CN105429720A - Related delay estimation method based on EMD reconstruction - Google Patents
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- CN105429720A CN105429720A CN201510833172.1A CN201510833172A CN105429720A CN 105429720 A CN105429720 A CN 105429720A CN 201510833172 A CN201510833172 A CN 201510833172A CN 105429720 A CN105429720 A CN 105429720A
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- 238000005070 sampling Methods 0.000 claims abstract description 5
- 230000006870 function Effects 0.000 claims description 19
- 238000000354 decomposition reaction Methods 0.000 claims description 8
- 230000002596 correlated effect Effects 0.000 claims description 6
- 238000001914 filtration Methods 0.000 claims description 5
- 238000010606 normalization Methods 0.000 claims description 5
- 230000000875 corresponding effect Effects 0.000 claims description 3
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
- G10L25/24—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters the extracted parameters being the cepstrum
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
- G10L25/18—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters the extracted parameters being spectral information of each sub-band
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
- G10L25/21—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters the extracted parameters being power information
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Abstract
The invention discloses a related delay estimation method based on EMD reconstruction, wherein the related delay estimation method comprises the following steps: 1) obtaining sampling sequences y1(n) and y2(n), and extracting preamble background noise sequences b1(n) and b2(n); 2) obtaining a cepstrum sequence (the formula is shown in the specification) of the sampling sequence y1(n), ad obtaining the cepstrum sequence (the formula is shown in the specification) of the preamble background noise sequence b1(n); 3) obtaining the sound channel response (the formula is shown in the specification) of the (the formula is shown in the specification) and the sound channel response (the formula is shown in the specification) of the (the formula is shown in the specification); obtaining the spectral envelope (the formula is shown in the specification) of the (the formula is shown in the specification) and the spectral envelope (the formula is shown in the specification) of the (the formula is shown in the specification); obtaining a spectral difference curve D1(omega) to obtain a normalized difference curve (the formula is shown in the specification); 4) obtaining a frequency band area with amplitude of (the formula is shown in the specification) larger than a threshold T1; 5) obtaining m basic mode components; 6) obtaining the power spectrum distribution curve of each group of components, calculating a ratio eta 1, and obtaining reconstruction signals of a target signal; and 7) detecting the peak value of a secondary related sequence (the formula is shown in the specification) of the reconstruction signals C1(n) and C2(n) of the target signal to obtain an estimated delay value (the formula is shown in the specification). The method is used for improving the accuracy and the stability of the delay estimation result under the condition of a low signal to noise ratio.
Description
Technical field
The present invention relates to array signal process technique, concrete a kind of Time Delay Estimation Based reconstructed based on EMD (EmpiricalModeDecomposition, empirical mode decomposition are called for short EMD).
Background technology
As the key technology of auditory localization, the estimation of sodar time delay is the hot issue of Array Signal Processing area research always, and has been applied to the multiple occasion such as underwater target tracking and location, Satellite tool kit, intelligent robot.
Traditional delay time estimation method has: all square filtering estimation technique of general cross correlation, self adaptation, mean square deviation function method etc.
General cross correlation, can list of references: " C.H.KnappandC.G.Carter.Thegeneralizedcorrelationmethodfo restimationoftimedelay [J] .IEEETrans, Acoustics, SpeechandSignalProcessing, 1976, 21 (2), pp.320-327. ", can accurate estimation time delay when high s/n ratio, but the weighting function that cannot pre-determine in the application of physical presence reverberation noise needed for it, can only replace by estimated value, under making the actual low signal-to-noise ratio of general cross correlation, certainty of measurement is poor and unstable.
The all square filtering estimation technique of self adaptation, can list of references: " F.A.Reed, P.L.FeintuchandN.J.Bershad.TimedelayestimationusingtheLM Sadaptivefilter – behavior [J] .IEEETransactionsonAcoustics, SpeechandSignalProcessing.1981, 29 (3), pp.561-571. ", by setting iterative initial value, parameter and adaptive learning carry out estimation time delay, but the method needs to suppose that interchannel background noise is incoherent white Gaussian noise, under actual noise environment, estimate that accuracy rate declines.
Mean square deviation function method, can list of references: " G.JacovittiandG.Scarano.DiscreteTimeTechniquesforTimeDel ayEstimation [J] .IEEETransactionsonSignalProcessing; 1993; 41 (2); pp.525-533. ", can reach best estimate when not having noise effect, but estimate that when there is actual noise accuracy reduces greatly, anti-changeable noise jamming ability is its greatest problem.
For the situation of low signal-to-noise ratio, above method is in the practical application of present stage, and effect is not very desirable, delay time estimation method of sane good in the urgent need to a kind of noiseproof feature in practical application.
