CN1805011A - Adaptive filter method and apparatus for improving speech quality of mobile communication apparatus - Google Patents

Adaptive filter method and apparatus for improving speech quality of mobile communication apparatus Download PDF

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CN1805011A
CN1805011A CNA2005101351203A CN200510135120A CN1805011A CN 1805011 A CN1805011 A CN 1805011A CN A2005101351203 A CNA2005101351203 A CN A2005101351203A CN 200510135120 A CN200510135120 A CN 200510135120A CN 1805011 A CN1805011 A CN 1805011A
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voice
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林中松
王箫程
邓昊
冯宇红
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Vimicro Corp
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Abstract

The invention relates to a self-adapting wave filter method which adjusts the self-adapting step length by computing the probability of the vice. It also relates to the self-adapting method application in the FD-NLMS analog of the mobile communicating device.

Description

A kind of adaptive filter method and device that improves the mobile communication equipment voice quality
Technical field
The present invention relates to the auto-adaptive filtering technique in the communications field, relate in particular to improved NLMS (the Frequency-Domain Normalized Least Mean Squares) algorithm that is used for mobile communication equipment.
Background technology
NLMS (Normalized Least Mean Squares) algorithm is a kind of common adaptive algorithm.Fig. 1 carries out the block diagram of noise reduction process for using the NLMS wave filter.Need two microphones when using the NLMS wave filter to carry out noise reduction process, one of them microphone is away from the speaker, and near noise source, the signal of being gathered is a reference noise signal; Another microphone is gathered by the voice signal of noise pollution as far as possible near the speaker.Yet in mobile communcations system, this requirement often is false.Because mobile communication equipment itself is size-constrained, often still can gather into more voice away from speaker's microphone.If the NLMS adaptive filter algorithm is not carried out suitable change, the voice after this situation will cause handling have tangible echo, and voice quality is descended.
FD-NLMS is frequency domain (Frequency-Domain) NLMS algorithm, it is a kind of form of NLMS algorithm, its purpose mainly is in order to save the calculated amount of NLMS auto adapted filtering, to replace the convolution on the time domain and ask relevant calculating in the method for using frequency domain to multiply each other.Its detailed principle document " sef-adapting filter theory " (Adaptive Filter Theory) that sees reference, Fourth Edition, SimonHaykin.
Fig. 2 has provided and has utilized FD-NLMS to eliminate the algorithm block diagram of noise processed in the prior art.
The step of the FD-NLMS algorithm of prior art (referring to) as follows:
1) N new reference noise sample v[0 of reference noise signal end input] ..., v[N-1], and and N the sample v ' [0] of last input,, v ' [N-1] forms the vector V of a new 2N length, V={v ' [0] ... v ' [N-1], v[0] ... v[N-1] };
2) V is carried out the FFT computing, obtain its FFT coefficient vector U, U=FFT (V)=u[0], u[1] ... u[2N-1] }, the length of U is 2N;
3) the FFT coefficient F of U and sef-adapting filter shock response WCarry out dot product, product is carried out IFFT, obtain Y '.Y ' is real number vector (imaginary part is 0), abandons the top n value of Y ', makes it become a new vector Y, Y={y[0] ..., y[N-1] }, its length is N;
4) the new noisy speech s[0 of noisy speech end input] ..., s[N-1], obtain E, E={e[0 after S and Y subtract each other] ..., e[N-1] }=s[0]-y[0] ..., s[N-1]-y[N-1], E is the voice signal after strengthening;
5) insert N 0 value in the front of E and form vector E ', E '=0 ..., and 0, e[0] ..., e[N-1] }, E ' is carried out the FFT computing obtain vector F, F=FFT (E ');
6) vector U is got conjugation and obtain U H, U H=U, U HCarry out dot product with F and obtain vector G '.G ' is carried out the IFFT computing obtain vector H, H=IFFT (G '), H are that length is the real number vector (imaginary part is 0) of 2N, the back N dimension value zero setting of H, and carry out the FFT computing, obtain vector G, and G is that length is the complex vector of 2N;
7) upgrade vector F W, F W=F W+ μ G, μ are constant, perhaps a μ=c/E n, c is a constant, E n(its method of estimation does not here describe in detail, asks for an interview list of references " AdaptiveFilter Theory ", 4 for the current energy that estimates ThEdith, Simon Haykin work);
8) get back to step 1, till phonetic entry is intact.
In said method, when calculating clean speech, in fact utilized following formula:
e [ n ] = s [ n ] - Σ i = 0 N - 1 u [ n - i ] w [ i ] - - - ( 1 )
In formula (1), s[n] represent that noisy speech is at n value constantly, u[n] represent that reference noise is at n value constantly, w[i] and be the coefficient of sef-adapting filter, sef-adapting filter is the N-1 rank.Formula (1) shows, if contain strong phonetic element among the reference noise u, this phonetic element is relevant with phonetic element among the noisy speech S, may cause the estimated value of auto adapted filtering y [ n ] = Σ i = 0 N - 1 u [ n - i ] w [ i ] Principal ingredient is not a noise, but the linear combination and the clean speech of current voice and former voice had correlationship.E[n like this] might concerning original pure voice, obtain a less value earlier, and also because e[n] may contain the linear combination of former phonetic element, cause a kind of echo phenomenon.
Summary of the invention
The purpose of this invention is to provide the adaptive filter method and the device that can improve the mobile communication equipment voice quality.
