CN101893698A - Noise source test and analysis method and device - Google Patents

Abstract

The invention discloses a noise source test and analysis method, which comprises the following steps of: 1) receiving sound waves radiated by a main noise source to be tested by adopting a microphone; 2) sampling the sound waves radiated by the main noise source to be tested, and saving and displaying the sound waves of the main noise source to be tested after analog-to-digital conversion; 3) processing the sound waves of the main noise source to be tested, which is saved by the step 2) by utilizing wavelet transform and high-order spectrum analysis technology to acquire a signal characteristic value for recognizing the noise source; and 4) estimating frequency response characteristics from the main noise source to be tested to the sensor by utilizing an independent component analysis method to estimate the direction of arrival of the sound source. The wavelet transform and bispectrum analysis-based noise source analysis method can eliminate the influence of non Gaussian noises, cannot cause loss of useful information of signals of the main noise source, and can accurately and effectively extract mode characteristics of the signals of the main noise source.

Description

Noise source test analytical approach and device thereof

Technical field

The present invention relates to a kind of sound source method for testing and analyzing, particularly noise source test analytical approach of analyzing based on wavelet transformation and higher-order spectrum and the device of realizing the method thereof.

Background technology

The auditory localization technology can be important techniques of location technology according to the position of determining target from the information of target.Along with location technology constantly develops, the auditory localization The Application of Technology is also increasingly extensive, particularly is applied in the strong industry spot of noise radiation.

Auditory localization often is used for locating and discerning strong sounding position, provides foundation to noise reduction.Traditional Noise Sources Identification method, as frequency spectrum analysis method, easy analysis is directly perceived, uses comparatively generally in the noise-source analysis of rotating machinery, but is subjected to the influence of the frequency response function of each sensor, can not discern main noise source sensitively.

Microphone is a kind of converting member that acoustic energy is converted to electric energy, and utilizing microphone that sound source is positioned is to adopt microphone array that the sound source position of the sound that sends is positioned.Microphone array is meant by a plurality of microphones and puts according to different locus, forms the device of a certain specific receiving system jointly.At present, microphone array mainly adopts time delay to estimate that (Time-Delay-estimator) technology and time delay summation beam-forming technology carry out auditory localization, and its concrete implementation step is:,

1) at first M microphone formed microphone array according to certain topological structure, pick up voice signal and other noise signal that sound source is sent.

The received signal F (t) that represents microphone array with mathematical formulae is:

F(t)＝[f ₁(t)f ₂(t)...f _M(t)] ^T (1)

Then, the signal that each microphone is received carries out the AD conversion

F(n)＝[f ₁(n)f ₂(n)...f _M(n)] ^T (2)

From every road signal that formula (2) calculates, choose a frame signal again and carry out Short Time Fourier Transform:

$S\left(\mathrm{\ω}\right)\underset{m=1}{\overset{N}{\mathrm{\Σ}}}F\left(n\right)w(n-m)\exp (-\mathrm{j\ωm})=\left[\begin{array}{c}\underset{m=1}{\overset{n}{\mathrm{\Σ}}}{f}_1\left(n\right)w(n-m)\exp (-\mathrm{j\ωm})\\ \underset{m=1}{\overset{N}{\mathrm{\Σ}}}{f}_M\left(n\right)w(n-m)\exp (-\mathrm{j\ωm})\end{array}\right]---\left(3\right)$

Wherein: w (n) is a window function, and m is mobile at every turn the counting of window function, and N is a frame length.Each Frequency point S (ω to the S (ω) of formula (3) _i):

$S\left({\mathrm{\ω}}_i\right)=\left[\begin{array}{c}{S}_1\left(i\right)\\ \·\\ \·\\ \·\\ S_M\left(i\right)\end{array}\right]---\left(4\right)$

2) step 1 is obtained each frequency matrix and carried out handling calculating mean space spectrum, its processing procedure is as follows:

1. obtain the frequency domain correlation matrix of speech frame

R(i)：R(i)＝E{S(ω _i)S ^H(ω _i)}(5)

2. above-mentioned correlation matrix is carried out characteristic value decomposition, obtain eigenwert and characteristic of correspondence vector;

