CN114167356A - Sound source positioning method and system based on polyhedral microphone array - Google Patents
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Abstract
The invention provides a sound source positioning method based on a polyhedral microphone array, which comprises the following steps of S1: collecting multi-channel data collected by a plurality of microphones forming a polyhedral structure; s2: iteratively processing the multi-channel data through an independent component analysis algorithm to obtain target sound source data after noise reduction; s3: the azimuth of the target sound source is determined based on the amount of attenuation of the target sound source by each microphone. The invention has the advantages that: the sound data are collected through the polyhedral microphone array, so that the receiving signals of the microphone array form a non-singular matrix, the sound source can be extracted through an independent component analysis algorithm, the direction of the sound source relative to the microphone array is judged through the attenuation of the sound source, the sound source is further positioned on the basis of sound source separation, and the requirements of scenes such as abnormal sound source positioning, fault detection and the like can be met.
Description
Technical Field
The invention relates to the technical field of sound positioning, in particular to a sound positioning method and system based on a polyhedral microphone array.
Background
Independent Component Analysis (ICA) is used as a statistical signal data Analysis tool, each Independent source signal can be effectively and blindly separated from a linear mixed complex signal under the condition of no prior knowledge, the technology is widely applied to the fields of vibration fault monitoring, image feature extraction and the like, and the invention patent application with publication number CN109409341A discloses a method for identifying the noise source of an approach aeroengine based on an ICA model.
One premise that the ICA algorithm can effectively separate independent source signals is that the array received signals are linear mixed, and the rank of the mixing matrix is not less than the number of signal sources. In a vibration detection application scenario, the received signals of the sensor array typically satisfy the above conditions. The sensor will have different attenuations with different distances from the vibration source, and the delay difference caused by different distances of the sensor can be ignored within a certain range because the sound wave has a high propagation speed in the solid, for example, the propagation speed in iron is 5000 m/s. Even if the sensors have a certain distance difference to the sound source, the model can still be approximately regarded as a linear superposition model. But the detection is different in air, the sound propagation speed in air is about 340m/s, and the time delay error cannot be ignored. If the microphone arrays are arranged very close to reduce the time delay error, the attenuation of each microphone is too close, the mixing matrix is close to the singular matrix, the numerical value of the ICA calculation process is unstable, and the result is also unreliable. Therefore, the linear ICA is not ideal when applied to the detection of sound signals propagating in the air.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a method for detecting and positioning sound in an air environment by using an ICA (independent component analysis) through a polyhedral microphone array.
The invention solves the technical problems through the following technical scheme: a sound source positioning method based on a polyhedral microphone array comprises the following steps,
s1: collecting multi-channel data collected by a plurality of microphones forming a polyhedral structure;
the polyhedral microphone array comprises a plurality of non-omni-directional microphones which are not parallel in a normal direction, and the distance between adjacent microphones is less than 1/5 of the wavelength of a target acoustic signal;
s2: iteratively processing the multi-channel data through an independent component analysis algorithm to obtain target sound source data after noise reduction;
s3: the azimuth of the target sound source is determined based on the amount of attenuation of the target sound source by each microphone.
According to the method, the sound data are collected through the polyhedral microphone array, so that the received signals of the microphone array approximately meet a linear superposition model, and the mixing matrix is a non-singular matrix, so that the sound source can be extracted through an independent component analysis algorithm, then the direction of the sound source relative to the microphone array is judged through the attenuation of the sound source, the sound source is further positioned on the basis of sound source separation, and the requirements of scenes such as abnormal sound source positioning, fault detection and the like can be met.
