CN114935698B - Background noise recognition method, device, electronic device and storage medium - Google Patents
Background noise recognition method, device, electronic device and storage medium Download PDFInfo
- Publication number
- CN114935698B CN114935698B CN202210372817.6A CN202210372817A CN114935698B CN 114935698 B CN114935698 B CN 114935698B CN 202210372817 A CN202210372817 A CN 202210372817A CN 114935698 B CN114935698 B CN 114935698B
- Authority
- CN
- China
- Prior art keywords
- background noise
- frequency
- signal
- value
- time domain
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000003860 storage Methods 0.000 title claims abstract description 24
- 238000001228 spectrum Methods 0.000 claims abstract description 58
- 230000007613 environmental effect Effects 0.000 claims abstract description 40
- 238000005457 optimization Methods 0.000 claims abstract description 20
- 230000008569 process Effects 0.000 claims abstract description 12
- 238000012545 processing Methods 0.000 claims description 22
- 238000004590 computer program Methods 0.000 claims description 9
- 238000009499 grossing Methods 0.000 claims description 9
- 238000012935 Averaging Methods 0.000 claims description 7
- 230000035772 mutation Effects 0.000 claims description 7
- 230000009466 transformation Effects 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 2
- 238000012544 monitoring process Methods 0.000 abstract description 14
- 230000002159 abnormal effect Effects 0.000 description 14
- 238000010586 diagram Methods 0.000 description 13
- 230000006870 function Effects 0.000 description 6
- 230000005540 biological transmission Effects 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 4
- 238000003491 array Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 239000000835 fiber Substances 0.000 description 2
- 238000012806 monitoring device Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000001902 propagating effect Effects 0.000 description 2
- 230000011218 segmentation Effects 0.000 description 2
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 229910052802 copper Inorganic materials 0.000 description 1
- 239000010949 copper Substances 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000011664 signaling Effects 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R29/00—Arrangements for measuring or indicating electric quantities not covered by groups G01R19/00 - G01R27/00
- G01R29/26—Measuring noise figure; Measuring signal-to-noise ratio
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R23/00—Arrangements for measuring frequencies; Arrangements for analysing frequency spectra
- G01R23/16—Spectrum analysis; Fourier analysis
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
- Radar Systems Or Details Thereof (AREA)
- Noise Elimination (AREA)
Abstract
The embodiment of the application discloses a background noise identification method, a device, electronic equipment and a storage medium, which relate to the technical field of radio environment monitoring; the method comprises the steps of obtaining an envelope spectrum of the environment signal to be detected, distinguishing whether the environment signal to be detected is in a high frequency band or a low frequency band according to a set frequency threshold value, and selecting a corresponding optimization strategy to process the envelope spectrum according to the distinguished high frequency band and low frequency band to obtain final background noise. The application utilizes the spectrum characteristics of the environmental signal to be detected to distinguish the high frequency band and the low frequency band of the environmental signal to be detected, and then the background noise for judgment is obtained by differentiating the envelope spectrum through different optimization strategies, so that the stability and the accuracy of a noise curve are ensured and improved, and the signal identification result is more accurate.
Description
Technical Field
The present application relates to the field of radio environment monitoring, and in particular, to a method and apparatus for identifying background noise, an electronic device, and a storage medium.
Background
In daily security work of a radio, electromagnetic signals in the environment are often acquired by a monitoring station or the like to accomplish monitoring and management of abnormal signals. How to distinguish between daily signals, background noise and abnormal signals is more complex in terms of the current increasingly complex electromagnetic environment.
In the related art, environmental spectrum monitoring data obtained by a monitoring station are averaged, a mean curve is generated, the mean curve is lifted to serve as background noise, and the lifted numerical value is dynamically processed according to the current environment. However, for some very signaling, in view of the low frequency of occurrence of the signal, the signal strength of the frequency band where the averaged signal is located is not improved too much, so that a false alarm phenomenon occurs in conventional monitoring, and if a special signal such as a frequency hopping signal occurs, a background noise curve cannot reflect the special signal, and a situation that a normal signal is wrongly reported as an abnormal signal is easy to occur.
It can be seen that how to effectively identify abnormal signals in background noise is one of the technical problems that need to be solved in the art.
Disclosure of Invention
In view of the above, embodiments of the present application provide a background noise recognition method, apparatus, electronic device, and storage medium for solving at least one problem in the background art.
