CN108594163B - Method for identifying sound source by using single microphone movement and inner product operation - Google Patents
Method for identifying sound source by using single microphone movement and inner product operation Download PDFInfo
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Abstract
The invention discloses a method for identifying a sound source by combining single microphone movement with inner product operation in the technical field of sound source identification, wherein a microphone is used for identifying the sound source, a sound pressure signal is collected in the movement process, and the collected signals cannot form a correlation relation in the time domain because the phase information of the actual point sound source cannot be determined, so that the correlation relation is found in the frequency domain of the signals by adopting the whole period sampling, and the position of the actual point sound source is identified by using the inner product operation; compared with a microphone array, the phase matching does not exist, and the cost is low; the high, medium and low frequency sound source identification resolution is high, and no spatial aliasing is generated.
Description
Technical Field
The invention relates to a sound source identification technology, in particular to a method for identifying a sound source by using single microphone movement so as to obtain the position of the sound source.
Background
Sound source identification is one of important contents of noise control engineering, and has important applications in low noise design of products, acoustic fault diagnosis and the like. The position of the sound source is obtained through sound source identification, so that the information such as the intensity of the sound source can be obtained, and the noise reduction from the sound source link is an effective noise control way; the running information of the mechanical equipment is obtained by analyzing the acoustic signals, so that faults can be found in time to avoid loss.
The existing sound source identification methods mainly include a near-field acoustic holography technology and a beam forming method, the sound source identification methods adopt a microphone array to acquire sound pressure signals, the microphone array is static when the sound source is identified by the microphone array, and in order to solve the problems that a low-frequency sound source identification resolution is low, a high-frequency sound source is easy to generate spatial aliasing and the like in the beam forming method, a mobile microphone array is also adopted to acquire signals. The microphone array is composed of hundreds of microphones mostly, and is composed of tens of microphones mostly, and the microphone array is used for measurement, so that the phase matching among the microphones is technically required, and the measurement cost is high.
Disclosure of Invention
The invention aims to solve the problems of the existing method for identifying a sound source by adopting a microphone array, provides a method for identifying the sound source by combining single microphone movement with inner product operation, has no problem of phase matching requirement and is low in measurement cost.
The invention discloses a method for identifying a sound source by combining single microphone movement and inner product operation, which adopts the technical scheme that the method sequentially comprises the following steps:
step 1: detecting the frequency f and the period T of an actual point sound source by adopting a mobile microphone;
step 2: enabling the microphone to move at a constant speed along a set route, and collecting a time domain sound pressure p (t) signal of an actual point sound source;
and step 3: dividing the time domain sound pressure p (t) signal into n segments at equal time intervals, and dividing the sound pressure p of each segmenti(t) the signals are each individually summed with a function e-jωtPerforming inner product operation to obtain the sound pressure frequency domain value p of the i-th sectioni(ω) constituting an actual point sound source sound pressure vector p; ω 2 pi f; e is the base number of the natural logarithm; j is an imaginary number; t is the sound pressure signal received by the moving microphone at time t;
and 4, step 4: performing mesh division in a region where an actual point sound source may exist, and assuming that a virtual sound source exists on nodes of a mesh;
and 5: obtaining sound pressure p generated by virtual sound source to moving microphonev(t) and corresponding ith segment frequency-domain value pvi(ω) constituting a virtual sound source sound pressure vector pv；
Step 6: for vector pvNormalization processing is carried out to obtain a vector ev；
And 7: the vector p and the vector e are comparedvInner product operation is carried out and then inner product modulo | non-conducting field is obtained through calculation<p,ev>And | l, searching out the position coordinate at the maximum value of the inner product module:
and 8: dividing finer grids in the surrounding area of the position of the maximum value of the internal integral mode, repeating the steps 5 to 8, and obtaining the position coordinate of the position of the maximum value of the internal integral mode after refining:
and step 9: and judging whether the position coordinate at the position of the refined internal integral mode maximum value reaches the required precision, if so, determining that the position corresponding to the position of the internal integral mode maximum value is the identified actual point sound source position.
