CN108417220B - Voice signal coding and decoding methods based on agent model Volterra modeling - Google Patents
Voice signal coding and decoding methods based on agent model Volterra modeling Download PDFInfo
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Abstract
A kind of voice signal coding and decoding methods based on agent model Volterra modeling construct prediction model by being pre-processed to the chaos voice signal of input, with Volterra modeling method, determine chaos voice signal prediction model and encode, decoding step forms.Existing artificial bee colony algorithm is improved since the present invention uses, preemphasis, adding window, framing pretreatment are carried out to the chaos voice signal of input, establish chaos voice signal prediction model, determine the parameter in chaos voice signal prediction model, complete the coding of chaos voice signal, according to the data having after encoding, conventionally it is decoded.The present invention utilizes the chaos feature of voice signal, rapidly and accurately realizes and is encoded, decoded to chaos voice signal, and with step, simple, easy to accomplish, high accuracy for examination, can be used for encoding chaos voice signal, decode.
Description
Technical field
The invention belongs to calculate and applied technical field, and in particular to Chaotic time series forecasting model.
Background technique
In recent years, reaching its maturity with hardware device and mechanics of communication, has change for the efficiency of transmission of voice
It is required that.Studies have found that voice signal time series is nonlinear, and show as apparent chaotic characteristic.Utilize chaos spy
Property building voice signal prediction model be considered as a kind of outstanding feasible method.Most of researcher's construction one non-thread
Property prediction model be all directly using Volterra modeling method:
And it is cumbersome to do phase space reconfiguration process.And it need to be built on the basis of voice signal chaotic characteristic using evolution algorithm
Voice signal Chaotic time series forecasting model is found.Existing evolution algorithm efficiency is lower, to particular problem without specific aim.It is existing
Somebody's work ant colony algorithm computational efficiency is low, solving precision is insufficient, existing observation peak stage Search equation are as follows:
The information of each iteration cannot be fully utilized.
Summary of the invention
Technical problem to be solved by the present invention lies in the above-mentioned prior art is overcome, provide a kind of step it is simple,
It is easy to accomplish, speed is fast, accuracy rate it is high based on agent model Volterra modeling voice signal coding and decoding methods.
Solving technical solution used by above-mentioned technical problem is to comprise the steps of:
(1) the chaos voice signal of input is pre-processed
In the chaos voice signal of input, the uniform frame of waveform is found as analysis frame, preemphasis is carried out, adding window, divides
Frame pretreatment.
Above-mentioned adding window pretreatment is carried out using following window function:
N is limited positive integer in formula.
(2) prediction model is constructed with Volterra modeling method
By the information of step (1) analysis frame, chaos voice signal prediction model is established by formula (2):
U (n-i τ) is the analysis frame signal of input in formula, and m is that the memory span of Chaotic time series forecasting model is limited
Positive integer, h1(i) and h2(i, j) is undetermined coefficient, and u (n-i τ) is the n-th-i τ sample of correspondence analysis frame, and n-i τ is step
(1) the sample serial number of analysis frame in, u (n-j τ) are the n-th-j τ sample of corresponding analysis frame, and n-j τ is analysis in step (1)
The sample serial number of frame, τ are delay times for limited positive integer, and j, n are limited positive integer.
(3) it determines chaos voice signal prediction model and encodes
The chaos voice signal of analysis frame in step (1) is determined into chaos voice signal institute with adaptive artificial bee colony algorithm
Corresponding delay time T, Embedded dimensions s, undetermined coefficient h1(i), undetermined coefficient h2(i, j), using agent model method as close
Like fitness function, Embedded dimensions s, delay time T, the undetermined coefficient h of high fitness are selected1(i) and undetermined coefficient h2(i,
J), it is obtained most as original fitness function by greedy selection method using the mean square error between predicted value and actual value
Good Embedded dimensions s, delay time T, undetermined coefficient h1(i) and h2(i, j) is substituted into above-mentioned formula (2), completes chaos voice signal
Coding.