In a first aspect, an embodiment of the present application provides a method for identifying background noise, including the following steps:
Collecting an environmental signal to be detected;
Acquiring an envelope spectrum of the environmental signal to be detected;
distinguishing whether the environmental signal to be detected is in a high frequency band or a low frequency band according to a set frequency threshold;
And selecting a corresponding optimization strategy to process the envelope spectrum according to the distinguished high and low frequency bands to obtain final background noise.
With reference to the first aspect of the present application, in an optional implementation manner, the step of processing the envelope spectrum according to the differentiated high-low frequency band by selecting a corresponding optimization strategy specifically includes:
If the frequency band of the environmental signal to be measured is a low frequency band, then segment trowelling the time domain envelope value in the envelope frequency spectrum on the frequency domain to obtain a frequency domain trowelling result, and lifting the result to be used as final background noise, or
If the frequency band of the environmental signal to be detected is a high frequency band, selecting the minimum value of the time domain envelope values of all frequency points in the envelope frequency spectrum as a background noise value of the environmental signal to be detected, and lifting the background noise value to be used as final background noise.
With reference to the first aspect of the present application, in an optional implementation manner, the step of segment-screeding the time domain envelope values in the envelope spectrum in the frequency domain specifically includes:
Segmenting on a frequency domain according to a set bandwidth, and averaging all time domain envelope values in each segment to obtain a first average value;
and then, solving an average value of all the time domain envelope values lower than the first average value in the segment to obtain a second average value, and taking the second average value as a frequency domain trowelling result of the segment.
With reference to the first aspect of the present application, in an optional implementation manner, after obtaining the second average value, the method further includes the step of:
if the difference value between the second average value and the real background noise value of the segment exceeds a set threshold value, taking the average value of the time domain envelope values of the first two frequency points of the segment as a frequency domain smoothing result of the segment, otherwise taking the second average value as the frequency domain smoothing result of the segment.
With reference to the first aspect of the present application, in an optional implementation manner, after obtaining the frequency domain trowelling result, before lifting the result, the method further includes the steps of:
if a frequency point has mutation, the value of the frequency point is corrected to be the average value of the two frequency points before and after the frequency point.
With reference to the first aspect of the present application, in an optional implementation manner, the step of obtaining an envelope spectrum of the environmental signal to be measured specifically includes:
acquiring time domain signals of all frequency points of an environment signal to be detected, and correcting burst signals in the time domain signals;
carrying out maximum value tracing on the corrected time domain signal to obtain the time domain envelope of each frequency point;
And performing time domain-to-frequency domain transformation on each time domain envelope to obtain an envelope spectrum.
With reference to the first aspect of the present application, in an optional implementation manner, the step of correcting the burst signal in the time domain signal specifically includes:
and correcting the value of the burst signal to be the average value of the two time domain signals before and after the burst signal.
In a second aspect, an embodiment of the present application provides a background noise recognition apparatus, including:
the signal acquisition module is configured to acquire an environmental signal to be detected;
A spectrum acquisition module configured to acquire an envelope spectrum of the environmental signal to be measured;
The frequency band distinguishing module is configured to distinguish whether the environment signal to be detected is in a high frequency band or a low frequency band according to a set frequency threshold value;
and the background noise optimization module is configured to process the envelope spectrum according to the distinguished high and low frequency bands by selecting a corresponding optimization strategy to obtain final background noise.
In a third aspect, an embodiment of the present application further provides an electronic device, including a processor and a memory;
the memory is used for storing computer executable instructions;
The processor is configured to execute the computer-executable instructions to implement the method for identifying background noise according to any one of the first aspect.
In a fourth aspect, an embodiment of the present application further provides a storage medium having stored thereon a computer program which, when executed by a processor, implements a method for identifying background noise as set forth in any one of the first aspects above.
The technical scheme provided by the embodiment of the application has the beneficial effects that the spectral characteristics of the environmental signal to be detected are utilized, the frequency range where the environmental signal to be detected is positioned is distinguished to be a high frequency range or a low frequency range by taking the set frequency threshold as a demarcation point, and then the background noise for judgment is obtained by distinguishing the envelope spectrum of the environmental signal to be detected according to the high frequency range and the low frequency range through different optimization strategies, so that the stability and the accuracy of a noise curve are ensured and improved, and the signal identification result is more accurate.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a background noise identification method provided in an embodiment of the present application;
FIG. 2 is a spectrum diagram of real-time values and background noise values of a low-frequency signal in a frequency domain;
FIG. 3 is a spectrum diagram of real-time values and background noise values of a high-frequency signal in the frequency domain;
FIG. 4 is a graph of a frequency spectrum of a real time value, a background noise value, and a threshold value of another low frequency signal in the frequency domain;
FIG. 5 is a block flow diagram of a method for identifying background noise according to an embodiment of the present application;
fig. 6 is a spectrum diagram of three frequency points selected in a broadcast frequency band in a time domain;
fig. 7 is a schematic structural diagram of a background noise recognition device according to an embodiment of the present application;
Fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
In order to make the technical scheme and the beneficial effects of the application more obvious and understandable, the following detailed description is given by way of example. Wherein the drawings are not necessarily to scale, and wherein local features may be exaggerated or minimized to more clearly show details of the local features, unless otherwise defined, technical and scientific terms used herein have the same meaning as those in the technical field to which the present application pertains.
