CN117031397B - Quick calculation method for positioning and evaluating noise source of moving object - Google Patents
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Abstract
The invention belongs to the technical field of sound source positioning, and particularly discloses a quick calculation method for positioning and evaluating a noise source of a moving object, which comprises the steps of constructing a sound source scanning surface of the moving object, and establishing a plane coordinate system on the sound source scanning surface; analyzing the motion paths of coordinate points of a scanning surface, finding out grid points with different motion paths, and pre-calculating and storing propagation delay time and sound pressure amplitude recovery coefficients corresponding to the coordinate points as acceleration factors; for other grid points on the same motion path, the propagation delay time and the sound pressure amplitude recovery coefficient are calculated by adopting an interpolation method, so that a large number of repeated calculations for all grid points are avoided.
Description
Technical Field
The invention belongs to the technical field of sound source positioning, and particularly relates to a quick calculation method for positioning and evaluating a noise source of a moving object.
Background
Noise is an environmental pollution source, and intense noise will have serious influence on physical and mental health of people. In 2022, china specially implements the method for preventing and controlling noise pollution of the people's republic of China, wherein the sixth chapter provides clear requirements for preventing and controlling noise pollution of transportation. When a transportation means such as an aviation craft, a railway rolling stock, an urban rail transit vehicle, a motor ship and the like runs, noise which interferes with the surrounding living environment is generated. In particular to pneumatic noise generated by civil aviation aircraft, high-speed trains and the like, and the energy of the pneumatic noise is in direct proportion to the 6-8 th power of the speed. As the operating speed increases, the aerodynamic noise will increase dramatically. In city management, in order to avoid motor vehicle whistle from interfering with production and life of residents, a warning mark for prohibiting whistle is arranged in many places, but how to improve the supervision efficiency is a problem to be solved urgently.
The noise problem in the transportation field is solved by accurately positioning and evaluating the noise source of the moving object, so that noise reduction research and supervision work can be developed in a targeted manner.
Under the influence of the Doppler effect, the noise frequency and amplitude will change during the process of moving objects approaching the observer. In general, time domain conventional beamforming algorithms are employed for moving object noise source localization and evaluation. The basic principle is as follows: the method comprises the steps of constructing a sound source scanning surface along with a moving object (a scanning surface grid point is a potential sound source point), processing sound signals measured by a microphone array by using a delay-sum algorithm to obtain a sound source diagram on the scanning surface, and further evaluating the position and the magnitude of a noise source. For large objects such as high-speed rails and airplanes, if sufficiently accurate results are to be obtained, the microphone array is required to have a high number of channels, a high acoustic signal sampling rate, and dense scanning surface grid points. Under the "two-to-one secret" requirement, conventional time domain beamforming algorithms would be very time consuming. To obtain a high resolution sound source map, calculations up to several hours are often required.
Disclosure of Invention
The invention aims to provide a rapid calculation method for positioning and evaluating a noise source of a moving object, which can rapidly position the noise source of the moving object.
In order to solve the technical problems, the invention adopts the following technical scheme: a rapid computing method for moving object noise source localization and assessment, comprising:
constructing a sound source scanning surface of a moving object, and establishing a plane coordinate system on the sound source scanning surface; the coordinate points on the plane coordinate system represent possible positions of the sound source on the moving object;
grouping all coordinate points, wherein the coordinate points with the same motion path are a group;
extracting one coordinate point from each group as a standard point, and calculating delay time and sound pressure amplitude recovery coefficient as acceleration factors;
traversing all coordinate points of a sound source scanning surface, and calculating delay time and sound pressure amplitude recovery coefficients of each microphone corresponding to each coordinate point with the same path as the standard point by combining an interpolation algorithm based on acceleration factors of each standard point; resampling to obtain time domain noise signals; summing all the time domain noise signals from each coordinate point to all microphones and averaging to obtain the time domain noise signal average value of the coordinate point;
and constructing a sound source diagram of the moving object by using the time domain noise signal average value of the coordinate points.