To achieve these goals, the invention provides a kind of adaptive filter method, it comprises the steps:
1) gathers one section Noisy Speech Signal;
2) gather one section reference noise signal;
3) with described reference noise input adaptive wave filter, described Noisy Speech Signal is carried out filtering according to current auto adapted filtering coefficient;
4) in Noisy Speech Signal, get rid of filtering reference noise afterwards and obtain the clean speech signal;
5) calculate the probability that contains voice in adaptive step and the noisy speech, adjust this adaptive step according to the probability that contains voice in the current reference noise;
6) according to clean speech signal, reference noise, adjusted auto adapted filtering step-length to current auto adapted filtering coefficient update.
According to a further aspect in the invention, also provide a kind of improved FD-NLMS algorithm that is applicable to mobile communication equipment, it comprises the steps:
1) N new reference noise sample v[0 of reference noise signal end input] ..., v[N-1], and and N the reference noise sample v ' [0] of last input,, v ' [N-1] forms the vector V of a new 2N length, V={v ' [0] ... v ' [N-1], v[0] ... v[N-1] };
2) V is carried out the FFT computing, obtain its FFT coefficient vector U, U=FFT (V)=u[0], u[1] ... u[2N-1] }, the length of U is 2N;
3) the FFT coefficient F of U and sef-adapting filter shock response WCarry out dot product, product is carried out IFFT, obtain Y ', wherein Y ' is the real number vector, and its imaginary part is 0, abandons the top n value of Y ', makes it become a new vector Y, Y={y[0] ..., y[N-1] }, its length is N;
4) the new noisy speech s[0 of noisy speech end input] ..., s[N-1], obtain E, E={e[0 after S and Y subtract each other] ..., e[N-1] }=s[0]-y[0] ..., s[N-1]-y[N-1], E is the language signal after strengthening;
5) insert N 0 value in the front of E and form vector E ', E '=0 ..., and 0, e[0] ..., e[N-1] }, E ' is carried out the FFT computing obtain vector F, F=FFT (E ');
6) vector U is got conjugation and obtain U H, U H=U, U HCarry out dot product with F and obtain vector G '.G ' is carried out the IFFT computing obtain vector H, H=IFFT (G '), H are that length is the real number vector of 2N, and its imaginary part is 0, the back N dimension value zero setting of H, and carries out the FFT computing, obtains vector G, and G is that length is the complex vector of 2N;
7) analytical procedure 2) in FFT coefficient vector U, calculate the probability that contains voice in adaptive step μ and the current reference noise, wherein μ is constant, perhaps μ=c/E n, c is a constant, En is the current energy that estimates; Adjust adaptive step μ according to the probability that contains voice in the current reference noise, when the probability that contains voice is high, reduce step size mu, when the probability that contains voice is low, increase step size mu;
8) upgrade vector F W, F W=F W+ μ G, F WUse when the filtering next time as the FFT coefficient of new sef-adapting filter shock response;
9) get back to step 1), till phonetic entry is intact.
In said method of the present invention, on the one hand, in above-mentioned steps 7) in, can carry out the voice posterior probability estimation to current FFT coefficient U with reference to noise, output vector p={p[0], ..., p[N-1] }, p[i wherein] be illustrated in the voice posterior probability that exists on each FFT coefficient, like this, it is p '=1-p that there is the posterior probability vector of noise in each FFT coefficient, and p ' and step size mu multiply each other then, obtain new adaptive step vector μ → = { μ [ 0 ] , . . . , μ [ N - 1 ] } , It has provided the adaptive step on each FFT coefficient.
Wherein, above-mentioned new adaptive step
Figure A20051013512000102
After posterior probability p ' by making noise and step size mu multiply each other, be multiplied by rule of thumb determined constant C 1 again and obtain, so that new adaptive step There is suitable size to guarantee convergence of algorithm.
On the other hand, in above-mentioned steps 7) in, can find the device processing through speech activity to current FFT coefficient U with reference to noise, the output probability α that voice exist in N reference noise sample of current input, calculate the probability β=1-α of noise in current existence, β and step size mu multiply each other then, obtain new adaptive step
Figure A20051013512000104
When speech activity finds that device adopts the mode of hard decision, its α=0 o'clock, expression does not have voice, α=1 o'clock, expression has voice; When speech activity found that device adopts the mode of soft-decision, its α was likelihood probability 0≤α≤1, the probability that the expression voice exist.