As shown in fig. 1, an embodiment of the present application provides a background noise identification method, which includes the following steps:
s101, collecting an environmental signal to be detected;
s102, acquiring an envelope spectrum of the environmental signal to be detected;
S103, distinguishing whether the environmental signal to be detected is in a high frequency band or a low frequency band according to a set frequency threshold value;
s104, selecting a corresponding optimization strategy to process the envelope spectrum according to the distinguished high and low frequency bands, and obtaining final background noise.
In the environment monitoring, when an environment signal to be detected is acquired, whether the real frequency of the environment signal to be detected is above or below a frequency domain threshold value can be determined, and the environment signal to be detected can be further distinguished into a high frequency band and a low frequency band.
In the embodiment of the present application, it is preferable that the range of the frequency threshold is set to 850±20MHz. In a preferred embodiment, the frequency threshold is approximately 850MHz. The low frequency band in the embodiment of the application is below about 850MHz, and is mostly a frequency band used by non-operators, and the curve characteristic of the envelope spectrum has the following characteristics:
(1) The background noise value is unstable, and the integrity of the local frequency band is raised or lowered;
(2) There is a large jitter in the level value of the full band.
The high frequency band in the embodiment of the application is above 850MHz, most of frequency bands used by operators or special frequency bands, and the curve characteristics of the envelope spectrum have the following characteristics:
(1) The background noise value is stable, and the integral elevation or reduction of the local frequency band does not exist;
(2) Has a broadband and stable signal.
As can be seen, there is a significant difference in the curve characteristics of the envelope spectrum between the high-frequency and low-frequency signals, as shown in fig. 2 and 3, compared with the process of directly averaging the spectrum data in the related art, the embodiment of the application uses the curve characteristics of the envelope spectrum of the environmental signal to be detected to distinguish the high-frequency and low-frequency bands of the environmental signal to be detected, and then uses different optimization strategies to distinguish the envelope spectrum to obtain the background noise for judgment, thereby ensuring and improving the stability and accuracy of the noise curve, making the signal identification result more accurate, and also accurately realizing the monitoring of the abnormal signal.
Step 102 and step 103 may be performed sequentially or synchronously.
As an optional specific solution of the embodiment of the present application, the step of obtaining the envelope spectrum of the environmental signal to be measured specifically includes:
acquiring time domain signals of all frequency points of an environment signal to be detected, and correcting burst signals in the time domain signals;
carrying out maximum value tracing on the corrected time domain signal to obtain the time domain envelope of each frequency point;
And performing time domain-to-frequency domain transformation on each time domain envelope to obtain an envelope spectrum.
Specifically, the specific step of correcting the burst signal in the time domain signal includes:
and correcting the value of the burst signal to be the average value of the two time domain signals before and after the burst signal.
Compared with the traditional average value calculation, the method filters out abnormal time domain signals, avoids erroneous judgment or missed judgment of abnormal signals with low signal strength, and reduces interference influence on a noise curve.
Optionally, the step of processing the envelope spectrum according to the distinguished high-low frequency band by selecting a corresponding optimization strategy specifically includes:
If the frequency band of the environmental signal to be measured is a low frequency band, then segment trowelling the time domain envelope value in the envelope frequency spectrum on the frequency domain to obtain a frequency domain trowelling result, and lifting the result to be used as final background noise, or
If the frequency band of the environmental signal to be detected is a high frequency band, selecting the minimum value of the time domain envelope values of all frequency points in the envelope frequency spectrum as a background noise value of the environmental signal to be detected, and lifting the background noise value to be used as final background noise.