Preferably, the method for iteratively processing the multi-channel data by the independent component analysis algorithm is,
step i: random initialization generates a demixing matrix w with modulus 11A zero mean Gaussian vector v with variance of 1, a whitening matrix Q, a Lagrange multiplier mu1And a similarity threshold epsilon, and the iteration number k is 1;
step ii: the received multi-channel data matrix y is subjected to a whitening operation, i.e.,
step iii: calculating sound source information x after kth iterative noise reduction processingk，
Step iv: calculating xkThe degree of similarity with the reference signal r,
sk＝G(xk,r)-ε
the formula of the similarity function is such that,
G(xk,r)＝-E{|xk 2|·r}
e { } represents a desired value;
step v: the parametric unmixing matrix and the lagrangian multiplier are updated,
μk+1＝max{0,μk+sk}
wherein the content of the first and second substances,
L(xk,y,wk)＝ρ·E{yF′(xk)}-0.5μkE{yG′(xk,r)}
δ(wk)＝ρ·E{yF″(xk)}-0.5μkE{yG″(xk,r)}
ρ＝E{F(x)}-E{F(v)}
eta is a constant, F' (x)k) And F' (x)k) Respectively represent F (x)k) First and second derivatives of;
step vi: if it is not
Delta is a preset empirical value, k is k +1, the step iii is returned, otherwise x is outputk。
Preferably, the method of determining the azimuth of the target sound source is,
the mixing matrix of the desired sound source is derived from the unmixing matrix and the whitening matrix,
Wmix＝pinv(wk,Q)
wherein pinv () represents a generalized inverse matrix, WmixA vector consisting of attenuation coefficients of the sound source to the respective microphones,
according to the position and direction of each microphone, the sound of each direction is calculatedAttenuation coefficient vector A of sound relative to polyhedral microphonenN is 1,2, N, obtaining attenuation coefficient vectors of N directions, the sound source direction is,
Preferably, the shape of the polyhedral microphone array comprises a first square frame at the center and four second square frames respectively connected with four edges of the first square frame, the normal included angles between the four second square frames and the first square frame are smaller than 90 degrees and equal, and a microphone is respectively arranged on each of the first square frame and each of the second square frames.
The invention also provides a sound source positioning system based on the polyhedral microphone array, which comprises,
the data acquisition module is used for acquiring multi-channel data acquired by a plurality of microphones forming a polyhedral structure;
the polyhedral microphone array comprises a plurality of non-omni-directional microphones that are not parallel in a normal direction, with adjacent microphones spaced less than 1/5 of a desired acoustic signal wavelength;
the noise reduction module is used for iteratively processing the multi-channel data through an independent component analysis algorithm to obtain target sound source data subjected to noise reduction;
and the positioning module is used for determining the azimuth of the target sound source based on the attenuation amount of each microphone to the target sound source.
Preferably, the method for iteratively processing the multi-channel data by the independent component analysis algorithm is,
step i: random initialization generates a demixing matrix w with modulus 11A zero mean Gaussian vector v with variance of 1, a whitening matrix Q, a Lagrange multiplier mu1And a similarity threshold epsilon, and the iteration number k is 1;
step ii: the received multi-channel data matrix y is subjected to a whitening operation, i.e.,
step iii: calculating sound source information x after kth iterative noise reduction processingk，
Step iv: calculating xkThe degree of similarity with the reference signal r,
sk＝G(xk,r)-ε
the formula of the similarity function is such that,
G(xk,r)＝-E{|xk 2|·r}
e { } represents a desired value;
step v: the parametric unmixing matrix and the lagrangian multiplier are updated,
μk+1＝max{0,μk+k}
wherein the content of the first and second substances,
L(xk,y,wk)＝ρ·E{yF′(xk)}-0.5μkE{yG′(xk,r)}
δ(wk)＝ρ·E{yF″(xk)}-0.5μkE{yG″(xk,r)}
ρ＝E{F(x)}-E{F(v)}
eta is a constant, F' (x)k) And F' (x)k) Respectively represent F (x)k) First and second derivatives of;
step vi: if it is not
Delta is a preset empirical value, k is k +1, the step iii is returned, otherwise x is outputk。
Preferably, the method of determining the azimuth of the target sound source is,
the mixing matrix of the desired sound source is derived from the unmixing matrix and the whitening matrix,
Wmix＝pinv(wk,Q)
wherein pinv () represents a generalized inverse matrix, WmixA vector consisting of attenuation coefficients of the sound source to the respective microphones,
according to the position and direction of each microphone, calculating the attenuation coefficient vector A of the sound of each direction relative to the polyhedral microphonenN is 1,2, N, obtaining attenuation coefficient vectors of N directions, the sound source direction is,
Preferably, the shape of the polyhedral microphone array comprises a first square frame at the center and four second square frames respectively connected with four edges of the first square frame, the normal included angles between the four second square frames and the first square frame are smaller than 90 degrees and equal, and a microphone is respectively arranged on each of the first square frame and each of the second square frames.
