CN105812068A - Noise suppression method and device based on Gaussian distribution weighting - Google Patents
Noise suppression method and device based on Gaussian distribution weighting Download PDFInfo
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- G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
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Abstract
The invention relates to a noise suppression method and a noise suppression device based on Gaussian distribution weighting. The noise suppression method comprises the steps of determining the local radius of a filter; determining a window length of the filter according to the local radius of the filter, and determining discrete signals in the window of the filter; determining corresponding mean values and variances according to the discrete signals in the window of the filter; determining a Gaussian function of the discrete signals in the window of the filter by using the mean values and the variances; determining the corresponding Gaussian function value of each discrete signal in the window of the filter by using the Gaussian function, and performing summation of the obtained Gaussian function values; determining the corresponding weighted value of each discrete signal in the window of the filter by using the corresponding Gaussian function value of each discrete signal in the window of the filter and the sum of the Gaussian function values; and performing noise suppression treatment on the discrete signals at the center of the window by using the weighted values by the filter.
Description
Technical field
The present invention relates to noise management technique field, particularly to a kind of noise suppressing method based on Gauss distribution weighting and device.
Background technology
Signal filtering technology is the core research topic in signal processing field.Signal filtering method is divided into linear filtering and nonlinear filtering.Digital signal and picture signal process with research in early days, and linear filter technology is the Main Means suppressing noise, and this has suitable mathematical expression form mainly due to linear filtering mode and easily designs and Implements.When linear filter technology is applied to nonadditivity noise signal, its result is often not satisfied.Signal is unavoidably subject to noise jamming in various degree in collection, transmitting procedure, sometimes even produces impact signal.At this time, it may be necessary to signal is carried out suitable process, to eliminate impact and noise contribution thereof.When adopting linear filter technology to be filtered, its effect is often barely satisfactory, can not obtain desirable effect.
Medium filtering is based on a kind of nonlinear signal processing technology that can effectively suppress noise that sequencing statistical is theoretical.For signal, the data in neighborhood are ranked up by it, adopt the intermediate value in neighborhood as currency.The method can effectively suppress impact noise, but causes that signal is smooth not after filtering.
Mean filter is based on signal local statistic information and signal is filtered linear filter, is equivalent to a low pass filter.The establishment of the method algorithm is convenient, performs speed fast, and the method is while realizing signal smoothing suppression noise, it is easy to the details of blurred signal.
Particle filter be by find one group state space propagate random sample probability density function is similar to, with sample average replace integral operation, thus obtain minimum variance distribution process.Particle filter can express the Posterior probability distribution based on observed quantity and controlled quentity controlled variable more accurately.But its subject matter is to need the posterior probability function of substantial amounts of sample size ability well approximation system.Adopting resampling technique additionally, due to algorithm, this can cause sample availability and multifarious loss, causes samples impoverishment phenomenon.
Other conventional wave filter also includes the nonlinear filters such as exponent filtering, shape filtering, weighted filtering.These filtering algorithms, all suitable in different aspects, are respectively arranged with it good and bad.For the noise jamming occurred in signal acquisition process in Hydropower Unit status monitoring field, or even impulsive disturbance, not yet there is good solution, the series of problems such as cause that Hydropower Unit runout protection system fails to come into operation completely all the time.
Summary of the invention
The main purpose of the embodiment of the present invention is in that to propose a kind of noise suppressing method based on Gauss distribution weighting and device, it is achieved containing Gaussian noise or even the suppression impacting ingredient noise, make the signal obtained truer.
For achieving the above object, the invention provides a kind of noise suppressing method based on Gauss distribution weighting, including:
Determine the local radius of wave filter；
Local radius according to described wave filter determines the length of window of wave filter, and determines discrete signal in the window of wave filter；
In window according to described wave filter, discrete signal determines the average and variance that current local radius is corresponding；
Described average and variance is utilized to determine the Gaussian function of discrete signal in the window of wave filter；
Described Gaussian function is utilized to determine the Gaussian function numerical value that in the window of wave filter, each discrete signal is corresponding, and to the Gaussian function numerical value summation obtained；
Utilize the weighted value corresponding with each discrete signal in the window determining wave filter of Gaussian function numerical value that each discrete signal in the window of wave filter is corresponding and Gaussian function numerical value；
The discrete signal of window center is carried out noise suppressed process by described wave filter exploitation right weight values.