For different high and low frequency bands, the low frequency band is relatively unstable in background noise value, namely the time domain envelope value is unstable in the frequency domain, so that the segmentation processing is carried out on the frequency domain to obtain a more accurate background noise for judgment so as to effectively distinguish the background noise from the abnormal noise by comparing with the actual background noise, and the high frequency band background noise value is stable, so that a lower value is found in the wide frequency domain and is used as the background noise value to compare with the actual background noise.
Wherein the time domain envelope value is the mean value of the time domain envelope of the frequency points.
For the high frequency band, under the condition of proper frequency band range, the minimum value of the time domain tracing value is taken as the bottom noise value of the whole frequency band. And in order to ensure that the signal identification result is more accurate, after the background noise value is determined, the background noise value needs to be properly adjusted to be larger by a surplus value, namely, the background noise curve is lifted, and the lifted background noise value is a threshold value.
In the case of the low frequency band, it is also necessary to perform processing in segments in the frequency domain.
Therefore, in the signals of the two frequencies of the high frequency and the low frequency, the signals of the high frequency are processed in a time domain mode independently due to the stability of the signals, the jumps of the signals of the low frequency are more, and the accuracy of the background noise for judgment obtained by the signals of the low frequency is effectively improved by adopting a processing mode of combining the time domain and the frequency domain.
Specifically, the step of piecewise trowelling on the frequency domain to process the time domain envelope values in the envelope spectrum specifically includes:
Segmenting on a frequency domain according to a set bandwidth, and averaging all time domain envelope values in each segment to obtain a first average value;
and then, solving an average value of all the time domain envelope values lower than the first average value in the segment to obtain a second average value, and taking the second average value as a frequency domain trowelling result of the segment.
In this embodiment, the traffic channel is segmented with a bandwidth of 0.01MHz, each segment is subjected to a frequency domain smoothing process, that is, an average value of time domain envelope values of each frequency point in the segment is obtained in each segment, and is defined as a first average value, and then all values lower than the first average value in the segment are taken out to obtain a new average value by taking the first average value as a standard, and are defined as a second average value. Normally, the second average value is not excessively different from the true background noise value of the segment, so that the second average value is used as a frequency domain screeding result of the segment, namely the background noise value of the segment.
Optionally, after obtaining the second average value, the method further includes the steps of:
if the difference value between the second average value and the real background noise value of the segment exceeds a set threshold value, taking the average value of the time domain envelope values of the first two frequency points of the segment as a frequency domain smoothing result of the segment, otherwise taking the second average value as the frequency domain smoothing result of the segment.
In this embodiment, the set threshold is determined according to practical experience, and for the case that the actual background noise value and the calculated background noise value have too large differences, the numerical average value of the first two frequency points is used for replacing, so that each section of narrower signal of the time domain tracing value can be smoothed.
To ensure accurate determination of the frequency domain trowelling result, optionally, after obtaining the frequency domain trowelling result, before lifting the result, the method further comprises the steps of:
if a frequency point has mutation, the value of the frequency point is corrected to be the average value of the two frequency points before and after the frequency point.
In this embodiment, the concept of frequency domain averaging is provided for the instability of the low-frequency signal, and meanwhile, the abnormal conditions possibly occurring in some frequency points are filtered and removed, so that the stability and accuracy of the finally obtained noise curve are ensured.
As shown in FIG. 4, taking a low-frequency signal as an example, two smoother curves are arranged above the actual background noise, wherein the lower one is a processed noise curve, namely a curve corresponding to the result of the frequency domain trowelling, and the upper one is a final noise curve obtained by lifting the noise curve by 5dB, and the final noise curve is taken as a bottom noise discrimination curve.
As the application of the embodiment of the application in electromagnetic environment monitoring, in actual monitoring noise, the final background noise is obtained to obtain a background noise curve, and the real-time signal is compared with the background noise curve, if a frequency point continuously exceeding the background noise appears, the frequency point is considered to be an abnormal frequency point, and an alarm signal needs to be sent out.
The background noise recognition method provided by the application is further explained below with reference to a specific example.
Fig. 5 shows a flow chart of this specific example, and as shown in the figure, first, the external monitoring device starts to perform monitoring, and acquires monitoring data. In practical applications, for example, a frequency band and an area to be monitored are specified, and spectrum data of a period of time around is obtained through an external monitoring device.