$R\left(i\right)=U_i\mathrm{diag}({\mathrm{\λ}}_1,...,{\mathrm{\λ}}_M)V_i^H---\left(6\right)$

3. the signal space of forming according to the big wisp proper vector of eigenwert is decomposed into noise subspace S and signal subspace

G：V _i＝[S _iMG _i](7)

4. obtain the position vector a of each searching position according to formula (8) _i(r, θ):

$a_i(r,\mathrm{\θ})=\left[\begin{array}{ccc}\frac{1}{r_1}{e}^{-j{\mathrm{\ω}}_i{\mathrm{\τ}}_1}& \frac{1}{r_m}{e}^{-j{\mathrm{\ω}}_i{\mathrm{\τ}}_m}& \frac{1}{r_M}{e}^{-j{\mathrm{\ω}}_i{\mathrm{\τ}}_M}\end{array}\right]---\left(8\right)$

Wherein: r _mBe that (r is θ) to the distance of m microphone from the search point; τ _m(r is θ) to travel-time of m microphone from the search point for sound source; ω _iRepresent i Frequency point.

Definition b _i(r θ) is:

$b_i(r,\mathrm{\θ})=\frac{a_i(r,\mathrm{\θ})}{\left|\right|a_i(r,\mathrm{\θ})\left|\right|}---\left(9\right)$

The two-dimensional space spectrum matrix of obtaining i Frequency point is:

$p_i(\mathrm{\θ},r,{\mathrm{\ω}}_i)=\frac{1}{\left|\right|b_i^H(r,\mathrm{\θ})G_i\left|\right|}---\left(10\right)$

After the processing by top 4 steps, select a frequency range and obtain the interior mean space spectrum of this frequency range:

$p_i(r,{\mathrm{\ω}}_i)=\frac{1}{k}\underset{i={\mathrm{\ω}}_L}{\overset{{\mathrm{\ω}}_H}{\mathrm{\Σ}}}{P}_i(\mathrm{\θ},r,{\mathrm{\ω}}_i)---\left(11\right)$

Wherein: ω _L, ω _HBe respectively the lower bound and the upper bound frequency of this frequency range, K=ω _H-ω _L+ 1.

3) to the P that obtains in the step 2 (r θ) carries out two-dimentional spectrum peak search, thereby finds the position of echo signal, promptly two-dimensional space spectrum matrix P (r finds out peak value in θ), and the coordinate of peak value correspondence is exactly the sound source position estimated value: (and r, θ).

But the major defect of this method is the location out of true, and all Frequency points are all asked spatial spectrum, and operand is big, and real-time is low, realizes the cost height, thereby can not effectively analyze and extract noise source, and bigger limitation is arranged in auditory localization.

Summary of the invention

The key point of auditory localization technology is how to discern tested sound source and estimate the orientation of sound wave from ground unrest, and what wherein press for solution is the resolution characteristic problem of basic matrix.In order to address the above problem, the object of the present invention is to provide a kind of noise source test analytical approach and device thereof, make it have the higher signal resolution characteristic.

The technical scheme that the present invention solves its technical matters employing is:

1, a kind of noise source test analytical approach may further comprise the steps:

1) adopt microphone to receive the sound wave of tested main noise source radiation;

2) sound wave of tested main noise source radiation is sampled; After the analog-to-digital conversion, the acoustical signal of tested main noise source is preserved, shown;

3) utilize wavelet transformation and higher-order spectrum analytical technology that the acoustical signal of the tested main noise source of step 2 preservation is handled, obtain the signal characteristic value, be used to discern noise source;

4) utilize the frequency response characteristic of independent component analysis method estimation, and then realize the estimation of sound source direction of arrival from tested main noise source to sensor.

Further, the microphone of described step 1 is a microphone array, and the microphone number is greater than 1.

Described step 3 comprises the steps:

1) voice signal is carried out wavelet transformation, voice signal is decomposed into the details component and the approximate component of different scale, by analysis to components at different levels, determine the scale component that main noise source and ground unrest are concentrated, and will contain the scale component filtering of ground unrest, the scale component that only keeps the main noise source place is as two spectrum estimated signals sequences;

2) with the burst behind the wavelet transformation, the two spectrums of the property advanced are estimated, calculate two spectrum signatures of main noise source.