The invention also provides an electronic processing device comprising at least one processor and a storage means storing at least one executable program, said at least one processor implementing the sound source localization method as described when said at least one executable program is executed by said at least one processor.
The present invention also provides a computer-readable storage medium storing a computer program which, when executed by a processor, is capable of implementing the sound source localization method.
The sound source positioning method based on the polyhedral microphone array has the advantages that: the sound data are collected through the polyhedral microphone array, so that the received signals of the microphone array approximately form a linear superposition model, and the mixing matrix is a non-singular matrix, so that the sound source can be extracted through an independent component analysis algorithm, and then the direction of the sound source relative to the microphone array is judged through the attenuation of the sound source, so that the sound source is further positioned on the basis of sound source separation, and the requirements of scenes such as positioning, fault detection and the like of abnormal sound sources can be met.
Drawings
Fig. 1 is a flowchart of a sound source localization method based on a polyhedral microphone array according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a polyhedral microphone array based on a sound source localization method of the polyhedral microphone array according to an embodiment of the present invention;
FIG. 3 is a schematic signal attenuation diagram of a non-omni-directional microphone according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of two non-omni directional microphone array configurations provided by an embodiment of the present invention;
fig. 5 is a schematic diagram of a sound source separation circuit of a sound source localization method based on a polyhedral microphone array according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a sound source localization system based on a polyhedral microphone array according to an embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention are described below in detail and completely with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the present embodiment provides a sound source localization method based on a polyhedral microphone array, including,
s1: collecting multi-channel data collected by a plurality of microphones forming a polyhedral structure;
the polyhedral microphone array comprises a plurality of non-omnidirectional microphones which are not parallel in a normal direction, and the distance between adjacent microphones is smaller than the wavelength of a target acoustic signal;
s2: iteratively processing the multi-channel data through an independent component analysis algorithm to obtain target sound source data after noise reduction;
s3: the azimuth of the target sound source is determined based on the amount of attenuation of the target sound source by each microphone.
In the embodiment, the sound data is collected through the polyhedral microphone array, so that the received signals of the microphone array approximately form a linear superposition model, and the mixing matrix is a nonsingular matrix, so that the sound source can be extracted through an independent component analysis algorithm, and then the direction of the sound source relative to the microphone array is judged through the attenuation of the sound source, so that the sound source is further positioned on the basis of sound source separation, and the requirements of scenes such as positioning, fault detection and the like of abnormal sound sources can be met.
Specifically, the sound source localization method based on the polyhedral microphone array provided by the embodiment includes,
s1: collecting multi-channel data collected by a plurality of microphones forming a polyhedral structure;
the polyhedral microphone array comprises a plurality of non-omnidirectional microphones which are not parallel in a normal direction, the distance between adjacent microphones is less than the wavelength of a target acoustic signal, preferably less than 1/5 of the wavelength of the target acoustic signal, and the polyhedral microphone array can be arranged in any structure on the basis of meeting the conditions.
