Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, completeSite preparation description, it is clear that described embodiments are some of the embodiments of the present invention, instead of all the embodiments.Based on this hairEmbodiment in bright, every other implementation obtained by those of ordinary skill in the art without making creative effortsExample, shall fall within the protection scope of the present invention.
It should be appreciated that ought use in this specification and in the appended claims, term " includes " and "comprising" instructionDescribed feature, entirety, step, operation, the presence of element and/or component, but one or more of the other feature, whole is not precludedBody, step, operation, the presence or addition of element, component and/or its set.
It is also understood that mesh of the term used in this description of the invention merely for the sake of description specific embodimentAnd be not intended to limit the present invention.As description of the invention and it is used in the attached claims, unless onOther situations are hereafter clearly indicated, otherwise " one " of singular, "one" and "the" are intended to include plural form.
It will be further appreciated that the term "and/or" used in description of the invention and the appended claims isRefer to any combination and all possible combinations of one or more of associated item listed, and including these combinations.
Referring to Fig. 1, it is the speaker provided in an embodiment of the present invention based on Recognition with Recurrent Neural Network and acoustic feature pointFlow diagram from method should be applied to server based on speaker's separation method of Recognition with Recurrent Neural Network and acoustic featureIn, this method is executed by the application software being installed in server.
As shown in Figure 1, the method comprising the steps of S110~S170.
S110, voice data to be identified transmitted by upload end is received.
In the present embodiment, when using upload end sound-recording function or video record function collected voice number to be identifiedAccording to when, in order to realize to the speaker of voice data to be identified separation, voice data to be identified need to be sent to clothes by uploading endBusiness device, carries out data processing to voice data to be identified by server and obtains speaker clustering result.
S120, the term vector set that the voice data to be identified is obtained by speech recognition, and obtained by speech recognitionThe MFCC feature vector set for taking the voice data to be identified carries out the term vector set and MFCC feature vector setFull connection, with feature vector after being merged.
In the present embodiment, it in order to carry out speaker's separation to voice data to be identified, needs first to extract voice to be identifiedThe corresponding term vector set of data and MFCC feature vector set.
Wherein, term vector set is obtained from the corresponding text data of the voice data to be identified.In one embodiment,As shown in Fig. 2, step S120 includes:
S121, obtained by speech recognition independent heat corresponding with respectively being segmented in the voice data to be identified encode word toAmount；
S122, will each independence corresponding with voice to be identified by the Word2Vec model for converting word to vectorHeat coding term vector is converted, and term vector set corresponding with voice data to be identified is obtained.
It i.e. first will independent heat coding term vector corresponding with respectively being segmented in the voice data to be identified with a linear layerThe word embeding layer being transformed into encoder, will be corresponding with voice to be identified each only by the Word2Vec model in word embeding layerVertical heat coding term vector is converted, and term vector set corresponding with voice data to be identified is obtained.
Wherein, MFCC feature vector set is directly acquired from the voice data to be identified.In one embodiment, stepS120 includes:
Feature extraction is carried out from the voice to be identified according to preset characteristic window, is obtained and the voice pair to be identifiedThe MFCC feature vector answered；Wherein, the length of window of the characteristic window is preset first time value, the characteristic windowMoving distance is preset second time value.
In the present embodiment, (MFCC's is full when carrying out feature extraction MFCC feature vector set from the voice to be identifiedTitle is Mel-scaleFrequency Cepstral Coefficients, indicates mel cepstrum coefficients), the spy can be presetThe length of window for levying window is 25ms, and the moving distance for presetting the characteristic window is 10ms.From length of window 25ms, it is mobile away fromThe MFCC feature that 13 dimensions are extracted in window from 10ms, is then averaged to word section, each word obtains the one of a 13*1Dimensional vector, to form MFCC feature vector set.By extracting MFCC feature vector corresponding with the voice to be identified, haveOne group of feature vector for obtaining voice physical information (spectrum envelope and details) progress encoding operation of effect.
In one embodiment, the term vector set and the MFCC feature vector set are input to phaseWith being connected entirely in the GRU model of number hidden layer, with feature vector after being merged.
For MFCC feature vector, using the hidden layer of 256 hiding layer units, the term vector of 256 sizes and 256 is greatlySmall output layer.And it enables the hidden layer number of plies of MFCC feature consistent with the hidden layer number of plies of word embeding layer, could export in this wayPreferably performance.