(4) it decodes
By smallest embedding dimension number s, delay time τ, the undetermined coefficient h of the chaos voice signal of extraction1(i) and h2(i, j)
Substitution formula (2), obtains the prediction model to induction signal, according to the data having after encoding, is conventionally decoded.
Adaptive artificial bee colony algorithm in step of the present invention (3) are as follows:
ω is weight coefficient between (0,1) in formula, and c1, c2 are Studying factors 2,It is the random number of [- 1,1], xbest
For the global optimum nectar source of each iteration, xijFor current nectar source position, i is the serial number of nectar source vector, and j is respective components,
xneighborFor the neighbouring nectar source position in current nectar source, neighbor is that vector serial number in nectar source cannot be equal to i, and ω is by following two formula
Son determines:
ω=ωmin+ρ(ωmax-ωmin) (4)
ωminThe upper bound for being ω is 0.2, ωmaxThe lower bound for being ω is 0.9, a 2, and maxcyle is the largest the number of iterations
Being the largest the number of iterations for 2000, a 2, maxcyle is 1000 or 1500 or 2000.
Agent model method in step of the present invention (3) are as follows:
(1) the Embedded dimensions s in the chaos Phase Space Reconstruction of Speech Signals of analysis frame, delay time T are added to original
In Volterra model, the m in formula (1) is replaced with s.
(2) according to the model for introducing s and τ belonging to step (1), using the agent model method of adaptive artificial bee colony algorithm
Determine undetermined coefficient h1(i) and h2(i, j).
Use radial base neural net as approximate fitness function, by approximate fitness function and true fitness function
Models coupling uses, approximate fitness function are as follows:
K in formula (| | x-ci| |) it is used kernel function, aiFor the value to be assessed, ciFor radial base neural net
Central point, true fitness function are as follows:
Y in formulaiFor actual value,For predicted value, L is prediction length.
Determine best undetermined coefficient h1(i)、h2Whether (i, j), detection mean square error reach requirement, are not up to error requirements,
Iteration again.
Since the present invention is using existing artificial bee colony algorithm is improved, the chaos voice signal of input is carried out
Preemphasis, adding window, framing pretreatment, establish chaos voice signal prediction model, determine in chaos voice signal prediction model
Parameter, complete chaos voice signal coding, according to have coding after data, be conventionally decoded.This hair
The bright chaos feature using voice signal, rapidly and accurately realizes and is encoded, decoded to chaos voice signal, has step
Simply, easy to accomplish, high accuracy for examination can be used for encoding chaos voice signal, decode.
Detailed description of the invention
Fig. 1 is process flow chart of the invention.
Fig. 2 is the waveform diagram that embodiment 1 inputs chaos voice signal phonetic symbol [b].
Fig. 3 is the experimental result that embodiment 1 determines chaos voice signal prediction model and encodes.
Fig. 4 is the experimental result that embodiment 2 determines chaos voice signal prediction model and encodes.
Fig. 5 is the experimental result that embodiment 3 determines chaos voice signal prediction model and encodes.
Specific embodiment
The present invention is described in more detail with reference to the accompanying drawings and examples, but the present invention is not limited to following embodiment party
Formula.
Embodiment 1
By taking phonetic symbol [b] in the chaos voice signal chosen in standard pronunciation mark corpus as an example, it is based on agent model
The voice signal coding and decoding methods step (as shown in Figure 1) of Volterra modeling is as follows:
(1) the chaos voice signal of input is pre-processed
Fig. 2 is the waveform diagram of the chaos voice signal phonetic symbol [b] of input, in the chaos voice signal phonetic symbol [b] of input,
The uniform frame of waveform is found as analysis frame, carries out preemphasis, adding window, framing pretreatment, preemphasis is conventional method, using biography
Delivery function carries out preemphasis.
Above-mentioned adding window pretreatment is carried out using following window function:
N is limited positive integer in formula.