The concrete steps of described step 4 are:

1) utilize microphone array to receive tested main noise source signal vector x (t);

When 2) utilizing-frequency analysis technique construction acoustical signal the time-the frequency matrix;

3) utilize the independent component analysis technology, by the time-frequency response characteristic of frequency Matrix Estimation from tested main noise source to sensor---separation matrix;

4) estimate that from separation matrix the sound source ripple reaches the value of azimuth angle theta.

Further, use formula

${\mathrm{WT}}_x(a,b)=\frac{1}{\sqrt{a}}\&Integral;x\left(t\right)\mathrm{\&Psi;}\left[\frac{t-b}{a}\right]=<x\left(t\right),{\mathrm{\&Psi;}}_{a,b}\left(t\right)>---\left(1\right)$

Carry out wavelet transformation, wherein a is a scale factor, and b is the time shift factor.Ψ _{A, b}(t) be that female small echo Ψ (t) is through displacement and the flexible cluster function that is produced.

Use formula

$C_{3X}({\mathrm{\&omega;}}_1,{\mathrm{\&omega;}}_2)=\underset{{\mathrm{\&tau;}}_1=-\&infin;}{\overset{\&infin;}{\mathrm{\&Sigma;}}}\underset{{\mathrm{\&tau;}}_2=-\&infin;}{\overset{\&infin;}{\mathrm{\&Sigma;}}}{c}_{3X}({\mathrm{\&tau;}}_1,{\mathrm{\&tau;}}_2)\exp \{-j\left({\mathrm{\&omega;}}_1{\mathrm{\&tau;}}_1\text{+}{\mathrm{\&omega;}}_2{\mathrm{\&tau;}}_2\right)\}---\left(2\right)$

Calculate two spectrum signatures of acoustical signal.

Realize that this noise source test analytical equipment comprises: microphone, signal conditioner, capture card, computing machine, data acquisition software and data analysis; After the sound wave of the tested main noise source that described microphone receives is amplified by the signal condition joint, transfer to computing machine through capture card, carry out analog to digital conversion and show, store acoustical signal in real time by data acquisition software, acoustical signal after the above-mentioned digitizing realizes the identification of noise source by the data analysis software computing.

Beneficial effect of the present invention:

Common statistical signal is handled three basic assumptions: linear, Gauss and stationarity, and in the actual noise source location, signal Processing then with how with non-linear, non-Gauss and non-stationary signal as analyzing and the object of handling.Double-spectrum analysis is a strong instrument of handling gaussian signal, and it characterizes random signal from order Probability Structure more, can suppress Gaussian noise fully in theory, in higher-order spectrum, the exponent number of two spectrums is minimum, and disposal route is the simplest, also comprises all characteristics of higher-order spectrum simultaneously.But when tested main noise source was submerged in the non-Gaussian Background noise, it is unable to do what one wishes that double-spectrum analysis just seems.And the advantage of wavelet transformation is non-stationary signal, has tangible time domain localization, can strengthen the prompting message that is hidden in the vibration signal effectively.

A kind of noise source test analytical approach provided by the invention, be based on the noise-source analysis method of small echo variation and double-spectrum analysis, can both eliminate the influence of non-Gauss's noise like part like this, can the useful information of main noise source signal not caused damage again, can extract the pattern feature of main noise source signal comparatively accurately and efficiently.

Description of drawings

Fig. 1 is a kind of noise source test analytical equipment structural representation

The specific embodiment mode

Below in conjunction with drawings and the specific embodiments principle of the present invention and particular content are elaborated.

A kind of main noise source method for testing and analyzing comprises the steps

1) adopt microphone to receive the sound wave of tested main noise source radiation.

Present embodiment adopts the MPA416 desire polarization free field measuring microphone of Beijing popularity, and this sensitivity of microphone height can link to each other with data collecting card, and strong signal output and lower background noise are provided, and is mainly used in array and acoustic measurement.The microphone number is selected 4.

2) sound wave of tested main noise source radiation is sampled; After the analog-to-digital conversion, the acoustical signal of tested main noise source is preserved, shown.