As an improvement, the X-axis of the planar coordinate system is parallel to the moving direction of the moving object, and the origin thereof is disposed at the center of the sound source scanning surface.
As an improvement, coordinate points having the same ordinate are divided into a group.
As an improvement, a coordinate point with an abscissa of 0 in each group is taken as a standard point.
As an improvement, the method for calculating the delay time is as follows:
using the formula:
，
，
a delay time is calculated, wherein,in order for the delay time to be a time delay,is the firstThe distance between the individual microphones and said standard point,is the propagation speed of sound wave in the air，，) Is the firstCoordinates of each microphone under the global coordinate system，，) For the coordinates of the standard point in the global coordinate system,is the movement speed of the moving object.
As a further improvement, the open root number operation in the iterative solution formula is adopted, which comprises:
selecting an approximation of an equation, assigning to the variables；
Will beThe value stored in the variableThen calculateAnd save the result in the variableThe method comprises the steps of carrying out a first treatment on the surface of the Repeating the steps untilAnd (3) withThe absolute value of the difference is greater than or equal to the specified accuracy requirement.
As an improvement, the method for calculating the sound pressure amplitude recovery coefficient is as follows:
using the formula:
,
,
calculating a sound pressure amplitude recovery coefficient, wherein,is the sound pressure amplitude recovery coefficient,for the mach number of the motion,for the included angle between the moving direction of the moving object and the standard point connecting line of the microphone-sound source scanning surface,is the firstThe distance between the individual microphones to the standard point of the sound source scanning surface,in order for the delay time to be a time delay,for the speed of movement of the moving object,is the abscissa of the standard point in the global coordinate system.
As an improvement, the interpolation algorithm is a linear interpolation algorithm.
As an improvement, the delay time and the sound pressure amplitude value of each grid point on the sound source scanning surface are calculated by a sound pressure amplitude value recovery system, the time domain noise signals are resampled, and the time domain noise signal average value of the corresponding coordinate point is calculated according to all microphone array element signals.
As an improvement, the parallel processing adopts CUDA parallel technology based on a computer graphic processing unit.
The invention has the advantages that: according to the method, through analyzing the motion paths of the coordinate points (namely all potential sound source points) of the scanning surface, grid points with different motion paths are found, and the propagation delay time and the sound pressure amplitude recovery coefficient corresponding to the coordinate points are calculated and stored in advance to serve as an acceleration factor. For other grid points (i.e., other potential source points) on the same motion path, their propagation delay times and acoustic pressure amplitude recovery coefficients are calculated using interpolation methods, avoiding a large number of repeated calculations for all grid points.
At the same time, a parallel algorithm is used to process the "triple loop" computational task. The method combines the two, greatly improves the calculation speed, lays a foundation for developing more advanced noise source positioning of the moving object and evaluating a high-resolution algorithm, and has important significance for noise reduction research and urban noise real-time supervision in the transportation field.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. Like elements or portions are generally identified by like reference numerals throughout the several figures. In the drawings, elements or portions thereof are not necessarily drawn to scale. It will be apparent to those of ordinary skill in the art that the drawings in the following description are of some embodiments of the invention and that other drawings may be derived from these drawings without inventive faculty.
FIG. 1 is a schematic diagram of a moving object and microphone array in accordance with the present invention;
FIG. 2 is a flow chart of the present invention;
FIG. 3 is a view of a sound source scan plane and a sound source map according to the present invention;
fig. 4 is a schematic structural diagram of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In this document, suffixes such as "module", "component", or "unit" used to represent elements are used only for facilitating the description of the present invention, and have no particular meaning in themselves. Thus, "module," "component," or "unit" may be used in combination.