(2) prediction model is constructed with Volterra modeling method
The information of step (1) analysis frame is shown in Fig. 3, the present embodiment sample length that therefrom intercepted length is 400, by formula (2)
Establish chaos voice signal prediction model:
U (n-i τ) is the analysis frame signal of input in formula, and m is that the memory span of Chaotic time series forecasting model is limited
Positive integer, h1(i) and h2(i, j) is undetermined coefficient, and u (n-i τ) is the n-th-i τ sample of correspondence analysis frame, and n-i τ is step
(1) the sample serial number of analysis frame in, u (n-j τ) are the n-th-j τ sample of corresponding analysis frame, and n-j τ is analysis in step (1)
The sample serial number of frame, τ are delay times for limited positive integer, and j, n are limited positive integer.
(3) it determines chaos voice signal prediction model and encodes
The chaos voice signal of analysis frame in step (1) is determined into chaos voice signal institute with adaptive artificial bee colony algorithm
Corresponding delay time T, Embedded dimensions s, undetermined coefficient h1(i), undetermined coefficient h2(i, j), adaptive artificial bee colony algorithm are as follows:
ω is weight coefficient between (0,1) in formula, and c1, c2 are Studying factors 2,It is the random number of [- 1,1], xbest
For the global optimum nectar source of each iteration, xijFor current nectar source position, i is the serial number of nectar source vector, and j is respective components,
xneighborFor the neighbouring nectar source position in current nectar source, neighbor is that vector serial number in nectar source cannot be equal to i, and ω is by following two formula
Son determines:
ω=ωmin+ρ(ωmax-ωmin) (4)
ωminThe upper bound for being ω is 0.2, ωmaxThe lower bound for being ω is 0.9, a 2, and maxcyle is the largest the number of iterations
It is 2000.
Using agent model method as approximate fitness function, select the Embedded dimensions s of high fitness, delay time T,
Undetermined coefficient h1(i), undetermined coefficient h2(i, j), the agent model method of the present embodiment are as follows:
(1) the Embedded dimensions s in the chaos Phase Space Reconstruction of Speech Signals of analysis frame, delay time T are added to original
In Volterra model, the m in formula (1) is replaced with s.
(2) according to the model for introducing s and τ belonging to step (1), using the agent model method of adaptive artificial bee colony algorithm
Determine undetermined coefficient h1(i) and h2(i, j):
Use radial base neural net as approximate fitness function, by approximate fitness function and true fitness function
Models coupling uses, approximate fitness function are as follows:
K in formula (| | x-ci| |) it is used kernel function, aiFor the value to be assessed, ciFor radial base neural net
Central point, true fitness function are as follows:
Y in formulaiFor actual value,For predicted value, L is prediction length；
Determine best undetermined coefficient h1(i)、h2Whether (i, j), detection mean square error reach requirement, are not up to error requirements,
Iteration again.
Using the mean square error between predicted value and actual value as original fitness function, by greedy selection method,
Greedy selection method is conventional method, obtains smallest embedding dimension number s, delay time T, undetermined coefficient h1(i) and undetermined coefficient h2
(i, j) is substituted into above-mentioned formula (2), completes the coding of chaos voice signal.
With adaptive artificial bee colony algorithm obtain chaos voice signal phonetic symbol [b] corresponding to delay time T be 8, insertion dimension
Number s is 12, undetermined coefficient h in chaos voice signal prediction model1(i) and undetermined coefficient h2(i, j) is shown in Table 1, table 2, Fig. 3.