With the acoustic signals of the tested main noise source radiation that collects, amplify through signal conditioner, by data collecting card, the analog signal conversion that collects is become digital signal, transfer to processing such as data acquisition software in the computing machine stores, demonstration.This example is selected the MC104 signal conditioner of popularity for use, and the PCI1714UL high-performance data capture card of selecting NI company for use is as the intermediate equipment that connects signal conditioner and computing machine.The data acquisition software of present embodiment is based on the data acquisition software of LabView Software Development Platform design.

3) utilize wavelet transformation and higher-order spectrum analytical technology that the acoustical signal of the tested main noise source of step 2 preservation is handled, obtain the signal characteristic value, be used to discern noise source.

This enforcement can be analyzed the noise signal that collects by the noise-source analysis system of Matlab software development by this software.Concrete steps:

At first, the voice signal that collects is carried out wavelet transformation, voice signal is decomposed into the details component and the approximate component of different scale, by analysis to components at different levels, determine the scale component that main noise source and ground unrest are concentrated, and will contain the scale component filtering of ground unrest, only keep the scale component at main noise source place, as two spectrum estimated signals sequences.

Wherein wavelet transformation is defined as:

For given signal x (t) ∈ L ²(R), the wavelet transformation of x (t) is defined as:

${\mathrm{WT}}_x(a,b)=\frac{1}{\sqrt{a}}\&Integral;x\left(t\right)\mathrm{\&Psi;}\left[\frac{t-b}{a}\right]=<x\left(t\right),{\mathrm{\&Psi;}}_{a,b}\left(t\right)>---\left(1\right)$

${\mathrm{\&Psi;}}_{a,b}\left(t\right)=\frac{1}{\sqrt{a}}\mathrm{\&Psi;}\left[\frac{t-b}{a}\right]---\left(2\right)$

In the formula: a＞0th, scale factor, b is the time shift factor.Ψ _{A, b}(t) be the cluster function of female small echo Ψ (t), be referred to as wavelet basis through being shifted and stretching and produced.Thus definition as can be known, wavelet transformation comes down to original signal and related operation through the wavelet function after flexible bunch.By adjusting yardstick, can obtain having the diverse location of the small echo of different time-frequency width with the coupling original signal, reach the localization analysis of signal.

A, b parameter be corresponding to frequency in time-frequency analysis and time variable, in the wavelet transformation always according to unlike signal the time-the frequency characteristic, carry out adaptive selection by algorithm.For example, in the noise source signal analysis, when analyzing yardstick a more than or equal to 4 the time, the gained result basic corresponding be that the ground unrest of high frequency disturbs.Therefore, our the analysis yardstick of choosing actual noise source signal wavelet transformation usually is 1: 3.

Then, with the burst behind the wavelet transformation, the two spectrums of the property advanced are estimated, calculate two spectrum signatures of main noise source.Orientating as of double-spectrum analysis wherein:

The defined formula of two spectrums is as follows:

$C_{3X}({\mathrm{\&omega;}}_1,{\mathrm{\&omega;}}_2)=\underset{{\mathrm{\&tau;}}_1=-\&infin;}{\overset{\&infin;}{\mathrm{\&Sigma;}}}\underset{{\mathrm{\&tau;}}_2=-\&infin;}{\overset{\&infin;}{\mathrm{\&Sigma;}}}{c}_{3X}({\mathrm{\&tau;}}_1,{\mathrm{\&tau;}}_2)\exp \{-j\left({\mathrm{\&omega;}}_1{\mathrm{\&tau;}}_1\text{+}{\mathrm{\&omega;}}_2{\mathrm{\&tau;}}_2\right)\}---\left(3\right)$

By the symmetry of three rank semi-invariants, can get the symmetry and the symmetric domains thereof of two spectrums.

C _3X(ω ₁，ω ₂)＝C _3X(ω ₂，ω ₁)＝C _3X(-ω ₂，ω ₁)＝C _3X(-ω ₁-ω ₂，ω ₂)

＝C _3X(ω ₁，-ω ₁-ω ₂)＝C _3X(-ω ₁-ω ₂，ω ₁)＝C _3X(ω ₂，-ω ₁-ω ₂) (4)

For real stochastic process, two spectrums have 12 symmetrical regions.Therefore, by following formula as can be known, as long as known main trigonum ω ₂〉=0, ω ₁〉=ω ₂, ω ₁+ ω ₂Two spectrums in the≤π just can be described all two spectrums fully.The flow process that two spectrums are calculated is as follows:

1. N data with acoustical signal x (t) are divided into the K section, and every section contains M sample, i.e. N=KM;

2. remove the average of every segment data;

3. calculate the estimated value of three rank semi-invariants of each data segment;

4. getting the mean value of the three rank semi-invariants of all sections estimates as three rank semi-invariants of whole observation data group;

5. three rank semi-invariants are estimated to carry out two-dimension fourier transform, the two spectrums that obtain acoustical signal x (t) are estimated.At last, the audio signal characteristics value that just can obtain collecting by the two spectrum estimations on the above-mentioned wavelet transformed domain.