Preferably, the step of the described local radius determining wave filter includes:
When data index value i is less than optimum local radius, then the local radius of wave filter is equal to data index value i；
When data index value i is be more than or equal to optimum local radius, and when data index value i deducts optimum local radius less than the number of discrete signal, then, the local radius of wave filter is equal to optimum local radius；
Otherwise, the local radius of wave filter deducts current data index value equal to the number of discrete signal and subtracts 1 again.
Preferably, the length of window of described wave filter adds 1 again equal to the local radius of the described wave filter of twice.
Preferably, the step that the discrete signal of window center is carried out noise suppressed process by described wave filter exploitation right weight values includes:
Discrete signal in window is carried out dot-product operation with corresponding weighted value by described wave filter, and this operation result is that the wave filter noise suppressed result to currency exports.
Preferably, described optimum local radius obtaining step includes:
Emulation signal is added noise；
Emulation signal after adding noise is carried out noise suppressed process, it is thus achieved that noise cancellation signal；
Obtain the mean square error between emulation signal and the noise cancellation signal corresponding to current radius；
Mean square error between mean square error between emulation signal and noise cancellation signal corresponding to current radius and emulation signal and the noise cancellation signal corresponding to upper Radius is compared；If the mean square error between emulation signal and noise cancellation signal corresponding to current radius is be more than or equal to the mean square error between emulation signal and the noise cancellation signal corresponding to upper Radius, then current radius is optimum local radius；Otherwise, lower Radius is as current radius, current radius, as upper Radius, compares the mean square error between the mean square error between emulation signal and the noise cancellation signal corresponding to current radius and emulation signal and the noise cancellation signal corresponding to upper Radius, until obtaining optimum local radius.
Accordingly, for achieving the above object, present invention also offers a kind of Noise Suppression Device based on Gauss distribution weighting, including:
Local radius determines unit, for determining the local radius of wave filter；
In window, discrete signal determines unit, for determining the length of window of wave filter according to the local radius of described wave filter, and determines discrete signal in the window of wave filter；
Average and variance determine unit, for determining, according to discrete signal in the window of described wave filter, the average and variance that current local radius is corresponding；
Gaussian function determines unit, for utilizing described average and variance to determine the Gaussian function of discrete signal in the window of wave filter；
Sum unit, for utilizing described Gaussian function to determine the Gaussian function numerical value that in the window of wave filter, each discrete signal is corresponding, and to the Gaussian function numerical value summation obtained；
Weighted value unit, for utilizing the weighted value corresponding with each discrete signal in the window determining wave filter of Gaussian function numerical value that in the window of wave filter, each discrete signal is corresponding and Gaussian function numerical value；
Noise suppression unit, carries out noise suppressed process for described wave filter exploitation right weight values to the discrete signal of window center.
Preferably, described local radius determine unit specifically for:
When data index value i is less than optimum local radius, then the local radius of wave filter is equal to data index value i；
When data index value i is be more than or equal to optimum local radius, and when data index value i deducts optimum local radius less than the number of discrete signal, then, the local radius of wave filter is equal to optimum local radius；
Otherwise, the local radius of wave filter deducts current data index value equal to the number of discrete signal and subtracts 1 again.
Preferably, in described window, discrete signal determines that the length of window of the wave filter that unit obtains adds 1 again equal to the local radius of the described wave filter of twice.
Preferably, the discrete signal in window is carried out dot-product operation with corresponding weighted value specifically for described wave filter by described noise suppression unit, and this operation result is that the wave filter noise suppressed result to currency exports.