Summary of the invention
The object of the invention is for the deficiencies in the prior art, and a kind of Time Delay Estimation Based based on EMD reconstruct is provided.This method can improve the Stability and veracity of time delay estimated result when low signal-to-noise ratio.
The technical scheme realizing the object of the invention is:
Based on the Time Delay Estimation Based of EMD reconstruct, comprise the steps:
1) gather two-way echo signal, obtain the sample sequence y of two-way echo signal
1(n), y
2(n), and the leading background noise sequence b extracting two-way echo signal channel
1(n), b
2(n), because there is delay inequality σ when two-way echo signal arrives two receiving sensors, therefore sample sequence y
1(n) and y
2also time delay σ is there is between (n);
2) to sample sequence y
1n () is carried out discrete Fourier transform and is obtained Y
1(ω), by Y
1(ω) amplitude is taken the logarithm and is obtained
right again
carry out inverse Fourier transform and obtain sample sequence y
1the cepstrum sequence of (n)
echo signal channel leading background noise sequence b is obtained by same mode
1the cepstrum sequence of (n)
3) will
carry out filtering, obtain respectively
sound channel response
with
sound channel response
right
carry out Fourier transform respectively to obtain
spectral enveloping line
spectral enveloping line
right
do difference and eliminate reciprocity noise, and divided by instantaneous background noise energy
obtain spectral difference curve D
1(ω), then to spectral difference curve D
1(ω) be normalized and obtain normalization spectral difference curve
4) obtain by threshold method
amplitude is higher than the ghz area of thresholding T1, suppose that the ghz area satisfied condition has k, the numerical value of k is relevant with the distribution of signal spectrum energy, and the numerical value obtained under varying environment condition is different, and voice signal composition contained by corresponding echo signal is dominated frequency range and is designated as: [ω
p11, ω
q11] ..., [ω
p1k, ω
q1k];
5) to sample sequence y
1n () carries out EMD decomposition, obtain m Intrinsic mode functions successively, be designated as: h
11(n), h
12(n) ..., h
1m(n), and the surplus r after a decomposition
1(n), such y
1(n) just can be expressed as Intrinsic mode functions and remainder and, namely
it is adaptive that EMD decomposes the component obtained, therefore the numerical value of m and sample sequence y
1n spectrum distribution situation that () is own is relevant, sample sequence y
1n () frequency content is abundanter, the numerical value of m is larger;
6) calculated by Fourier transform and obtain every group component h
11(n) ... h
1mthe Power Spectrum Distribution curve H of (n)
11(ω) ... H
1m(ω); Calculate the Power Spectrum Distribution curve of every group component at frequency range [ω
p11, ω
q11] ..., [ω
p1k, ω
q1k] in amplitude to add up the ratio η cumulative with overall amplitude
1if, the η that respective components calculates
1be greater than predetermined threshold value TH, then this component is as a road echo signal composition reconstruction signal C
1one of the component of (n), otherwise this component is abandoned;
7) by another road sample sequence y
2n () is according to step 2)-step 6) carry out same operation, obtain another road echo signal reconstruction signal C
2n (), supposes that the signal after the reconstruct of two-way echo signal is expressed as C
1(n) and C
2n (), by C
1(n) and C
2n () carries out that secondary is relevant obtains C
1(n), C
2the secondary correlated series of (n)
to secondary correlated series
peak value carry out detection and can obtain time delay estimated value
(can list of references: " and Tang Juan, the great man of virtue and ability of row. based on the delay time estimation method [J] that secondary is relevant. computer engineering, 33 (21), pp.266-267. in 2007 ").
Described sample sequence y
1(n), y
2n () is the time sampling sequence of two transducer synchronous acquisitions, sample frequency is Fs=8000Hz;
Described thresholding T1 is 0.7-0.9, and preferred value is 0.8.
Described predetermined threshold value TH is 0.5.
Described spectral difference curve D
1(ω) be:
Described normalization spectral difference curve
for:
Described ratio η
1for:
Wherein H (ω) represents the Fourier transform of any Intrinsic mode functions function.
This method is estimated for the time delay in low signal-to-noise ratio situation, the estimation of secondary associated time delays is combined with empirical mode decomposition algorithm, makes difference method by cepstrum separation, Fourier transform and frequency spectrum, achieves the selection that EMD decomposes rear useful signal dominant component.This method improves the Stability and veracity of time delay estimated result when low signal-to-noise ratio.
Accompanying drawing explanation
Fig. 1 is embodiment method flow schematic diagram.
Embodiment
Below in conjunction with drawings and Examples, content of the present invention is further elaborated, but is not limitation of the invention.