4) utilize the frequency response characteristic of independent component analysis method estimation from tested main noise source to sensor, and then realize the estimation of sound source direction of arrival, its step is as follows:

1. utilize hyperchannel microphone array or measurement mechanism to receive original sound-source signal vector x (t);

When 2. utilizing-frequency analysis technology (as short time discrete Fourier transform) make up acoustical signal the time-the frequency matrix;

3. utilize independent component analysis method, by the time-frequency response characteristic of frequency Matrix Estimation from tested main noise source to sensor---separation matrix;

4. estimate that from separation matrix the sound source ripple reaches the value of azimuth angle theta.

The ultimate principle of DOA estimation is:

Suppose to have the source signal s of P mixing _p(t) and Q sensors observe signal

Wherein, h _Qp(k) impulse response of expression from source p to sensor q.Allow d _qThe position (linear sensor arrangement) of expression sensor q, θ _pExpression source s _pOrientation (become with the quadrature of sensor array 90 degree).

By L point short time DFT, time-domain signal x _q(t) x _q(t) convert frequency domain time series signal X to _q(f), f=0 wherein, f _s/ L ..., f _s(L-1)/and L, m is a frame index.Therefore, mixture model on frequency can by

Represent.

Though signal mixes under the condition of echoing,, can be similar to and obtain an impulse response h _Qp(k) frequency response H _Pq(f) be

Wherein c is a velocity of propagation.If θ _pAs a variable θ, can obtain a guiding vector:

$a(f,\mathrm{\&theta;})={[{\mathrm{<figure-callout\; id="2"\; label="e\; j"\; filenames="HSA00000178563000011.png"\; state="\{\{state\}\}">e</figure-callout>}}^j,...,{\mathrm{<figure-callout\; id="2"\; label="e\; j"\; filenames="HSA00000178563000011.png"\; state="\{\{state\}\}">e</figure-callout>}}^j]}^T---\left(5\right)$

Thereby the observed quantity that sensor obtains can be expressed as

Wherein, X (f) is a Q dimensional vector X (f)=[X ₁(f) ..., X _Q(f)] ^T

If the separation matrix that is obtained by the ICA method can extract sound-source signal, and guarantee W (f) H (f)=I, thereby by H (f)=W ^-1(f) estimate the frequency response of commingled system.Here need to consider the yardstick of ICA decomposition and the uncertainty of signal source ordering.Therefore, should compare with the real frequency response of commingled system, each row of H (f) have yardstick and signal source ordering arbitrarily.Each element H of matrix H (f) _Pq(f) has amplitude arbitrarily.Because the approximate value of commingled system Do not meet this condition of front, so, rebuild at initial point and had decay A _Qp(real-valued) and phase modulation (PM)

The model of commingled system:

By calculating and corresponding two the element H of same source p _Qp(f) and H _{Q ' p}(f) ratio between, can ignore the uncertainty of yardstick:

$H_{\mathrm{qp}}/H_{q^{\&prime;}p}=A_{\mathrm{qp}}/A_{q^{\&prime;}\mathrm{<figure-callout\; id="2"\; label="p\; e\; j"\; filenames="HSA00000178563000011.png"\; state="\{\{state\}\}">p</figure-callout>}}{e}^j{2\mathrm{\&pi;}{\mathrm{fc}}^{-1}(d_q-d_{q^{\&prime;}})\cos {\mathrm{\&theta;}}_p}^{}---\left(7\right)$

Can obtain computer azimuth angle θ by top formula _pA formula

${\mathrm{\&theta;}}_p={\cos }^{-1}\frac{\mathrm{angle}(H_{\mathrm{qp}}/H_{q^{\&prime;}p})}{2\mathrm{\&pi;}{\mathrm{fc}}^{-1}(d_q-d_{q^{\&prime;}})}---\left(8\right)$

If to the cos that is discussed ^-1Absolute value greater than 1, θ then _pBecome plural number, and can not get azimuth information.In this case, formula (7) can calculate q and q ' with another.From formula, this method can obtain any amount of source signal.

