than the low threshold, it is triggered and a low-level signal is output;

[0101] S2. Divide the acoustic detection module data obtained in step S1 into several data sets in units of time period T, and count the number of times the acoustic detection module is triggered in each data set;

[0102] S3, select the time domain T with the largest total number of times the acoustic detection module is triggered in step S2. a and adjacent time domain T a-1 , T a+1 The acoustic detection module data in the time domain T a-1 , T a , T a+1 Merge into a new sound source signal dataset;

[0103] S4, repeat the above steps S2 to S3 until the sound source signal data of the required time length of 600ms is obtained, and the influence of abnormal signals is eliminated. The valid data in the statistical data set of the number of acoustic sensor triggering obtained by dividing by T = 600ms is as follows Figure 7 As shown;

[0104] S5, let "0" be the unknown sound source center, and the delay difference P between adjacent sensor nodes ki for:

[0105]

[0106] In formula (1), P i is the triggered acoustic sensor node, t k is the trigger delay of acquisition, i=1, 2, ..., Q, k=1, 2, ..., Q;

[0107] Figure 8 To obtain the data obtained after the first delay of the high-level threshold trigger signal, Figure 8It can be seen that the first delays of the three acoustic sensors are 7374146ms, 7374161ms and 7374280ms respectively. The delay differences are 15ms and 119ms respectively. The distance differences obtained by the delay differences are 5.1m and 40.5m respectively, which are basically consistent with the theoretical values. Therefore, the present invention is effective in calculating the sound source position by selecting the first delay of the acoustic sensor.

[0108] Subsequent steps S6 to S13 are the same as those in Example 1 and are not described in detail here, so as to complete the rapid assessment and positioning of the sound source based on the clustering concept.

[0109] Example 4

[0110] like Figure 1 A method for quickly evaluating and locating a sound source based on clustering idea is shown, comprising the following steps:

[0111] S1. In this embodiment 4, four groups of acoustic sensors are arranged in the east, south, west and north directions of the sound source, respectively. Each group of sensors includes three sensors, which are arranged at 30, 40 and 60 meters away from the sound source, respectively. The sound signal is collected by the acoustic detection module, and the trigger time is sorted by the acoustic detection module; the acoustic detection module includes a high threshold and a low threshold, the initial value of the high threshold is 120dB, and the initial value of the low threshold is 90dB. When the decibel value of the sound signal exceeds the high threshold or is lower than the low threshold, it is triggered and a low-level signal is output;

[0112] S2. For the acoustic detection module data obtained in step S1, divide it into several data sets in units of time period T according to the acquisition time, and count the number of times the acoustic detection module is triggered in each data set;

[0113] S3, select the time domain T with the largest total number of times the acoustic detection module is triggered in step S2. a and adjacent time domain T a-1 、T a+1 The acoustic detection module data in the time domain T a-1 、T a 、T a+1 Merge into a new sound source signal data set, in this embodiment 4, a=11;

[0114] S4, repeat the above steps S2 to S3 until the sound source signal data with the required time length of 600ms is obtained (such as Fig. 9 As shown), eliminate the influence of abnormal signals;

[0115] S5, let "0" be the unknown sound source center, the delay difference between adjacent sensor nodes (such as Fig.10 (shown) ki for:

[0116]

[0117] In formula (1), P i is the triggered acoustic sensor node, t k is the trigger delay of acquisition, i=1, 2, ..., Q, k=1, 2, ..., Q;

[0118] Subsequent steps S6 to S13 are the same as those in Example 1 and are not described in detail here, so as to complete the rapid assessment and positioning of the sound source based on the clustering concept.

[0119] Fig.11 The sound source position is obtained by grid clustering analysis, which shows the effectiveness of the present invention in determining the sound source position by completing grid division.