The present invention also provides a kind of adaptive filter device, and it comprises: sef-adapting filter is used for estimating according to reference noise signal the noise of voice signal; Subtracter is used for deducting the estimated noise that comes out of sef-adapting filter from Noisy Speech Signal, obtains and voice signal behind the noise is eliminated in output, and the voice signal that will eliminate simultaneously behind the noise feeds back to sef-adapting filter as feedback signal; The step-length control module, it calculates the probability that contains voice in the current reference noise, thereby the described sef-adapting filter of the probability that has noise in the acquisition reference noise calculates adaptive step μ, exist the probability of noise to adjust adaptive step μ in the reference noise that is obtained according to the step-length control module, upgrade the auto adapted filtering coefficient, feed back to wherein feedback signal according to described subtracter then and reappraise noise in the voice signal
The present invention also provides a kind of frequency domain adaptive filtering device that improves the mobile communication equipment voice quality, and described mobile communication equipment comprises at least two Mikes, and one of them Mike is used to import reference noise signal, and another Mike is used to import noisy speech.Described frequency domain adaptive filtering device comprises: input block, be used for N new reference noise sample v[0 of input],, v[N-1] and last N the reference noise sample v ' [0] that imports ... v ' [N-1] forms the vector V of a new 2N length, V={v ' [0] ..., v ' [N-1], v[0] ... v[N-1] }; The FFT arithmetic element is used for vector V is carried out the FFT computing, obtains its FFT coefficient vector U, U=FFT (V)=u[0], u[1] ... u[2N-1] }, the length of U is 2N; Filter unit is used for the FFT coefficient F with vector U and sef-adapting filter shock response WCarry out dot product; The IFFT arithmetic element is used for the resultant product of filter unit is carried out IFFT, obtains Y ', and wherein Y ' is the real number vector, and its imaginary part is 0, abandons the top n value of Y ', makes it become a new vector Y, Y={y[0] ..., y[N-1] }, its length is N; Output unit is used for the new noisy speech vector S=s[0 with noisy speech end input] ..., s[N-1] obtain vector E after subtracting each other with vector Y, E={e[0] ..., e[N-1] }=s[0]-y[0],, s[N-1]-y[N-1], the voice signal E after output strengthens; Auto adapted filtering coefficient update unit, the front of the voice signal vector E that it is exported at output unit 5 insert N 0 value formation vector E '=0 ..., 0, e[0] ..., e[N-1] }, E ' is carried out the FFT computing obtains vector F, F=FFT (E '); Vector U is got conjugation obtain U H, U H=U, U HCarry out dot product with F and obtain vector G '; G ' is carried out the IFFT computing obtain vector H, H=IFFT (G '), H are that length is the real number vector of 2N, and its imaginary part is 0, the back N dimension value zero setting of H, and carries out the FFT computing, obtains vector G, and G is that length is the complex vector of 2N; Analyze the resulting FFT coefficient vector of FFT arithmetic element U and obtain upgrading step size mu, pass through F W=F W+ μ G upgrades the FFT coefficient vector F of sef-adapting filter shock response WWherein said auto adapted filtering updating block comprises the step-length control module, and it analyzes the resulting FFT coefficient vector of FFT arithmetic element U, calculates the probability that contains voice in adaptive step μ and the current reference noise, and wherein μ is constant, perhaps μ=c/E n, c is a constant, En is the current energy that estimates; Adjust adaptive step μ according to the probability that contains voice in the current reference noise, when the probability that contains voice is high, reduce step size mu, when the probability that contains voice is low, increase step size mu.
According to said apparatus of the present invention, described step-length control module comprises: voice posterior probability estimation unit, current FFT coefficient U with reference to noise is carried out the voice posterior probability estimation, output vector p={p[0] ..., p[N-1], p[i wherein] be illustrated in the voice posterior probability that exists on each FFT coefficient, i=0 ..., N-1; Noise probability calculation unit, according to voice posterior probability estimation unit resultant voice posterior probability p[i], obtaining the posterior probability vector that there is noise in each FFT coefficient is p '=1-p, p ' and step size mu are multiplied each other, obtain new adaptive step vector μ={ μ [0], ..., μ [N-1] }, μ [i] has provided the adaptive step on each FFT coefficient.
According to said apparatus of the present invention, described step-length control module comprises: speech activity is found device, current FFT coefficient U with reference to noise is handled the output probability α that voice exist in current N reference noise sample; Noise probability calculation unit according to finding the resultant probability α of device at speech activity, calculates the probability β=1-α of noise in current existence, and β and step size mu multiply each other then, obtains the new self-adaptation step
Figure A20051013512000121
Described speech activity finds that device adopts the mode of hard decision, and when α=0, expression does not have voice, α=1 o'clock, and expression has voice.
Utilize the present invention, can avoid the echo phenomenon, and when effectively eliminating noise, do not cause the reduction of voice quality.
Description of drawings
Fig. 1 carries out the block diagram of noise reduction process for using the NLMS wave filter;
Fig. 2 has provided and has utilized FD-NLMS to eliminate the algorithm block diagram of noise processed in the prior art;
Fig. 3 has provided the synoptic diagram of adaptive filter method of the present invention;
Fig. 4 has provided the algorithm block diagram that the FD-NLMS of utilization of the present invention eliminates noise processed;
Fig. 5 has provided a kind of implementation of step-length control of the present invention;
Fig. 6 has provided the another kind of implementation of step-length control of the present invention;
Fig. 7 has provided the synoptic diagram of adaptive filter device of the present invention;
Fig. 8 has provided the adaptive filter device structural representation of raising mobile communication equipment voice quality of the present invention;
Fig. 9 has provided the synoptic diagram of a kind of step-length control module of the present invention;
Figure 10 has provided the synoptic diagram of another kind of step-length control module of the present invention.
Embodiment
Describe the specific embodiment of the present invention in detail below in conjunction with accompanying drawing, but these embodiments are not limitation of the present invention.
Because the mobile communication equipment size is little, so when the small type mobile devices that adopts the diamylose gram, as mobile phone, when having two adjacent speech sources and noise source, the diamylose gram, promptly Mike A and Mike B record speech source, suppose that Mike A is near and far away from noise source from speech source, Mike B is near and far away from speech source from noise source, at this moment, can be recorded to the voice signal of a two-channel by Mike A and B.Yet because the existence of noise source, Mike A and B can be recorded to noise, thereby cause both poor sound quality.Be example just more than with two adjacent speech sources and noise source, in fact, multiple often speech source and noise source coexistence, at this moment, noise will become more serious to the influence of voice.