Since the uncertainty of ordering, θ _pMay be not and s _pCorresponding, but corresponding with the another one source signal.Yet, by calculating all p=1 ..., the θ of P _p, can obtain the orientation of institute's active signal.By formula (8), the orientation can be by approximate estimating.

For the situation of two sound sources, can prove θ _pCan be obtained by formula (7), same, the minimum value of zero direction in the directional diagram also can be obtained by (7).In the directional diagram, | B _r(f, θ) |=w _r(f) a (f, θ), w wherein _r(f) be that the r of separation matrix frequency response is capable.When | B _r(f, θ) | for hour, θ is corresponding to be zero direction.Work as omission

During with f, minimize expression formula and be

$J\left(\mathrm{\&theta;}\right)=B_r\left(\mathrm{\&theta;}\right)\&CenterDot;B_r{\left(\mathrm{\&theta;}\right)}^{*}(W_{r1}{e}^{j{\mathrm{\&alpha;}}_1\cos \mathrm{\&theta;}}+W_{r2}{e}^{j{\mathrm{\&alpha;}}_2\cos \mathrm{\&theta;}})\&CenterDot;({\mathrm{<figure-callout\; id="1"\; label="W\; r"\; filenames="HSA00000178563000011.png"\; state="\{\{state\}\}">W</figure-callout>}}_r^1{e}^{-j{\mathrm{\&alpha;}}_1\cos \mathrm{\&theta;}}+{\mathrm{<figure-callout\; id="2"\; label="W\; r"\; filenames="HSA00000178563000011.png"\; state="\{\{state\}\}">W</figure-callout>}}_r^2{e}^{-j{\mathrm{\&alpha;}}_2\cos \mathrm{\&theta;}})---\left(9\right)$

As α=α ₂-α ₁The time, first order derivative and second derivative are respectively

$\frac{\mathrm{dJ}}{\mathrm{d\&theta;}}=-\mathrm{\&alpha;}\sin \mathrm{\&theta;}\&CenterDot;2\mathrm{im}\left(W_{r1}{\mathrm{<figure-callout\; id="2"\; label="W\; r"\; filenames="HSA00000178563000011.png"\; state="\{\{state\}\}">W</figure-callout>}}_r^2{e}^{-\mathrm{j\&alpha;}\cos \mathrm{\&theta;}}\right),$

Wherein, re and im represent real part and imaginary part.If

Be zero,

Be positive number, then the value of J (θ) is minimum.

$\mathrm{angle}(-W_{r1}{\mathrm{<figure-callout\; id="2"\; label="W\; r"\; filenames="HSA00000178563000011.png"\; state="\{\{state\}\}">W</figure-callout>}}_r^2)=\mathrm{\&alpha;}\cos {\mathrm{\&theta;}}_r^{\mathrm{null}}\&DoubleLeftRightArrow;{\mathrm{\&theta;}}_r^{\mathrm{null}}={\cos }^{-1}\frac{\mathrm{angle}(-{\mathrm{<figure-callout\; id="1"\; label="W\; r"\; filenames="HSA00000178563000011.png"\; state="\{\{state\}\}">W</figure-callout>}}_r/W_{r2})}{2\mathrm{\&pi;}{\mathrm{fc}}^{-1}(d_2-d_1)}---\left(10\right)$

Therefore, zero direction be by

Capable form of r, and Be by

With Obtain.Thereby can see θ ₁With

Be of equal value:

${\mathrm{\&theta;}}_1={\cos }^{-1}\frac{\mathrm{angle}(H_{21}/H_{11})}{2\mathrm{\&pi;}{\mathrm{fc}}^{-1}(d_2-d_1)}={\cos }^{-1}\frac{\mathrm{angle}(-W_{21}/W_{22})}{2\mathrm{\&pi;}{\mathrm{fc}}^{-1}(d_2-d_1)}={\mathrm{\&theta;}}_2^{\mathrm{null}}---\left(11\right)$

Can obtain all position angles according to formula (8) and (11).