Best undetermined coefficient h in 1 embodiment 1 of table1(i)
| h1(1) | h1(1) | h1(1) | h1(1) | h1(1) | h1(1) | h1(1) | h1(1) |
| 1 | ‐0.0020 | ‐0.0531 | ‐0.0898 | ‐0.1363 | 0.0555 | 0.6349 | ‐0.0617 |
Best undetermined coefficient h in 2 embodiment 1 of table2(i, j)
| h2(i, j) | I=1 | I=2 | I=3 | I=4 | I=5 | I=6 | I=7 | I=8 |
| J=1 | 0.8258 | -0.4758 | 0.2718 | 1 | -1 | 0.1292 | -1 | 0.7767 |
| J=2 | 0.0449 | -0.0179 | 0.1362 | -0.1184 | 1 | 0.3567 | -0.3045 | |
| J=3 | 0.5248 | 0.2685 | -0.9564 | 0.7436 | -0.3485 | 0.3652 | ||
| J=4 | -0.9852 | 0.5326 | 0.2134 | 0.3452 | 0.2741 | |||
| J=5 | 0.1245 | 0.5236 | -12354 | 1 | ||||
| J=6 | -0.9654 | 0.1455 | 0.2542 | |||||
| J=7 | 0.6532 | 0.8541 | ||||||
| J=8 | 0.8745 |
By table 1, table 2, Fig. 3 as it can be seen that in chaos voice signal phonetic symbol [b] optimal embedding dimension s be 12, the optimum delay time
It is 8, undetermined coefficient h1(i)、h2When (i, j) is data in table, the worst error of sample cumulative is 0.199474, has reached error
Therefore range is output in the file of formulation.The file exported in figure is found, is substituted into above-mentioned formula (2), chaos voice is completed
The coding of signal.
(4) it decodes
By smallest embedding dimension number s, delay time τ, the undetermined coefficient h of phonetic symbol [b] in the chaos voice signal of extraction1(i)
And h2(i, j) substitute into formula (2), obtain the prediction model to induction signal, according to have coding after data, conventionally into
Row decoding.
Embodiment 2
By taking phonetic symbol [b] in the chaos voice signal chosen in standard pronunciation mark corpus as an example, it is based on agent model
Steps are as follows for the voice signal coding and decoding methods of Volterra modeling:
(1) the chaos voice signal of input is pre-processed
It is same as Example 1 that pre-treatment step is carried out to the chaos voice signal of input.
(2) prediction model is constructed with Volterra modeling method
It is same as Example 1 with Volterra modeling method building prediction model step.
(3) it determines chaos voice signal prediction model and encodes
The chaos voice signal of analysis frame in step (1) is determined into chaos voice signal institute with adaptive artificial bee colony algorithm
Corresponding delay time T, Embedded dimensions s, undetermined coefficient h1(i), undetermined coefficient h2(i, j), adaptive artificial bee colony algorithm are as follows:
ω is weight coefficient between (0,1) in formula, and c1, c2 are Studying factors 2,It is the random number of [- 1,1], xbest
For the global optimum nectar source of each iteration, xijFor current nectar source position, i is the serial number of nectar source vector, and j is respective components,
xneighborFor the neighbouring nectar source position in current nectar source, neighbor is that vector serial number in nectar source cannot be equal to i, and ω is by following two formula
Son determines:
ω=ωmin+ρ(ωmax-ωmin) (9)
ω in formulaminThe upper bound for being ω is 0.2, ωmaxThe lower bound for being ω is 0.9, a 2, and maxcyle is the largest iteration
Number is 1000.
Using agent model method as approximate fitness function, select the Embedded dimensions s of high fitness, delay time T,
Undetermined coefficient h1(i), undetermined coefficient h2The agent model method of (i, j), the present embodiment are same as Example 1.With adaptive artificial
Ant colony algorithm obtains that delay time T corresponding to chaos voice signal phonetic symbol [b] is 8, Embedded dimensions s is 12, chaos voice signal
Undetermined coefficient h in prediction model1(i) and undetermined coefficient h2(i, j) is shown in Table 3, table 4, Fig. 4.
Best undetermined coefficient h in 3 embodiment 2 of table1(i)
| h1(1) | h1(1) | h1(1) | h1(1) | h1(1) | h1(1) | h1(1) | h1(1) |
| 1 | 1.1321 | 0.0672 | -0.4031 | 0.0203 | -0.2818 | 0.1010 | 0.2818 |
Best undetermined coefficient h in 4 embodiment 2 of table2(i, j)
Other steps are same as Example 1.