Preferably, described local radius determines that unit includes:
Simulator and noise signaling module, for adding noise to emulation signal；
Noise elimination module, for carrying out noise suppressed process to the emulation signal after adding noise, it is thus achieved that noise cancellation signal；
Mean square error module, for obtaining the mean square error between emulation signal and the noise cancellation signal corresponding to current radius；
Optimum local radius determines module, compares for the mean square error between the mean square error between emulation signal and the noise cancellation signal corresponding to current radius and emulation signal and the noise cancellation signal corresponding to upper Radius；If the mean square error between emulation signal and noise cancellation signal corresponding to current radius is be more than or equal to the mean square error between emulation signal and the noise cancellation signal corresponding to upper Radius, then current radius is optimum local radius；Otherwise, lower Radius is as current radius, current radius, as upper Radius, compares the mean square error between the mean square error between emulation signal and the noise cancellation signal corresponding to current radius and emulation signal and the noise cancellation signal corresponding to upper Radius, until obtaining optimum local radius.
Technique scheme has the advantages that
1, adaptive polo placement weights strategy proposed by the invention, enriches weighting filter method for designing；
2, for containing normal distribution noise and impact noise signal, the more conventional mean filter of this filter filtering effect and fixed weighting coefficient filter are effective；
3, the relatively complicated approach such as shape filtering, particle filter, inventive algorithm is simple, it is achieved easily.
Accompanying drawing explanation
In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, the accompanying drawing used required in embodiment or description of the prior art will be briefly described below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skill in the art, under the premise not paying creative work, it is also possible to obtain other accompanying drawing according to these accompanying drawings.
The method flow schematic diagram that Fig. 1 provides for the embodiment of the present invention；
The device schematic diagram that Fig. 2 provides for the embodiment of the present invention；
The system schematic that Fig. 3 provides for the embodiment of the present invention；
Fig. 4 is the graph of relation of local radius and mean square error；
Fig. 5 is the muting signal waveforms of the present embodiment；
Fig. 6 is the signal waveforms of the Noise of the present embodiment；
Fig. 7 is the signal waveforms after the de-noising of the present embodiment.
Detailed description of the invention
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is only a part of embodiment of the present invention, rather than whole embodiments.Based on the embodiment in the present invention, the every other embodiment that those of ordinary skill in the art obtain under not making creative work premise, broadly fall into the scope of protection of the invention.
One skilled in the art will appreciate that embodiments of the present invention can be implemented as a kind of system, device, equipment, method or computer program.Therefore, the disclosure can be implemented as following form, it may be assumed that the form that hardware, completely software (including firmware, resident software, microcode etc.), or hardware and software completely combines.
According to the embodiment of the present invention, it is proposed that a kind of noise suppressing method based on Gauss distribution weighting and device.
In this article, it is to be understood that in involved term:
Gaussian function: Gaussian function is a kind of widely used function in mathematical statistics.Its definition is as follows, if it is μ that stochastic variable X obeys a mathematic expectaion, standard variance is σ2Gauss distribution, be designated as: X～N (μ, σ2), then its probability density function is:
Additionally, any number of elements in accompanying drawing is all unrestricted for example, and any name is only used for distinguishing, and does not have any limitation.
Below with reference to some representative embodiments of the present invention, explaination principles of the invention and spirit in detail.
Summary of the invention
The technical program relates to a kind of equipment, method and apparatus, and the technical program supposes the discrete signal Normal Distribution in present filter window, the average of the discrete signal in window and variance, it is determined that Gaussian function expression formula；Utilize this Gaussian function expression formula to determine the Gaussian function numerical value that in window, discrete signal is corresponding, and Gaussian function numerical value is sued for peace.By each Gaussian function numerical value corresponding for discrete signal in the window sum divided by Gaussian function numerical value, so that it is determined that the weighted value that in window, discrete signal is corresponding.With corresponding weighted value, discrete signal in window is made point multiplication operation, and its result is the noise cancellation signal of wave filter output.This technical scheme has the effect realizing suppressing while Gaussian noise and impact noise concurrently.
After the ultimate principle describing the present invention, introduce the various non-limiting embodiment of the present invention in detail below.