Wavelet transformation and higher-order spectrum analysis are mainly used in the analysis and the identification of noise source, with the frequency response characteristic of independent component analysis method estimation from the source to the sensor, and then the estimation of realization sound source direction of arrival.Their binding data collecting devices have been formed the noise source test analytic system based on wavelet transformation and higher-order spectrum analysis jointly.

As shown in Figure 1, realize the noise source test analytical equipment of this algorithm, comprise microphone (1), signal conditioner (2), capture card (3), computing machine (4), data acquisition software (5) and data analysis software (6); The sound wave that is stung main noise source that described microphone (1) receives, after signal conditioner (2) amplification, transfer to computing machine (4) through capture card (3), carry out analog to digital conversion and show, store acoustical signal in real time by carrying out data acquisition software (5), realize the test of tested main noise source by data analysis software (6) computing, comprise DOA estimation and identification of sound source.

Above embodiment only for explanation technological thought of the present invention, can not limit protection scope of the present invention with this, every technological thought that proposes according to the present invention, and any change of being done on the technical scheme basis all falls within the protection domain of the present invention.

Claims (7)

1. a noise source test analytical approach is characterized in that, may further comprise the steps:

1) adopt microphone to receive the sound wave of tested main noise source radiation;

2) sound wave of tested main noise source radiation is sampled; After the analog-to-digital conversion, the acoustical signal of tested main noise source is preserved, shown;

3) utilize wavelet transformation and higher-order spectrum analytical technology that the acoustical signal of the tested main noise source of step 2 preservation is handled, obtain the signal characteristic value, be used to discern noise source;

4) utilize the frequency response characteristic of independent component analysis method estimation, and then realize the estimation of sound source direction of arrival from tested main noise source to sensor.

2. a kind of noise source test analytical approach according to claim 1 is characterized in that the microphone of described step 1 is a microphone array, and the microphone number is greater than 1.

3. a kind of noise source test analytical approach according to claim 1 is characterized in that described step 3 comprises the steps:

1) voice signal is carried out wavelet transformation, voice signal is decomposed into the details component and the approximate component of different scale, by analysis to components at different levels, determine the scale component that main noise source and ground unrest are concentrated, and will contain the scale component filtering of ground unrest, the scale component that only keeps the main noise source place is as two spectrum estimated signals sequences;

2) with the burst behind the wavelet transformation, the two spectrums of the property advanced are estimated, calculate two spectrum signatures of main noise source.

4. a kind of noise source test analytical approach according to claim 3 is characterized in that: use formula

${\mathrm{WT}}_x(a,b)=\frac{1}{\sqrt{a}}\&Integral;x\left(t\right)\mathrm{\&Psi;}\left[\frac{t-b}{a}\right]=<x\left(t\right),{\mathrm{\&Psi;}}_{a,b}\left(t\right)>$

Carry out wavelet transformation, wherein a is a scale factor, and b is the time shift factor.Ψ _{A, b}(t) be that female small echo Ψ (t) is through displacement and the flexible cluster function that is produced.

5. a kind of noise source test analytical approach according to claim 3 is characterized in that: use formula

Calculate two spectrum signatures of acoustical signal.

6. a kind of noise source test analytical approach according to claim 1 is characterized in that the concrete steps of described step 4 are:

1) utilize microphone array to receive tested main noise source signal vector x (t);

When 2) utilizing-frequency analysis technique construction acoustical signal the time-the frequency matrix;

3) utilize the independent component analysis technology, by the time-frequency response characteristic of frequency Matrix Estimation from tested main noise source to sensor---separation matrix;

4) estimate that from separation matrix the sound source ripple reaches the value of azimuth angle theta.

7. realize the described noise source test analytical equipment of claim 1 for one kind, it is characterized in that this device comprises microphone, signal conditioner, computing machine, data acquisition software and data analysis software; The sound wave of the tested main noise source that described microphone receives transfers to computing machine through capture card after saving amplification by signal condition, carries out analog to digital conversion and shows, stores acoustical signal in real time by data acquisition software, by the identification of data analysis software realization noise source.

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