Referring to fig. 2, in the present embodiment, the polyhedral microphone array structure includes a first block at the center andthe microphone is arranged on the first square frame and the second square frame respectively, so that the requirements that the normal direction is not parallel and the distance is smaller than the wavelength of an acoustic signal are met, and the microphone is a non-omnidirectional microphone. In conjunction with fig. 3, non-omni-directional microphones attenuate sound differently in different directions, with the smallest attenuation at the normal direction and the greater the angle from the normal, the greater the attenuation. Referring to fig. 4, for an array composed of two non-omnidirectional microphones, the two microphones are located on different planes and are both non-omnidirectional microphones, the interfering sound source s1 and the sound source to be detected s2 radiate sound waves to the microphone array from different positions, and the attenuation coefficients from the interfering sound source s1 to the two microphones are set as a1And a2The attenuation coefficients of the sound source s2 to be detected to the two microphones are respectively b1And b2Since the array pitch is very small, much smaller than the signal wavelength, the delay can be ignored here, only the amplitude attenuation is considered, and the array received signal amplitude is modeled as,
the matrix is in the form of a matrix,
wherein, a is a mixed matrix, and an important condition that the independent component analysis algorithm can effectively separate the signal s2 to be detected from the mixed signal is that the mixed matrix a is a nonsingular matrix, and as can be seen from fig. 3 and 4, a is inevitably present due to the different directions of the two microphones1>a2And b1<b2The mixing matrix a must then be a non-singular matrix.
S2: iteratively processing the multi-channel data through an independent component analysis algorithm to obtain target sound source data after noise reduction;
referring to fig. 5, the method for iteratively processing the multi-channel data by the independent component analysis algorithm is,
step i: random initialization generates a demixing matrix w with modulus 11A zero mean Gaussian vector v with variance of 1, a whitening matrix Q, a Lagrange multiplier mu1And a similarity threshold epsilon, and the iteration number k is 1;
step ii: the received multi-channel data matrix y is subjected to a whitening operation, i.e.,
step iii: calculating sound source information x after kth iterative noise reduction processingk，
Step iv: calculating xkThe degree of similarity with the reference signal r,
sk＝G(xk,r)-γ
the formula of the similarity function is such that,
G(xk,r)＝-E{|xk 2|·r}
e { } represents a desired value;
step v: the parametric unmixing matrix and the lagrangian multiplier are updated,
μk+1＝max{0,μk+sk}
wherein the content of the first and second substances,
L(xk,y,wk)＝ρ·E{yF′(xk)}-0.5μkE{yG′(xk,r)}
δ(wk)＝ρ·E{yF″(xk)}-0.5μkE{yG″(xk,r)}
ρ＝E{F(x)}-E{F(v)}
eta is a constant, empirical value, F' (x)k) And F' (x)k) Respectively represent F (x)k) First and second derivatives of;
step vi: if it is not
Delta is a preset empirical value, k is k +1, the step iii is returned, otherwise x is outputk。
S3: determining the azimuth of the target sound source based on the attenuation amount of each microphone to the target sound source by,
the mixing matrix of the desired sound source is derived from the unmixing matrix and the whitening matrix,
Wmix＝pinv(wk,Q)
wherein pinv () represents a generalized inverse matrix, WmixA vector consisting of attenuation coefficients of the sound source to the respective microphones,
according to the position and direction of each microphone, calculating the attenuation coefficient vector A of the sound of each direction relative to the polyhedral microphonenN is 1,2, N, obtaining attenuation coefficient vectors of N directions, the sound source direction is,
When the polyhedral microphone is constructed, the attenuation coefficient vector A can be calculated or determined experimentallynThen, howeverAnd then, the estimation direction of the sound source can be determined through calculation, so that the sound source positioning is realized.
In conjunction with fig. 6, the present embodiment also provides a sound source localization system based on a polyhedral microphone array, including,
the data acquisition module is used for acquiring multi-channel data acquired by a plurality of microphones forming a polyhedral structure;
the polyhedral microphone array comprises a plurality of non-omni-directional microphones which are not parallel in a normal direction, and the distance between adjacent microphones is less than 1/5 of the wavelength of a target acoustic signal;
the noise reduction module is used for iteratively processing the multi-channel data through an independent component analysis algorithm to obtain target sound source data subjected to noise reduction;
and the positioning module is used for determining the azimuth of the target sound source based on the attenuation amount of each microphone to the target sound source.
The present embodiment further provides an electronic processing device, including at least one processor and a storage device storing at least one execution program, where when the at least one execution program is executed by the at least one processor, the at least one processor performs the following method:
s1: collecting multi-channel data collected by a plurality of microphones forming a polyhedral structure;
the polyhedral microphone array comprises a plurality of non-omni-directional microphones which are not parallel in a normal direction, and the distance between adjacent microphones is less than 1/5 of the wavelength of a target acoustic signal;
s2: iteratively processing the multi-channel data through an independent component analysis algorithm to obtain target sound source data after noise reduction;
s3: the azimuth of the target sound source is determined based on the amount of attenuation of the target sound source by each microphone.