Complete the coding and decoding of chaos voice signal phonetic symbol [b].
Embodiment 3
By taking phonetic symbol [b] in the chaos voice signal chosen in standard pronunciation mark corpus as an example, it is based on agent model
Steps are as follows for the voice signal coding and decoding methods of Volterra modeling:
(1) the chaos voice signal of input is pre-processed
It is same as Example 1 that pre-treatment step is carried out to the chaos voice signal of input.
(2) prediction model is constructed with Volterra modeling method
It is same as Example 1 with Volterra modeling method building prediction model step.
(3) it determines chaos voice signal prediction model and encodes
The chaos voice signal of analysis frame in step (1) is determined into chaos voice signal institute with adaptive artificial bee colony algorithm
Corresponding delay time T, Embedded dimensions s, undetermined coefficient h1(i), undetermined coefficient h2(i, j), adaptive artificial bee colony algorithm are as follows:
ω is weight coefficient between (0,1) in formula, and c1, c2 are Studying factors 2,It is the random number of [- 1,1], xbest
For the global optimum nectar source of each iteration, xijFor current nectar source position, i is the serial number of nectar source vector, and j is respective components,
xneighborFor the neighbouring nectar source position in current nectar source, neighbor is that vector serial number in nectar source cannot be equal to i, and ω is by following two formula
Son determines:
ω=ωmin+ρ(ωmax-ωmim) (12)
ω in formulaminThe upper bound for being ω is 0.2, ωmaxThe lower bound for being ω is 0.9, a 2, and maxcyle is the largest iteration
Number is 1500.
Using agent model method as approximate fitness function, select the Embedded dimensions s of high fitness, delay time T,
Undetermined coefficient h1(i), undetermined coefficient h2The agent model method of (i, j), the present embodiment are same as Example 1.With adaptive artificial
Ant colony algorithm obtains that delay time T corresponding to chaos voice signal phonetic symbol [b] is 8, Embedded dimensions s is 12, chaos voice signal
Undetermined coefficient h in prediction model1(i) and undetermined coefficient h2(i, j) is shown in Table 5, table 6, Fig. 5.
Table 5 applies the best undetermined coefficient h in example 31(i)
| h1(1) | h1(1) | h1(1) | h1(1) | h1(1) | h1(1) | h1(1) | h1(1) |
| 1 | 0.2119 | -0.4320 | -0.0315 | 0.0995 | 0.0014 | -0.1405 | 0.0898 |
Best undetermined coefficient h in 6 embodiment 3 of table2(i, j)
| h2(i, j) | I=1 | I=2 | I=3 | I=4 | I=5 | I=6 | I=7 | I=8 |
| J=1 | 0.2358 | -0.9652 | 0.2148 | 0.3541 | -1 | 0.7022 | -1 | 0.3354 |
| J=2 | 0.6249 | -0.6931 | 0.3654 | -0.6944 | 0.2982 | 0.6367 | -0.4508 | |
| J=3 | 0.9852 | 0.7564 | -0.2485 | 0.4267 | 0.5130 | 0.7452 | ||
| J=4 | -0.3498 | 0.3215 | 0.3124 | 0.2347 | 0.7824 | |||
| J=5 | 0.7545 | 0.1453 | -0.1154 | 1.2647 | ||||
| J=6 | -0.5496 | 0.3265 | 0.3542 | |||||
| J=7 | 0.3541 | 0.4516 | ||||||
| J=8 | 0.1264 |
Other steps are same as Example 1.
Complete the coding and decoding of chaos voice signal phonetic symbol [b].
According to above-mentioned principle, different phonetic symbols in the chaos voice signal chosen in standard pronunciation mark corpus, using being based on
The voice signal coding and decoding methods of agent model Volterra modeling, can code and decode different phonetic symbols.