Application scenarios overview
In hydroelectric power plant when monitoring rotating machinery shaft displacement, make the noise containing similar impact composition in signal owing to artificially causing axle surface to contain projection or depression when manufacturing, install or debug；Arranging big quantity sensor in condition monitoring system, sensor is installed under strong magnetic environment wherein a lot of to be used for monitoring Generator Vibration, throw, office put etc., as easy as rolling off a log is interfered so that signal produces distortion.It addition, the packet of sampled signal is when transmitting by network, cause being distorted the abnormal impact of generation in the signal etc. in continuous sampling signal due to electromagnetic interference.Use the technical program, it is possible to the effective information of sensor signal is reduced, and suppress the noise of the interference signal under strong magnetic environment, the electromagnetic interference signal in network transmission and similar impact composition.The vibration of electromotor in accurate monitoring system, throw, office put.
Illustrative methods
Below in conjunction with application scenarios, with reference to Fig. 1, the method for exemplary embodiment of the invention is introduced.
It should be noted that above-mentioned application scenarios is for only for ease of the spirit and principle of understanding the present invention and illustrates, embodiments of the present invention are unrestricted in this regard.On the contrary, embodiments of the present invention can apply to any scene of being suitable for.
Referring to Fig. 1, for the method flow schematic diagram that the embodiment of the present invention provides.As it can be seen, the step of noise suppressing method includes:
Step 101): determine the local radius of wave filter；
In a step 101, the local radius of wave filter has three kinds of situations.The discrete signal x (0) being N with length ... x (N-1) is example.It is respectively as follows: when data index value i is less than optimum local radius r0Time, then the local radius r of wave filter is equal to data index value i；
When data index value i is be more than or equal to optimum local radius r0, and data index value i deducts optimum local radius r less than the number N of discrete signal0Time, then the local radius r of wave filter is equal to optimum local radius r0；
Otherwise, the local radius r of wave filter deducts current data index value i equal to the number N of discrete signal and subtracts 1 again.
Optimum local radius r0Determine that step includes:
Emulation signal is added noise；
Emulation signal after adding noise is carried out noise suppressed process, it is thus achieved that noise cancellation signal；
Obtain the mean square error between emulation signal and the noise cancellation signal corresponding to current radius；
Mean square error between mean square error between emulation signal and noise cancellation signal corresponding to current radius and emulation signal and the noise cancellation signal corresponding to upper Radius is compared；If the mean square error between emulation signal and noise cancellation signal corresponding to current radius is be more than or equal to the mean square error between emulation signal and the noise cancellation signal corresponding to upper Radius, then current radius is optimum local radius；Otherwise, lower Radius is as current radius, current radius, as upper Radius, compares the mean square error between the mean square error between emulation signal and the noise cancellation signal corresponding to current radius and emulation signal and the noise cancellation signal corresponding to upper Radius, until obtaining optimum local radius.
Step 102): determine the length of window of wave filter according to the local radius of described wave filter, and determine discrete signal in the window of wave filter；
In a step 102, the length of window of described wave filter adds 1 again equal to the local radius r of the described wave filter of twice.
Step 103): determine, according to stochastic signal in the window of described wave filter, the average and variance that current local radius is corresponding；
Step 104): utilize described average and variance to determine the Gaussian function of discrete signal in the window of wave filter；
Step 105): utilize described Gaussian function to determine the Gaussian function numerical value that in the window of wave filter, each discrete signal is corresponding, and to the Gaussian function numerical value summation obtained；
Step 106): utilize the weighted value corresponding with each discrete signal in the window determining wave filter of Gaussian function numerical value that each discrete signal in the window of wave filter is corresponding and Gaussian function numerical value；
Step 107): the discrete signal of window center is carried out noise suppressed process by described wave filter exploitation right weight values.
In step 107, the discrete signal in window is carried out dot-product operation with corresponding weighted value by described wave filter, and this operation result is that the wave filter noise suppressed result to currency exports.
Although it should be noted that, describe the operation of the inventive method in the accompanying drawings with particular order, but, this does not require that or implies and must operate to perform these according to this particular order, or having to carry out all shown operation could realize desired result.Additionally or alternatively, it is convenient to omit some step, multiple steps are merged into a step and performs, and/or a step is decomposed into the execution of multiple step.