S130, it feature vector after the fusion is input in encoder encodes, obtain coding result.
In the present embodiment, it is encoded since feature vector after the fusion to be input in encoder, it is known that codingMFCC feature vector and term vector are merged in device, MFCC feature vector and term vector are carried out by linear layers some in encoderFull connection, feature vector after being merged.Then feature vector after fusion is input to the GRU model used in encoder (i.e.Gating cycle unit), coding result can be obtained.
In one embodiment, step S130 includes:
Feature vector after the fusion is input in encoder and carries out nonlinear change conversion to obtain intermediate semanteme；
The Automobile driving probability distribution respectively segmented in the intermediate semanteme is obtained by attention mechanism, to obtain and instituteState intermediate semantic corresponding coding result.
In the present embodiment, attention mechanism is usually used in coder-decoder frame.Encoder is exactly as its name suggests to defeatedEnter sentence Source (such as sentence in the corresponding text of voice data to be identified) to be encoded, input sentence Source is led toIt crosses nonlinear transformation and is converted into intermediate semantic expressiveness C, wherein C=F (x1, x2... ..., xm)；For decoder, taskIt is according to the intermediate semantic expressiveness C for inputting sentence Source and the historical information y generated before1, y2... ... yi-1To generateThe i moment word y to be generatedi=(Ci, y1, y2... ... yi-1), CiAutomobile driving for participle i in the intermediate semanteme is generalRate distribution.Even in the encoder and the decoder between introduce attention model, the above process such as y1=f (C1), y2=f (C2,Y1), y3=f (C3, y1, y2) namely each Ci may correspond to the Automobile driving probability point of different source statement sub-wordsCloth, this attention mechanism for allowing for model can help to capture the most important part of the feature of speaker.
Attention mechanism in Sequence Learning task have huge castering action, in codec framework, byA model is added in coding section, data weighting transformation is carried out to source data sequence, or introduce A model in decoding end, to target dataIt is weighted variation, sequence is can effectively improve and the system under the natural way of sequence is showed.
S140, it is decoded, obtains and feature vector after described merge using the coding result as the input of decoderCorresponding segmentation result；Wherein, the segmentation result includes word sequence and voice conversion symbol.
In the present embodiment, decoder output is the segmentation result for including word sequence and voice conversion symbol.For example, toIdentify the corresponding text of voice data are as follows: hello hi my name is James hi James.The segmentation of decoder outputAs a result are as follows: hello#A hi#B my name is James#A hi James.
Decoder loss function calculate when, have ignored the ID of speaker, and be only concerned speaker be grouped situation.ThanSuch as, voice conversion symbol sequence ABA is considered as being equivalent to BAB.Because the original of conversion symbol sequence can be calculated in calculating processVersion and flipped version, and select loss reduction that as penalty values.This loss function can also be to avoid study instructionPractice the probability concentrated in target sequence between conversion symbol and word.
S150, it will be distributed apart from nearest voice conversion symbol to corresponding word in the segmentation result away from each word, withThe prediction that voice conversion is carried out to the segmentation result obtains voice conversion and accords with corresponding Speaker Identification result.
In the present embodiment, in order to realize the accuracy for maximizing voice conversion prediction, a movement-overlapping is usedDesign.For example, using the window inswept entire part from the beginning to the end of a 32 word length.For each window, with trainingGood sequence carrys out predictive conversion symbol sequence to series model.
In one embodiment, as shown in figure 3, step S150 includes:
Segmentation result in S151, acquisition decoder；
S152, the word that marker in the segmentation result is directed toward first place is obtained, to originate word as current；
S153, it will be distributed into the segmentation result often away from each word apart from nearest voice conversion symbol in segmentation resultA word, to establish converting vector；
S154, the converting vector is stored into voice conversion sequence matrix；
S155, marker direction position is moved right into a word to update current starting word；
Whether S156, the current starting word of judgement are the word that last bit is in segmentation result, if currently starting word is not segmentationAs a result it is in the word of last bit in, returns to step S153, if currently starting word is that the word in segmentation result in last bit executesStep S157；
The process of the prediction of S157, end voice conversion.