Embodiment:
With reference to Fig. 1, based on the Time Delay Estimation Based of EMD reconstruct, comprise the steps:
1) gather two-way echo signal, obtain the sample sequence y of two-way echo signal
1(n), y
2(n), and the leading background noise sequence b extracting two-way echo signal channel
1(n), b
2(n), because there is delay inequality σ when two-way echo signal arrives two receiving sensors, therefore sample sequence y
1(n) and y
2also time delay σ is there is between (n);
2) to sample sequence y
1n () is carried out discrete Fourier transform and is obtained Y
1(ω), by Y
1(ω) amplitude is taken the logarithm and is obtained
right again
carry out inverse Fourier transform and obtain sample sequence y
1the cepstrum sequence of (n)
echo signal channel leading background noise sequence b is obtained by same mode
1the cepstrum sequence of (n)
3) will
carry out filtering, obtain respectively
sound channel response
with
sound channel response
right
carry out Fourier transform respectively to obtain
spectral enveloping line
spectral enveloping line
right
do difference and eliminate reciprocity noise, and divided by instantaneous background noise energy
obtain spectral difference curve D
1(ω), then to spectral difference curve D
1(ω) be normalized and obtain normalization spectral difference curve
4) obtain by threshold method
amplitude is higher than the ghz area of thresholding T1, suppose that the ghz area satisfied condition has k, the numerical value of k is relevant with the distribution of signal spectrum energy, and the numerical value obtained under varying environment condition is different, and voice signal composition contained by corresponding echo signal is dominated frequency range and is designated as: [ω
p11, ω
q11] ..., [ω
p1k, ω
q1k];
5) to sample sequence y
1n () carries out EMD decomposition, obtain m Intrinsic mode functions successively, be designated as: h
11(n), h
12(n) ..., h
1m(n), and the surplus r after a decomposition
1(n), such y
1(n) just can be expressed as Intrinsic mode functions and remainder and, namely
it is adaptive that EMD decomposes the component obtained, therefore the numerical value of m and sample sequence y
1n spectrum distribution situation that () is own is relevant, sample sequence y
1n () frequency content is abundanter, the numerical value of m is larger;
6) calculated by Fourier transform and obtain every group component h
11(n) ... h
1mthe Power Spectrum Distribution curve H of (n)
11(ω) ... H
1m(ω); Calculate the Power Spectrum Distribution curve of every group component at frequency range [ω
p11, ω
q11] ..., [ω
p1k, ω
q1k] in amplitude to add up the ratio η cumulative with overall amplitude
1if, the η that respective components calculates
1be greater than predetermined threshold value TH, then this component is as a road echo signal composition reconstruction signal C
1one of the component of (n), otherwise this component is abandoned;
7) by another road sample sequence y
2n () is according to step 2)-step 6) carry out same operation, obtain another road echo signal reconstruction signal C
2n (), supposes that the signal after the reconstruct of two-way echo signal is expressed as C
1(n) and C
2n (), by C
1(n) and C
2n () carries out that secondary is relevant obtains C
1(n), C
2the secondary correlated series of (n)
to secondary correlated series
peak value carry out detection and can obtain time delay estimated value
Described sample sequence y
1(n), y
2n () is the time sampling sequence of two transducer synchronous acquisitions, sample frequency is Fs=8000Hz;
Described thresholding T1 value is 0.8.
Described predetermined threshold value TH is 0.5.
Step 3) middle acquisition
with
sound channel response comprises following flow process:
Inverted frequency axle arranges rectangular window function window (n), window width n
0, window function is expressed as:
n＝0,1,...,N-1
The width of window function is relevant with the length of sample frequency and Fourier transform, in order to make
with
envelope after Fourier transform is real function, window function demand fulfillment symmetry;
Will
be multiplied with window function, obtain sound channel response
Described spectral difference curve D
1(ω) be:
Described normalization spectral difference curve
for:
Described ratio η
1for:
Wherein H (ω) represents the Fourier transform of any Intrinsic mode functions function.