Exemplary means
After the method describing exemplary embodiment of the invention, it follows that respectively the device of exemplary embodiment of the invention is introduced with reference to Fig. 2.
As in figure 2 it is shown, the device block diagram provided for the embodiment of the present invention.Noise Suppression Device includes:
Local radius determines unit 201, for determining the local radius of wave filter；
Described local radius determine unit 201 specifically for:
When data index value i is less than optimum local radius, then the local radius of wave filter is equal to data index value i；
When data index value i is be more than or equal to optimum local radius, and when data index value i deducts optimum local radius less than the number of discrete signal, then the local radius of wave filter is equal to optimum local radius；
Otherwise, the local radius of wave filter deducts current data index value equal to the number of discrete signal and subtracts 1 again.
Further, for the optimum local radius that foregoing is directed to, described local radius determines that unit includes:
Simulator and noise signaling module, for adding noise to emulation signal；
Noise elimination module, for carrying out noise suppressed process to the emulation signal after adding noise, it is thus achieved that noise cancellation signal；
Mean square error module, for obtaining the mean square error between emulation signal and the noise cancellation signal corresponding to current radius；
Optimum local radius determines module, compares for the mean square error between the mean square error between emulation signal and the noise cancellation signal corresponding to current radius and emulation signal and the noise cancellation signal corresponding to upper Radius；If the mean square error between emulation signal and noise cancellation signal corresponding to current radius is be more than or equal to the mean square error between emulation signal and the noise cancellation signal corresponding to upper Radius, then current radius is optimum local radius；Otherwise, lower Radius is as current radius, current radius, as upper Radius, compares the mean square error between the mean square error between emulation signal and the noise cancellation signal corresponding to current radius and emulation signal and the noise cancellation signal corresponding to upper Radius, until obtaining optimum local radius.
In window, discrete signal determines unit 202, for determining the length of window of wave filter according to the local radius of described wave filter, and determines discrete signal in the window of wave filter；
In described window, discrete signal determines that the length of window of the wave filter that unit 202 obtains adds 1 again equal to the local radius of the described wave filter of twice.
Average and variance determine unit 203, for determining, according to discrete signal in the window of described wave filter, the average and variance that current local radius is corresponding；
Gaussian function determines unit 204, for utilizing described average and variance to determine the Gaussian function of discrete signal in the window of wave filter；
Sum unit 205, for utilizing described Gaussian function to determine the Gaussian function numerical value that in the window of wave filter, each discrete signal is corresponding, and to the Gaussian function numerical value summation obtained；
Weighted value unit 206, for utilizing the weighted value corresponding with each discrete signal in the window determining wave filter of Gaussian function numerical value that in the window of wave filter, each discrete signal is corresponding and Gaussian function numerical value；
Noise suppression unit 207, carries out noise suppressed process for described wave filter exploitation right weight values to the discrete signal of window center.
Further, the discrete signal in window is carried out dot-product operation with corresponding weighted value specifically for described wave filter by described noise suppression unit 207, and this operation result is the result that the discrete signal noise suppressed in window is processed by wave filter.
Although additionally, be referred to some unit of device in above-detailed, but this division is only not enforceable.It practice, according to the embodiment of the present invention, the feature of two or more unit above-described and function can embody in a unit.Equally, the feature of an above-described unit and function can also Further Division for be embodied by multiple unit.
Example devices
Based on above-mentioned exemplary means and method, the present embodiment also proposes a kind of equipment, as shown in Figure 3.This system is used for noise suppressed；Including:
Memorizer a, is used for storing request instruction；
Processor b, it couples with described memorizer, and this processor is configured to perform storage request instruction in which memory, wherein, the application program that described processor is configured for:
Determine the local radius of wave filter；
Local radius according to described wave filter determines the length of window of wave filter, and determines discrete signal in the window of wave filter；
In window according to described wave filter, discrete signal determines the average and variance that current local radius is corresponding；
Described average and variance is utilized to determine the Gaussian function of discrete signal in the window of wave filter；
Described Gaussian function is utilized to determine the Gaussian function numerical value that in the window of wave filter, each discrete signal is corresponding, and to the Gaussian function numerical value summation obtained；
Utilize the weighted value corresponding with each discrete signal in the window determining wave filter of Gaussian function numerical value that each discrete signal in the window of wave filter is corresponding and Gaussian function numerical value；
The discrete signal of window center is carried out noise suppressed process by described wave filter exploitation right weight values.