The terms "upper," "lower," "inner," "outer," "front," "rear," "one end," "the other end," and the like herein refer to an orientation or positional relationship based on that shown in the drawings, merely for convenience of description and to simplify the description, and do not denote or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the invention. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted," "configured to," "connected," and the like, herein, are to be construed broadly as, for example, "connected," whether fixedly, detachably, or integrally connected, unless otherwise specifically defined and limited; the two components can be mechanically connected, can be directly connected or can be indirectly connected through an intermediate medium, and can be communicated with each other. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Herein, "and/or" includes any and all combinations of one or more of the associated listed items.
Herein, "plurality" means two or more, i.e., it includes two, three, four, five, etc.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Chinese patent application CN 201910924363.7 discloses a whistle vehicle automatic identification device and method based on acoustic array, the device includes: annular acoustic array: collecting sound signals; the image acquisition module records audio and/or video information; the signal analysis module is used for judging whistling and carrying out sound source localization analysis; and the communication module is used for storing the information and uploading the information to the server. The method comprises the following steps: s01: collecting sound signals; s02: judging whether whistling exists or not, if so, entering the next step, and if not, returning to the first step; s03: recording audio and/or video information for evidence collection, determining a target vehicle through sound source positioning analysis, and simultaneously acquiring information such as license plates through an image acquisition module; s04: and saving the evidence obtaining information and uploading the evidence obtaining information to a server.
In the prior art, the focusing surface of the acoustic array is divided into grids according to a certain interval, the delay of each grid point relative to each microphone is obtained according to the grid points and the spherical wave theory on the grids, the delay summation is carried out on each grid point of the focusing surface according to the signals of the microphones, and the position with the maximum beam output is the true sound source position finally. That is, this prior art performs delay computation on all grid points, so that the computation time is long, and the system resources are loaded.
Based on the technical problems, the invention provides a rapid method for positioning and evaluating a noise source of a moving object, which is suitable for the object moving at a uniform linear speed, as shown in fig. 1 and 3. For convenience of description and understanding, the model is simplified in this embodiment, the moving object is an object with a length of 5m and a height of 2m (the width of the microphone array is negligible because the microphone array is arranged on the side of the moving object), and the moving object moves linearly at a constant speed of 30 m/s. A white noise source exists in the center position (0, 0) of the object, a simple harmonic source with the frequency of 2000Hz exists in the upper right position (0.5 ), and the amplitudes of sound signals are all 1Pa. A 31 channel microphone array was placed 2m away from the object with an aperture of 0.5m and the center of the array was facing the center of the object when the object was moved to the array. The response frequency of the array microphone ranges from 20Hz to 20kHz, and the microphones on the array surface are arranged according to a multi-arm spiral rule. The data acquisition system is triggered to acquire an acoustic signal when the moving object is at a distance of 20m from the microphone array. The sampling rate of the signal was set to 51.2kHz and the acquisition time was 2s.
As shown in fig. 2, the specific steps of the present invention include:
s1, constructing a sound source scanning surface of a moving object, and establishing a plane coordinate system on the sound source scanning surface; coordinate points on the planar coordinate system (i.e., individual grid points on the source scanning surface) represent locations on the moving object where the source may be present.
As shown in fig. 1, a sound source scanning plane is constructed along a longitudinal section of a moving object. Rectangular grids with the abscissa range of-2.5 m and 2.5m (namely, the abscissa is parallel to the motion direction of the moving object) and the ordinate range of-1.0 m and 1.0m (namely, the ordinate is parallel to the height direction of the longitudinal section of the moving object) constructed on the sound source scanning surface, wherein the grid spacing is 0.01m, and therefore, the number of grid points of the scanning surface is 100701. In order to facilitate the later calculation, the X axis of the plane coordinate system is parallel to the moving direction of the moving object, and the origin of the X axis is arranged in the center of the sound source scanning surface. Preferably, the sound source scanning plane is parallel to the plane of the microphone array.
S2, grouping all coordinate points, wherein the coordinate points with the same motion path are in a group.