In this case,, reduce to export the echo in the voice, improve voice quality, can adopt following adaptive filter method in order to reduce or to eliminate noise.As shown in Figure 3, suppose that the signal that Mike A is recorded is a noisy speech, the signal that Mike B is recorded is a reference noise.Adaptive filter method of the present invention comprises the steps:
Gather one section Noisy Speech Signal and one section reference noise signal;
With the reference noise signal input adaptive wave filter of being gathered 11;
Sef-adapting filter 11 is according to its auto adapted filtering coefficient, utilizes reference noise signal to estimate noise in the Noisy Speech Signal;
Utilize subtracter 12, deduct the estimated noise that goes out of sef-adapting filter 11 in the Noisy Speech Signal from channel A, obtain the clean speech signal;
The clean speech signal is offered sef-adapting filter 11 as feedback signal;
Calculate the probability that contains voice in adaptive step μ and the noisy speech, wherein μ=c/E n, c is a constant, E nBe the energy of the current reference noise signal that estimates, adjust adaptive step μ, when the probability that contains voice is high, reduce adaptive step μ, when the probability that contains voice is low, increase adaptive step μ according to the probability that contains voice in the current reference noise;
According to clean speech signal, reference noise signal, adjusted auto adapted filtering step size mu, upgrade current auto adapted filtering coefficient according to following formula:
W [ k + 1 ] = W [ k ] + μ ‾ Σ i = 0 L - 1 U ( kL + i ) e ( kL + i )
U(kL+i)＝{u(kL+i)，u(kL+i-1)……u(kL+i-N+1)}
Here, suppose that sef-adapting filter is the N rank, W[k+1] be the k+1 time auto adapted filtering coefficient, L upgrades an auto adapted filtering coefficient W[k after representing L reference noise sample], u (kL+i) represents kL+i reference noise value constantly, e[kL+i] represent the kL+i clean noise figure in the moment.Wherein L can be for greater than 1 natural number, and L equals N usually.
In above-mentioned adaptive filter method of the present invention, adjusted auto adapted filtering step size mu can be undertaken by following dual mode:
A kind of mode is, carries out the voice posterior probability estimation to current with reference to noise, obtains to exist the posterior probability of noise, and this probability and adaptive step multiply each other then, obtain new adaptive step.Particularly, utilize existing NMLS algorithm computation to go out self-adaptation step size mu=c/E n, c is a constant, E nEnergy for the current reference noise signal that estimates; Import L reference noise signal, this L reference noise signal and before this L reference noise signal form the vector of length 2L, be transformed in the frequency domain by FFT, estimate the probability P that voice exist on each Frequency point then, P=P={p[0], P[1] ... p[2L-1] }.About the method for estimation of P, at document " Speech enhancement for non-stationary noise environments ", IsraelCohen *, Baruch Berdugo, Signal Processing (2001), Elsevier discloses a kind of.Calculate the adaptive step on each Frequency point
Figure A20051013512000141
:
μ＝u·c 1·(1-P)＝{u·c 1·(1-p[0])，u·c 1·(1-p[1])，...，u·c 1·p[2L-1]}
Wherein, the constant of C1 for rule of thumb drawing.
Another kind of mode is, finds that through speech activity device handles with reference to noise to current, calculates the probability of the current existence of noise, and this probability and adaptive step multiply each other then, obtain new adaptive step.Particularly, utilize existing NMLS algorithm computation to go out self-adaptation step size mu=c/E n, c is a constant, E nEnergy for the current reference noise signal that estimates; Find the individual reference noise signal of device input L (L＞=1) to speech activity; Speech activity is found the value of device according to a current L reference noise signal, estimates the Probability p that voice exist in a current L reference noise, and p is a scalar; Calculate the adaptive step μ of this L reference noise signal correspondence, μ=uc 2(1-p).C wherein 2Be the constant that rule of thumb draws.
Said method of the present invention can be applied in the existing NLMS adaptive filter algorithm, to make improvements.As shown in Figure 4, it has provided the frequency domain adaptive filtering method of raising mobile communication equipment voice quality of the present invention, it is used to comprise at least two Mikes' mobile communication equipment, one of them Mike is the reference noise signal input end, another Mike is the noisy speech input end, here, the phonetic element of signals with noise end is stronger than the phonetic element of reference noise input end, a little less than the noise contribution of the noise contribution of signals with noise end than reference noise input end.The frequency domain adaptive filtering method of raising mobile communication equipment voice quality of the present invention comprises the steps:
1) N new reference noise sample v[0 of reference noise signal end input] ..., v[N-1], and and N the reference noise sample v ' [0] of last input,, v ' [N-1] forms the vector V of a new 2N length, V={v ' [0] ... v ' [N-1], v[0] ... v[N-1] };
2) V is carried out the FFT computing, obtain its FFT coefficient vector U, U=FFT (V)=u[0] ..., u[2N-1] }, the length of U is 2N;
3) the FFT coefficient FW of U and sef-adapting filter shock response carries out dot product, and product is carried out IFFT, obtains Y ', and wherein Y ' is real number vector (imaginary part is 0), abandon the top n value of Y ', make it become a new vector Y, Y={y0],, y[N-1] }, its length is N;
4) the new noisy speech s[0 of noisy speech end input] ..., s[N-1], obtain E, E={e[0 after S and Y subtract each other] ..., e[N-1] }=s[0]-y[0] ..., s[N-1]-y[N-1], the voice signal E after output strengthens;
5) insert N 0 value in the front of E and form vector E ', E '=0 ..., and 0, e[0] ..., e[N-1] }, E ' is carried out the FFT computing obtain vector F, F=FFT (E ');
6) vector U is got conjugation and obtain U H, U H=U, U HCarry out dot product with F and obtain vector G '.G ' is carried out the IFFT computing obtain vector H, H=IFFT (G '), H are that length is the real number vector (imaginary part is 0) of 2N, the back N dimension value zero setting of H, and carry out the FFT computing, obtain vector G, and G is that length is the complex vector of 2N;
7) analyze in step 2) in FFT coefficient vector U, calculate the probability that contains voice in step size mu and the current reference noise, wherein μ is constant, perhaps μ=c/E n, c is a constant, En is the energy of the current reference noise signal that estimates; Adjusting adaptive step μ according to the probability that contains voice in the current reference noise is μ , when the probability that contains voice is high, reduce step size mu, when the probability that contains voice is low, increase step size mu; Wherein the computing method of step size mu are identical with the computing method of step size mu in the existing FD-NLMS algorithm;
8) upgrade vector F W, F W=F W+ μ G.