Claims (6)
1., based on the Time Delay Estimation Based of EMD reconstruct, it is characterized in that, comprise the steps:
1) gather two-way echo signal, obtain the sample sequence y of two-way echo signal
1(n), y
2(n), and the leading background noise sequence b extracting two-way echo signal channel
1(n), b
2(n), because there is delay inequality σ when two-way echo signal arrives two receiving sensors, therefore sample sequence y
1(n) and y
2also time delay σ is there is between (n);
2) to sample sequence y
1n () is carried out discrete Fourier transform and is obtained Y
1(ω), by Y
1(ω) amplitude is taken the logarithm and is obtained
right again
carry out inverse Fourier transform and obtain sample sequence y
1the cepstrum sequence of (n)
echo signal channel leading background noise sequence b is obtained by same mode
1the cepstrum sequence of (n)
3) will
carry out filtering, obtain respectively
sound channel response
with
sound channel response
right
carry out Fourier transform respectively to obtain
spectral enveloping line
spectral enveloping line
right
do difference and eliminate reciprocity noise, and divided by instantaneous background noise energy
obtain spectral difference curve D
1(ω), then to spectral difference curve D
1(ω) be normalized and obtain normalization spectral difference curve
4) obtain by threshold method
amplitude, higher than the ghz area of thresholding T1, supposes that the ghz area satisfied condition has k, and voice signal composition contained by corresponding echo signal is dominated frequency range and is designated as: [ω
p11, ω
q11] ..., [ω
p1k, ω
q1k];
5) to sample sequence y
1n () carries out EMD decomposition, obtain m Intrinsic mode functions successively, be designated as: h
11(n), h
12(n) ..., h
1m(n), and the surplus r after a decomposition
1(n), such y
1(n) just can be expressed as Intrinsic mode functions and remainder and, namely
6) calculated by Fourier transform and obtain every group component h
11(n) ... h
1mthe Power Spectrum Distribution curve H of (n)
11(ω) ... H
1m(ω); Calculate the Power Spectrum Distribution curve of every group component at frequency range [ω
p11, ω
q11] ..., [ω
p1k, ω
q1k] in amplitude to add up the ratio η cumulative with overall amplitude
1if, the η that respective components calculates
1be greater than predetermined threshold value TH, then this component is as a road echo signal composition reconstruction signal C
1one of the component of (n), otherwise this component is abandoned;
7) by another road sample sequence y
2n () is according to step 2)-step 6) carry out same operation, obtain another road echo signal reconstruction signal C
2n (), the signal after the reconstruct of two-way echo signal is expressed as C
1(n) and C
2n (), by C
1(n) and C
2n () carries out that secondary is relevant obtains C
1(n), C
2the secondary correlated series of (n)
to secondary correlated series
peak value carry out detection and can obtain time delay estimated value
2. the Time Delay Estimation Based based on EMD reconstruct according to claim 1, is characterized in that, described sample sequence y
1(n), y
2n () is the time sampling sequence of two transducer synchronous acquisitions, sample frequency is Fs=8000Hz.
3. the Time Delay Estimation Based based on EMD reconstruct according to claim 1, it is characterized in that, described thresholding T1 is 0.7-0.9.
4. the Time Delay Estimation Based based on EMD reconstruct according to claim 1, it is characterized in that, described thresholding T1 is 0.8.
5. the Time Delay Estimation Based based on EMD reconstruct according to claim 1, is characterized in that: described predetermined threshold value TH is 0.5.
6. the Time Delay Estimation Based based on EMD reconstruct according to claim 1, is characterized in that: described spectral difference curve D
1(ω) be:
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109116301A (en) * | 2018-08-14 | 2019-01-01 | 中国电子科技集团公司第三十八研究所 | A kind of reaching time-difference measurement method based on reliability estimating |
| CN109884893A (en) * | 2019-02-28 | 2019-06-14 | 西安理工大学 | Dynamic lag estimation method between a kind of multi-process variable |
| CN110514294A (en) * | 2019-08-30 | 2019-11-29 | 鞍钢矿业***有限公司 | A kind of blasting vibration signal noise-reduction method based on EMD and VMD |
| CN113395124A (en) * | 2021-08-17 | 2021-09-14 | 清华大学 | Time delay estimation method and device based on time shift variance |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109116301A (en) * | 2018-08-14 | 2019-01-01 | 中国电子科技集团公司第三十八研究所 | A kind of reaching time-difference measurement method based on reliability estimating |
| CN109116301B (en) * | 2018-08-14 | 2023-02-28 | 中国电子科技集团公司第三十八研究所 | Time difference of arrival measuring method based on confidence degree estimation |
| CN109884893A (en) * | 2019-02-28 | 2019-06-14 | 西安理工大学 | Dynamic lag estimation method between a kind of multi-process variable |
| CN110514294A (en) * | 2019-08-30 | 2019-11-29 | 鞍钢矿业***有限公司 | A kind of blasting vibration signal noise-reduction method based on EMD and VMD |
| CN113395124A (en) * | 2021-08-17 | 2021-09-14 | 清华大学 | Time delay estimation method and device based on time shift variance |
| CN113395124B (en) * | 2021-08-17 | 2021-11-02 | 清华大学 | Time delay estimation method and device based on time shift variance |
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