Next, time domain tracing is performed. The time domain tracing can be called time domain maximum tracing, and whether the time domain tracing is high-frequency band or low-frequency band, the single frequency point is characterized by similar sine waves with wave crests and wave troughs in the time domain, and firstly, the wave trough data in the time domain must be removed. Fig. 6 shows a spectrogram of three frequency points 95.7MHz, 96.5MHz and 91.1MHz selected in a broadcast frequency band in a time domain, wherein actual values are curves of irregular jump in the graph, and the curves are continuously traced according to maximum values to form an envelope. In addition, the interference of the burst signal needs to be removed, a limiting value is set, and if the signal intensity of a certain frequency point has mutation with the signal intensity of the same frequency point before and after the signal intensity of the same frequency point twice, and the mutation value exceeds the limiting value, the frequency point is considered as the burst signal, and the burst signal is corrected to be the average value of the two signals before and after the burst signal. And finally, carrying out edge tracing on the corrected time domain signal, specifically carrying out edge tracing in a mode of sampling to obtain the maximum value, and averaging all points to obtain the average value of a certain frequency point. And carrying out time domain edge tracing and average value taking on each frequency point, wherein the obtained spectrogram is shown in fig. 2 and 3, and the curve in the graph is a background noise value which is not processed in the frequency domain, and is called as a time domain edge tracing value or a time domain envelope value.
Therefore, the level value of the frequency point can be ensured to be mostly smaller than the value, and the envelope is observed to find the characteristics that a lower value (real background noise) is unstable in a low frequency band and stable in a high frequency band, the frequency point value of a signal is large, and a broadband signal can lead to continuous high values.
For a high frequency band, taking the minimum value of the time domain tracing value as the bottom noise of the whole frequency band under the condition of proper size of the frequency band range. In a large number of observation processes, the applicant finds that, after the background noise value is taken for the high frequency band, the residual value needs to be properly adjusted, so that the signal identification result is more accurate.
For the low frequency band, processing in frequency domain segmentation is also required.
Next, it is determined whether or not it is a high frequency signal. And if the judgment result is negative, executing the step of trowelling in the frequency domain. Specifically, the time domain tracing value obtained by the low frequency band is utilized to smooth the signal into the background noise through two steps by utilizing the adjacent frequency condition.
First, segment processing is performed. The traffic channel is segmented with a certain bandwidth (e.g., 0.1 MHz), and each segment is processed separately. In each section, taking an average value 1, taking all values lower than the average value 1 by taking the average value 1 as a standard, and obtaining an average value 2. It will be appreciated that the true background noise value of the frequency band should not differ too much from the average value 2, and for a frequency band that differs too much, the numerical average value of the first two frequency points is used to replace it, so that each segment of narrower signal of the time domain tracing value can be smoothed.
And secondly, performing comprehensive treatment. And correcting the frequency points with mutation to be the average value of the left frequency point and the right frequency point. And adds a value to the final noise curve as the final background noise. As shown in fig. 4, there are two smoother curves above the background noise, with the lower one being the processed noise curve and the upper one being the noise curve added by 5dB as the final background noise discrimination curve.
In practical application, background noise is obtained through calculation by a background noise algorithm, noise is applied to monitoring, a real-time signal is compared with a background noise curve, if a frequency point continuously exceeding background noise appears, the frequency point is considered to be an abnormal frequency point, and an alarm is required to be immediately carried out.
According to the background noise calculation method in the specific example, the high frequency and the low frequency are subjected to differential processing, the low frequency signals are subjected to comprehensive processing of a time domain and a frequency domain due to excessive hopping, and the high frequency signals are relatively stable and are processed in a time domain mode. The high-frequency signal and the low-frequency signal can be distinguished to perform different optimization aiming at different characteristics of the signals, and the accuracy of the result is improved. For the traditional mean value calculation mode, the thought of filtering abnormal points is provided, and for some sudden signals in daily life, if the sudden signals are not removed, the noise curve can be influenced, and some abnormal signals with low signal strength can be misjudged and missed. In addition, for the instability of the low-frequency signal, the specific example provides frequency domain averaging processing, filters abnormal conditions possibly occurring in certain frequency points, and ensures the stability and accuracy of a noise curve.
On this basis, the embodiment of the application also provides a background noise recognition device, fig. 7 shows the structure of the background noise recognition device, and as shown in the figure, the background noise recognition device 700 comprises:
A signal acquisition module 701 configured to acquire an environmental signal to be measured;
A spectrum acquisition module 702 configured to acquire an envelope spectrum of the environmental signal under test;
a frequency band distinguishing module 703 configured to distinguish whether the environmental signal to be measured is in a high frequency band or a low frequency band according to a set frequency threshold;
And the background noise optimization module 704 is configured to select a corresponding optimization strategy to process the envelope spectrum according to the distinguished high and low frequency bands to obtain final background noise.
