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CN112863540A - Sound source distribution visualization method and computer program product - Google Patents

Sound source distribution visualization method and computer program product Download PDF

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Publication number
CN112863540A
CN112863540A CN201911186137.XA CN201911186137A CN112863540A CN 112863540 A CN112863540 A CN 112863540A CN 201911186137 A CN201911186137 A CN 201911186137A CN 112863540 A CN112863540 A CN 112863540A
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sound source
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source distribution
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physical signal
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王智中
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Ruijie International Co ltd
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    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/06Transformation of speech into a non-audible representation, e.g. speech visualisation or speech processing for tactile aids
    • G10L21/10Transforming into visible information
    • G10L21/14Transforming into visible information by displaying frequency domain information
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01HMEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H17/00Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/27Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the analysis technique
    • G10L25/30Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the analysis technique using neural networks

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  • Signal Processing (AREA)
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Abstract

本发明提供一种声源分布可视化方法及电脑程式产品,其方法包括:读取检测目标的一目标影像;在目标影像标示一检测边界及在检测边界设置多个检测点,各检测点具有专属编码;对应各检测点输入检测目标运作过程产生的一物理信号;通过频谱叠加计算各物理信号的频谱分布,分析各物理信号的频宽范围,并且通过一分析运算处理取得各物理信号的频宽范围内的时间波形,以产生各物理信号的一特征信号;以及将各特征信号通过一神经网路运算,形成可视化特征的一影像声源分布,影像声源分布配合目标影像呈现于检测边界中;借此,能够即时、快速且准确取得声源分布。

Figure 201911186137

The present invention provides a sound source distribution visualization method and a computer program product. The method includes: reading a target image of a detection target; marking a detection boundary on the target image and setting a plurality of detection points on the detection boundary, each detection point has a dedicated Coding; input a physical signal generated by the operation process of the detection target corresponding to each detection point; calculate the spectral distribution of each physical signal through spectrum superposition, analyze the frequency bandwidth range of each physical signal, and obtain the frequency bandwidth of each physical signal through an analysis operation process A time waveform within the range to generate a characteristic signal of each physical signal; and each characteristic signal is calculated by a neural network to form an image sound source distribution of visual features, and the image sound source distribution is presented in the detection boundary in conjunction with the target image. ; Thereby, the sound source distribution can be obtained in real time, quickly and accurately.