Obviously, since the object moves linearly at a uniform speed and the X axis of the coordinate system of the sound source scanning surface is parallel to the moving direction of the moving object, coordinate points with the same ordinate (namely grid points on the sound source scanning surface) have the same moving path. In the grouping, only the coordinate points having the same ordinate are required to be grouped into one group, and in this embodiment, 201 groups are used in total.
S3, extracting one coordinate point from any group as a standard point, and calculating delay time and sound pressure amplitude recovery coefficient as acceleration factors.
In this embodiment, for the sake of easy calculation, the coordinate point with the abscissa of 0 in each group is taken as the standard point, that is, the delay time and the sound pressure amplitude recovery coefficient of 201 grid points with the ordinate of [ -1.0m,1.0m ] are extracted as the "acceleration factor".
Specifically, the step of calculating the delay time is: using the formula(1) The delay time is calculated.
Wherein,in order for the delay time to be a time delay,is the firstThe distance between each microphone and the corresponding coordinate point on the sound source scanning surface (i.e. the propagation distance between the mth microphone array element and a certain grid point or a certain potential sound source), and:
（2）；
wherein,is the propagation speed of sound wave in the air，，) Is the firstCoordinates of the microphones in a global coordinate system; (，，) The coordinates of a coordinate point (i.e., potential sound source) on the surface are scanned for coordinates in the global coordinate system, and the coordinates of the potential sound source are time-varying.
In particular, the method comprises the steps of,setting variableThe step of solving the root number operation in the formula by adopting an iteration method in the formula (2) comprises the following steps:
selecting an approximation of an equation, assigning to the variables；
Will beThe value stored in the variableThen calculateAnd save the result in the variableThe method comprises the steps of carrying out a first treatment on the surface of the Repeating the steps untilAnd (3) withThe absolute value of the difference is larger than or equal to a preset precision requirement.
In some embodiments, the step of calculating the sound pressure amplitude recovery factor is:
using the formula(3) And calculating a sound pressure amplitude recovery coefficient.
Wherein,is the sound pressure amplitude recovery coefficient,for kinematic Mach number:，the included angle between the moving direction of the moving object and the connecting line of the grid points of the microphone-scanning surface is as follows:（4），is the first in the microphone arrayThe distances between the microphones and the corresponding coordinate points on the sound source scanning surface,in order for the delay time to be a time delay,for the speed of movement of the moving object,is the propagation velocity of sound waves in the air.
Accordingly, when the above formulas (1) and (3) are used to calculate the standard points with the abscissa of 0 in each group (i.e., with the abscissa of 0 and the ordinate of [ -1.0m,1.0 m)]201 standard points of (a)Sum sound pressure amplitude recovery coefficientThe method specifically comprises the following steps:
（5）；
（6）
wherein,。
specifically, when calculating the distance from each standard point with the abscissa of 0 to the corresponding microphone, that is, when calculating the root in the formula (5), the iterative algorithm is adopted, that is, an approximation of one equation is selected, and the value is assigned to the variableThe method comprises the steps of carrying out a first treatment on the surface of the Then willThe value stored in the variableThen calculateAnd save the result in the variableThe method comprises the steps of carrying out a first treatment on the surface of the Repeating the steps untilAnd (3) withThe absolute value of the difference is larger than or equal to a preset precision requirement. And the delay time calculated according to the above formulas (5) and (6)Sum sound pressure amplitude recovery coefficientAnd storing for standby.
The delay time and the sound pressure amplitude recovery coefficient of each standard point are calculated by the above method to be used as acceleration factors. The acceleration factor refers to that the delay time and the sound pressure amplitude recovery coefficient of the standard point can be used as the reference, so that the delay time and the sound pressure amplitude recovery coefficient of other coordinate points in the same group can be calculated through other simple algorithms without calculating each coordinate point according to the calculation method (namely, each step of calculating the delay time and the sound pressure amplitude recovery coefficient of each standard point).