9) get back to step 1), till phonetic entry is intact.
Fig. 5 has provided a kind of implementation method of step-length control of the present invention.As shown in Figure 5, current FFT coefficient vector U with reference to noise is carried out the voice posterior probability estimation, exports a vector p={p[0] ..., p[N-1], p[i] be illustrated in the posterior probability that has voice on each FFT coefficient.The voice posterior probability estimation is a kind of existing technology, about the visible list of references of its specific algorithm " Speechenhancement for non-stationary noise environments ", Israel Cohen *, BaruchBerdugo, Signal Processing (2001), Elsevier.After voice posterior probability p calculated, each FFT coefficient existed the posterior probability p ' of noise to draw by p '=1-p.P ' and step size mu (scalar) multiply each other, and obtain new adaptive step μ → = { μ [ 0 ] , . . . , μ [ N - 1 ] } 。Wherein μ adapts to the step-length that step size computation draws with existing FD-NLMS algorithm, about the concrete mode of its calculating referring to document " Adaptive Filter Theory ", 4 ThEdition, Simon Haykin work. μ → = { μ [ 0 ] , . . . , μ [ n - 1 ] } Provided the adaptive step on each FFT coefficient.In order to make adaptive step be more suitable for the present situation, new adaptive step
Figure A20051013512000154
Can be multiplied by rule of thumb determined constant C 1 again and obtain by after p ' and step size mu (scalar) are multiplied each other.
Fig. 6 has provided another kind of step-length control implementation method of the present invention.Speech activity among Fig. 6 finds that device (Voice Activity Detector is called for short VAD) is a prior art.The FFT coefficient vector U of reference noise is input to speech activity and finds device VAD, and VAD is output as factor alpha.α is illustrated in the probability that voice exist in N the reference noise sample of current input.VAD has hard decision or two kinds of forms of soft-decision.When adopting hard decision, speech activity is found device output α=0 (not having voice) or α=1 (voice are arranged), and when adopting soft-decision, speech activity finds that device is output as likelihood probability 0≤α≤1, the probability that the expression voice exist.Use β=1-α to calculate the probability β of noise in current existence, β and step size mu multiply each other then, obtain new adaptive step In such cases
Figure A20051013512000162
Be scalar, promptly on all FFT coefficient points, all adopt identical adaptation step-length
The present invention also provides a kind of adaptive filter device, and as shown in Figure 7, it comprises: sef-adapting filter is used for estimating according to reference noise signal the noise of voice signal; Subtracter is used for deducting the estimated noise that comes out of sef-adapting filter from Noisy Speech Signal, obtains and voice signal behind the noise is eliminated in output, and the voice signal that will eliminate simultaneously behind the noise feeds back to sef-adapting filter as feedback signal; The step-length control module, it calculates the probability that contains voice in the reference noise, thereby obtains to exist in the reference noise probability of noise; Wherein, described sef-adapting filter calculates adaptive step μ, exist the probability of noise to adjust adaptive step μ in the reference noise that is obtained according to the step-length control module, upgrade the auto adapted filtering coefficient, feed back to wherein feedback signal according to described subtracter then and reappraise noise in the voice signal.
Wherein, described step-length control module can be realized by following scheme: it comprises: voice posterior probability estimation unit, carry out the voice posterior probability estimation to current with reference to noise, the voice posterior probability that obtains existing; Noise probability calculation unit according to the resulting voice posterior probability in voice posterior probability estimation unit, obtains existing the posterior probability of noise.Described step-length control module can also be realized by following proposal: it comprises speech activity discovery device, handles the probability α that voice exist in the output reference noise sample with reference to noise to current; Noise probability calculation unit according to finding the resultant probability α of device at speech activity, calculates the probability β=1-α of noise in current existence, and β and adaptive step μ multiply each other then, obtain new adaptive step
Figure A20051013512000164
Wherein, described speech activity finds that device can adopt the mode of hard decision, and when α=0, expression does not have voice, α=1 o'clock, and expression has voice.Described speech activity finds that device also can adopt the mode of soft-decision, and α is likelihood probability 0≤α≤1.
Step-length control module wherein can be independent of the sef-adapting filter setting, as shown in Figure 7; Also can be arranged within the sef-adapting filter.When the step-length control module was arranged in the sef-adapting filter, can also combine with the adaptive step computing function constituted a step size computation and control module.