Optionally, the background noise optimization module 704 is specifically configured to, if the frequency band of the environmental signal to be detected is a low frequency band, segment and smear the time domain envelope value in the envelope spectrum on the frequency domain to obtain a frequency domain smear result, and lift the result as final background noise, or if the frequency band of the environmental signal to be detected is a high frequency band, select the minimum value of the time domain envelope values of all frequency points in the envelope spectrum as the background noise value of the environmental signal to be detected, and lift the background noise value as final background noise.
Optionally, the noise floor optimization module 704 is configured to segment and smear the time domain envelope values in the envelope spectrum in a frequency domain, and specifically includes segments in the frequency domain according to a set bandwidth, averages all the time domain envelope values in each segment to obtain a first average value, averages all the time domain envelope values lower than the first average value in the segment to obtain a second average value, and uses the second average value as a frequency domain smear result of the segment.
Optionally, after obtaining the second average value, the noise floor optimization module 704 is further configured to use the average value of the time domain envelope values of the first two frequency points of the segment as the frequency domain smoothing result of the segment if the difference value between the second average value and the true noise floor value of the segment exceeds a set threshold value, and use the second average value as the frequency domain smoothing result of the segment otherwise.
Optionally, after obtaining the frequency domain screeding result and before lifting the result, the noise floor optimization module 704 is further configured to correct the value of a frequency point to be the average value of two frequency points before and after the frequency point if the frequency point has a mutation.
Optionally, the spectrum acquisition module 702 is specifically configured to acquire a time domain signal of each frequency point of the environmental signal to be detected, correct a burst signal in the time domain signal, perform maximum value edge tracing on the corrected time domain signal to obtain a time domain envelope of each frequency point, and perform time domain-to-frequency domain conversion on each time domain envelope to obtain an envelope spectrum.
Optionally, the spectrum acquisition module 702 is configured to modify the burst signal in the time domain signal specifically includes modifying a value of the burst signal to be an average value of two time domain signals before and after the burst signal.
It should be noted that the embodiments of the apparatus provided in the present application are described in detail in the above method embodiments, and are not described in detail herein.
The embodiment of the application also provides a computer readable storage medium. The computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the background noise identification method of any of the embodiments described above.
Embodiments of the present application may be a system, method, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for causing a processor to implement aspects of the present application. The computer program product may be written 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. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present application are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information for computer readable program instructions, which can execute the computer readable program instructions.
A 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. A computer readable storage medium is a tangible device that can hold and store instructions for use by an instruction execution device. The readable storage medium may include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of a readable storage medium (a non-exhaustive list) include a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical encoding device, punch cards or in-groove protrusion structures such as those having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.
Various aspects of the present application are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable medium having the instructions stored therein includes an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The embodiment of the application also provides electronic equipment. Fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the application. As shown, the electronic device 800 includes one or more processors 801 and a memory 802, with computer executable instructions stored in the memory 802, and the processor 801 executing the computer executable instructions to implement steps in the background noise identification method of any of the embodiments described above.
The processor 801 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 802 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. Volatile memory can 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 a computer readable storage medium and the processor 801 may execute the program instructions to implement the steps in the text recognition method and/or other desired functions of the various embodiments of the present application above.
In one example, electronic device 800 may also include input devices and output devices that are interconnected by a bus system and/or other forms of connection mechanisms (not shown).
In addition, the input device may also include, for example, a keyboard, a mouse, a microphone, and the like. The output means may output various information to the outside, and may include, for example, a display, a speaker, a printer, and a communication network and a remote output device connected thereto, and the like.
Of course, only a part of the components of the electronic device 800 relevant to the present application are shown in fig. 8 for simplicity, and components such as a bus, an input device/output interface, and the like are omitted. In addition, the electronic device 800 may include any other suitable components depending on the particular application.
The background noise recognition method, the background noise recognition device, the computer-readable storage medium and the electronic device provided by the embodiments of the present application belong to the same concept, and the technical features in the technical solutions described in the embodiments may be arbitrarily combined without collision.
It should be understood that the above examples are illustrative and are not intended to encompass all possible implementations encompassed by the claims. Various modifications and changes may be made in the above embodiments without departing from the scope of the disclosure. Likewise, the individual features of the above embodiments can also be combined arbitrarily to form further embodiments of the invention which may not be explicitly described. Therefore, the above examples merely represent several embodiments of the present invention and do not limit the scope of protection of the patent of the present invention.
