CN109147753A - The smallest convex combination noise-reduction method of difference based on square-error and square-error logarithm - Google Patents
The smallest convex combination noise-reduction method of difference based on square-error and square-error logarithm Download PDFInfo
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- CN109147753A CN109147753A CN201810816410.1A CN201810816410A CN109147753A CN 109147753 A CN109147753 A CN 109147753A CN 201810816410 A CN201810816410 A CN 201810816410A CN 109147753 A CN109147753 A CN 109147753A
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
- G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
- G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
- G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
- G10K11/178—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
- G10K11/1785—Methods, e.g. algorithms; Devices
- G10K11/17853—Methods, e.g. algorithms; Devices of the filter
- G10K11/17854—Methods, e.g. algorithms; Devices of the filter the filter being an adaptive filter
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
- G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
- G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
- G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
- G10K11/178—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
- G10K11/1787—General system configurations
- G10K11/17879—General system configurations using both a reference signal and an error signal
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
- G10K2210/00—Details of active noise control [ANC] covered by G10K11/178 but not provided for in any of its subgroups
- G10K2210/30—Means
- G10K2210/301—Computational
- G10K2210/3028—Filtering, e.g. Kalman filters or special analogue or digital filters
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Abstract
The invention discloses a kind of the smallest convex combination noise-reduction methods of difference for being based on square-error and square-error logarithm, its step is mainly: A, reference signal acquires, the output signal of sound transducer near acquisition noise source, obtain the discrete value x (n) of reference signal, the input signal vector of its filter is X (n)=[x (n), x (n-1) ..., x (n-L+1)]T；B, convex combination, by big step-length filter output signal y1(n) and small step-length filter output signal y2(n) convex combination is carried out by weight λ (n) and obtains junction filter output signal y (n), y (n)=λ (n) y1(n)+(1‑λ(n))y2(n)；C, residual error signal acquires the output signal of the sound transducer near loudspeaker, obtains offsetting the voice signal d (n) after noise, and obtain residual error signal e (n)；D, junction filter tap weights vector updates；E, filter weight updates；F, filter weight limits；G, the step of repeating, enabling n=n+1, repeat A~F.Its fast convergence rate, steady-state error is small, and anti-acoustic capability is good.
Description
Technical field
The invention belongs to the adaptive control technology fields of noise.
Background technique
As people's living standard increasingly improves, noise pollution increasingly attracts people's attention, especially some special
Working environment for a long time be under critical noisy, personnel's health of work at this will receive serious influence；Simultaneously
Noise also will affect the performance of equipment, shorten the service life of equipment.In order to eliminate and reduce these harm and pollution, it is necessary to adopt
Effective noise control (noise reduction) measure is taken, the noise got worse is controlled.
Active noise control (noise reduction) technology is the new technology that recent decades grow up in field of noise control.With
The development of electronic technology, by sef-adapting filter technology be applied to Active noise control, it has also become Active noise control research
Emphasis.This noise-reduction method can reach good noise reduction effect for low-frequency noise, and have it is small in size, it is light-weight, easily
In control the advantages that；Active noise control (noise reduction) is exactly to be led to according to two sound wave destructive interferences or Reduction of acoustic radiation principle
It crosses algorithm and estimates the size of noise, phase, and then generate, the counteracting sound of opposite in phase equal in magnitude with the sound wave of noise
Source；It offsets sound source to cancel out each other with noise, to achieve the purpose that noise reduction.
In current Active noise control, more common noise control (noise reduction) method is minimum quasi- based on square-error
Then, when filter weight is updated, increment item relevant to residual error e (n) is e (n).Its filter weight updates and remnants
The linear positive correlation of error e (n), so that filter weight updated value is more sensitive to error, so that convergence rate is very fast, surely
State error is smaller, but it still needs further improvement for its convergence rate and steady-state error.
Summary of the invention
The object of the invention is just to provide a kind of the smallest convex combination noise reduction of difference for being based on square-error and square-error logarithm
Method, the fast convergence rate of this method, steady-state error is small, excellent noise reduction effect.