Claims (1)
1. a kind of voice signal coding and decoding methods based on agent model Volterra modeling, it is characterised in that by following step
Rapid composition:
(1) the chaos voice signal of input is pre-processed
In the chaos voice signal of input, the uniform frame of waveform is found as analysis frame, progress preemphasis, adding window, framing are pre-
Processing；
Above-mentioned adding window pretreatment is carried out using following window function:
N is limited positive integer in formula；
(2) prediction model is constructed with Volterra modeling method
By the information of step (1) analysis frame, chaos voice signal prediction model is established by formula (2):
In formula u (n-i τ) be input analysis frame signal, m be Chaotic time series forecasting model memory span be it is limited just
Integer, h1(i) and h2(i, j) is undetermined coefficient, and u (n-i τ) is the n-th-i τ sample of correspondence analysis frame, and n-i τ is step (1)
The sample serial number of middle analysis frame, u (n-j τ) are the n-th-j τ sample of corresponding analysis frame, and n-j τ is analysis frame in step (1)
Sample serial number, τ be delay time be limited positive integer, j,nFor limited positive integer；
(3) it determines chaos voice signal prediction model and encodes
Corresponding to the chaos voice signal of analysis frame in step (1) is determined chaos voice signal with adaptive artificial bee colony algorithm
Delay time T, Embedded dimensions s, undetermined coefficient h1(i), undetermined coefficient h2(i, j), using agent model method as approximate suitable
Response function selects Embedded dimensions s, delay time T, the undetermined coefficient h of high fitness1(i) and undetermined coefficient h2(i, j) is adopted
With the mean square error between predicted value and actual value as original fitness function, by greedy selection method, obtain best embedding
Enter dimension s, delay time T, undetermined coefficient h1 (i) and h2(i, j) is substituted into above-mentioned formula (2), completes the volume of chaos voice signal
Code；
Above-mentioned adaptive artificial bee colony algorithm are as follows:
ω is weight coefficient between (0,1) in formula, and c1, c2 are Studying factors 2,It is the random number of [- 1,1], xbestIt is every
The global optimum nectar source of secondary iteration, xijFor current nectar source position, i is the serial number of nectar source vector, and j is respective components, xneighbor
For the neighbouring nectar source position in current nectar source, neighbor is that vector serial number in nectar source cannot be equal to i, and ω is true by following two formula
It is fixed:
ω=ωmin+ρ(ωmax-ωmin) (4)
ωminThe upper bound for being ω is 0.2, ωmaxThe lower bound for being ω is 0.9, a 2, and maxcyle is the largest the number of iterations and is
It is 1000 or 1500 or 2000 that 2000, a 2, maxcyle, which are the largest the number of iterations,；
Above-mentioned agent model method are as follows:
(1) the Embedded dimensions s in the chaos Phase Space Reconstruction of Speech Signals of analysis frame, delay time T are added to original
In Volterra model, the m in formula (1) is replaced with s；
(2) it according to the model for introducing s and τ belonging to step (1), is determined using the agent model method of adaptive artificial bee colony algorithm
Undetermined coefficient h1(i) and h2(i, j):
Use radial base neural net as approximate fitness function, by approximate fitness function and true fitness function model
It is used in combination, approximate fitness function are as follows:
K in formula (| | x-ci| |) it is used kernel function, aiFor the value to be assessed, ciFor the center of radial base neural net
Point, true fitness function are as follows:
Y in formulaiFor actual value,For predicted value, L is prediction length；
Determine best undetermined coefficient h1(i)、h2Whether (i, j), detection mean square error reach requirement, are not up to error requirements, again
Iteration；
(4) it decodes
By smallest embedding dimension number s, delay time τ, the undetermined coefficient h of the chaos voice signal of extraction1(i) and h2(i, j) is substituted into
Formula (2), obtains the prediction model to induction signal, according to the data having after encoding, is conventionally decoded.
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