Fig. 8 has provided the frequency domain adaptive filtering device of raising mobile communication equipment voice quality of the present invention.Described mobile communication equipment comprises at least two microphones, and a Mike is used to import reference noise signal, and a Mike is used to import noisy speech.The frequency domain adaptive filtering device comprises input block 1, FFT arithmetic element 2, auto adapted filtering coefficient update unit 5, filter unit 3, IFFT arithmetic element 4, output unit 6.Wherein input block 1 will be imported N new reference noise sample v[0] ..., v[N-1] and last N the reference noise sample v ' [0] that imports ... v ' [N-1] forms the vector V of a new 2N length, V={v ＇ [0] ... v ' [N-1], v[0] ... v[N-1] }; 2 pairs of vector V of FFT arithmetic element are carried out the FFT computing, obtain its FFT coefficient vector U, U=FFT (V)=u[0], u[1] ... u[2N-1] }, the length of U is 2N; Filter unit 3 is with the FFT coefficient F of vector U and sef-adapting filter shock response WCarry out dot product; 4 pairs of resultant products of filter unit of IFFT arithmetic element carry out IFFT, obtain Y ', and wherein Y ' is the real number vector, and its imaginary part is 0, abandons the top n value of Y ', make it become a new vector Y, Y={y[0] ..., y[N-1] }, its length is N; Output unit 6 is used for noisy speech vector S=s[0 that noisy speech end input is new] ..., s[N-1] obtain vector E after subtracting each other with vector Y, E={e[0] ..., e[N-1] }=s[0]-y[0],, s[N-1]-y[N-1], E is the voice signal after strengthening.The front of the voice signal vector E that it is exported at output unit 5 insert N 0 value formation vector E '=0 ..., 0, e[0] ..., e[N-1] }, E ' is carried out the FFT computing obtains vector F, F=FFT (E '); Vector U is got conjugation obtain U H, U H=U, U HCarry out dot product with F and obtain vector G '; G ' is carried out the IFFT computing obtain vector H, H=IFFT (G '), H are that length is the real number vector of 2N, and its imaginary part is 0, the back N dimension value zero setting of H, and carries out the FFT computing, obtains vector G, and G is that length is the complex vector of 2N; Analyze the resulting FFT coefficient vector of FFT arithmetic element U, pass through F W=F W+ μ G upgrades the FFT coefficient vector F of sef-adapting filter shock response W, wherein μ is the adaptive step of control renewal speed; Wherein said auto adapted filtering updating block 5 comprises step size computation and control module 51, it is used to analyze FFT arithmetic element 2 resulting FFT coefficient vector U, calculate the probability that contains voice in adaptive step μ and the current reference noise, wherein μ is constant, perhaps μ=c/E n, c is a constant, En is the current energy that estimates; Adjusting adaptive step μ according to the probability that contains voice in the current reference noise is μ, when the probability that contains voice is high, reduces step size mu, when the probability that contains voice is low, increases step size mu.
In frequency domain adaptive filtering device shown in Figure 8, described step size computation and control module 51 can adopt structure shown in Figure 9.As shown in Figure 9, step size computation and control module 51 comprise: voice posterior probability estimation unit 511, current FFT coefficient U with reference to noise is carried out the voice posterior probability estimation, output vector p={p[0] ..., p[N-1], p[i wherein] be illustrated in the voice posterior probability that exists on each FFT coefficient, i=0 ..., N-1; Noise probability calculation unit 512 is according to voice posterior probability estimation unit 511 resultant voice posterior probability p[i], obtaining the posterior probability vector that there is noise in each FFT coefficient is p '=1-p, and p ' and step size mu are multiplied each other, and obtains new adaptive step vector μ → = { μ [ 0 ] , . . . , μ [ N - 1 ] } , μ [i] has provided the adaptive step on each FFT coefficient.Wherein in order to make new adaptive step
Figure A20051013512000182
More be applicable to current environment, after the posterior probability p ' that makes noise multiplies each other with step size mu, can be multiplied by rule of thumb determined constant C 1 again.
In frequency domain adaptive filtering device shown in Figure 8, described step size computation and control module 51 also can adopt structure shown in Figure 10.As shown in figure 10, step size computation and control module 51 comprise: speech activity is found device 513, current FFT coefficient U with reference to noise is handled the output probability α that voice exist in current N reference noise sample; Noise probability calculation unit 512 according to finding the resultant probability α of device at speech activity, calculates the probability β=1-α of noise in current existence, and β and step size mu multiply each other then, obtain new adaptive step
Figure A20051013512000183
Described speech activity finds that device 513 can adopt the mode of hard decision also can adopt the mode of soft-decision.When speech activity found that device 513 adopts the mode of hard decisions, in α=0 o'clock, expression did not have voice, α=1 o'clock, and expression has voice; When adopting the mode of soft-decision, α is likelihood probability 0≤α≤1.
In said apparatus, described step size computation and control module 51 also can be divided into the step size computation module and these two functional modules of step-length control module realize according to function.
The present invention can be applied to mobile communication equipment, for example in the mobile phone etc., can improve voice quality effectively, reduces noise, avoids echo.
Foregoing is not to be used for limiting the specific embodiment of the present invention, and all modification and change or combinations of carrying out according to the main inventive concept of this method all should belong to protection domain of the presently claimed invention.