I.e. in prediction, extracted from text and audio file respectively 32 words term vector and 32 dimensions MFCC feature toAmount.The prediction of specific speaker's conversion sequence is carried out by Fig. 4 and following steps:
51) segmentation result is obtained from decoder；
52) conversion is established in such a way that conversion symbol nearest away from each word in segmentation result is distributed to each wordVector；
53) in the voice conversion sequence matrix for accumulating the converting vector there are one；
54) move right a word distance and by next group of 32 term vectors and 32 tie up MFCC feature vector inputInto encoder；
After window is moved to end, determined by way of majority voting belonging to voice conversion symbol.In this way, it is equivalent to 32 kinds of different predictions and determines.
S160, the Speaker Identification result is passed through into cluster, obtains speaker clustering result.
In the present embodiment, using the cluster mode for being based on bayesian information criterion (BIC), and the feature usedIt is the other MFCC feature of frame level.In more speaker's speech Separations, the Speaker Identification result is clustered, is exactly oneVoice flow is clustered into the voice flow of multiple speakers by the cluster process of voice flow.
S170, the speaker clustering result is sent to the corresponding upload end of the voice data to be identified.
In the present embodiment, after completing the identification of speaker clustering result, the speaker clustering result will be sent outUpload end corresponding to the voice data to be identified is sent, voice data to be identified is completed and carries out speaker point on server lineFrom.
The method achieve the sequences for having merged vocabulary and acoustics characteristic information to go to carry out dividing for speaker to series modelFrom can capture including the encoded information before and after voice conversion point.
The embodiment of the present invention also provides a kind of speaker's separator based on Recognition with Recurrent Neural Network and acoustic feature, the baseIt is aforementioned special based on Recognition with Recurrent Neural Network and acoustics for executing in speaker's separator of Recognition with Recurrent Neural Network and acoustic featureAny embodiment of speaker's separation method of sign.Specifically, referring to Fig. 5, Fig. 5 is provided in an embodiment of the present invention based on followingThe schematic block diagram of speaker's separator of ring neural network and acoustic feature.It should be based on Recognition with Recurrent Neural Network and acoustic featureSpeaker's separator 100 can be configured in server.
As shown in figure 5, speaker's separator 100 based on Recognition with Recurrent Neural Network and acoustic feature includes phonetic incepting listFirst 110, Fusion Features unit 120, coding unit 130, decoding unit 140, speaker's predicting unit 150, speaker clustering listFirst 160, result transmission unit 170.
Voice receiving unit 110 uploads voice data to be identified transmitted by end for receiving.
In the present embodiment, when using upload end sound-recording function or video record function collected voice number to be identifiedAccording to when, in order to realize to the speaker of voice data to be identified separation, voice data to be identified need to be sent to clothes by uploading endBusiness device, carries out data processing to voice data to be identified by server and obtains speaker clustering result.
Fusion Features unit 120, for obtaining the term vector set of the voice data to be identified by speech recognition, andThe MFCC feature vector set that the voice data to be identified is obtained by speech recognition, the term vector set and MFCC is specialSign vector set is connected entirely, with feature vector after being merged.
In the present embodiment, it in order to carry out speaker's separation to voice data to be identified, needs first to extract voice to be identifiedThe corresponding term vector set of data and MFCC feature vector set.
Wherein, term vector set is obtained from the corresponding text data of the voice data to be identified.In one embodiment,As shown in fig. 6, Fusion Features unit 120 includes:
Independent heat coding term vector acquiring unit 121, for being obtained and the voice data to be identified by speech recognitionIn respectively segment corresponding independent heat coding term vector；
Term vector set acquiring unit 122, for by the Word2Vec model for converting word to vector will with toThe corresponding each independent heat coding term vector of identification voice is converted, and term vector collection corresponding with voice data to be identified is obtainedIt closes.
It i.e. first will independent heat coding term vector corresponding with respectively being segmented in the voice data to be identified with a linear layerThe word embeding layer being transformed into encoder, will be corresponding with voice to be identified each only by the Word2Vec model in word embeding layerVertical heat coding term vector is converted, and term vector set corresponding with voice data to be identified is obtained.
Wherein, MFCC feature vector set is directly acquired from the voice data to be identified.In one embodiment, featureIntegrated unit 120 is also used to:
Feature extraction is carried out from the voice to be identified according to preset characteristic window, is obtained and the voice pair to be identifiedThe MFCC feature vector answered；Wherein, the length of window of the characteristic window is preset first time value, the characteristic windowMoving distance is preset second time value.