The technical scheme adopted by the invention for realizing the object of the invention is that one kind is based on square-error and square-error logarithm
The smallest convex combination noise-reduction method of difference, its step are as follows:
A, reference signal acquires
The output signal of sound transducer near acquisition noise source obtains the discrete value x of the reference signal of current time n
(n), it by the discrete value x (n) of the reference signal between current time n to moment n-L+1, x (n-1) ..., x (n-L+1), constitutes
The sef-adapting filter input vector X (n) of current time n, X (n)=[x (n), x (n-1) ..., x (n-L+1)]T, wherein L=
128 be filter tap number, and T represents transposition operation；
The filter input vector X (n) of current time n is respectively obtained into current time by convex combination sef-adapting filter
The big step-length filter output signal y of n1(n), y1(n)=W1 T(n) the small step-length filter of X (n) and current time n exports letter
Number y2(n), y2(n)=W2 T(n)X(n)；Wherein, W1(n) and W2It (n) is respectively in current time n convex combination sef-adapting filter
Big step-length filter tap weights vector and small step-length filter tap weights vector, initial value be null vector；
B, convex combination
By the big step-length filter output signal y of current time n1(n) and small step-length filter output signal y2(n), pass through
The big step-length filter weight λ (n) of current time n carries out convex combination and obtains the filter bank output signal y of current time n
(n), y (n)=λ (n) y1(n)+(1-λ(n))y2(n)；
Wherein, the calculation formula of big step-length filter weight λ (n) isValue range is 0~1；α(n)
For the hybrid parameter of current time n, initial value 0；
It send the filter bank output signal y (n) of current time n to loudspeaker again, issues current time n's by loudspeaker
Speaker output signalWherein, * indicates that convolution algorithm, s are the impulse response of loudspeaker；
Meanwhile calculating the big step-length component in the speaker output signal of current time n
With the small step-length component in the speaker output signal of current time n
C, residual error signal
The output signal for acquiring the sound transducer near loudspeaker obtains current time n and offsets the sound letter after noise
Number d (n), and obtain the residual error signal e (n) of current time n,And then respectively obtain current time
The big step-length residual error signal e of n1(n),With the small step-length residual error signal e of current time n2
(n),
D, junction filter tap weights vector updates
If the hybrid parameter α (n) of current time n is less than hybrid parameter threshold value σ, the big step-length of subsequent time n+1 is filtered
The tap weights vector W of wave device1(n+1) and the tap weights vector W of small step-length filter2(n+1) it is respectively as follows:
If the hybrid parameter a (n) of current time n is more than or equal to hybrid parameter threshold value σ, the big step of subsequent time n+1
The tap weights vector W of long filter1(n+1) and the tap weights vector W of small step-length filter2(n+1) it is respectively as follows:
Wherein, μ1For the step-length of big step-length filter, value is 0.008~0.01；μ2For the step of small step-length filter
Long, value is 0.003~0.008；φ is positive threshold parameter, and value is 0.0001~0.01；Hybrid parameter threshold value σ's takes
Value is 4~5；
E, filter weight updates
Update obtains the hybrid parameter a (n+1) of subsequent time n+1:
Wherein, μαIt is a constant, value range is 1~1000；
Update obtains the big step-length filter weight λ (n+1) of subsequent time n+1:
F, filter weight limits
If negative value of the hybrid parameter α (n+1) of subsequent time n+1 less than hybrid parameter threshold value σ, i.e. α (n+1) <-σ,
Then enable α (n+1)=- σ；If the hybrid parameter α (n+1) of subsequent time n+1 is more than or equal to hybrid parameter threshold value σ, i.e. α (n+1)
>=σ then enables α (n+1)=σ；
G, it repeats
The step of enabling n=n+1, repeating A~F.