The present embodiments also provide a computer-readable storage medium storing a computer program which, when executed by a processor, is capable of implementing the method of:
s1: collecting multi-channel data collected by a plurality of microphones forming a polyhedral structure;
the polyhedral microphone array comprises a plurality of non-omni-directional microphones which are not parallel in a normal direction, and the distance between adjacent microphones is less than 1/5 of the wavelength of a target acoustic signal;
s2: iteratively processing the multi-channel data through an independent component analysis algorithm to obtain target sound source data after noise reduction;
s3: the azimuth of the target sound source is determined based on the amount of attenuation of the target sound source by each microphone.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A sound source positioning method based on a polyhedral microphone array is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
s1: collecting multi-channel data collected by a plurality of microphones forming a polyhedral structure;
the polyhedral microphone array comprises a plurality of non-omni-directional microphones which are not parallel in a normal direction, and the distance between adjacent microphones is less than 1/5 of the wavelength of a target acoustic signal;
s2: iteratively processing the multi-channel data through an independent component analysis algorithm to obtain target sound source data after noise reduction;
s3: the azimuth of the target sound source is determined based on the amount of attenuation of the target sound source by each microphone.
2. The sound source localization method based on the polyhedral microphone array as claimed in claim 1, wherein: the method for processing the multichannel data by the independent component analysis algorithm in an iterative way is that,
step i: random initialization generates a demixing matrix w with modulus 11A zero mean Gaussian vector v with variance of 1, a whitening matrix Q, a Lagrange multiplier mu1And a similarity threshold epsilon, and the iteration number k is 1;
step ii: the received multi-channel data matrix y is subjected to a whitening operation, i.e.,
step iii: calculating sound source information x after kth iterative noise reduction processingk，
Step iv: calculating xkThe degree of similarity with the reference signal r,
sk＝G(xk，r)-ε
the formula of the similarity function is such that,
G(xk，r)＝-E{|xk 2|·r}
e { } represents a desired value;
step v: the parametric unmixing matrix and the lagrangian multiplier are updated,
μk+1＝max{0，μk+sk}
wherein the content of the first and second substances,
L(xk，y，wk)＝ρ·E{yF′(xk)}-0.5μkE{yG′(xk，r)}
δ(wk)＝ρ·E{yF″(xk)}-0.5μkE{yG″(xk，r)}
ρ＝E{F(x)}-E{F(v)}
eta is a constant, F' (x)k) And F' (x)k) Respectively represent F (x)k) First and second derivatives of;
step vi: if it is not
Delta is a preset empirical value, k is k +1, the step iii is returned, otherwise x is outputk。
3. The sound source localization method based on the polyhedral microphone array as claimed in claim 2, wherein: the method for determining the azimuth of the target sound source is,
the mixing matrix of the desired sound source is derived from the unmixing matrix and the whitening matrix,
Wmix＝pinv(wk，Q)
wherein pinv () represents a generalized inverse matrix, WmixA vector consisting of attenuation coefficients of the sound source to the respective microphones,
according to the position and direction of each microphone, calculating the attenuation coefficient vector A of the sound of each direction relative to the polyhedral microphonenN is 1,2, …, N, and obtains attenuation coefficient vectors of N directions, the sound source direction is,
4. The sound source localization method based on the polyhedral microphone array as claimed in claim 1, wherein: the shape of the polyhedral microphone array comprises a first square frame positioned at the center and four second square frames respectively connected with four edges of the first square frame, the normal included angles of the four second square frames and the first square frame are smaller than 90 degrees and equal, and a microphone is respectively arranged on the first square frame and each second square frame.