Claims (9)
1. A method for identifying background noise, comprising the steps of:
Collecting an environmental signal to be detected;
Acquiring an envelope spectrum of the environmental signal to be detected;
distinguishing whether the environmental signal to be detected is in a high frequency band or a low frequency band according to a set frequency threshold;
selecting a corresponding optimization strategy according to the distinguished high and low frequency bands to process the envelope spectrum, so as to obtain final background noise;
The step of processing the envelope spectrum according to the distinguished high and low frequency bands by selecting a corresponding optimization strategy specifically comprises the following steps:
If the frequency band of the environmental signal to be measured is a low frequency band, then segment trowelling the time domain envelope value in the envelope frequency spectrum on the frequency domain to obtain a frequency domain trowelling result, and lifting the result to be used as final background noise, or
If the frequency band of the environmental signal to be detected is a high frequency band, selecting the minimum value of the time domain envelope values of all frequency points in the envelope frequency spectrum as a background noise value of the environmental signal to be detected, and lifting the background noise value to be used as final background noise.
2. The method for identifying background noise according to claim 1, wherein the step of processing the time domain envelope values in the envelope spectrum in a piecewise screeding manner in the frequency domain comprises:
Segmenting on a frequency domain according to a set bandwidth, and averaging all time domain envelope values in each segment to obtain a first average value;
and then, solving an average value of all the time domain envelope values lower than the first average value in the segment to obtain a second average value, and taking the second average value as a frequency domain trowelling result of the segment.
3. The method of identifying background noise according to claim 2, further comprising the step of, after obtaining the second average value:
if the difference value between the second average value and the real background noise value of the segment exceeds a set threshold value, taking the average value of the time domain envelope values of the first two frequency points of the segment as a frequency domain smoothing result of the segment, otherwise taking the second average value as the frequency domain smoothing result of the segment.
4. The method for identifying background noise according to claim 1, further comprising the steps of, after obtaining the result of the frequency domain trowelling, before lifting the result:
if a frequency point has mutation, the value of the frequency point is corrected to be the average value of the two frequency points before and after the frequency point.
5. The method for identifying background noise according to claim 1, wherein the step of obtaining the envelope spectrum of the environmental signal under test specifically comprises:
acquiring time domain signals of all frequency points of an environment signal to be detected, and correcting burst signals in the time domain signals;
carrying out maximum value tracing on the corrected time domain signal to obtain the time domain envelope of each frequency point;
And performing time domain-to-frequency domain transformation on each time domain envelope to obtain an envelope spectrum.
6. The method for identifying background noise according to claim 5, wherein the step of correcting the burst signal in the time domain signal specifically comprises:
and correcting the value of the burst signal to be the average value of the two time domain signals before and after the burst signal.
7. A background noise recognition device, comprising:
the signal acquisition module is configured to acquire an environmental signal to be detected;
A spectrum acquisition module configured to acquire an envelope spectrum of the environmental signal to be measured;
The frequency band distinguishing module is configured to distinguish whether the environment signal to be detected is in a high frequency band or a low frequency band according to a set frequency threshold value;
The background noise optimizing module is configured to process the envelope spectrum according to the distinguished high and low frequency bands by selecting a corresponding optimizing strategy to obtain final background noise;
The background noise optimization module is specifically configured to segment and smear time domain envelope values in the envelope spectrum on a frequency domain to obtain a frequency domain smear result if the frequency band of the environment signal to be measured is a low frequency band, and lift the result as final background noise, or select the minimum value of the time domain envelope values of all frequency points in the envelope spectrum as the background noise value of the environment signal to be measured if the frequency band of the environment signal to be measured is a high frequency band, and lift the background noise value as final background noise.
8. An electronic device comprising a processor and a memory;
the memory is used for storing computer executable instructions;
the processor configured to execute the computer-executable instructions to implement the background noise identification method according to any one of claims 1 to 6.