S4, traversing coordinate points of a sound source scanning surface, calculating delay time and sound pressure amplitude recovery coefficients of each microphone in a microphone array corresponding to each coordinate point at each time point, and resampling to obtain a time domain noise signal; summing the time domain noise signals of all microphones of each coordinate point and averaging to obtain the average value of the time domain noise signals of the coordinate point; the delay time and the sound pressure amplitude recovery coefficient of the coordinate point are obtained by calculating an interpolation algorithm based on the same group of acceleration factors.
In this step, a certain coordinate point is first selected, a certain microphone in the microphone array is selected, and then the delay time from the coordinate point to the microphone and the sound pressure amplitude recovery coefficient are calculated according to a fixed time step. And finally, sampling the reconstructed noise source signal according to a fixed time step. And after the steps are finished, the calculation of the delay time and the sound pressure amplitude recovery coefficient of the next microphone is selected again, and sampling is repeated until the calculation of the delay time and the sound pressure amplitude recovery coefficient from the coordinate point to all microphones is finished, and then all the microphone time domain noise signals of the coordinate point are summed and averaged to obtain the time domain noise signal average value of the coordinate point. And circulating the method until the time domain noise signals of all coordinate points in the sound source scanning surface coordinate system are averaged.
In this embodiment, after the acceleration factor is established, the delay time and the sound pressure amplitude recovery coefficient of each of the remaining coordinate points (i.e., each grid point with a horizontal-vertical sign other than 0 on the sound source scanning plane) are not repeatedly calculated according to the above formulas (1) and (3), but are obtained by interpolation calculation, such as linear interpolation calculation, with the delay time and the sound pressure amplitude recovery coefficient of the acceleration factor as references, so that the calculation amount is greatly reduced. On the MATLAB platform, the interpolation algorithm is implemented by calling the interp1 function.
In addition, in order to further improve the calculation efficiency, the embodiment adopts a parallel method to process a 'triple loop' calculation task formed by the coordinate points of the scanning surface of the sound source surface, the time domain noise signals and the microphone. Specifically, "triple-loop" processing is performed using CUDA (Compute Unified Device Architecture) parallel technology based on a GPU (Graphic processing unit graphics processor). CUDA is a row computing framework proposed by NVIDIA, and by utilizing the parallel computing capability of the GPU, large-scale scientific computing, data processing and machine learning tasks which cannot be born by the CPU can be accelerated.
S5, constructing a sound source diagram of the moving object by using the time domain noise signal average value of each coordinate point.
As shown in FIG. 3, a white noise source exists in the center position (0, 0) of the sound source diagram, a simple harmonic sound source with the frequency of 2000Hz exists in the upper right position (0.5 ), the amplitudes of sound signals are 1Pa, the sound signals are consistent with a preset model, and the calculation speed is more than 100 times of that of a conventional time domain waveform beam forming algorithm. The invention can ensure the accuracy of the calculation result, greatly reduce the calculation amount, shorten the calculation time and reduce the system overhead.
As shown in fig. 4, the present invention further provides a fast positioning system for a noise source of a moving object, including:
the microphone array comprises a plurality of microphones and is used for collecting sound wave signals of a moving object;
the upper computer is used for positioning a sound source on the moving object according to the sound wave signals acquired by the microphone array; the upper computer includes:
the sound source scanning surface construction module is used for constructing a sound source scanning surface of a moving object and establishing a plane coordinate system on the sound source scanning surface; grid points on the planar coordinate system represent potential sound sources on the moving object;
the acceleration factor calculation module is used for grouping all coordinate points, wherein the coordinate points with the same motion path are a group; extracting a coordinate point (namely a grid point) from each group as a standard point, and calculating delay time and sound pressure amplitude recovery coefficient as acceleration factors; preferably, grid points with an abscissa of 0 are extracted as standard points;
the time domain noise signal average value acquisition module is used for traversing coordinate points of a sound source scanning surface, calculating delay time and sound pressure amplitude recovery coefficients of each microphone in the microphone array corresponding to each coordinate point at each time point, and resampling to obtain a time domain noise signal; the delay time and the sound pressure amplitude recovery coefficient of each coordinate point are obtained by calculating an interpolation algorithm based on the same group of acceleration factors; summing all microphone array element signals of each coordinate point and averaging to obtain a time domain noise signal average value on the coordinate point;
and the sound source diagram construction module is used for constructing a sound source diagram of the moving object by utilizing the time domain noise signal average value of the coordinate points.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising several instructions for causing a computer terminal (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The embodiments of the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those having ordinary skill in the art without departing from the spirit of the present invention and the scope of the claims, which are to be protected by the present invention.
Claims (9)
1. A rapid computing method for the localization and evaluation of a noise source of a moving object, comprising:
constructing a sound source scanning surface of a moving object, and establishing a plane coordinate system on the sound source scanning surface; coordinate points on the plane coordinate system represent potential sound source points;
grouping all coordinate points, wherein the coordinate points with the same motion path are in a group;
extracting one coordinate point from each group as a standard point, and calculating delay time and sound pressure amplitude recovery coefficient as acceleration factors;
traversing all coordinate points of the sound source scanning surface, calculating delay time and sound pressure amplitude recovery coefficients corresponding to each microphone of each coordinate point with the same path as the standard point by combining an interpolation algorithm based on the acceleration factor of each standard point, and resampling to obtain a time domain noise signal; summing the time domain noise signals from each coordinate point to all microphones and averaging to obtain the respective time domain noise signal average value of each coordinate point;
calculating the sound pressure amplitude recovery coefficient of the standard pointThe method of (1) is as follows:
using the formula:
the sound pressure amplitude recovery coefficient is calculated,
wherein,for movement Mach number>Is the included angle between the moving direction of the moving object and the connecting line of the microphone and the standard point, and +.>；/>For delay time, +.>Is->Distance between the individual microphones and said standard point, -/-, a>For the speed of movement of the moving object, +.>For the abscissa of the standard point in the global coordinate system,/o>Is->The abscissa of the microphones in the global coordinate system;
and constructing a sound source diagram of the moving object by using the time domain noise signal average value of all the coordinate points.
2. A rapid computing method for the localization and assessment of noise sources of moving objects according to claim 1, characterized in that:
the X axis of the plane coordinate system is parallel to the motion direction of the moving object, and the origin of the plane coordinate system is arranged at the center of the sound source scanning surface.
3. A rapid computing method for the localization and assessment of noise sources of moving objects according to claim 2, characterized in that: and dividing the coordinate points with the same ordinate in the plane coordinate system into a group.
4. A rapid calculation method for the localization and assessment of noise sources of moving objects according to claim 3, characterized in that: and taking the coordinate point with the abscissa of 0 in each group as the standard point.
5. The method for quickly calculating the positioning and evaluation of the noise source of the moving object according to claim 4, wherein the step of calculating the delay time of the standard point specifically comprises the following steps:
using the formula:
，
，
calculating delay time;
wherein,for delay time, +.>Is->Distance between the individual microphones and said standard point, -/-, a>Is the sound wave propagation velocity in the air, (-)>，/>，/>) Is->Coordinates of the individual microphones in the global coordinate system, (-j->，/>，) For the coordinates of the standard point under the global coordinate system, +.>Is the movement speed of the moving object.
6. The rapid calculation method for positioning and evaluating a noise source of a moving object according to claim 5, wherein the open root number operation of the distance is solved by adopting an iterative method, and the method specifically comprises the following steps:
assigning an approximate root of an equation to a variable；
Will beThe value stored in the variable->Then calculate +.>And saves the result in the variable +.>The method comprises the steps of carrying out a first treatment on the surface of the Repeating the step until->And->The absolute value of the difference is larger than or equal to a preset precision requirement.
7. A rapid computing method for the localization and assessment of noise sources of moving objects according to claim 1, characterized in that: the interpolation algorithm is a linear interpolation algorithm.
8. A rapid computing method for the localization and assessment of noise sources of moving objects according to claim 1, characterized in that: and the three steps of calculating the delay time and the sound pressure amplitude recovery coefficient of the coordinate point, resampling the time domain noise signal and calculating the time domain noise signal average value of the corresponding coordinate point according to all microphone array element signals are processed in parallel.
9. A rapid computing method for moving object noise source localization and assessment as defined in claim 8, wherein: the parallel processing adopts CUDA parallel technology based on a computer graphic processing unit.
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Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB578729A (en) * | 1938-06-03 | 1946-07-10 | George Edward Condliffe | Improvements in or relating to electrical systems for determining the direction of sound sources |
| CA2348894A1 (en) * | 1998-11-16 | 2000-05-22 | The Board Of Trustees Of The University Of Illinois | Binaural signal processing techniques |
| CN101688819A (en) * | 2007-03-28 | 2010-03-31 | 爱斯福光电工程公司 | Be used for determining the method and the facility of Differential Group Delay and polarization mode dispersion |
| KR101217254B1 (en) * | 2012-01-13 | 2012-12-31 | 한국과학기술연구원 | Generation technique of inter-channel time delay(itd) maps for shifted robot artificial ear, the use of estimation of sound source direction |
| CN102869959A (en) * | 2010-04-28 | 2013-01-09 | 米托尔斯有限公司 | Ultrasonic flow meter |
| CN106328156A (en) * | 2016-08-22 | 2017-01-11 | 华南理工大学 | Microphone array voice reinforcing system and microphone array voice reinforcing method with combination of audio information and video information |
| CN106772248A (en) * | 2016-12-09 | 2017-05-31 | 中国空气动力研究与发展中心低速空气动力研究所 | Noise source localization method of the High Speed Train Models in acoustic wind tunnel |
| CN112487730A (en) * | 2020-10-30 | 2021-03-12 | 南京航空航天大学 | Phase angle control-based multi-rotor aircraft noise suppression method |
| CN113933786A (en) * | 2021-09-13 | 2022-01-14 | 武汉声与声科技合伙企业(有限合伙) | Novel four-microphone industrial noise source positioning system |
| CN114046957A (en) * | 2021-11-16 | 2022-02-15 | 成都流体动力创新中心 | Three-dimensional shear layer correction method for open wind tunnel far field noise measurement |
| CN114088332A (en) * | 2021-11-24 | 2022-02-25 | 成都流体动力创新中心 | Wind tunnel background noise correction method for extracting sound signals of rotating blades |
| CN114323245A (en) * | 2021-12-08 | 2022-04-12 | 中国运载火箭技术研究院 | Ultrasonic touch sound field measurement system and method based on mechanical arm moving platform |
| CN114527427A (en) * | 2022-01-27 | 2022-05-24 | 华南理工大学 | Low-frequency beam forming sound source positioning method based on spherical microphone array |
| CN115902776A (en) * | 2022-12-09 | 2023-04-04 | 中南大学 | A Sound Source Localization Method Based on Passive Sound Signals |
| CN116309921A (en) * | 2023-04-24 | 2023-06-23 | 厦门大学 | Delay summation acoustic imaging parallel acceleration method based on CUDA technology |