In the present embodiment, (MFCC's is full when carrying out feature extraction MFCC feature vector set from the voice to be identifiedTitle is Mel-scaleFrequency Cepstral Coefficients, indicates mel cepstrum coefficients), the spy can be presetThe length of window for levying window is 25ms, and the moving distance for presetting the characteristic window is 10ms.From length of window 25ms, it is mobile away fromThe MFCC feature that 13 dimensions are extracted in window from 10ms, is then averaged to word section, each word obtains the one of a 13*1Dimensional vector, to form MFCC feature vector set.By extracting MFCC feature vector corresponding with the voice to be identified, haveOne group of feature vector for obtaining voice physical information (spectrum envelope and details) progress encoding operation of effect.
In one embodiment, the term vector set and the MFCC feature vector set are input to phaseWith being connected entirely in the GRU model of number hidden layer, with feature vector after being merged.
For MFCC feature vector, using the hidden layer of 256 hiding layer units, the term vector of 256 sizes and 256 is greatlySmall output layer.And it enables the hidden layer number of plies of MFCC feature consistent with the hidden layer number of plies of word embeding layer, could export in this wayPreferably performance.
Coding unit 130 is encoded for feature vector after the fusion to be input in encoder, obtains coding knotFruit.
In the present embodiment, it is encoded since feature vector after the fusion to be input in encoder, it is known that codingMFCC feature vector and term vector are merged in device, MFCC feature vector and term vector are carried out by linear layers some in encoderFull connection, feature vector after being merged.Then feature vector after fusion is input to the GRU model used in encoder (i.e.Gating cycle unit), coding result can be obtained.
In one embodiment, coding unit 130 includes:
Intermediate semanteme acquiring unit, carries out nonlinear change for feature vector after the fusion to be input in encoderConversion is to obtain intermediate semanteme；
Attention mechanism processing unit, for obtaining the attention respectively segmented in the intermediate semanteme by attention mechanismAllocation probability distribution, to obtain and the intermediate semantic corresponding coding result.
In the present embodiment, attention mechanism is usually used in coder-decoder frame.Encoder is exactly as its name suggests to defeatedEnter sentence Source (such as sentence in the corresponding text of voice data to be identified) to be encoded, input sentence Source is led toIt crosses nonlinear transformation and is converted into intermediate semantic expressiveness C, wherein C=F (x1, x2... ..., xm)；For decoder, taskIt is according to the intermediate semantic expressiveness C for inputting sentence Source and the historical information y generated before1, y2... ... yi-1To generateThe i moment word y to be generatedi=(Ci, y1, y2... ... yi-1), CiAutomobile driving for participle i in the intermediate semanteme is generalRate distribution.Even in the encoder and the decoder between introduce attention model, the above process such as y1=f (C1), y2=f (C2,Y1), y3=f (C3, y1, y2) namely each Ci may correspond to the Automobile driving probability point of different source statement sub-wordsCloth, this attention mechanism for allowing for model can help to capture the most important part of the feature of speaker.
Attention mechanism in Sequence Learning task have huge castering action, in codec framework, byA model is added in coding section, data weighting transformation is carried out to source data sequence, or introduce A model in decoding end, to target dataIt is weighted variation, sequence is can effectively improve and the system under the natural way of sequence is showed.
Decoding unit 140 obtains merging with described for being decoded the coding result as the input of decoderThe corresponding segmentation result of feature vector afterwards；Wherein, the segmentation result includes word sequence and voice conversion symbol.
In the present embodiment, decoder output is the segmentation result for including word sequence and voice conversion symbol.For example, toIdentify the corresponding text of voice data are as follows: hello hi my name is James hi James.The segmentation of decoder outputAs a result are as follows: hello#A hi#B my name is James#A hi James.
Decoder loss function calculate when, have ignored the ID of speaker, and be only concerned speaker be grouped situation.ThanSuch as, voice conversion symbol sequence ABA is considered as being equivalent to BAB.Because the original of conversion symbol sequence can be calculated in calculating processVersion and flipped version, and select loss reduction that as penalty values.This loss function can also be to avoid study instructionPractice the probability concentrated in target sequence between conversion symbol and word.