Figure 201911186137

Description

Sound source distribution visualization method and computer program product
Technical Field
The present invention relates to a visualization technology, and more particularly, to a sound source distribution visualization method and computer program product.
Background
In the field of noise control, correct identification of a noise source is the basis for effective noise improvement, so the accuracy of sound source identification and localization will affect the effect of noise control, and the influence of noise can be effectively controlled or correctly evaluated only under the conditions of really mastering the position, intensity distribution, speed distribution, density distribution and the like of the noise source, and further, the noise caused by structure vibration is reduced, so that the noise of the structure is optimized. For example, the noise control technique is applied to the power machine diagnosis industry, and not only can assist engineers in determining the fault point of the power machine and evaluating the influence caused by the noise source, but also can improve the accuracy of the judgment of the engineers.
In the prior art, a sound source is searched by using a sound intensity method in the technology of identifying the sound source, a plurality of grids need to be distinguished from a detection target space, a sound intensity value of a region is measured in each grid by using a sound intensity meter (sound intensity probe), and then the current sound intensity distribution is reduced and measured by using an interpolation method, so that the purpose of positioning the sound source is achieved.
In addition, U.S. Pat. No. 20050225497 discloses a method for recognizing a sound source by using a beam forming array (beam forming array) technique, however, the beam forming array technique can only recognize a far-field sound field, and has a poor recognition performance for an unsteady sound source, and has disadvantages that it is impossible to perform an instantaneous operation, it is impossible to recognize a sound field with different coordinates synchronously, and it is necessary to change the shape of an array microphone in order to prevent a spatial distortion.
Disclosure of Invention
In order to solve the above problems, the present invention provides a method for visualizing sound source distribution, which can obtain the visualized distribution of the sound source in real time, quickly and accurately by matching the visual characteristics with the analysis operation and the neural network operation.
An embodiment of the present invention provides a sound source distribution visualization method, including: an image creating step: reading a target image of a detection target; a marking step: marking a detection boundary on the target image and setting a plurality of detection points on the detection boundary, wherein each detection point has a special code; a signal acquisition step: inputting a physical signal generated in the operation process of the detection target corresponding to each detection point; an operation processing step: calculating the spectrum distribution of each physical signal through spectrum superposition to analyze the bandwidth range of each physical signal, and acquiring a time waveform in the bandwidth range of each physical signal through analysis and calculation processing to generate a characteristic signal of each physical signal; and a visualization step: and calculating each characteristic signal through a neural network to form an image sound source distribution of the visual characteristic, wherein the image sound source distribution is presented in the detection boundary in cooperation with the target image.
In one embodiment, the intensity variation of each feature signal is generated by calculating each feature signal through a neural network to obtain the distance between each detection point, so as to form the image sound source distribution of the visual feature.
In one embodiment, a continuous and smooth image sound source distribution is formed between the detection points by a bi-harmonic spline interpolation method.
In one embodiment, the distribution of the image sound source of the visual features exhibits color variations according to the intensity of each feature signal.
In one embodiment, the analysis operation is a time-frequency analysis, each physical signal obtains a time waveform within a bandwidth range of each physical signal through the analysis operation, and the characteristic signal for selectively generating each physical signal is provided as a root mean square value or a maximum value of the waveform.
In one embodiment, the neural Network operation is a regression neural Network method (GRNN) or a Supervised neural Network method (Supervised Learning Network).
In one embodiment, when the detection target is a constant-speed device, each physical signal is input corresponding to each detection point in a step-by-step manner, and each physical signal corresponds to the exclusive code memory of each detection point.
In one embodiment, when the detection target is a variable-speed device, each physical signal is input corresponding to each detection point in a synchronous manner, and each physical signal corresponds to the exclusive code memory of each detection point.
In one embodiment, the physical signal is a sound signal or a vibration signal.
One embodiment of the present invention provides a computer program product comprising a non-transitory computer readable medium having instructions recorded thereon, which when executed by a computer implement the method of any of the above embodiments.
Through the above, the invention can instantly, rapidly and accurately obtain the visual image distribution of the sound source by matching the physical signal generated in the operation process of the detection target with the visual characteristics through analysis operation and neural network operation, thereby solving the problem that the prior art cannot instantly and accurately obtain the sound source.
Furthermore, the invention can form the image sound source distribution with visual characteristics by analyzing and calculating and neural network calculation without considering the linear or nonlinear sound source transmission path.
In addition, the invention can be applied to equipment for detecting and measuring the rotating speed or changing the rotating speed so as to improve the application range of the invention.
Drawings
FIG. 1 is a schematic diagram of the method steps of an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating the detection of a boundary marked on a target image according to the present invention;
FIG. 3 is a schematic diagram of a plurality of inspection points arranged on an inspection boundary according to the present invention;
FIG. 4 is a schematic diagram of inputting physical signals corresponding to various detection points according to the present invention;
FIG. 5 is a schematic diagram of calculating the spectral distribution of each physical signal by spectral superposition according to the present invention;
FIG. 6 is a schematic diagram illustrating the distribution of the image sound source in cooperation with the presentation of the target image within the detection boundary according to the present invention.
Description of the reference numerals
I target image
B detecting the boundary
P detection point
C-specific coding
SI image sound source distribution
S1 image creating step
S2 marking step
S3 Signal acquisition step
S4 arithmetic processing step
And S5 visualization step.
Detailed Description
For the purpose of illustrating the central concepts of the present invention as embodied in the above summary, reference will now be made to specific embodiments. Various objects in the embodiments are depicted in terms of scale, dimensions, deformation, or displacement suitable for illustration, rather than in terms of actual component proportions, as previously described.
Referring to fig. 1 to 6, the present invention provides a method for visualizing sound source distribution, including:
an image creating step S1: reading a target image I of a detection target; the detection target can be fixed-speed equipment or variable-speed equipment; the target image I of the detected target is obtained by a camera device (e.g., a camera, a video camera, or a smart mobile device), or is generated by a drawing method.
A labeling step S2: marking a detection boundary B on the target image I obtained in the image establishing step S1, and setting a plurality of detection points P on the detection boundary B, each detection point P having a dedicated code C, wherein the detection boundary B is a closed range, and the detection boundary B can be a closed rectangular shape, a polygonal shape, or a circular curve shape, and in the embodiment of the present invention, the detection boundary B is a closed rectangular shape, as shown in fig. 2; furthermore, the number of the detecting points P can be set according to the requirement of the user, the dedicated code C of each detecting point P can be a number or an english letter, and the dedicated code C is used to identify each detecting point P, in the embodiment of the present invention, the number of the detecting points P is 12, the dedicated code C of each detecting point P is a number, and the dedicated codes C of each detecting point P are 1 to 12, respectively, as shown in fig. 3 and 4.
A signal acquiring step S3: after the marking step S2, a physical signal generated in the operation process of the detection target is inputted corresponding to each detection point P, as shown in fig. 4; wherein, the physical signal is a sound signal or a vibration signal; when the physical signal is a sound signal, the physical signal generated in the operation process of the detection target can be read by a sound reading device (such as an independent microphone, a built-in microphone of an intelligent mobile device or a multi-bit recording pen, but the invention is not limited thereto); when the physical signal is a vibration signal, the physical signal can pass through a vibration sensor (for example, a displacement sensor, a velocity sensor, an acceleration sensor or an accelerometer, but the invention is not limited thereto).
Furthermore, when the detected object is a constant speed device, a single sound reading device or vibration sensor is used to read the physical signal corresponding to each detecting point P step by step according to the type of the physical signal to be read, and the read physical signal is memorized corresponding to the exclusive code C of each detecting point P.
In addition, when the detected object is a variable-speed device, a plurality of sound reading devices or a plurality of vibration sensors are used according to the types of physical signals to be read, each sound reading device or each vibration device is placed at the actual position of the physical detected object corresponding to each detection point P, the physical signals are input into each detection point P through each sound reading device or each vibration device in a synchronous mode, and the read physical signals are memorized corresponding to the exclusive codes C of each detection point P.
An arithmetic processing step S4: the physical signals of each detection point P obtained in the signal obtaining step S3 are subjected to spectrum superposition to calculate the spectrum distribution of each physical signal, so as to analyze the bandwidth range of each physical signal, and a time waveform within the bandwidth range of each physical signal is obtained through an analyzing and calculating process to generate a characteristic signal of each physical signal, as shown in fig. 5; in the embodiment of the invention, the analysis operation is time-frequency analysis, and the characteristic signal of the physical signal can be a root mean square value or a waveform maximum value; after the bandwidth range of each physical signal is analyzed, each physical signal can obtain a time waveform in the bandwidth range of each physical signal through analysis and calculation processing, and a characteristic signal for selectively generating each physical signal is provided as a root mean square value or a waveform maximum value, wherein the bandwidth range of each physical signal can be set or preset by a user.
A visualization step S5: the feature signal of each detection point P obtained in the operation step S4 is operated through a neural network to form an image sound source distribution SI of visual features, and the image sound source distribution SI is presented in the detection boundary B in cooperation with the target image I, wherein the image sound source distribution SI is superimposed on the target image I and does not display each detection point P, as shown in fig. 6.
In the embodiment of the present invention, the neural Network operation is a regression neural Network method (GRNN) or a Supervised neural Network method (Supervised Learning Network); the image sound source distribution of the visual features presents color changes according to the intensity of each feature signal. Further explanation is as follows: calculating each characteristic signal through a neural network to obtain the intensity variation of each characteristic signal generated by the distance difference between the detection points P, for example: when 12 detection points P exist, the distance from each detection point P to the rest detection points P is different, and the intensity change of different characteristic signals can be generated among the detection points P; then, a continuous and smooth image sound source distribution SI is formed between the detecting points P by bi-harmonic spline interpolation method, as shown in FIG. 6.
Some embodiments according to the invention comprise an data carrier with electronically readable control signals capable of cooperating with a programmable computer system such that one of the methods described in the invention is performed. Generally, embodiments of the present invention can be implemented as a computer program product having program code operative to perform one of the methods described above when the computer program product is executed on a terminal device; wherein the program code may for example be stored on a machine readable carrier.
In other embodiments of the invention, a computer program product stored on a machine-readable carrier for performing the methods described herein can be included. In other words, an embodiment of the inventive method is thus a computer program having program code for performing one of the methods described herein when the computer program product is executed on a terminal device, such as a computer or a smart mobile device.
Therefore, when the embodiment of the invention is a computer program product with program codes, the target image I of the detection target can be obtained through signal connection between the terminal device and the camera device; or generating a target image I of the detection target on the terminal device in a drawing mode. Furthermore, the terminal device can be connected with a sound reading device or a vibration sensor through signals to acquire physical signals.
In summary, the present invention has the following effects:
the invention matches the physical signal generated in the operation process of the detection target with the visual characteristic through analysis operation and neural network operation, and can instantly, quickly and accurately obtain the visual image distribution of the sound source.
The invention can form the image sound source distribution with visual characteristics by analysis operation and neural network operation without considering the linear or nonlinear sound source transmission path.
The invention can be applied to equipment for detecting and measuring the rotating speed or changing the rotating speed so as to improve the application range of the invention.
The above examples are provided only for illustrating the present invention and are not intended to limit the scope of the present invention. All such modifications and variations are within the scope of the invention as determined by the appended claims.

Claims (10)

1.一种声源分布可视化方法,其特征在于,其包括:1. a sound source distribution visualization method, is characterized in that, it comprises: 一影像建立步骤:读取检测目标的一目标影像;An image creation step: reading a target image of the detection target; 一标示步骤:在该目标影像标示一检测边界及在该检测边界设置多个检测点,各检测点具有专属编码;A marking step: marking a detection boundary on the target image and setting a plurality of detection points on the detection boundary, and each detection point has an exclusive code; 一获取信号步骤:对应各检测点输入检测目标运作过程产生的一物理信号;A signal acquisition step: corresponding to each detection point inputting a physical signal generated by the operation process of the detection target; 一运算处理步骤:通过频谱叠加计算各物理信号的频谱分布,分析各物理信号的频宽范围,并且通过一分析运算处理取得各物理信号的频宽范围内的时间波形,以产生各物理信号的一特征信号;以及One operation processing step: calculate the spectral distribution of each physical signal through spectrum superposition, analyze the frequency bandwidth range of each physical signal, and obtain the time waveform within the frequency bandwidth range of each physical signal through an analysis operation process, so as to generate the frequency range of each physical signal a characteristic signal; and 一可视化步骤:将各特征信号通过一神经网路运算,形成可视化特征的一影像声源分布,该影像声源分布配合该目标影像呈现于该检测边界中。A visualization step: operating each feature signal through a neural network to form an image sound source distribution of visualized features, and the image sound source distribution is presented in the detection boundary in conjunction with the target image. 2.如权利要求1所述的声源分布可视化方法,其特征在于:将各特征信号通过该神经网路运算,取得各检测点间距离不同而产生各特征信号的强度变化,形成可视化特征的该影像声源分布。2. The method for visualizing sound source distribution according to claim 1, characterized in that: each characteristic signal is calculated by the neural network, and the distance between each detection point is different to generate the intensity change of each characteristic signal, forming a visual characteristic of The sound source distribution of the image. 3.如权利要求2所述的声源分布可视化方法,其特征在于:将各检测点间以双调和样条内插方法,形成连续且平滑性的该影像声源分布。3 . The method for visualizing sound source distribution according to claim 2 , wherein a biharmonic spline interpolation method is used between each detection point to form a continuous and smooth distribution of the image sound source. 4 . 4.如权利要求3所述的声源分布可视化方法,其特征在于:可视化特征的该影像声源分布依据各特征信号的强度呈现颜色变化。4 . The method for visualizing sound source distribution according to claim 3 , wherein the image sound source distribution of the visualized feature exhibits a color change according to the intensity of each feature signal. 5 . 5.如权利要求4所述的声源分布可视化方法,其特征在于:该分析运算为时频分析,各物理信号通过该分析运算处理取得各物理信号的频宽范围内的时间波形,提供选择产生各物理信号的特征信号为均方根值或波形最大值。5. The method for visualizing sound source distribution according to claim 4, wherein the analysis operation is a time-frequency analysis, and each physical signal obtains a time waveform within the frequency bandwidth of each physical signal through the analysis and operation processing, and provides selection The characteristic signal that produces each physical signal is the root mean square value or the maximum value of the waveform. 6.如权利要求1所述的声源分布可视化方法,其特征在于:该神经网路运算为回归神经网路法或监督式类神经网路法。6 . The sound source distribution visualization method according to claim 1 , wherein the neural network operation is a regression neural network method or a supervised neural network method. 7 . 7.如权利要求1所述的声源分布可视化方法,其特征在于:当检测目标为定转速设备时,以逐步方式对应各检测点输入各物理信号,各物理信号对应各检测点的专属编码记忆。7. The method for visualizing sound source distribution according to claim 1, wherein when the detection target is a device with a fixed rotational speed, each physical signal is input corresponding to each detection point in a step-by-step manner, and each physical signal corresponds to the exclusive code of each detection point memory. 8.如权利要求1所述的声源分布可视化方法,其特征在于:当检测目标为变转速设备时,以同步方式对应各检测点输入各物理信号,各物理信号对应各检测点的专属编码记忆。8. The method for visualizing sound source distribution according to claim 1, wherein when the detection target is a variable speed device, each physical signal is input corresponding to each detection point in a synchronous manner, and each physical signal corresponds to the exclusive code of each detection point memory. 9.如权利要求1所述的声源分布可视化方法,其特征在于:该物理信号为声音信号或振动信号。9 . The method for visualizing sound source distribution according to claim 1 , wherein the physical signal is a sound signal or a vibration signal. 10 . 10.一种电脑程式产品,其特征在于:包括多个指令的一非暂时性电脑可读媒体,所述指令在通过一电脑执行时实施如权利要求1至9的声源分布可视化方法。10. A computer program product, characterized by a non-transitory computer-readable medium comprising a plurality of instructions that, when executed by a computer, implement the sound source distribution visualization method of claims 1 to 9.
CN201911186137.XA 2019-11-28 2019-11-28 Sound source distribution visualization method and computer program product Pending CN112863540A (en)

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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH0981066A (en) * 1995-09-14 1997-03-28 Toshiba Corp Display device
CN101556187A (en) * 2009-05-07 2009-10-14 广东美的电器股份有限公司 Statistically optimal near-field acoustical holography used for visual recognition of air-conditioner noise sources and operation method thereof
CN104346531A (en) * 2014-10-30 2015-02-11 重庆大学 Hospital acoustic environment simulation system based on social force model
CN106488358A (en) * 2015-09-09 2017-03-08 上海其高电子科技有限公司 Optimize sound field imaging localization method and system
CN106934149A (en) * 2017-03-09 2017-07-07 哈尔滨工业大学 A kind of Forecasting Methodology of calculating crowd noise stack result in space
CN107688165A (en) * 2017-07-11 2018-02-13 国网山西省电力公司电力科学研究院 A kind of extra-high voltage transformer vibration noise source localization method

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH0981066A (en) * 1995-09-14 1997-03-28 Toshiba Corp Display device
CN101556187A (en) * 2009-05-07 2009-10-14 广东美的电器股份有限公司 Statistically optimal near-field acoustical holography used for visual recognition of air-conditioner noise sources and operation method thereof
CN104346531A (en) * 2014-10-30 2015-02-11 重庆大学 Hospital acoustic environment simulation system based on social force model
CN106488358A (en) * 2015-09-09 2017-03-08 上海其高电子科技有限公司 Optimize sound field imaging localization method and system
CN106934149A (en) * 2017-03-09 2017-07-07 哈尔滨工业大学 A kind of Forecasting Methodology of calculating crowd noise stack result in space
CN107688165A (en) * 2017-07-11 2018-02-13 国网山西省电力公司电力科学研究院 A kind of extra-high voltage transformer vibration noise source localization method

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Application publication date: 20210528