The invention only uses one microphone to identify the sound source, collects the sound pressure signal in the moving process, and the collected signal does not form a correlation relation in the time domain because the phase information of the actual point sound source can not be determined, so the correlation relation is found in the frequency domain of the signal by adopting the whole period sampling, and the position of the actual point sound source is identified by utilizing the inner product operation. Compared with a microphone array, the phase matching does not exist, and the cost is low. The high, medium and low frequency sound source identification resolution is high, and no spatial aliasing is generated.
Drawings
The invention is described in further detail below with reference to the drawings and the detailed description;
fig. 1 is a schematic view at the time of plane sound source identification;
FIG. 2 is a schematic diagram of spatial sound source identification;
fig. 3 is a flow chart of the present invention.
Detailed Description
Referring to fig. 1, an actual point sound source exists in a space, the frequency of the actual point sound source is f, the actual point sound source generates sound pressure radiation to the surroundings, and a sound pressure p (t) signal generated by the actual point sound source to a microphone moving at a constant speed v is:
in the formula (1), a is the sound source intensity, r (t) is the distance between the moving microphone and the actual point sound source at time t, ω ═ 2 π f is the sound wave angular frequency, k is the wave number,
is the initial phase of the sound source, e is the base number of the natural logarithm, j represents the imaginary number; t is t1Where t-r (t)/c is the propagation velocity of sound in air, and the sound pressure signal received by the moving microphone at time t is the actual point sound source at t1The sound pressure signal sent out at any moment.
Assuming that a virtual sound source exists at any position in space, the intensity of the virtual sound source is set to be 1, the initial phase is set to be 0, and the virtual sound source generates sound pressure radiation to a microphone which moves at a constant speed v, and the radiation sound pressure pv(t) is:
in the formula (2), tv1＝t-rv(t)/c，rv(t) represents the distance of the virtual sound source from the microphone at time t.
Since the phase information of the actual point sound source is unknown, the sound pressure p (t) signal of the actual point sound source and the sound pressure p of the virtual sound source are different from the phase of the virtual sound sourcev(t) the signals do not form a correlation in the time domain, and in order to find the correlation, the time domain signals of the actual point sound sources are converted into the frequency domain. Dividing the whole sampling signal into n segments with equal time interval, each segment having time length TsTake a certain segment of signal as an example, and e-jωtPerforming inner product operation to obtain the sound pressure frequency domain value p of the i-th sectioni(ω) is:
in the formula (3), i is more than 1 and less than or equal to n, tiIndicating the starting time, T, of the selected i-th segment signals+tiIs the end time of the ith segment signal, dt is the sign of the integration operation. The speed v of the microphone movement is a small quantity relative to the speed c of sound, and because the sampling is fast, the sampling time of each segment is short, and at the time TsR (t) can be approximated to a constant value r, so that the integral of the negative frequency term of the latter term of equation (3) can be written as:
get TsIs an integral multiple of the sound pressure signal period of the actual point sound source, so that the integral result of the formula (4) is approximately 0, and the sound pressure frequency domain value p of the ith section of the sound pressure of the actual point sound sourcei(ω) is:
in the same way, the sound pressure p of the virtual sound sourcev(t) processing the signal, dividing the signal into n segments at equal time intervals, and corresponding to the ith segment of the sound pressure of the virtual sound sourceValue of threshold pvi(ω) is:
when the virtual sound source coincides with the actual point sound source position, r (t) and rv(t) same, pi(omega) and pvi(ω) is proportional, and a correlation is found.
The present invention utilizes r (t) and r when the virtual sound source coincides with the actual point sound source positionv(t) same, pi(omega) and pvi(ω) this correlation, in proportion, is combined with an inner product operation for sound source identification. Processing each segment of signal, integrating the frequency domain values of n segments of signal into the vector p [ p ] of sound pressure of actual point sound source according to the segmentation sequence1(ω),p2(ω),...,pi(ω),...,pn(ω)]The virtual sound pressure signals are integrated into a vector p of the virtual sound source sound pressure according to the same methodv[pv1(ω),pv2(ω),...,pvi(ω),...,pvn(ω)]And then carrying out normalization treatment on the sample:
in the formula (7) | | pvI represents a vector pvThe die of (1). The vector p and the vector e are comparedvPerforming inner product operation<p,ev>Obtaining inner product modulo (| non-conducting phosphor) of the two<p,ev>L. According to the Cauchy Schwarz theorem, if and only if the vector p and the vector evAnd obtaining the maximum value of the inner integral module during linear correlation, searching the maximum value of the inner integral module in the area where the actual point sound source is located by using an optimization algorithm, and finishing the identification of the sound source position by using the position of the corresponding virtual sound source when obtaining the maximum value, namely the position of the actual point sound source. The method comprises the following specific steps:
1. the frequency of the actual point source is detected using a moving microphone. Before moving the microphone, the microphone is made to stand at a point position, sound pressure signals of an actual point sound source are collected, and then the frequency f and the period T of the actual point sound source are obtained through Fourier transform.
2. And (3) enabling the microphone to move at a constant speed along a set route, and acquiring data in the moving process to obtain a sound pressure p (t) signal of the actual point sound source, wherein the signal is a time domain signal.
3. The obtained time domain sound pressure p (T) signal is segmented at equal time intervals and divided into n segments, and the time length of each segment of signal is Ts，TsIs an integral multiple of the sound pressure signal period T. Sound pressure p of each segmenti(t) the signals are each individually summed with a function e-jωtPerforming inner product operation to obtain the sound pressure frequency domain value p of the i-th sectioni(ω)：
tiIs the starting time of the ith segment of signal; e is the base number of the natural logarithm; j represents an imaginary number; ω 2 pi f, which is the acoustic angular frequency; t is the sound pressure signal received by the moving microphone at time t; dt is the sign of the integration operation.
The operation results are integrated one by one in sequence to construct the sound pressure vector p [ p ] of the actual point sound source1(ω),p2(ω),...,pi(ω),...,pn(ω)]。
4. In the possible existing area of the actual point sound source, the area is divided by a rough grid, a point sound source is assumed to exist on the nodes of the grid, the intensity of the point sound source is set to be 1, the phase of the point sound source is set to be 0, and the point sound source is called as a constructed virtual sound source.
5. The sound pressure p of the virtual sound source generated by the virtual sound source to the surrounding can be obtained by calculationv(t) is:
r in formula (9)v(t) is the distance between the moving microphone and the virtual sound source at time t, and k is the wave number; t is tv1＝tv-rv(t)/c, c is the speed of sound propagation in air, and the moving microphone receives the sound pressure signal at time tIs the virtual sound source at tv1The sound pressure signal sent out at any moment.
Then, the sound pressure p to the virtual sound sourcev(t) processing the signal according to the formula (10) to obtain the ith frequency domain value p of the sound pressure of the virtual sound sourcevi(ω)：
The operation results are integrated one by one in sequence to construct a sound pressure vector p related to the virtual sound sourcev[pv1(ω),pv2(ω),...,pvi(ω),...,pvn(ω)]。
6. For the virtual sound source sound pressure vector p obtained in the step 5vCarrying out normalization processing to obtain a normalized vector ev：
In the formula | | pvI represents a vector pvThe die of (1).
7. The actual point sound source sound pressure vector p and the vector e obtained in the step 3 are usedvDo inner product operation<p,ev>Calculating to obtain inner product modulo (| luminance)<p,ev>L. Obtaining inner product module values on each node, calculating the number of cells in the inner product module<p,ev>Searching out the maximum value in the | | value, and recording the position coordinate at the maximum value
8. And (3) dividing finer grids in the surrounding area of the position of the maximum value of the internal integral model, and repeating the step 5 to the step 8 by using a step-by-step refining optimization method to obtain the position coordinate of the position of the maximum value of the internal integral model after refining.
9. And finally, judging whether the position coordinate at the position of the refined inner integral mode maximum value reaches the required precision, if not, returning to the step 8 to continue the operation, and if the precision requirement is met, determining the position corresponding to the inner integral mode maximum value as the identified actual point sound source position, thus obtaining the identified result.
Two embodiments of the present invention are provided below, which are a method for identifying a planar midpoint sound source and a method for identifying a spatial midpoint sound source, respectively:
example 1
A certain point sound source exists in a plane, the frequency of the radiated sound wave of the point sound source is 1000Hz, the position coordinates of the point sound source are assumed to be (0.4226,0.3113), the coordinate unit is meter, and as shown in figure 1, the identification precision requirement reaches the millimeter level. The identification steps are as follows:
detecting the frequency of the sound source
The microphone is placed at a point outside the plane, and the distance AO from the plane is 0.5 m. Firstly, the microphone is static, sound pressure signals are collected, Fourier transform is applied to the collected signals, and the frequency f of the sound source sound wave is 1000Hz and the period T is 0.001 s.
(II) sound pressure signal is collected by uniform motion of microphone
And enabling the microphone to linearly move at a constant speed along the X-axis direction, setting the movement speed v to be 1m/s, and acquiring a time-domain sound pressure signal of one second at a sampling frequency of 4000Hz, and recording the time-domain sound pressure signal as p (t).
(III) constructing an actual sound source sound pressure vector in a frequency domain
Dividing the collected time-domain sound pressure signal into 200 segments, wherein each segment has a time length of 5 sound wave periods, and the time length T of the segments0.005 s. Obtaining the operation result p of each segment by using the formula (8)i(ω) which are integrated in order into a vector p [ p ] with respect to the actual sound source sound pressure1(ω),p2(ω),...,p200(ω)]。
(IV) constructing virtual Sound Source
In a 1 × 1m area where a sound source is located, the area is divided by a coarse grid, the grid interval is 0.1m, as shown in fig. 1, it is assumed that a virtual sound source exists at a node of the grid, the intensity of the sound source is 1, and the initial phase is 0.
(V) constructing vector of virtual sound source sound pressure in frequency domain
The virtual sound source generates sound pressure radiation to the surroundings, and the sound pressure generated by the virtual sound source to the mobile microphone can be obtained by calculating according to the formula (9) pv(t) signal, p in the same way as in step (III)v(t) segmenting, and obtaining the operation result p of each segment by using a formula (10)vi(ω) integrating the results thereof in order into a vector p with respect to the sound pressure of the virtual sound sourcev[pv1(ω)，pv2(ω)，...，Pv200(ω)]。
(VI) normalization processing
For the obtained virtual sound source sound pressure vector pvCarrying out normalization processing to obtain a vector
(VII) carrying out inner product operation and searching inner product module extreme value
The sound pressure vector p and the vector e of the actual point sound source are comparedvPerforming inner product operation to obtain inner product modulo | luminance<p，ev>And obtaining inner product module values on all the nodes, searching out the maximum value from the inner product module values, and recording that the position coordinate at the maximum value is (0.4, 0.3).
(VIII) optimally calculating the maximum value
And (5) dividing a finer grid of 0.01m in an area of 0.1m around (0.4, 0.3) the position of the maximum value of the inner product mode, repeating the step (five) to the step (eight) by using a progressive refinement optimization method, and obtaining the position coordinate of the refined maximum value of the inner product mode of (0.42, 0.32).
(nine) obtaining the recognition result by precision judgment
Judging the position coordinates (0.42, 0.32) obtained in the last step, not meeting the millimeter-scale precision requirement, returning to the step (eight) to continue the operation, obtaining the position coordinates (0.423, 0.311) at the maximum value of the inner product mode after progressive refinement, judging, meeting the millimeter-scale precision requirement, and then (0.423, 0.311) is the recognized sound source position, and the recognition result is accurate, as shown in the following table 1:
TABLE 1 results of plane point sound source identification
| Sound source position coordinate x (m) | Sound source position coordinates y (m) | |
| Theoretical value | 0.4226 | 0.3113 |
| Identification value | 0.423 | 0.311 |
Example 2
A sound source exists in a certain point in space, the radiation sound wave frequency of the sound source is 800Hz, the coordinates of the sound source in the space are (0.2123, 0.3558 and 0.7912), the coordinate unit is meter, and as shown in figure 2, the identification requirement reaches the millimeter level. The identification steps are as follows:
detecting the frequency of the sound source
The initial position of the microphone is (0.5, 0, 0), and the distance AO from the area where the sound source is located is 0.6 m. Firstly, the microphone is static, sound pressure signals are collected, Fourier transform is applied to the collected signals, and the frequency f of the sound source sound waves is 800Hz, and the period T is 0.00125 s.
(II) sound pressure signal is collected by uniform motion of microphone
The microphone is made to move on a plane x0y along a set circular track at a constant speed, the radius R of the motion track is 0.5m, the speed v is 0.52m/s, and a time-domain sound pressure signal of 3 seconds is collected at a sampling frequency of 4000Hz and is recorded as p (t).
(III) constructing an actual sound source sound pressure vector in a frequency domain
Dividing the collected time-domain sound pressure signal into 160 segments, each segment having a time length of 15 sound wave periodsTime length T of time segments0.01875 s. Obtaining the operation result p of each segment by using the formula (8)i(ω) which are integrated in order into a vector p [ p ] with respect to the actual sound source sound pressure1(ω),p2(ω),...,p160(ω)]。
(IV) constructing virtual Sound Source
In a 1 × 1 × 1m area where a sound source is located, a rough grid is used for division, the interval is 0.1m, a virtual sound source is assumed to exist on a node of the grid, the intensity of the sound source is 1, and the phase is 0.
(V) constructing vector of virtual sound source sound pressure in frequency domain
The virtual sound source generates sound pressure radiation to the surroundings, and the sound transmission to the movement can be obtained by applying the calculation of the formula (9)
Sound pressure signal p generated by the devicev(t) applying the same method as in the third step to pv(t) segmenting, and obtaining the operation result p of each segment by using a formula (10)vi(ω) integrating the results thereof in order into a vector p with respect to the sound pressure of the virtual sound sourcev[pv1(ω),pv2(ω),...,pv160(ω)]。
(VI) normalization processing
For the obtained virtual sound source sound pressure vector pvCarrying out normalization processing to obtain a vector
(VII) carrying out inner product operation and searching inner product module extreme value
The vector p and the vector e of the actual sound source sound pressure are comparedvPerforming inner product operation to calculate inner product module value | non-conducting phosphor<p,ev>L. After obtaining the inner product modulus values on all the nodes, the maximum value is searched out from the inner product modulus values, and the position coordinate at the maximum value is recorded to be (0.2,0.4, 0.8).
(VIII) optimally calculating the maximum value
And (4) dividing a finer grid by 0.01m in a region 0.1m around the position (0.2,0.4,0.8) of the maximum value of the inner product mode, and repeating the steps (five) to (eight) by using a progressive refinement optimization method to obtain the position coordinates (0.21,0.36,0.79) at the position of the refined maximum value of the inner product mode.