Speaker's predicting unit 150, for will be accorded with away from each word apart from nearest voice conversion in the segmentation resultDistribution obtains voice conversion and accords with corresponding theory to corresponding word to carry out the prediction of voice conversion to the segmentation resultTalk about people's recognition result.
In the present embodiment, in order to realize the accuracy for maximizing voice conversion prediction, a movement-overlapping is usedDesign.For example, using the window inswept entire part from the beginning to the end of a 32 word length.For each window, with trainingGood sequence carrys out predictive conversion symbol sequence to series model.
In one embodiment, as shown in fig. 7, speaker's predicting unit 150 includes:
Segmentation result acquiring unit 151, for obtaining the segmentation result in decoder；
Current starting word acquiring unit 152 is directed toward the first word for obtaining marker in the segmentation result, using asCurrent starting word；
Converting vector acquiring unit 153, for will divide away from each word apart from nearest voice conversion symbol in segmentation resultIt is assigned to each word in the segmentation result, to establish converting vector；
Converting vector storage unit 154, for storing the converting vector into voice conversion sequence matrix；
Word updating unit 155 is originated, for marker direction position to be moved right a word to update current riseBeginning word；
Last bit word judging unit 156 currently originates whether word is the word that last bit is in segmentation result for judging, if working asPreceding starting word is not the word that last bit is in segmentation result, returns and executes and will speak away from each word apart from nearest in segmentation resultPeople converts symbol distribution each word into the segmentation result, the step of to establish converting vector, if currently starting word is segmentation knotWord in fruit in last bit executes the step of process for terminating the prediction of voice conversion；
Process end unit 157, the process of the prediction for terminating voice conversion.
I.e. in prediction, extracted from text and audio file respectively 32 words term vector and 32 dimensions MFCC feature toAmount.The prediction of specific speaker's conversion sequence is carried out by Fig. 4 and following steps:
51) segmentation result is obtained from decoder；
52) conversion is established in such a way that conversion symbol nearest away from each word in segmentation result is distributed to each wordVector；
53) in the voice conversion sequence matrix for accumulating the converting vector there are one；
54) move right a word distance and by next group of 32 term vectors and 32 tie up MFCC feature vector inputInto encoder；
After window is moved to end, determined by way of majority voting belonging to voice conversion symbol.In this way, it is equivalent to 32 kinds of different predictions and determines.
Speaker clustering unit 160, for the Speaker Identification result by cluster, to be obtained speaker clustering knotFruit.
In the present embodiment, using the cluster mode for being based on bayesian information criterion (BIC), and the feature usedIt is the other MFCC feature of frame level.In more speaker's speech Separations, the Speaker Identification result is clustered, is exactly oneVoice flow is clustered into the voice flow of multiple speakers by the cluster process of voice flow.
As a result transmission unit 170, it is corresponding for the speaker clustering result to be sent to the voice data to be identifiedUpload end.
In the present embodiment, after completing the identification of speaker clustering result, the speaker clustering result will be sent outUpload end corresponding to the voice data to be identified is sent, voice data to be identified is completed and carries out speaker point on server lineFrom.
The arrangement achieves the sequences for having merged vocabulary and acoustics characteristic information to go to carry out dividing for speaker to series modelFrom can capture including the encoded information before and after voice conversion point.
Above-mentioned speaker's separator based on Recognition with Recurrent Neural Network and acoustic feature can be implemented as computer programForm, the computer program can be run in computer equipment as shown in Figure 8.
Referring to Fig. 8, Fig. 8 is the schematic block diagram of computer equipment provided in an embodiment of the present invention.The computer equipment500 be server, and server can be independent server, is also possible to the server cluster of multiple server compositions.
Refering to Fig. 8, which includes processor 502, memory and the net connected by system bus 501Network interface 505, wherein memory may include non-volatile memory medium 503 and built-in storage 504.
The non-volatile memory medium 503 can storage program area 5031 and computer program 5032.The computer program5032 are performed, and processor 502 may make to execute speaker's separation method based on Recognition with Recurrent Neural Network and acoustic feature.
The processor 502 supports the operation of entire computer equipment 500 for providing calculating and control ability.
The built-in storage 504 provides environment for the operation of the computer program 5032 in non-volatile memory medium 503, shouldWhen computer program 5032 is executed by processor 502, processor 502 may make to execute based on Recognition with Recurrent Neural Network and acoustic featureSpeaker's separation method.