Claims (19)

1. an adaptive filter method is characterized in that, comprises the steps:
1) gathers one section Noisy Speech Signal;
2) gather one section reference noise signal;
3) with described reference noise input adaptive wave filter, described Noisy Speech Signal is carried out filtering according to current auto adapted filtering coefficient;
4) from Noisy Speech Signal, get rid of filtering reference noise afterwards and obtain the clean speech signal;
5) calculate the probability that contains voice in adaptive step and the noisy speech, adjust this adaptive step according to the probability that contains voice in the current reference noise;
6) according to clean speech signal, reference noise, adjusted adaptive step to current auto adapted filtering coefficient update.
2. method according to claim 1 is characterized in that,
In described step 5), when the probability that contains voice is high, reduce adaptive step, when the probability that contains voice is low, increase adaptive step.
3. method according to claim 1 and 2 is characterized in that,
In described step 5), carry out the voice posterior probability estimation to current with reference to noise, obtain to exist the posterior probability of noise, this probability and adaptive step multiply each other then, obtain new adaptive step.
4. method according to claim 1 and 2 is characterized in that,
In described step 5), find device processing with reference to noise through speech activity to current, calculate the probability of the current existence of noise, this probability and adaptive step multiply each other then, obtain new adaptive step.
5. frequency domain adaptive filtering installation method that improves the mobile communication equipment voice quality, be used to comprise at least two Mikes' mobile communication equipment, one of them Mike is the reference noise signal input end, and another Mike is the noisy speech input end, it is characterized in that, comprise the steps:
1) N new reference noise sample v[0 of reference noise signal end input] ..., v[N-1], and and N the reference noise sample v ' [0] of last input,, v ' [N-1] forms the vector V of a new 2N length, V={v ' [0] ... v ' [N-1], v[0] ... v[N-1] };
2) vector V is carried out the FFT computing, obtain its FFT coefficient vector U, U=FFT (V)=u[0], u[1] ... u[2N-1] }, the length of U is 2N;
3) the FFT coefficient F of vector U and sef-adapting filter shock response WCarry out dot product, product is carried out IFFT, obtain Y ', wherein Y ' is the real number vector, and its imaginary part is 0, abandons the top n value of Y ', makes it become a new vector Y, Y={y[0] ..., y[N-1] }, its length is N;
4) the new noisy speech S=s[0 of noisy speech end input] ..., s[N-1] obtain E, E={e[0 after subtracting each other with Y] ..., e[N-1] }=s[0]-y[0] ..., s[N-1]-y[N-1], the voice signal vector E after output strengthens;
5) insert N 0 value in the front of voice signal vector E and form vector E ', E '=0 ..., and 0, e[0] ..., e[N-1] }, E ' is carried out the FFT computing obtain vector F, F=FFT (E ');
6) vector U is got conjugation and obtain U H, U H=U, U HCarry out dot product with F and obtain vector G '; G ' is carried out the IFFT computing obtain vector H, H=IFFT (G '), H are that length is the real number vector of 2N, and its imaginary part is 0, the back N dimension value zero setting of H, and carries out the FFT computing, obtains vector G, and G is that length is the complex vector of 2N;
7) analytical procedure 2) in FFT coefficient vector U, calculate the probability that contains voice in adaptive step μ and the current reference noise; Adjusting adaptive step μ according to the probability that contains voice in the current reference noise is μ;
8) upgrade vector F W, F W=F W+ μ G, F WUse when the filtering next time as the FFT coefficient of new sef-adapting filter shock response;
9) get back to step 1), till phonetic entry is intact.
6. method according to claim 5 is characterized in that,
In described step 7), current FFT coefficient U with reference to noise is carried out the voice posterior probability estimation, output vector p={p[0], ..., p[N-1] }, p[i wherein] be illustrated in the voice posterior probability that exists on each FFT coefficient, like this, it is p '=1-p that there is the posterior probability vector of noise in each FFT coefficient, p ' and step size mu multiply each other then, obtain new adaptive step vector μ=μ [0] ..., μ [N-1] }, it has provided the adaptive step on each FFT coefficient.
7. method according to claim 6 is characterized in that, new adaptive step μ is multiplied by rule of thumb determined constant C 1 again and obtains after multiplying each other with step size mu by the posterior probability p ' that makes noise.
8. method according to claim 5 is characterized in that,
In described step 7), current FFT coefficient U with reference to noise is found the device processing through speech activity, the output probability α that voice exist in N reference noise sample of current input, calculate the probability β=1-α of noise in current existence, β and step size mu multiply each other then, obtain new adaptive step μ.
9. method according to claim 8 is characterized in that,
In described step 7), speech activity finds that device adopts the mode of hard decision, and when α=0, expression does not have voice, α=1 o'clock, and expression has voice.
10. method according to claim 8 is characterized in that,
In described step 7), speech activity finds that device adopts the mode of soft-decision, and at this moment Shu Chu α is likelihood probability 0≤α≤1.
11. an adaptive filter device comprises:
Sef-adapting filter is used for estimating according to reference noise signal the noise of voice signal;
Subtracter is used for deducting the estimated noise that comes out of sef-adapting filter from Noisy Speech Signal, obtains and voice signal behind the noise is eliminated in output, and the voice signal that will eliminate simultaneously behind the noise feeds back to sef-adapting filter as feedback signal;
It is characterized in that, also comprise:
The step-length control module, it calculates the probability that contains voice in the current reference noise, thereby obtains to exist in the reference noise probability of noise,
Described sef-adapting filter calculates adaptive step μ, exist the probability of noise to adjust adaptive step μ in the reference noise that is obtained according to the step-length control module, upgrade the auto adapted filtering coefficient, feed back to wherein feedback signal according to described subtracter then and reappraise noise in the voice signal.