| CN116755036A (en) * | 2023-06-21 | 2023-09-15 | 中国林业科学研究院资源信息研究所 | A sound source positioning system for wild animal sounds |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9143375B1 (en) * | 2014-03-09 | 2015-09-22 | Allen LeRoy Limberg | Iterative-diversity COFDM broadcasting with improved shaping gain |
-
2023
- 2023-10-07 CN CN202311278534.6A patent/CN117031397B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB578729A (en) * | 1938-06-03 | 1946-07-10 | George Edward Condliffe | Improvements in or relating to electrical systems for determining the direction of sound sources |
| CA2348894A1 (en) * | 1998-11-16 | 2000-05-22 | The Board Of Trustees Of The University Of Illinois | Binaural signal processing techniques |
| CN101688819A (en) * | 2007-03-28 | 2010-03-31 | 爱斯福光电工程公司 | Be used for determining the method and the facility of Differential Group Delay and polarization mode dispersion |
| CN102869959A (en) * | 2010-04-28 | 2013-01-09 | 米托尔斯有限公司 | Ultrasonic flow meter |
| KR101217254B1 (en) * | 2012-01-13 | 2012-12-31 | 한국과학기술연구원 | Generation technique of inter-channel time delay(itd) maps for shifted robot artificial ear, the use of estimation of sound source direction |
| CN106328156A (en) * | 2016-08-22 | 2017-01-11 | 华南理工大学 | Microphone array voice reinforcing system and microphone array voice reinforcing method with combination of audio information and video information |
| CN106772248A (en) * | 2016-12-09 | 2017-05-31 | 中国空气动力研究与发展中心低速空气动力研究所 | Noise source localization method of the High Speed Train Models in acoustic wind tunnel |
| CN112487730A (en) * | 2020-10-30 | 2021-03-12 | 南京航空航天大学 | Phase angle control-based multi-rotor aircraft noise suppression method |
| CN113933786A (en) * | 2021-09-13 | 2022-01-14 | 武汉声与声科技合伙企业(有限合伙) | Novel four-microphone industrial noise source positioning system |
| CN114046957A (en) * | 2021-11-16 | 2022-02-15 | 成都流体动力创新中心 | Three-dimensional shear layer correction method for open wind tunnel far field noise measurement |
| CN114088332A (en) * | 2021-11-24 | 2022-02-25 | 成都流体动力创新中心 | Wind tunnel background noise correction method for extracting sound signals of rotating blades |
| CN114323245A (en) * | 2021-12-08 | 2022-04-12 | 中国运载火箭技术研究院 | Ultrasonic touch sound field measurement system and method based on mechanical arm moving platform |
| CN114527427A (en) * | 2022-01-27 | 2022-05-24 | 华南理工大学 | Low-frequency beam forming sound source positioning method based on spherical microphone array |
| CN115902776A (en) * | 2022-12-09 | 2023-04-04 | 中南大学 | A Sound Source Localization Method Based on Passive Sound Signals |
| CN116309921A (en) * | 2023-04-24 | 2023-06-23 | 厦门大学 | Delay summation acoustic imaging parallel acceleration method based on CUDA technology |
| CN116755036A (en) * | 2023-06-21 | 2023-09-15 | 中国林业科学研究院资源信息研究所 | A sound source positioning system for wild animal sounds |
Non-Patent Citations (5)
| Title |
|---|
| Bai, MR ; Chen, YS and Lo, YY.A two-stage noise source identification technique based on a farfield random parametric array.《 JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA》.2017,2978-2988. * |
| 基于TSVD的广义逆波束形成对扩展性噪声源的识别;叶虹敏;王强;袁昌明;范昕炜;谷小红;;传感技术学报(第04期);全文 * |
| 基于遗传算法的动态优化波叠加噪声源识别方法;李兵;杨殿阁;郑四发;李克强;连小珉;;机械工程学报(第12期);全文 * |
| 潘邦雄,罗瀚波,赵秀亮等.利用声压幅值相关性识别声源特性研究.《噪声与振动控制》.2022,第42卷116-122. * |
| 马瑞贤 ; 刘占生 ; 吴桂华 ; 张广辉 ; 何鹏.考虑任意来流方向的运动力源辐射声压解析式.《船舶力学》.2020,全文. * |
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