[0120] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for rapid assessment and localization of sound sources based on clustering, characterized in that: The following steps are involved: S1, collecting sound signals through the sound detection module, and triggering time sorting through the sound detection module; S2, for the acoustic detection module data obtained in step S1, divide it into several data sets in units of time period T according to the size of the acquisition time, and count the number of times the acoustic detection module is triggered in each data set; S3, select the time domain T with the largest total number of times the acoustic detection module is triggered in step S2. a and adjacent time domain T a-1 , T a+1 The acoustic detection module data in the adjacent time domains has a time difference of less than 10ms, and the time domain T a-1 , T a , T a+1 Merge into a new sound source signal dataset; S4, repeating the above steps S2 to S3 until the sound source signal data of the required time length is obtained to eliminate the influence of abnormal signals; S5, let "0" be the unknown sound source center, and the delay difference P between adjacent sensor nodes ki for: In formula (1), P i is the triggered acoustic sensor node, t k is the trigger delay of acquisition, i=1, 2, ..., Q, k=1, 2, ..., Q; S6, dividing and clustering the delay differences between adjacent sensor nodes determined in step S5 according to the time range to obtain a data set D: In formula (2), v is the speed of sound propagation in the medium, ε is the error of the acoustic signal trigger delay; R is the distance position of the triggered sensor; S7, the data set D obtained in step S6 is arranged in descending order according to the delay size, and the data set is divided according to the value of the error ε, and the divided data set D' is obtained as follows: D′=[A(x1,y1),B(x2,y2),C(x3,y3),D(x4,y4),E(x5,y5)]; Formula (3) The size of the divided data set D' in the X and Y directions is divided into grids of Δx and Δy respectively; S8. Connect any two points A, B, C, D, E in step S7 to obtain a straight line y between points i and j. ij (i≠j) is: S9, take the coordinate point of the grid Y axis in step S7 as the reference point, traverse all the straight lines of formula (4) in step S8, and obtain the straight line y ij (i≠j) Coordinates that intersect the X axis for: According to formula (5), the grid position through which the corresponding straight line passes is determined as: S10, traverse the grids that each straight line obtained by equation (6) passes through, and cluster analysis and count the number of times each grid passes through the straight line, and select the coordinates of the grid with the maximum number of times, which is the estimated position of the sound source S11, using the shortest delay node to perform weighted average processing on all nodes, and adjusting the sound source estimation position in step S10 to improve the sound source estimation accuracy; Extract the minimum delay t1 of D' in equation (3) and select the corresponding reference point A(x1,y1), and the position difference between OA can be obtained as: S12: The estimated position of the sound source in step S10 according to formula (7) Adjust and estimate twice, we can get: In formula (8), l represents the number of points contained in the data set D', r x 、r y Indicates the amount of adjustment of the sound source position; S13, select each data set in formula (3) as a reference point in turn, repeat the above steps S11 to S12, and perform mean processing on the obtained sound source position to complete the rapid evaluation and positioning of the sound source based on the clustering idea.

2. According to the method for rapid assessment and positioning of sound sources based on clustering concept in claim 1, it is characterized in that: In step S1, the sound detection module includes a high threshold and a low threshold, the initial value of the high threshold is 120dB, and the initial value of the low threshold is 90dB. When the decibel value of the sound signal exceeds the high threshold or is lower than the low threshold, it is triggered and outputs a low-level signal.

3. The method for rapid assessment and positioning of sound sources based on clustering concept according to claim 1, characterized in that: In step S4, the required time length is 18 seconds.

4. The method for rapid assessment and localization of sound sources based on clustering concept according to claim 1, characterized in that: In step S6, v is the speed of sound propagation in the air, v=340 m / s; the error ε of the acoustic signal trigger delay is determined according to the distance at which the sensors are arranged.

5. The method for rapid assessment and positioning of sound sources based on clustering concept according to claim 1, characterized in that: In step S7, the values ​​of the grid sizes Δx and Δy are determined according to the distance resolution of the sampling nodes.

6. The method for rapid assessment and localization of sound sources based on clustering concept according to claim 1, characterized in that: In step S12, the sound source position adjustment amount r x 、r y for:

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