12. adaptive filter device according to claim 11 is characterized in that,
Described step-length control module comprises:
Voice posterior probability estimation unit carries out the voice posterior probability estimation to current with reference to noise, the voice posterior probability that obtains existing;
Noise probability calculation unit, according to the resulting voice posterior probability in voice posterior probability estimation unit, obtain existing the posterior probability of noise,, obtain new adaptive step μ existing the posterior probability and the adaptive step μ of noise to multiply each other.
13. adaptive filter device according to claim 11 is characterized in that,
Speech activity is found device, handles the probability α that voice exist in the output reference noise sample with reference to noise to current;
Noise probability calculation unit according to finding the resultant probability α of device at speech activity, calculates the probability β=1-α of noise in current existence, and β and adaptive step μ multiply each other then, obtain new adaptive step μ.
14. frequency domain adaptive filtering device that improves the mobile communication equipment voice quality, described mobile communication equipment comprises at least two Mikes, one of them Mike is used to import reference noise signal, another Mike is used to import noisy speech, it is characterized in that described frequency domain adaptive filtering device comprises:
Input block is used for N new reference noise sample v[0 of input] ..., v[N-1] and last N the reference noise sample v ' [0] that imports,, v ' [N-1] forms the vector V of a new 2N length, V={v ' [0] ... v ' [N-1], v[0] ... v[N-1] };
The FFT arithmetic element is used for vector V is carried out the FFT computing, obtains its FFT coefficient vector U, U=FFT (V)=u[0], u[1] ... u[2N-1] }, the length of U is 2N; Filter unit is used for the FFT coefficient F with vector U and sef-adapting filter shock response WCarry out dot product;
The IFFT arithmetic element is used for the resultant product of filter unit is carried out IFFT, obtains Y ', and wherein Y ' is the real number vector, and its imaginary part is 0, abandons the top n value of Y ', makes it become a new vector Y, Y={y[0] ..., y[N-1] }, its length is N;
Output unit is used for the new noisy speech vector S=s[0 with noisy speech end input] ..., s[N-1] obtain vector E after subtracting each other with vector Y, E={e[0] ..., e[N-1] }=s[0]-y[0],, s[N-1]-y[N-1], the voice signal E after output strengthens;
Auto adapted filtering coefficient update unit, the front of the voice signal vector E that it is exported at output unit insert N 0 value formation vector E '=0 ..., 0, e[0] ..., e[N-1] }, E ' is carried out the FFT computing obtains vector F, F=FFT (E '); Vector U is got conjugation obtain U H, U H=U, U HCarry out dot product with F and obtain vector G '; G ' is carried out the IFFT computing obtain vector H, H=IFFT (G '), H are that length is the real number vector of 2N, and its imaginary part is 0, the back N dimension value zero setting of H, and carries out the FFT computing, obtains vector G, and G is that length is the complex vector of 2N; U obtains upgrading step size mu by the resulting FFT coefficient vector of FFT arithmetic element, utilizes F then W=F W+ μ G upgrades the FFT coefficient vector F of sef-adapting filter shock response W,
Described auto adapted filtering updating block comprises:
Step size computation and control module, it utilizes the resulting FFT coefficient vector of FFT arithmetic element U, calculates the probability that contains voice in adaptive step μ and the current reference noise; Adjusting adaptive step μ according to the probability that contains voice in the current reference noise is μ, when the probability that contains voice is high, reduces step size mu, when the probability that contains voice is low, increases step size mu.
15. device according to claim 14 is characterized in that, described step size computation and control module comprise:
The step size computation unit, it utilizes the resulting FFT coefficient vector of FFT arithmetic element U, calculates the probability that contains voice in adaptive step μ and the current reference noise;
The step-length control module, adjusting adaptive step μ according to the probability that contains voice in the current reference noise is μ, when the probability that contains voice is high, reduces step size mu, when the probability that contains voice is low, increases step size mu.
16., it is characterized in that described step size computation and control module or step-length control module comprise according to claim 14 or 15 described devices:
Voice posterior probability estimation unit carries out the voice posterior probability estimation to current with reference to noise, output vector p={p[0] ..., p[N-1], p[i wherein] be illustrated in the voice posterior probability that exists on each FFT coefficient, i=0 ..., N-1;
Noise probability calculation unit, according to voice posterior probability estimation unit resultant voice posterior probability p[i], obtaining the posterior probability vector that there is noise in each FFT coefficient is p '=1-p, p ' and step size mu are multiplied each other, obtain new adaptive step vector μ={ μ [0], ..., μ [N] }, μ [i] has provided the adaptive step on each FFT coefficient.
17., it is characterized in that described step size computation and control module or step-length control module comprise according to claim 14 or 16 described devices:
Speech activity is found device, current FFT coefficient U with reference to noise is handled the output probability α that voice exist in current N reference noise sample;
Noise probability calculation unit according to finding the resultant probability α of device at speech activity, calculates the probability β=1-α of noise in current existence, and β and step size mu multiply each other then, obtain new adaptive step μ.
18. device according to claim 17 is characterized in that, described speech activity finds that device adopts the mode of hard decision, and when α=0, expression does not have voice, α=1 o'clock, and expression has voice.
19. device according to claim 17 is characterized in that, described speech activity finds that device adopts the mode of soft-decision, and α is likelihood probability 0≤α≤1.
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