Compared with prior art, the beneficial effects of the present invention are:
One, the present invention is based on the difference e of square-error and square-error logarithm2(n)-φln(e2(n)+φ) the smallest criterion,
When showing that filter weight updates, increment item relevant to residual error e (n) isAnd show
Have based on square-error minimum criteria, when filter weight is updated, linearly positively related increment item is e with residual error e (n)
(n).The present invention is updated compared with existing error quadratic power minimum method, filter weight not only with residual error e (n) at line
Property be positively correlated, also on this basis, increase the product term being positively correlated with itSo that of the invention
Filter weight updated value and residual error e (n) positive correlation degree are higher, fast convergence rate strong to the tracing property of error, stable state
Error is small.
Two, convex combination filter of the invention, by the low steady of the fast convergence of big step-length filter and small step-length filter
State error combines, while guaranteeing to expect that filter has faster convergence rate and lower steady-state error.To further increase
The convergence rate and anti-acoustic capability of filter.
Specific embodiment
Embodiment
A kind of specific embodiment of the invention is: difference of the one kind based on square-error and square-error logarithm is the smallest convex
Method of reducing noise for combined, its step are as follows:
A, reference signal acquires
The output signal of sound transducer near acquisition noise source obtains the discrete value x of the reference signal of current time n
(n), it by the discrete value x (n) of the reference signal between current time n to moment n-L+1, x (n-1) ..., x (n-L+1), constitutes
The sef-adapting filter input vector X (n) of current time n, X (n)=[x (n), x (n-1) ..., x (n-L+1)]T, wherein L=
128 be filter tap number, and T represents transposition operation；
The filter input vector X (n) of current time n is respectively obtained into current time by convex combination sef-adapting filter
The big step-length filter output signal y of n1(n), y1(n)=W1 T(n) the small step-length filter of X (n) and current time n exports letter
Number y2(n), y2(n)=W2 T(n)X(n)；Wherein, W1(n) and W2It (n) is respectively in current time n convex combination sef-adapting filter
Big step-length filter tap weights vector and small step-length filter tap weights vector, initial value be null vector；
B, convex combination
By the big step-length filter output signal y of current time n1(n) and small step-length filter output signal y2(n), pass through
The big step-length filter weight λ (n) of current time n carries out convex combination and obtains the filter bank output signal y of current time n
(n), y (n)=λ (n) y1(n)+(1-λ(n))y2(n)；
Wherein, the calculation formula of big step-length filter weight λ (n) isValue range is 0~1；α(n)
For the hybrid parameter of current time n, initial value 0；
It send the filter bank output signal y (n) of current time n to loudspeaker again, issues current time n's by loudspeaker
Speaker output signalWherein, * indicates that convolution algorithm, s are the impulse response of loudspeaker；
Meanwhile calculating the big step-length component in the speaker output signal of current time n
With the small step-length component in the speaker output signal of current time n
C, residual error signal
The output signal for acquiring the sound transducer near loudspeaker obtains current time n and offsets the sound letter after noise
Number d (n), and obtain the residual error signal e (n) of current time n,And then respectively obtain current time
The big step-length residual error signal e of n1(n),With the small step-length residual error signal e of current time n2
(n),
D, junction filter tap weights vector updates
If the hybrid parameter α (n) of current time n is less than hybrid parameter threshold value σ, the big step-length of subsequent time n+1 is filtered
The tap weights vector W of wave device1(n+1) and the tap weights vector W of small step-length filter2(n+1) it is respectively as follows:
If the hybrid parameter a (n) of current time n is more than or equal to hybrid parameter threshold value σ, the big step of subsequent time n+1
The tap weights vector W of long filter1(n+1) and the tap weights vector W of small step-length filter2(n+1) it is respectively as follows:
Wherein, μ1For the step-length of big step-length filter, value is 0.008~0.01；μ2For the step of small step-length filter
Long, value is 0.003~0.008；φ is positive threshold parameter, and value is 0.0001~0.01；Hybrid parameter threshold value σ's takes
Value is 4~5；
E, filter weight updates
Update obtains the hybrid parameter a (n+1) of subsequent time n+1:
Wherein, μαIt is a constant, value range is 1~1000；
Update obtains the big step-length filter weight λ (n+1) of subsequent time n+1:
F, filter weight limits
If negative value of the hybrid parameter α (n+1) of subsequent time n+1 less than hybrid parameter threshold value σ, i.e. α (n+1) <-σ,
Then enable α (n+1)=- σ；If the hybrid parameter α (n+1) of subsequent time n+1 is more than or equal to hybrid parameter threshold value σ, i.e. α (n+1)
>=σ then enables α (n+1)=σ；
G, it repeats
N=n+1 is enabled, the step of A~F is repeated.