The embodiment of the present invention also provides for a kind of computer-readable program, and wherein when performing described program in the electronic device, described program makes the method that computer performs the noise suppressed based on Gauss distribution weighting as described in Figure 1 in described electronic equipment.
The embodiment of the present invention also provides for a kind of storage has the storage medium of computer-readable program, wherein said computer-readable program to make the method that computer performs the noise suppressed based on Gauss distribution weighting as described in Figure 1 in the electronic device.
Embodiment
In order to describe feature and the operation principle of the present invention more intuitively, describe below in conjunction with a practice scene.
As shown in Figure 4, for the graph of relation of local radius Yu mean square error.In the present embodiment, emulation signal is selected to have the local radius of least mean-square error as optimum local radius.In the present embodiment, optimum local radius is 5.
As it is shown in figure 5, be the muting signal waveforms of the present embodiment.As shown in Figure 6, for the signal waveforms of the Noise of the present embodiment.As it is shown in fig. 7, be the signal waveforms after the de-noising of the present embodiment.In Figure 5, the signal that name is " bumps " of not Noise.Through operation, adding noise and the impact noise of normal distribution, comparison diagram 5 and Fig. 6 discovery at bumps signal, there is change in the waveform of signal.With optimum local radius for 5 for prerequisite, adopt the technical program that the signal of Fig. 6 is carried out noise suppressed process, it is thus achieved that the signal waveform shown in Fig. 7.Comparison diagram 5 and Fig. 7 are it is found that the signal waveform in two figure is basically identical, it can be seen that noise and impact noise to the normal distribution added are effectively suppressed, and with reality closely, noise suppression effect is fine for the oscillogram after noise suppressed.
Above detailed description of the invention; the purpose of the present invention, technical scheme and beneficial effect have been further described; it is it should be understood that; these are only the specific embodiment of the present invention; the protection domain being not intended to limit the present invention; all within the spirit and principles in the present invention, any amendment of making, equivalent replacement, improvement etc., should be included within protection scope of the present invention.
Claims (10)
1. the noise suppressing method based on Gauss distribution weighting, it is characterised in that including:
Determine the local radius of wave filter；
Local radius according to described wave filter determines the length of window of wave filter, and determines discrete signal in the window of wave filter；
In window according to described wave filter, discrete signal determines the average and variance that current local radius is corresponding；
Described average and variance is utilized to determine the Gaussian function of discrete signal in the window of wave filter；
Described Gaussian function is utilized to determine the Gaussian function numerical value that in the window of wave filter, each discrete signal is corresponding, and to the Gaussian function numerical value summation obtained；
Utilize the weighted value corresponding with each discrete signal in the window determining wave filter of Gaussian function numerical value that each discrete signal in the window of wave filter is corresponding and Gaussian function numerical value；
The discrete signal of window center is carried out noise suppressed process by described wave filter exploitation right weight values.
2. the method for claim 1, it is characterised in that the step of the described local radius determining wave filter includes:
When data index value i is less than optimum local radius, then the local radius of wave filter is equal to data index value i；
When data index value i is be more than or equal to optimum local radius, and when data index value i deducts optimum local radius less than the number of discrete signal, then, the local radius of wave filter is equal to optimum local radius；
Otherwise, the local radius of wave filter deducts current data index value equal to the number of discrete signal and subtracts 1 again.
3. method as claimed in claim 1 or 2, it is characterised in that the length of window of described wave filter adds 1 again equal to the local radius of the described wave filter of twice.
4. method as claimed in claim 1 or 2, it is characterised in that the step that the discrete signal of window center is carried out noise suppressed process by described wave filter exploitation right weight values includes:
Discrete signal in window is carried out dot-product operation with corresponding weighted value by described wave filter, and this operation result is that the wave filter noise suppressed result to currency exports.