5. A sound source positioning system based on a polyhedral microphone array is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
the data acquisition module is used for acquiring multi-channel data acquired by a plurality of microphones forming a polyhedral structure;
the polyhedral microphone array comprises a plurality of non-omni-directional microphones which are not parallel in a normal direction, and the distance between adjacent microphones is less than 1/5 of the wavelength of a target acoustic signal;
the noise reduction module is used for iteratively processing the multi-channel data through an independent component analysis algorithm to obtain target sound source data subjected to noise reduction;
and the positioning module is used for determining the azimuth of the target sound source based on the attenuation amount of each microphone to the target sound source.
6. The sound source positioning system based on the polyhedral microphone array as claimed in claim 5, wherein: the method for processing the multichannel data by the independent component analysis algorithm in an iterative way is that,
step i: random initialization generates a demixing matrix w with modulus 11A zero mean Gaussian vector v with variance of 1, a whitening matrix Q, a Lagrange multiplier mu1And a similarity threshold epsilon, and the iteration number k is 1;
step ii: the received multi-channel data matrix y is subjected to a whitening operation, i.e.,
step iii: calculating sound source information x after kth iterative noise reduction processingk，
Step iv: calculating xkThe degree of similarity with the reference signal r,
sk＝G(xk，r)-ε
the formula of the similarity function is such that,
G(xk，r)＝-E{|xk 2|·r}
e { } represents a desired value;
step v: the parametric unmixing matrix and the lagrangian multiplier are updated,
μk+1＝max{0，μk+sk}
wherein the content of the first and second substances,
L(xk，y，wk)＝ρ·E{yF′(xk)}-0.5μkE{yG′(xk，r)}
δ(wk)＝ρ·E{yF″(xk)}-0.5μkE{yG″(xk，r)}
ρ＝E{F(x)}-E{F(v)}
eta is a constant, F' (x)k) And F' (x)k) Respectively represent F (x)k) First and second derivatives of;
step vi: if it is not
Delta is a preset empirical value, k is k +1, the step iii is returned, otherwise x is outputk。
7. The sound source positioning system based on the polyhedral microphone array as claimed in claim 6, wherein: the method for determining the azimuth of the target sound source is,
the mixing matrix of the desired sound source is derived from the unmixing matrix and the whitening matrix,
Wmix＝pinv(wk，Q)
wherein pinv () represents a generalized inverse matrix, WmixA vector consisting of attenuation coefficients of the sound source to the respective microphones,
according to the position and direction of each microphone, calculating the attenuation coefficient vector A of the sound of each direction relative to the polyhedral microphonenN is 1,2, …, N, and obtains attenuation coefficient vectors of N directions, the sound source direction is,
8. The sound source positioning system based on the polyhedral microphone array as claimed in claim 5, wherein: the shape of the polyhedral microphone array comprises a first square frame positioned at the center and four second square frames respectively connected with four edges of the first square frame, the normal included angles of the four second square frames and the first square frame are smaller than 90 degrees and equal, and a microphone is respectively arranged on the first square frame and each second square frame.
9. An electronic processing device, characterized by: comprising at least one processor and a storage device having at least one executable program stored thereon, the at least one processor implementing the method according to any one of claims 1-4 when the at least one executable program is executed by the at least one processor.
10. A computer-readable storage medium storing a computer program, characterized in that: the computer program is capable of implementing the method of any one of claims 1-4 when executed by a processor.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115184868A (en) * | 2022-07-04 | 2022-10-14 | 杭州爱谱科技有限公司 | Method for positioning three-dimensional position of noise source |
| CN116859339A (en) * | 2023-09-01 | 2023-10-10 | 北京圣传创世科技发展有限公司 | Method for separating and positioning sound source in polygonal area |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115184868A (en) * | 2022-07-04 | 2022-10-14 | 杭州爱谱科技有限公司 | Method for positioning three-dimensional position of noise source |
| CN116859339A (en) * | 2023-09-01 | 2023-10-10 | 北京圣传创世科技发展有限公司 | Method for separating and positioning sound source in polygonal area |
| CN116859339B (en) * | 2023-09-01 | 2023-11-17 | 北京圣传创世科技发展有限公司 | Method for separating and positioning sound source in polygonal area |
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