Claims (1)
1. one kind is based on the smallest convex combination noise-reduction method of difference of square-error and square-error logarithm, its step are as follows:
A, reference signal acquires
The output signal of sound transducer near acquisition noise source obtains the discrete value x (n) of the reference signal of current time n,
By the discrete value x (n) of the reference signal between current time n to moment n-L+1, x (n-1) ..., x (n-L+1), constitute current
The sef-adapting filter input vector X (n) of moment n, X (n)=[x (n), x (n-1) ..., x (n-L+1)]T；Wherein L=128
It is filter tap number, T represents transposition operation；
The filter input vector X (n) of current time n is respectively obtained current time n's by convex combination sef-adapting filter
Big step-length filter output signal y1(n), y1(n)=W1 T(n) the small step-length filter output signal y of X (n) and current time n2
(n), y2(n)=W2 T(n)X(n)；Wherein, W1(n) and W2It (n) is respectively big in current time n convex combination sef-adapting filter
The tap weights vector of the tap weights vector of step-length filter and small step-length filter, initial value are null vector；
B, convex combination
By the big step-length filter output signal y of current time n1(n) and small step-length filter output signal y2(n), by current
The big step-length filter weight λ (n) of moment n carries out convex combination, obtains the filter bank output signal y (n) of current time n, y
(n)=λ (n) y1(n)+(1-λ(n))y2(n)；
Wherein, the calculation formula of big step-length filter weight λ (n) isValue range is 0~1；α (n) is to work as
The hybrid parameter of preceding moment n, initial value 0；
It send the filter bank output signal y (n) of current time n to loudspeaker again, the loudspeaking of current time n is issued by loudspeaker
Device output signal Wherein, * indicates that convolution algorithm, s are the impulse response of loudspeaker；
Meanwhile calculating the big step-length component in the speaker output signal of current time n With work as
Small step-length component in the speaker output signal of preceding moment n
C, residual error signal
The output signal for acquiring the sound transducer near loudspeaker obtains current time n and offsets the voice signal d after noise
(n), and the residual error signal e (n) of current time n is obtained,And then respectively obtain current time n's
Big step-length residual error signal e1(n),With the small step-length residual error signal e of current time n2(n),
D, junction filter tap weights vector updates
If the hybrid parameter α (n) of current time n is less than hybrid parameter threshold value σ, the big step-length filter of subsequent time n+1
Tap weights vector W1(n+1) and the tap weights vector W of small step-length filter2(n+1) it is respectively as follows:
If the hybrid parameter a (n) of current time n is more than or equal to hybrid parameter threshold value σ, the big step-length of subsequent time n+1 is filtered
The tap weights vector W of wave device1(n+1) and the tap weights vector W of small step-length filter2(n+1) it is respectively as follows:
Wherein, μ1For the step-length of big step-length filter, value is 0.008~0.01；μ2For the step-length of small step-length filter,
Value is 0.003~0.008；φ is positive threshold parameter, and value is 0.0001~0.01；The value of hybrid parameter threshold value σ is 4
~5；
E, filter weight updates
Update obtains the hybrid parameter a (n+1) of subsequent time n+1:
Wherein, μαIt is a constant, value range is 1~1000；
Update obtains the big step-length filter weight λ (n+1) of subsequent time n+1:
F, filter weight limits
If the hybrid parameter α (n+1) of subsequent time n+1 is less than the negative value of hybrid parameter threshold value σ, i.e. α (n+1) <-σ then enables α
(n+1)=- σ；If the hybrid parameter α (n+1) of subsequent time n+1 is more than or equal to hybrid parameter threshold value σ, i.e. α (n+1) >=σ, then
Enable α (n+1)=σ；
G, it repeats
The step of enabling n=n+1, repeating A~F.
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