The network interface 505 is for carrying out network communication, such as the transmission of offer data information.Those skilled in the art canTo understand, structure shown in Fig. 8, only the block diagram of part-structure relevant to the present invention program, is not constituted to this hairThe restriction for the computer equipment 500 that bright scheme is applied thereon, specific computer equipment 500 may include than as shown in the figureMore or fewer components perhaps combine certain components or with different component layouts.
Wherein, the processor 502 is for running computer program 5032 stored in memory, to realize the applicationSpeaker's separation method in embodiment based on Recognition with Recurrent Neural Network and acoustic feature.
It will be understood by those skilled in the art that the embodiment of computer equipment shown in Fig. 8 is not constituted to computerThe restriction of equipment specific composition, in other embodiments, computer equipment may include components more more or fewer than diagram, orPerson combines certain components or different component layouts.For example, in some embodiments, computer equipment can only include depositingReservoir and processor, in such embodiments, the structure and function of memory and processor are consistent with embodiment illustrated in fig. 8,Details are not described herein.
It should be appreciated that in embodiments of the present invention, processor 502 can be central processing unit (CentralProcessing Unit, CPU), which can also be other general processors, digital signal processor (DigitalSignal Processor, DSP), specific integrated circuit (Application Specific Integrated Circuit,ASIC), ready-made programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logicDevice, discrete gate or transistor logic, discrete hardware components etc..Wherein, general processor can be microprocessor orPerson's processor is also possible to any conventional processor etc..
Computer readable storage medium is provided in another embodiment of the invention.The computer readable storage medium can be withFor non-volatile computer readable storage medium.The computer-readable recording medium storage has computer program, wherein calculatingMachine program is realized in the embodiment of the present application when being executed by processor and is separated based on the speaker of Recognition with Recurrent Neural Network and acoustic featureMethod.
It is apparent to those skilled in the art that for convenience of description and succinctly, foregoing description is setThe specific work process of standby, device and unit, can refer to corresponding processes in the foregoing method embodiment, and details are not described herein.Those of ordinary skill in the art may be aware that unit described in conjunction with the examples disclosed in the embodiments of the present disclosure and algorithmStep can be realized with electronic hardware, computer software, or a combination of the two, in order to clearly demonstrate hardware and softwareInterchangeability generally describes each exemplary composition and step according to function in the above description.These functions are studied carefullyUnexpectedly the specific application and design constraint depending on technical solution are implemented in hardware or software.Professional technicianEach specific application can be used different methods to achieve the described function, but this realization is it is not considered that exceedThe scope of the present invention.
In several embodiments provided by the present invention, it should be understood that disclosed unit and method, it can be withIt realizes by another way.For example, the apparatus embodiments described above are merely exemplary, for example, the unitIt divides, only logical function partition, there may be another division manner in actual implementation, can also will be with the same functionUnit set is at a unit, such as multiple units or components can be combined or can be integrated into another system or someFeature can be ignored, or not execute.In addition, shown or discussed mutual coupling, direct-coupling or communication connection canBe through some interfaces, the indirect coupling or communication connection of device or unit, be also possible to electricity, mechanical or other shapesFormula connection.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unitThe component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multipleIn network unit.Some or all of unit therein can be selected to realize the embodiment of the present invention according to the actual needsPurpose.
It, can also be in addition, the functional units in various embodiments of the present invention may be integrated into one processing unitIt is that each unit physically exists alone, is also possible to two or more units and is integrated in one unit.It is above-mentioned integratedUnit both can take the form of hardware realization, can also realize in the form of software functional units.
If the integrated unit is realized in the form of SFU software functional unit and sells or use as independent productWhen, it can store in one storage medium.Based on this understanding, technical solution of the present invention is substantially in other words to existingThe all or part of part or the technical solution that technology contributes can be embodied in the form of software products, shouldComputer software product is stored in a storage medium, including some instructions are used so that a computer equipment (can bePersonal computer, server or network equipment etc.) execute all or part of step of each embodiment the method for the present inventionSuddenly.And storage medium above-mentioned include: USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), magnetic disk orThe various media that can store program code such as person's CD.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, anyThose familiar with the art in the technical scope disclosed by the present invention, can readily occur in various equivalent modifications or replaceIt changes, these modifications or substitutions should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with rightIt is required that protection scope subject to.