9. A storage medium having stored thereon a computer program which, when executed by a processor, implements a method of identifying background noise according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210372817.6A CN114935698B (en) | 2022-04-07 | 2022-04-07 | Background noise recognition method, device, electronic device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210372817.6A CN114935698B (en) | 2022-04-07 | 2022-04-07 | Background noise recognition method, device, electronic device and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114935698A CN114935698A (en) | 2022-08-23 |
| CN114935698B true CN114935698B (en) | 2025-03-18 |
Family
ID=82861769
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210372817.6A Active CN114935698B (en) | 2022-04-07 | 2022-04-07 | Background noise recognition method, device, electronic device and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114935698B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116567269A (en) * | 2023-04-27 | 2023-08-08 | 北京博识广联科技有限公司 | Spectrum monitoring data compression method based on signal-to-noise separation |
| CN120956367A (en) * | 2024-01-18 | 2025-11-14 | 福建星海通信科技有限公司 | A method and system for real-time detection of channel quality monitoring and evaluation |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101185120A (en) * | 2005-04-01 | 2008-05-21 | 高通股份有限公司 | Systems, methods, and devices for high-band burst suppression |
| CA2861916A1 (en) * | 2011-12-30 | 2013-07-04 | Huawei Technologies Co., Ltd. | Method, apparatus, and system for processing audio data |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101199003B (en) * | 2005-04-22 | 2012-01-11 | 高通股份有限公司 | Systems, methods, and apparatus for gain factor attenuation |
| US7546237B2 (en) * | 2005-12-23 | 2009-06-09 | Qnx Software Systems (Wavemakers), Inc. | Bandwidth extension of narrowband speech |
| AU2012217162B2 (en) * | 2011-02-14 | 2015-11-26 | Fraunhofer-Gesellschaft Zur Foerderung Der Angewandten Forschung E.V. | Noise generation in audio codecs |
| US20170292977A1 (en) * | 2016-04-08 | 2017-10-12 | Tektronix, Inc. | Linear noise reduction for a test and measurement system |
| CN110471015A (en) * | 2019-09-05 | 2019-11-19 | 国网北京市电力公司 | Method and device for determining sensor detection threshold, storage medium and processor |
| CN114267371A (en) * | 2021-12-30 | 2022-04-01 | 思必驰科技股份有限公司 | Dereverberation method, electronic device, and storage medium |
-
2022
- 2022-04-07 CN CN202210372817.6A patent/CN114935698B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101185120A (en) * | 2005-04-01 | 2008-05-21 | 高通股份有限公司 | Systems, methods, and devices for high-band burst suppression |
| CA2861916A1 (en) * | 2011-12-30 | 2013-07-04 | Huawei Technologies Co., Ltd. | Method, apparatus, and system for processing audio data |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114935698A (en) | 2022-08-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114935698B (en) | Background noise recognition method, device, electronic device and storage medium | |
| CN111200569B (en) | Broadband signal detection and identification method and device | |
| CN106443203A (en) | Pulse signal detection system and method | |
| CN114025379A (en) | A kind of broadband multi-signal detection method, device and equipment | |
| EP3681042B1 (en) | Detection and tracking of interferers in a rf spectrum with multi-lane processing | |
| CN120567041B (en) | A fault identification method and system for photovoltaic power generation system | |
| CN113270107A (en) | Method and device for acquiring noise loudness in audio signal and electronic equipment | |
| CN116758056A (en) | A method for detecting defects in electrical terminal production | |
| CN120074712B (en) | Broadband fast spectrum detection sensing method, device, equipment and storage medium | |
| CN120811326A (en) | Logistic regression-based voltage signal self-adaptive filtering method | |
| CN110535549A (en) | A frequency domain energy detection method for 230MHz frequency band | |
| CN120336957B (en) | Intelligent recognition system and method based on time-frequency feature fusion and self-adaptive classification | |
| CN106877901B (en) | A kind of detection method of low noise than direct sequence signal | |
| CN105281791B (en) | A kind of interference detection method in OFDM wireless communication systems | |
| CN117390373B (en) | A communication transmission equipment commissioning, testing, maintenance and management method and system | |
| CN117715274A (en) | Lamp control method, device, terminal and storage medium in non-line-of-sight scene | |
| EP4113869B1 (en) | Method for determining pilot power, communication device, and storage medium | |
| KR20240042623A (en) | System and method for time-frequency separation of multiple wireless signals | |
| CN121049674B (en) | Self-adaptive frequency selecting method, device, equipment and storage medium for partial discharge narrowband monitoring | |
| CN117310406B (en) | Partial discharge detection method, device and storage medium | |
| CN121071623B (en) | Power supply network ground fault discrimination method and system based on zero sequence electric parameter signals | |
| CN119269981B (en) | A noise reduction method, device and dielectric for partial discharge detection | |
| CN116593772A (en) | Resolution bandwidth adaptive interference signal identification method, system and equipment | |
| CN111781434A (en) | A unified identification method for over-band oscillation of power system | |
| CN117200817A (en) | An anti-interference processing method and system for VHF band burst signals |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |