CN117877495A - Transformer working condition recognition voiceprint feature extraction method, device and medium - Google Patents
Transformer working condition recognition voiceprint feature extraction method, device and medium Download PDFInfo
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Abstract
The invention discloses a method, a device and a medium for extracting voiceprint features for transformer working condition identification. The method comprises the following steps: acquiring a power spectrum of an acquired sound signal near the transformer by adopting a multi-frame windowing average method; acquiring a steady-state trend spectrum representing a stable environmental noise component according to the power spectrum; extracting a harmonic spectrum of the power spectrum based on the power frequency of the power grid; subtracting the spectrum trend from the harmonic spectrum to obtain a detrend harmonic spectrum of the transformer vibration voiceprint; from the detrend harmonic spectrum, it is determined whether it is a transformer voiceprint feature.
Description
Technical Field
The invention relates to the technical field of voiceprint recognition, in particular to a voiceprint feature extraction method, device and medium for transformer working condition recognition.
Background
The technology is characterized in that an acoustic sensor is arranged on the transformer, a computing device is used for analyzing and processing acoustic signals collected by the sensor and extracting characteristics, and then the extracted voiceprint characteristics are used for carrying out state identification and fault monitoring on the transformer. The existing mainstream scheme is mainly migrated by voice voiceprint recognition technology in the field of voice recognition, and features in the field of voice recognition such as a spectrogram, a logarithmic mel spectrum, a mel cepstrum coefficient and the like are mainly adopted as voiceprint features for transformer working condition recognition.
The acoustic signal of the transformer mainly comes from periodic vibration generated by magnetostriction effect between the transformer iron core and the winding, the acoustic signal generated by the vibration presents a vibration signal mainly based on the fundamental frequency of the power frequency of the power grid and the frequency multiplication thereof, and the signal characteristics are obviously different from those of the voice signal; meanwhile, the corpus signal for voice recognition is generally purer, and the acoustic signal for transformer working condition recognition is generally required to be collected in the transformer substation environment, so that the influence of environmental interference is unavoidable.
The existing voiceprint extraction method is used for voice recognition, the main voiceprint characteristics adopted by the recognition are designed according to the characteristics of voice signals, the performance is good in a voice scene, and the optimization design is not carried out according to the characteristics of the voice signals of the transformer and the applicable scene. The spectrogram (also called spectrogram) is mainly suitable for short-time stationary signals, but for stationary signals, a large amount of redundant data exists, and environmental background noise interference is not considered; logarithmic mel spectrum and mel cepstrum coefficient in voice voiceprint recognition are divided according to mel frequency bands by adopting a plurality of filter banks, the divided frequency bands are designed towards the characteristics of human ear hearing, but each filter sub-band is limited in number, and each sub-band possibly comprises a plurality of groups of transformer vibration related harmonic frequency points, so that critical information in an extracted signal about transformer vibration generated acoustic signal is lost. Meanwhile, the spectrogram of the spectral subtraction processing commonly used in the voice recognition pretreatment realizes the removal of the stable components of the environmental noise, however, the stable components in the transformer vibration signal and the environment interference acoustic signal are stable signals, and the method cannot process the interference caused by the removal of the stable components in the stable environmental noise.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method, a device and a medium for extracting voiceprint features for identifying working conditions of a transformer.
According to one aspect of the invention, there is provided a method for extracting voiceprint features for identifying working conditions of a transformer, comprising:
acquiring a power spectrum of an acquired sound signal near the transformer by adopting a multi-frame windowing average method;
acquiring a steady-state trend spectrum representing a stable environmental noise component according to the power spectrum;
extracting a harmonic spectrum of the power spectrum based on the power frequency of the power grid;
subtracting the spectrum trend from the harmonic spectrum to obtain a detrend harmonic spectrum of the transformer vibration voiceprint;
from the detrend harmonic spectrum, it is determined whether it is a transformer voiceprint feature.
Optionally, the calculation formula of the power spectrum is:
wherein G is power spectrum, x n For each frame of acoustic signal, win n For the window function corresponding to each frame of acoustic signal, P is the power spectrum of each frame of acoustic signal, and N is the number of frames of acoustic signal.
Optionally, obtaining a steady state trend spectrum representing a steady state ambient noise component from the power spectrum includes:
performing median filtering on the power spectrum to obtain a steady-state trend spectrum representing a steady-state environment noise component, wherein the calculation formula of the steady-state trend spectrum is as follows:
wherein Z is steady state trend spectrum, G w_sort Sequencing the power spectrum data points of the periodic graph method in each sliding window with the odd length w from small to large, and taking the sequenced power spectrum data pointsData at the point.
Optionally, the extraction formula of the harmonic spectrum is:
wherein G is 50*n For all 50Hz frequency multiplication in the power spectrum, a harmonic spectrum X is formed 50 N is the number of frames of the acoustic signal.
Optionally, the formula for the detrending harmonic spectrum is:
C＝X 50 -Z 50
wherein C is the detrend harmonic spectrum, X 50 Is of harmonic spectrum, Z 50 All frequency doubling points at 50Hz of the steady state trend spectrum.
Optionally, determining whether to be a transformer voiceprint feature based on the detrending harmonic spectrum includes:
normalizing the detrending harmonic spectrum to obtain the harmonic energy duty ratio after detrending;
whether the harmonic energy duty cycle is a transformer voiceprint feature is determined based on whether the transformer feature is present.
Optionally, the calculation formula of the harmonic energy ratio is:
where Q is the harmonic energy duty cycle, C is the detrending harmonic spectrum, and ΣC is the total energy of the detrending harmonic spectrum.
According to another aspect of the present invention, there is provided a transformer condition recognition voiceprint feature extraction apparatus, comprising:
the first acquisition module is used for acquiring the power spectrum of the acquired sound signal near the transformer by adopting a multi-frame windowing average method;
the second acquisition module is used for acquiring a steady-state trend spectrum representing a stable environmental noise component according to the power spectrum;
the extraction module is used for extracting a harmonic spectrum of the power spectrum based on the power frequency of the power grid;
the third acquisition module is used for subtracting the spectrum trend from the harmonic spectrum to acquire a detrend harmonic spectrum of the transformer vibration voiceprint;
and the determining module is used for determining whether the transformer voiceprint characteristic is obtained according to the detrending harmonic spectrum.
According to a further aspect of the present invention there is provided a computer readable storage medium storing a computer program for performing the method according to any one of the above aspects of the present invention.
According to still another aspect of the present invention, there is provided an electronic device including: a processor; a memory for storing the processor-executable instructions; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method according to any of the above aspects of the present invention.
Therefore, the method extracts harmonic spectrum components belonging to the transformer vibration generated acoustic signals in the acoustic signal power spectrum, removes stationary noise base interference in the environment through median filtering, and finally carries out energy normalization on the extracted harmonic spectrum components. The method can effectively remove the interference of the environmental signals, the sensitivity and the volume gain of the acquired sensor, and has low characteristic data quantity and high reliability.
Drawings
Exemplary embodiments of the present invention may be more completely understood in consideration of the following drawings:
FIG. 1 is a flowchart of a method for extracting voiceprint features for identifying transformer operating conditions according to an exemplary embodiment of the present invention;
FIG. 2 is another flow chart of a method for identifying voiceprint features of a transformer operating mode according to an exemplary embodiment of the present invention;
FIG. 3 is a schematic diagram of an original power spectrum provided by an exemplary embodiment of the present invention;
FIG. 4 is a graph illustrating median filtered spectra provided in an exemplary embodiment of the invention;
FIG. 5 is a schematic diagram of a transformer harmonic voiceprint feature provided in an exemplary embodiment of the present invention;
FIG. 6 is a schematic illustration of median filtering effects provided by an exemplary embodiment of the present invention;
FIG. 7 is a schematic diagram of a transformer condition recognition voiceprint feature extraction apparatus according to an exemplary embodiment of the present invention;
fig. 8 is a structure of an electronic device provided in an exemplary embodiment of the present invention.
Detailed Description
Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some embodiments of the present invention and not all embodiments of the present invention, and it should be understood that the present invention is not limited by the example embodiments described herein.
It should be noted that: the relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise.
It will be appreciated by those of skill in the art that the terms "first," "second," etc. in embodiments of the present invention are used merely to distinguish between different steps, devices or modules, etc., and do not represent any particular technical meaning nor necessarily logical order between them.
It should also be understood that in embodiments of the present invention, "plurality" may refer to two or more, and "at least one" may refer to one, two or more.
It should also be appreciated that any component, data, or structure referred to in an embodiment of the invention may be generally understood as one or more without explicit limitation or the contrary in the context.
In addition, the term "and/or" in the present invention is merely an association relationship describing the association object, and indicates that three relationships may exist, for example, a and/or B may indicate: a exists alone, A and B exist together, and B exists alone. In the present invention, the character "/" generally indicates that the front and rear related objects are an or relationship.
It should also be understood that the description of the embodiments of the present invention emphasizes the differences between the embodiments, and that the same or similar features may be referred to each other, and for brevity, will not be described in detail.
Meanwhile, it should be understood that the sizes of the respective parts shown in the drawings are not drawn in actual scale for convenience of description.
The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the techniques, methods, and apparatus should be considered part of the specification.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
Embodiments of the invention are operational with numerous other general purpose or special purpose computing system environments or configurations with electronic devices, such as terminal devices, computer systems, servers, etc. Examples of well known terminal devices, computing systems, environments, and/or configurations that may be suitable for use with the terminal device, computer system, server, or other electronic device include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, small computer systems, mainframe computer systems, and distributed cloud computing technology environments that include any of the foregoing, and the like.
Electronic devices such as terminal devices, computer systems, servers, etc. may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, etc., that perform particular tasks or implement particular abstract data types. The computer system/server may be implemented in a distributed cloud computing environment in which tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computing system storage media including memory storage devices.
Exemplary method
Fig. 1 is a flowchart of a method for extracting voiceprint features for identifying transformer working conditions according to an exemplary embodiment of the present invention. The embodiment can be applied to an electronic device, as shown in fig. 1, the method 100 for extracting the voiceprint feature of the transformer working condition recognition includes the following steps:
step 101, acquiring a power spectrum of an acquired sound signal near a transformer by adopting a multi-frame windowing average method;
step 102, obtaining a steady-state trend spectrum representing a stable environmental noise component according to the power spectrum;
step 103, extracting a harmonic spectrum of the power spectrum based on the power frequency of the power grid;
step 104, subtracting the spectrum trend from the harmonic spectrum to obtain a detrend harmonic spectrum of the transformer vibration voiceprint;
step 105, determining whether the transformer voiceprint features according to the detrending harmonic spectrum.
Specifically, the application provides a dedicated voiceprint feature extraction processing method for identifying working conditions of a transformer, wherein a flow chart of the method is shown in fig. 2, and the method comprises the following steps:
1. the power spectrum estimation of the received sound signal is obtained by adopting a Welch method (multi-frame windowing average) on the sound signal collected near the transformer, and the calculation formula is as follows
Wherein x is n For each frame of acoustic signal, win n And (3) as a corresponding window function, P is the power spectrum of each frame, the signal is divided into N frames, the power spectrum is respectively obtained, and then the average is carried out, namely the power spectrum G of the periodic graph method.
I.e. an acoustic signal containing ambient noise, as shown in fig. 3.
2. The power spectrum in the step 1 is subjected to median filtering to obtain a steady-state trend representing a steady-state environmental noise component, and the calculation formula is as follows
Wherein G is w_sort Sequencing the power spectrum data points of the periodic graph method in each sliding window with the odd length w from small to large, and taking the sequenced power spectrum data pointsData at points (i.e., intermediate points) that constitute the median filtered power spectrum Z, as shown in fig. 4. Further, the effect of the median filtering is schematically shown in fig. 5.
3. The power frequency of the power grid is taken as the fundamental frequency, the harmonic spectrum is extracted from the power spectrum in the step 1, and the calculation formula is as follows
Frequency multiplication power spectrum G of all 50Hz in the power spectrum of the periodic chart method 50*n Reserved, composition of harmonic spectrum X 50 ；
4. Subtracting the spectrum trend of the relevant frequency point in the step 2 from the harmonic spectrum component in the step 3 to obtain a detrend harmonic spectrum representing the vibration voiceprint component of the transformer, wherein the calculation formula is as follows
C＝X 50 -Z 50
Wherein X is 50 Z for the harmonic spectrum extracted in step 3 50 For the spectral trend extracted in step 2 (only taking all the frequency doubling points of 50 Hz), the subtraction gives a detritus harmonic spectrum C, as shown in fig. 6.
5. Carrying out energy normalization on the detrending harmonic spectrum in the step 4 to obtain the harmonic energy duty ratio after detrending and eliminate the interference introduced by the gain of the sensor, wherein the calculation formula is as follows:
and (3) normalizing the detrending harmonic spectrum C in the step 4 by total energy to obtain the detrending harmonic energy duty ratio Q.
6. The output result of the step 5 is identified according to the operating condition of the transformer, according to the characteristics of the acoustic signal of the transformer (the harmonic signal composed of the fundamental frequency of 50Hz and the frequency multiplication thereof) in the application guideline for measuring the sound level of the 10.1 part of the power transformer of GB_T1094.101-2008, whether the obvious transformer characteristic (50 Hz harmonic characteristic) exists in the harmonic energy duty ratio after trend removal obtained in the step 5 is judged, and if the non-transformer operation is judged or the non-transformer characteristic is not judged, the non-transformer operation is not output; and if the operation of the transformer is judged, outputting the final transformer voiceprint characteristic.
Therefore, the method extracts harmonic spectrum components belonging to the transformer vibration generated acoustic signals in the acoustic signal power spectrum, removes stationary noise base interference in the environment through median filtering, and finally carries out energy normalization on the extracted harmonic spectrum components. The method can effectively remove the interference of the environmental signals, the sensitivity and the volume gain of the acquired sensor, and has low characteristic data quantity and high reliability.
Exemplary apparatus
Fig. 7 is a schematic structural diagram of a transformer working condition recognition voiceprint feature extraction device according to an exemplary embodiment of the present invention. As shown in fig. 7, the apparatus 700 includes:
a first obtaining module 10, configured to obtain a power spectrum of an acoustic signal near the collected transformer by using a multi-frame windowed averaging method;
a second obtaining module 720, configured to obtain a steady-state trend spectrum representing a stable environmental noise component according to the power spectrum;
an extracting module 730, configured to extract a harmonic spectrum of the power spectrum based on the power frequency of the power grid;
a third obtaining module 740, configured to subtract the spectrum trend from the harmonic spectrum to obtain a detrend harmonic spectrum of the transformer vibration voiceprint;
a determining module 750 is configured to determine whether the characteristic is a transformer voiceprint based on the detritus harmonic spectrum.
Optionally, the calculation formula of the power spectrum is:
wherein G is power spectrum, x n For each frame of acoustic signal, win n For the window function corresponding to each frame of acoustic signal, P is the power spectrum of each frame of acoustic signal, and N is the number of frames of acoustic signal.
Optionally, obtaining a steady state trend spectrum representing a steady state ambient noise component from the power spectrum includes:
performing median filtering on the power spectrum to obtain a steady-state trend spectrum representing a steady-state environment noise component, wherein the calculation formula of the steady-state trend spectrum is as follows:
wherein Z is steady state trend spectrum, G w_sort Sequencing the power spectrum data points of the periodic graph method in each sliding window with the odd length w from small to large, and taking the sequenced power spectrum data pointsData at the point.
Optionally, the extraction formula of the harmonic spectrum is:
wherein G is 50*n For all 50Hz frequency multiplication in the power spectrum, a harmonic spectrum X is formed 50 N is the number of frames of the acoustic signal.
Optionally, the formula for the detrending harmonic spectrum is:
C＝X 50 -Z 50
wherein C is the detrend harmonic spectrum, X 50 Is of harmonic spectrum, Z 50 All frequency doubling points at 50Hz of the steady state trend spectrum.
Optionally, the determining module 750 includes:
the obtaining submodule is used for carrying out normalization processing on the detrending harmonic spectrum and obtaining the harmonic energy duty ratio after detrending;
and the determining submodule is used for determining whether the harmonic energy duty ratio is the transformer voiceprint characteristic according to the fact that whether the transformer characteristic exists or not.
Optionally, the calculation formula of the harmonic energy ratio is:
where Q is the harmonic energy duty cycle, C is the detrending harmonic spectrum, and ΣC is the total energy of the detrending harmonic spectrum.
Exemplary electronic device
Fig. 8 is a structure of an electronic device provided in an exemplary embodiment of the present invention. As shown in fig. 8, the electronic device 80 includes one or more processors 81 and memory 82.
The processor 81 may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.
Memory 82 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM) and/or cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like. One or more computer program instructions may be stored on the computer readable storage medium that can be executed by the processor 81 to implement the methods of the software programs of the various embodiments of the present invention described above and/or other desired functions. In one example, the electronic device may further include: an input device 83 and an output device 84, which are interconnected by a bus system and/or other forms of connection mechanisms (not shown).
In addition, the input device 83 may also include, for example, a keyboard, a mouse, and the like.
The output device 84 can output various information to the outside. The output means 84 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, etc.
Of course, only some of the components of the electronic device relevant to the present invention are shown in fig. 8 for simplicity, components such as buses, input/output interfaces, etc. being omitted. In addition, the electronic device may include any other suitable components depending on the particular application.
Exemplary computer program product and computer readable storage Medium
In addition to the methods and apparatus described above, embodiments of the invention may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform steps in a method according to various embodiments of the invention described in the "exemplary methods" section of this specification.
The computer program product may write program code for performing operations of embodiments of the present invention in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the invention may also be a computer-readable storage medium, having stored thereon computer program instructions, which when executed by a processor, cause the processor to perform steps in a method according to various embodiments of the invention described in the "exemplary method" section of the description above.
The computer readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The basic principles of the present invention have been described above in connection with specific embodiments, however, it should be noted that the advantages, benefits, effects, etc. mentioned in the present invention are merely examples and not intended to be limiting, and these advantages, benefits, effects, etc. are not to be considered as essential to the various embodiments of the present invention. Furthermore, the specific details disclosed herein are for purposes of illustration and understanding only, and are not intended to be limiting, as the invention is not necessarily limited to practice with the above described specific details.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different manner from other embodiments, so that the same or similar parts between the embodiments are mutually referred to. For system embodiments, the description is relatively simple as it essentially corresponds to method embodiments, and reference should be made to the description of method embodiments for relevant points.
The block diagrams of the devices, systems, apparatuses, systems according to the present invention are merely illustrative examples and are not intended to require or imply that the connections, arrangements, configurations must be made in the manner shown in the block diagrams. As will be appreciated by one of skill in the art, the devices, systems, apparatuses, systems may be connected, arranged, configured in any manner. Words such as "including," "comprising," "having," and the like are words of openness and mean "including but not limited to," and are used interchangeably therewith. The terms "or" and "as used herein refer to and are used interchangeably with the term" and/or "unless the context clearly indicates otherwise. The term "such as" as used herein refers to, and is used interchangeably with, the phrase "such as, but not limited to.
The method and system of the present invention may be implemented in a number of ways. For example, the methods and systems of the present invention may be implemented by software, hardware, firmware, or any combination of software, hardware, firmware. The above-described sequence of steps for the method is for illustration only, and the steps of the method of the present invention are not limited to the sequence specifically described above unless specifically stated otherwise. Furthermore, in some embodiments, the present invention may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.
It is also noted that in the systems, devices and methods of the present invention, components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present invention. The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the invention. Thus, the present invention is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit embodiments of the invention to the form disclosed herein. Although a number of example aspects and embodiments have been discussed above, a person of ordinary skill in the art will recognize certain variations, modifications, alterations, additions, and subcombinations thereof.
Claims (11)
1. The method for extracting the voiceprint features of the transformer working condition recognition is characterized by comprising the following steps of:
acquiring a power spectrum of an acquired sound signal near the transformer by adopting a multi-frame windowing average method;
acquiring a steady-state trend spectrum representing a stable environmental noise component according to the power spectrum;
extracting a harmonic spectrum of the power spectrum based on power frequency of a power grid;
subtracting the spectrum trend from the harmonic spectrum to obtain a detrend harmonic spectrum of the transformer vibration voiceprint;
and determining whether the transformer voiceprint characteristic is determined according to the detrending harmonic spectrum.
2. The method of claim 1, wherein the power spectrum is calculated by the formula:
wherein G is power spectrum, x n For each frame of acoustic signal, win n For the window function corresponding to each frame of acoustic signal, P is the power spectrum of each frame of acoustic signal, and N is the number of frames of acoustic signal.
3. The method of claim 1, wherein obtaining a steady state trend spectrum representing a steady state ambient noise component from the power spectrum comprises:
performing median filtering on the power spectrum to obtain the steady-state trend spectrum representing steady-state environmental noise components, wherein the calculation formula of the steady-state trend spectrum is as follows:
wherein Z is steady state trend spectrum, G w_sort Sequencing the power spectrum data points of the periodic graph method in each sliding window with the odd length w from small to large, and taking the sequenced power spectrum data pointsData at the point.
4. The method of claim 1, wherein the extraction formula of the harmonic spectrum is:
wherein G is 50*n For all 50Hz frequency multiplication in the power spectrum, a harmonic spectrum X is formed 50 N is the number of frames of the acoustic signal.
5. The method of claim 1, wherein the formula for the detrending harmonic spectrum is:
C＝X 50 -Z 50
wherein C is the detrend harmonic spectrum, X 50 Is of harmonic spectrum, Z 50 All frequency doubling points at 50Hz of the steady state trend spectrum.
6. The method of claim 1, wherein determining whether it is a transformer voiceprint feature based on the detrending harmonic spectrum comprises:
normalizing the detrending harmonic spectrum to obtain the harmonic energy duty ratio after detrending;
and determining whether the harmonic energy duty ratio is a transformer voiceprint characteristic according to whether the transformer characteristic exists or not.
7. The method of claim 1, wherein the harmonic energy ratio is calculated as:
where Q is the harmonic energy duty cycle, C is the detrending harmonic spectrum, and ΣC is the total energy of the detrending harmonic spectrum.
8. The utility model provides a transformer operating mode discernment voiceprint feature extraction element which characterized in that includes:
the first acquisition module is used for acquiring the power spectrum of the acquired sound signal near the transformer by adopting a multi-frame windowing average method;
the second acquisition module is used for acquiring a steady-state trend spectrum representing a stable environmental noise component according to the power spectrum;
the extraction module is used for extracting a harmonic spectrum of the power spectrum based on the power frequency of the power grid;
the third acquisition module is used for subtracting the spectrum trend from the harmonic spectrum to acquire a detrend harmonic spectrum of the transformer vibration voiceprint;
and the determining module is used for determining whether the harmonic wave is the voiceprint characteristic of the transformer according to the detrending harmonic wave spectrum.
9. The apparatus of claim 8, wherein the determining module comprises:
the obtaining submodule is used for carrying out normalization processing on the detrending harmonic spectrum and obtaining the harmonic energy duty ratio after detrending;
and the determining submodule is used for determining whether the harmonic energy duty ratio is the transformer voiceprint characteristic according to the fact that whether the transformer characteristic exists or not.
10. A computer readable storage medium, characterized in that the storage medium stores a computer program for executing the method of any of the preceding claims 1-7.
11. An electronic device, the electronic device comprising:
a processor;
a memory for storing the processor-executable instructions;
the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method of any of the preceding claims 1-7.
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