(nine) obtaining the recognition result by precision judgment
Judging the position coordinates (0.21,0.36 and 0.79) obtained in the last step, not meeting the millimeter-grade precision requirement, returning to the step (eight) to continue the operation, obtaining the position coordinates at the maximum value of the inner product mode after progressive refinement as (0.212,0.356 and 0.791), judging and meeting the millimeter-grade precision requirement, wherein (0.212,0.356 and 0.791) is the recognized sound source position, and the recognition result is accurate, as shown in the following table 2:
table 2 recognition results of spatial point sound sources
| Sound source position coordinate x (m) | Sound source position coordinates y (m) | Sound source position coordinate z (m) | |
| Theoretical value | 0.2123 | 0.3558 | 0.7912 |
| Identification value | 0.212 | 0.356 | 0.791 |
Claims (7)
1. A method for identifying sound source by using single microphone movement and combining inner product operation is characterized by sequentially adopting the following steps:
step 1: detecting the frequency f and the period T of an actual point sound source by adopting a mobile microphone;
step 2: enabling the microphone to move at a constant speed along a set route, and collecting a time domain sound pressure p (t) signal of an actual point sound source;
and step 3: dividing the time domain sound pressure p (t) signal into n segments at equal time intervals, and dividing the sound pressure p of each segmenti(t) the signals are each individually summed with a function e-jωtPerforming inner product operation to obtain the sound pressure frequency domain value p of the i-th sectioni(ω) constituting an actual point sound source sound pressure vector p; ω 2 pi f; e is the base number of the natural logarithm; j is an imaginary number; t is the moment when the microphone receives the sound pressure signal;
and 4, step 4: performing mesh division in a region where an actual point sound source may exist, and assuming that a virtual sound source exists on nodes of a mesh;
and 5: obtaining the sound pressure p generated by the virtual sound source to the mobile microphonev(t) and corresponding ith segment frequency-domain value pvi(ω) constituting a virtual sound source sound pressure vector pv；
Step 6: for virtual sound source sound pressure vector pvNormalization processing is carried out to obtain a vector ev；
And 7: the sound pressure vector p and the vector e of the actual point sound source are comparedvInner product operation is carried out and then inner product modulo | non-conducting field is obtained through calculation<p,ev>And | l, searching out the position coordinate at the maximum value of the inner product module:
and 8: dividing finer grids in the surrounding area of the position of the maximum value of the internal integral mode, repeating the steps 5 to 8, and obtaining the position coordinate of the position of the maximum value of the internal integral mode after refining:
and step 9: and judging whether the position coordinate at the position of the refined internal integral mode maximum value reaches the required precision, if so, determining that the position corresponding to the refined internal integral mode maximum value is the identified actual point sound source position.
2. The method of claim 1 wherein the single microphone movement is combined with an inner product operation to identify the sound source, wherein: in step 1, the microphone is made to stand at a point position before the microphone is moved, and the frequency f and the period T of an actual point sound source are obtained through Fourier transform of collected sound pressure signals.
3. The method of claim 1 wherein the single microphone movement is combined with an inner product operation to identify the sound source, wherein: in step 3, the sound pressure p of each segmenti(T) the time length of the signal is Ts，TsIs an integer multiple of the period T; sound pressure frequency domain value of i-th section
tiIs the starting time of the ith segment of the signal.
4. The method of claim 1 wherein the single microphone movement is combined with an inner product operation to identify the sound source, wherein: in step 4, the intensity of the virtual sound source is 1 and the phase is 0.
5. A method of identifying a sound source using a single microphone move in combination with an inner product operation as claimed in claim 3, wherein: in step 5, the sound pressure of the virtual sound source
Ith segment frequency domain value
rv(t) is the distance between the moving microphone and the virtual sound source at time t, and k is the wave number; t is tv1＝tv-rv(t)/c, c is the propagation speed of sound in air, and the sound pressure signal received by the moving microphone at the time t is the virtual sound source at tv1The sound pressure signal sent out at any moment.
6. The method of claim 1 wherein the single microphone movement is combined with an inner product operation to identify the sound source, wherein: in the step 6, the process of the present invention,
||pvis the vector pvThe die of (1).
7. The method of claim 1 wherein the single microphone movement is combined with an inner product operation to identify the sound source, wherein: in step 9, when it is judged that the required accuracy is not achieved, the process returns to step 8.
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