5. method as claimed in claim 2, it is characterised in that described optimum local radius obtaining step includes:
Emulation signal is added noise；
Emulation signal after adding noise is carried out noise suppressed process, it is thus achieved that noise cancellation signal；
Obtain the mean square error between emulation signal and the noise cancellation signal corresponding to current radius；
Mean square error between mean square error between emulation signal and noise cancellation signal corresponding to current radius and emulation signal and the noise cancellation signal corresponding to upper Radius is compared；If the mean square error between emulation signal and noise cancellation signal corresponding to current radius is be more than or equal to the mean square error between emulation signal and the noise cancellation signal corresponding to upper Radius, then current radius is optimum local radius；Otherwise, lower Radius is as current radius, current radius, as upper Radius, compares the mean square error between the mean square error between emulation signal and the noise cancellation signal corresponding to current radius and emulation signal and the noise cancellation signal corresponding to upper Radius, until obtaining optimum local radius.
6. the Noise Suppression Device based on Gauss distribution weighting, it is characterised in that including:
Local radius determines unit, for determining the local radius of wave filter；
In window, discrete signal determines unit, for determining the length of window of wave filter according to the local radius of described wave filter, and determines discrete signal in the window of wave filter；
Average and variance determine unit, for determining, according to discrete signal in the window of described wave filter, the average and variance that current local radius is corresponding；
Gaussian function determines unit, for utilizing described average and variance to determine the Gaussian function of discrete signal in the window of wave filter；
Sum unit, for utilizing described Gaussian function to determine the Gaussian function numerical value that in the window of wave filter, each discrete signal is corresponding, and to the Gaussian function numerical value summation obtained；
Weighted value unit, for utilizing the weighted value corresponding with each discrete signal in the window determining wave filter of Gaussian function numerical value that in the window of wave filter, each discrete signal is corresponding and Gaussian function numerical value；
Noise suppression unit, carries out noise suppressed process for described wave filter exploitation right weight values to the discrete signal of window center.
7. device as claimed in claim 6, it is characterised in that described local radius determine unit specifically for:
When data index value i is less than optimum local radius, then the local radius of wave filter is equal to data index value i；
When data index value i is be more than or equal to optimum local radius, and when data index value i deducts optimum local radius less than the number of discrete signal, then, the local radius of wave filter is equal to optimum local radius；
Otherwise, the local radius of wave filter deducts current data index value equal to the number of discrete signal and subtracts 1 again.
8. device as claimed in claims 6 or 7, it is characterised in that in described window, discrete signal determines that the length of window of the wave filter that unit obtains adds 1 again equal to the local radius of the described wave filter of twice.
9. device as claimed in claims 6 or 7, it is characterized in that, discrete signal in window is carried out dot-product operation with corresponding weighted value specifically for described wave filter by described noise suppression unit, and this operation result is that the wave filter noise suppressed result to currency exports.
10. device as claimed in claim 7, it is characterised in that described local radius determines that unit includes:
Simulator and noise signaling module, for adding noise to emulation signal；
Noise elimination module, for carrying out noise suppressed process to the emulation signal after adding noise, it is thus achieved that noise cancellation signal；
Mean square error module, for obtaining the mean square error between emulation signal and the noise cancellation signal corresponding to current radius；
Optimum local radius determines module, compares for the mean square error between the mean square error between emulation signal and the noise cancellation signal corresponding to current radius and emulation signal and the noise cancellation signal corresponding to upper Radius；If the mean square error between emulation signal and noise cancellation signal corresponding to current radius is be more than or equal to the mean square error between emulation signal and the noise cancellation signal corresponding to upper Radius, then current radius is optimum local radius；Otherwise, lower Radius is as current radius, current radius, as upper Radius, compares the mean square error between the mean square error between emulation signal and the noise cancellation signal corresponding to current radius and emulation signal and the noise cancellation signal corresponding to upper Radius, until obtaining optimum local radius.
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