CN107351853A - A kind of active noise reduction seat suitable for high ferro business class - Google Patents
A kind of active noise reduction seat suitable for high ferro business class Download PDFInfo
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- CN107351853A CN107351853A CN201710506465.8A CN201710506465A CN107351853A CN 107351853 A CN107351853 A CN 107351853A CN 201710506465 A CN201710506465 A CN 201710506465A CN 107351853 A CN107351853 A CN 107351853A
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61D—BODY DETAILS OR KINDS OF RAILWAY VEHICLES
- B61D33/00—Seats
- B61D33/0007—Details; Accessories
-
- 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
- G10K2210/10—Applications
- G10K2210/128—Vehicles
- G10K2210/1283—Trains, trams or the like
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- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Soundproofing, Sound Blocking, And Sound Damping (AREA)
- Fittings On The Vehicle Exterior For Carrying Loads, And Devices For Holding Or Mounting Articles (AREA)
Abstract
Presently disclosed a kind of active noise reduction seat suitable for high ferro business class, the active noise reduction seat include base, backrest and headrest, in addition to：The upper reference noise sensor of backrest and headrest and error microphone being arranged in head of passenger scope of activities, MIMO active noise reductions controller, two loudspeakers.The present invention is higher by a series of degree of accuracy of incoherent optimal noise control modes obtained with reference to acoustic mode, it can more match with the noise pattern in high ferro business class, echo signal is sent further according to optimal noise control mode, realize more obvious to the wideband noise reduction of the noise in high ferro business class, noise reduction.And by constantly adjusting sampling period, sef-adapting filter exponent number, convergence factor, wave filter weight coefficient adaptively is changed, the convergence rate of system can be improved on the premise of ensureing that system is stable, and obtain the Noise measarement in broad frequency band.
Description
Technical field
The present invention relates to noise control technique field, more particularly to a kind of active noise reduction seat suitable for high ferro business class
Chair.
Background technology
With the improvement of people ' s living standards, rail vehicle transportation industry develops rapidly, and increasing people selects high ferro
Trip, its requirement of riding comfort to high ferro also improve constantly, and the noise situations wherein in compartment are increasingly becoming passenger and entered
One of standard that ranks car comfortableness judges.On the basis of noise level in current high ferro business class, Noise measarement effect is improved
Fruit, resting and sleeping for passenger provides a more quiet environment, turns into field of noise control one and more popular grinds
Study carefully.
Conventional Noise control program is more to realize certain noise reduction by using sound absorption structure and sound-absorbing material, this
Kind passive type Noise measarement can effectively reduce the noise of high band, and then poor for the control effect of low-frequency noise.Using
Active noise reduction means can be effectively controlled low-frequency noise, and current active noise control program only can be in narrower frequency
With interior reduction noise frequency, its application in rail transit train is also extremely limited.
The content of the invention
It is an object of the present invention to provide a kind of active control system of the noise in high ferro business class, to solve the above problems.
The present invention solves technical problem and adopted the following technical scheme that：
A kind of active noise reduction seat suitable for high ferro business class, the active noise reduction seat include base, backrest and head
Pillow, in addition to：
Reference noise sensor, the reference noise sensor are arranged on backrest and headrest in head of passenger scope of activities
On, for gathering main noise source noise x [n]；
Error microphone, the error microphone are arranged on the backrest and headrest in head of passenger scope of activities, are used for
Gather the residual noise e [n] after the Noise measarement in high ferro business class；
MIMO active noise reduction controllers, the MIMO active noise reductions controller respectively with the reference noise sensor and mistake
Poor microphone is connected, and for receiving main noise source noise x [n] and residual noise e [n], and extracts a series of incoherent references
Acoustic mode S [n], the path transfer function H [n] for noise control mode prediction is obtained, adaptive adjustment sampling period T
The scope of span, sef-adapting filter exponent number N span and convergence factor μ, ultimately generates optimal Noise measarement
Pattern；
Two loudspeakers, described two loudspeakers be separately positioned on headrest lower section both sides press close at human ear, and with it is described
MIMO active noise reduction controllers are connected, and are sent out for receiving optimal noise control mode, and according to optimal noise control mode
Go out echo signal Y [n] to reduce the noise in high ferro business class.
Preferably, the MIMO active noise reductions controller, including：
With reference to acoustic mode extraction unit, the acoustic mode extraction unit is connected with the reference noise sensor, uses
In reception main noise source noise x [n], and it is a series of incoherent with reference to acoustic mode S according to main noise source noise x [n] extractions
[n]；
Noise control mode predicting unit, the noise control mode predicting unit respectively with the error microphone, ginseng
Examine microphone with reference to acoustic mode extraction unit to be connected, for analyzing echo signal from the road being issued at residual noise collection
Footpath transmission function, the path transfer function H [n] for noise control mode prediction is obtained, and be based on FX-LMS algorithms, adaptively
Span, sef-adapting filter exponent number N span and the convergence factor μ scope in sampling period T are adjusted, further according to institute
State convergence factor μ, path transfer function H [n], a series of incoherent reference acoustic mode S [n], residual noise e [n] and masters
Noises from noise sources x [n] generates optimal noise control mode.
Preferably, it is described with reference to acoustic mode extraction unit, including：
Subelement is extracted with reference to acoustic mode, it is described to be made an uproar with reference to acoustic mode extraction subelement for receiving main noise sound source
Sound x [n], and refer to acoustic mode according to main noise source noise x [n] extractions are a series of；
Subelement is separated with reference to acoustic mode, it is described to be carried with reference to acoustic mode separation subelement with described with reference to acoustic mode
Take subelement, for receive it is a series of refer to acoustic mode, and will be adjusted to minimum with reference to the mutual information between acoustic mode, obtain
Obtain a series of incoherent with reference to acoustic mode S [n].
Preferably, it is described that a series of processes with reference to acoustic mode are extracted according to main noise source noise x [n], including：
Function will be extractedIt is applied in main noise source noise x [n]：
Preferably, a series of processes for obtaining incoherent reference acoustic mode S [n], including：
Contrast function α is acted on a series of with reference to acoustic mode S'[n] in：
α (S'[n])=I [S'[1] ... S'[k]],
Function is extracted using adjust automatically algorithm adjust automaticallyA series of with reference to acoustic mode S'[n] between it is mutual
Information I [S'[1] ... S'[k]] it is minimum when, the acoustic mode that is exported is a series of incoherent with reference to acoustic mode S [n].
Preferably, the noise control mode predicting unit, including：
Path transfer function analyzes subelement, and path transfer function analysis subelement is used to analyzing echo signal from raising
Sound device to error microphone path transfer function, and obtain for noise control mode prediction path transfer function H [n]；
Sef-adapting filter, the sef-adapting filter pass with path transfer function analysis subelement, error respectively
Sound device, it is connected with reference to acoustic mode extraction unit with reference to microphone, the path of noise control mode prediction is used for for receiving
Transfer function H [n], residual noise e [n] and a series of incoherent reference acoustic mode S [n], and FX-LMS algorithms are based on, from
Adapt to span, sef-adapting filter exponent number N span and the convergence factor μ scope in adjustment sampling period T, then root
According to filter weight coefficient formulas ω [n+1]=ω [n]+μ (e [n] H [n] x [n]), the convergence factor μ's
In the range of adjust μ value, update weight coefficient w, until residual noise e (n) convergence when convergence factor μ minimums when, generation most
Excellent noise control mode Y [n]=wS [n].
Preferably, the process for obtaining the path transfer function H [n] for noise control mode prediction, including：
Judge j-th of path transfer function H [j] whether so that residual noise e (n) restrains in n sampling number；
If convergence, noise control mode prediction is carried out using H [j]；
If not restraining, judge (j+1) individual path transfer function H [j+1] whether so that residual noise e (n) adopts at n
Restrained in number of samples.
Preferably, it is described to be based on FX-LMS algorithms, the process of the adaptive span for adjusting sampling period T, including：
Sampling period T is adjusted, makes T=1/fs≤1/2f0, wherein, fs is sample frequency, f0For the optimal noise control of output
The frequency range upper limit of molding formula.
Preferably, it is described to be based on FX-LMS algorithms, the process of the adaptive scope for adjusting sef-adapting filter exponent number N, bag
Include：
In reference noise frequency discontinuity, N values are raised；
In reference noise frequency stabilization, N values are reduced.
Preferably, it is described to be based on FX-LMS algorithms, the adaptive process for adjusting convergence factor μ scopes, including：
The adaptive adjustment convergence factor μ of adjustment so that μ meets stability of a system condition and system convergence condition.
It can be seen that the active noise reduction seat disclosed in this invention suitable for high ferro business class, crosses and is arranged on head of passenger work
The main noise x [n] at the reference microphone pick locomotive seat on backrest and headrest in dynamic scope, by being arranged on passenger's head
On backrest and headrest in portion's scope of activities error microphone acquisition noise control after residual noise e [n], by respectively with
The MIMO active noise reductions controller that the reference noise sensor is connected with error microphone extracts a series of incoherent references
Acoustic mode S [n], the path transfer function H [n] for noise control mode prediction is obtained, adaptive adjustment sampling period T
The scope of span, sef-adapting filter exponent number N span and convergence factor μ, generates optimal noise control mode,
The loudspeaker being finally connected with the MIMO active noise reductions controller sends echo signal Y according to optimal noise control mode
[n] is to reduce the noise in high ferro business class.
Compared with prior art, the active noise reduction seat disclosed in this invention suitable for high ferro business class passes through a series of
The degree of accuracy of the incoherent optimal noise control mode obtained with reference to acoustic mode S [n] is higher, more can be with high ferro business class
Interior noise pattern matches, and echo signal Y [n] is sent further according to optimal noise control mode, by thus according to
The echo signal Y [n] that the optimal noise control mode of more high accuracy is sent, then in echo signal Y [n] and high ferro business class
The matching degree of noise is also higher, and so as to realize the wideband noise reduction to the noise in high ferro business class, noise reduction is more
Substantially.
In addition, by constantly adjusting sampling period, sef-adapting filter exponent number, convergence factor, adaptively filter is changed
Ripple device weight coefficient, the convergence rate of system can be improved on the premise of ensureing that system is stable, and obtained in broad frequency band
Noise measarement.
Brief description of the drawings
Fig. 1 is a kind of active noise reduction armchair structure schematic diagram suitable for high ferro business class provided by the present invention；
Before and after Fig. 2 opens for a kind of active noise reduction seat suitable for high ferro business class provided by the present invention in drivers' cab
Noise profile figure.
Embodiment
To make the purpose, technical scheme and advantage of the embodiment of the present invention clearer, below in conjunction with the embodiment of the present invention
In accompanying drawing, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is
Part of the embodiment of the present invention, rather than whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art
The every other embodiment obtained under the premise of creative work is not made, belongs to the scope of protection of the invention.
Technical scheme is further elaborated with reference to embodiment and accompanying drawing.
The present invention, which implements, discloses a kind of active noise reduction seat suitable for high ferro business class, as shown in figure 1, including：
Reference noise sensor, the reference noise sensor are arranged on backrest and headrest in head of passenger scope of activities
On, for gathering main noise source noise x [n].In the present embodiment, in the headrest front, there is provided 5 to refer to microphone.Institute
State main noise to can be understood as during locomotive driving, by the engine compartment noise under different rotating speeds, vehicle-mounted voice band device spoke
Make an uproar to lead in the headrest noise set positioning of (human ear scope of activities) nearby and make an uproar in road under the middle low frequency range noise and different road conditions penetrated
Sound x [n].Wherein, the matrix that x [n] is made up of sampling of the n discrete time to main noise.The acoustic properties of main noise include
Amplitude, phase and frequency.
Error microphone, the error microphone are arranged on the backrest and headrest in head of passenger scope of activities, are used for
Gather the residual noise e [n] after the Noise measarement in high ferro business class.In the present embodiment, the headrest front is provided with 6
Error microphone.The e [n] is actually by noise remaining after locomotive seat Noise measarement, it is understood that is
The noise that the people being sitting on locomotive seat can experience.Residual noise e [n] samples institute for n discrete time to residual noise
The matrix of composition.
MIMO active noise reduction controllers, the MIMO active noise reductions controller respectively with the reference noise sensor and mistake
Poor microphone is connected, and for receiving main noise source noise x [n] and residual noise e [n], and extracts a series of incoherent references
Acoustic mode S [n], the path transfer function H [n] for noise control mode prediction is obtained, adaptive adjustment sampling period T
The scope of span, sef-adapting filter exponent number N span and convergence factor μ, ultimately generates optimal Noise measarement
Pattern.Wherein, the MIMO active noise reductions controller, including：
With reference to acoustic mode extraction unit, the acoustic mode extraction unit is connected with the reference noise sensor, uses
In reception main noise source noise x [n], and it is a series of incoherent with reference to acoustic mode S according to main noise source noise x [n] extractions
[n]；
Noise control mode predicting unit, the noise control mode predicting unit respectively with the error microphone, ginseng
Examine microphone with reference to acoustic mode extraction unit to be connected, for analyzing echo signal from the road being issued at residual noise collection
Footpath transmission function, the path transfer function H [n] for noise control mode prediction is obtained, and be based on FX-LMS algorithms, adaptively
Span, sef-adapting filter exponent number N span and the convergence factor μ scope in sampling period T are adjusted, further according to institute
State convergence factor μ, path transfer function H [n], a series of incoherent reference acoustic mode S [n], residual noise e [n] and masters
Noises from noise sources x [n] generates optimal noise control mode.
Further, it is described with reference to acoustic mode extraction unit, including：
Subelement is extracted with reference to acoustic mode, it is described to be made an uproar with reference to acoustic mode extraction subelement for receiving main noise sound source
Sound x [n], and a series of with reference to acoustic mode S'[n according to main noise source noise x [n] extractions].It is described according to main noise source noise
X [n] extractions are a series of with reference to acoustic mode S'[n] process, include again：
Function will be extractedIt is applied in main noise source noise x [n]：Wherein, function is extractedIt is tool
There is the linear mapping function of memory function, perform convolution algorithm, the acoustic mode mainly includes three key elements of noise signal,
That is amplitude, phase and frequency information.Main noise x [n] matrix elements and a series of with reference to acoustic mode S'[n] between matrix element
For mapping relations.
Subelement is separated with reference to acoustic mode, it is described to be carried with reference to acoustic mode separation subelement with described with reference to acoustic mode
It is take subelement, a series of with reference to acoustic mode S'[n for receiving], and will refer to acoustic mode S'[n] between mutual information tune
It is whole a series of incoherent with reference to acoustic mode S [n] to minimum, acquisition.Described obtain a series of incoherent refers to acoustic mode
Formula S [n] process, including：
Contrast function α is acted on a series of with reference to acoustic mode S'[n] in：
α (S'[n])=I [S'[1] ... S'[k]],
Function is extracted using adjust automatically algorithm adjust automaticallyA series of with reference to acoustic mode S'[n] between it is mutual
Information I [S'[1] ... S'[k]] it is minimum when, the acoustic mode that is exported is a series of incoherent with reference to acoustic mode S [n].Its
In, extract function using adjust automatically algorithm adjust automaticallyProcess it is as follows：
(1) if I ＞ 0, are adjustedChange x [n] and S'[n] between mapping relations, continue to judge；
(2) if I=0, useExtraction function as extraction process.
A series of incoherent reference acoustic mode S [n] are separated from default main noise source come out most
Representative acoustic mode.Active noise reduction system disclosed in the present embodiment is applied to polymorphic type sound source by function is extracted
In reference noise input, by Adaptive adjusting algorithm extract the optimization of function, can effectively ensure extracted acoustic mode
The degree of accuracy.
The noise control mode predicting unit, including：
Path transfer function analyzes subelement, and path transfer function analysis subelement is used to analyzing echo signal from raising
Sound device to error microphone path transfer function, and obtain for noise control mode prediction path transfer function H [n].
It is a period that the process, which can be understood as time span corresponding to n sampled point, if system includes E loudspeaker and F is missed
Poor microphone, then EF bang path is formed between loudspeaker and error microphone.Now, it is described to obtain for Noise measarement mould
The path transfer function H [n] of formula prediction process, including：Judge whether j-th of path transfer function H [j] causes residual noise
E (n) is restrained in n sampling number, if convergence, noise control mode prediction is carried out using H [j]；If not restraining, judge
Whether (j+1) individual path transfer function H [j+1] causes residual noise e (n) to be restrained in n sampling number.
Sef-adapting filter, the sef-adapting filter pass with path transfer function analysis subelement, error respectively
Sound device, it is connected with reference to acoustic mode extraction unit with reference to microphone, the path of noise control mode prediction is used for for receiving
Transfer function H [n], residual noise e [n] and a series of incoherent reference acoustic mode S [n], and FX-LMS algorithms are based on, from
Adapt to span, sef-adapting filter exponent number N span and the convergence factor μ scope in adjustment sampling period T, then root
According to filter weight coefficient formulas ω [n+1]=ω [n]+μ (e [n] H [n] x [n]), the convergence factor μ's
In the range of adjust μ value, update weight coefficient w, until residual noise e (n) convergence when convergence factor μ minimums when, generation most
Excellent noise control mode Y [n]=wS [n].
Wherein, it is described to be based on FX-LMS algorithms, the process of the adaptive span for adjusting sampling period T, including：
Sampling period T is adjusted, makes T=1/fs≤1/2f0, wherein, fs is sample frequency, f0For the optimal noise control of output
The frequency range upper limit of molding formula.By sampling period T size influences the upper limit of the frequency range of gathered reference noise
F, and T=1/fs, fs >=2f；Then T is smaller, and f is bigger, you can bigger (the analyzable reference of reference noise upper frequency limit of analysis
Noisy frequency range is consistent with optimal noise control mode frequency range), as T=1/fs≤1/2f0(i.e. f≤f0) when, it can obtain
Obtain larger noise reduction frequency range.
In addition, filter order N size can influence to export the frequency range upper limit f of optimal noise control mode0If increase
Big N, then filter cutoff frequency f0Reduce, and the signal frequency upper limit of its controlling loudspeaker output also decreases.It is optimal to make an uproar
The frequency range of acoustic control pattern determines noise abatement with the matching degree of reference noise frequency range.Pass through analytical sampling cycle T
With sef-adapting filter exponent number N relation, the adaptive span for adjusting the sampling period, can effectively ensure that can noise reduction noise
Frequency bandwidth.
It is described to be based on FX-LMS algorithms, the process of the adaptive scope for adjusting sef-adapting filter exponent number N, including：
In reference noise frequency discontinuity, N values are raised；
In reference noise frequency stabilization, N values are reduced.
The frequency bandwidth for the optimal noise control mode that MIMO active noise reduction controllers are exported is (0, f0), if increase N,
Then filter cutoff frequency f0It can reduce, analyzable reference noise frequency range and optimal noise control mode frequency range one
Cause, so adaptively adjusting N values, the operand of algorithm filter can be reduced so that system Fast Convergent.It is i.e. adaptive by analyzing
Filter order N and reference noise frequency bandwidth relation are answered, the adaptive scope for adjusting N, optimal noise reduction can ensured
On the premise of accelerate system convergence rate.
It is described to be based on FX-LMS algorithms, the adaptive process for adjusting convergence factor μ scopes, including：
The adaptive adjustment convergence factor μ of adjustment so that μ meets stability of a system condition and system convergence condition.Wherein,
The condition of the stability of a system is：0 ＜ μ ＜ 1/ (NP), N are filter order, and P is the power of speaker output signal.It is described
System convergence refers in specific iteration time whether FX-RBF algorithms reach convergence.In the present embodiment, the selection of μ values,
Need to meet the stability of a system and system algorithm convergence simultaneously.
By analyzing sef-adapting filter exponent number N and convergence factor μ relation, the adaptive scope for adjusting μ, can ensure
The stability of system.
Two loudspeakers, described two loudspeakers be separately positioned on headrest lower section both sides press close at human ear, and with it is described
MIMO active noise reduction controllers are connected, and are sent out for receiving optimal noise control mode, and according to optimal noise control mode
Go out echo signal Y [n] to reduce the noise in high ferro business class.
Because optimal noise control mode Y [n] can most match with a series of incoherent reference acoustic mode S [n],
The echo signal (sound) then sent according to optimal noise control mode could effectively offset main noise, reach optimal
Noise reduction.
It can be seen that the active noise reduction seat disclosed in this invention suitable for high ferro business class, crosses and is arranged on head of passenger work
The main noise x [n] at the reference microphone pick locomotive seat on backrest and headrest in dynamic scope, by being arranged on passenger's head
On backrest and headrest in portion's scope of activities error microphone acquisition noise control after residual noise e [n], by respectively with
The MIMO active noise reductions controller that the reference noise sensor is connected with error microphone extracts a series of incoherent references
Acoustic mode S [n], the path transfer function H [n] for noise control mode prediction is obtained, adaptive adjustment sampling period T
The scope of span, sef-adapting filter exponent number N span and convergence factor μ, generates optimal noise control mode,
The loudspeaker being finally connected with the MIMO active noise reductions controller sends echo signal Y according to optimal noise control mode
[n] is to reduce the noise in high ferro business class.
Compared with prior art, the active noise reduction seat disclosed in this invention suitable for high ferro business class passes through a series of
The degree of accuracy of the incoherent optimal noise control mode obtained with reference to acoustic mode S [n] is higher, more can be with high ferro business class
Interior noise pattern matches, and echo signal Y [n] is sent further according to optimal noise control mode, by thus according to
The echo signal Y [n] that the optimal noise control mode of more high accuracy is sent, then in echo signal Y [n] and high ferro business class
The matching degree of noise is also higher, and so as to realize the wideband noise reduction to the noise in high ferro business class, noise reduction is more
Substantially.
In addition, by constantly adjusting sampling period, sef-adapting filter exponent number, convergence factor, adaptively filter is changed
Ripple device weight coefficient, the convergence rate of system can be improved on the premise of ensureing that system is stable, and obtained in broad frequency band
Noise measarement.
As shown in Fig. 2 the present embodiment is further disclosed under certain speed, this active for being applied to high ferro business class is dropped
Make an uproar seat before opening (OFF) and open after (ON), the noise spectrum curve measured in Noise measarement region, by contrast can
Know, active noise reduction significant effect of the active noise reduction scheme provided by the present invention in wide frequency range.
Finally it should be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
The present invention is described in detail with reference to the foregoing embodiments, it will be understood by those within the art that：It still may be used
To be modified to the technical scheme described in foregoing embodiments, or equivalent substitution is carried out to which part technical characteristic；
And these modification or replace, do not make appropriate technical solution essence depart from various embodiments of the present invention technical scheme spirit and
Scope.
Claims (10)
1. a kind of active noise reduction seat suitable for high ferro business class, the active noise reduction seat includes base, backrest and headrest,
Characterized in that, also include：
Reference noise sensor, the reference noise sensor are arranged on the backrest and headrest in head of passenger scope of activities,
For gathering main noise source noise x [n]；
Error microphone, the error microphone is arranged on the backrest and headrest in head of passenger scope of activities, for gathering
Residual noise e [n] after Noise measarement in high ferro business class；
MIMO active noise reduction controllers, the MIMO active noise reductions controller pass with the reference noise sensor and error respectively
Sound device is connected, and for receiving main noise source noise x [n] and residual noise e [n], and extracts and a series of incoherent refers to acoustics
Mode S [n], obtain the path transfer function H [n] for noise control mode prediction, the adaptive value for adjusting sampling period T
The scope of scope, sef-adapting filter exponent number N span and convergence factor μ, ultimately generates optimal noise control mode；
Two loudspeakers, described two loudspeakers be separately positioned on headrest lower section both sides press close at human ear, and with the MIMO master
Dynamic noise reduction controller is connected, and target is sent for receiving optimal noise control mode, and according to optimal noise control mode
Signal Y [n] is to reduce the noise in high ferro business class.
2. it is applied to the active noise reduction seat of high ferro business class according to claim 1, it is characterised in that the MIMO is actively
Noise reduction controller, including：
With reference to acoustic mode extraction unit, the acoustic mode extraction unit is connected with the reference noise sensor, for connecing
Main noise source noise x [n] is received, and it is a series of incoherent with reference to acoustic mode S [n] according to main noise source noise x [n] extractions；
Noise control mode predicting unit, the noise control mode predicting unit is respectively with the error microphone, with reference to biography
Sound device is connected with reference to acoustic mode extraction unit, is passed for analyzing echo signal from the path being issued at residual noise collection
Delivery function, the path transfer function H [n] for noise control mode prediction is obtained, and be based on FX-LMS algorithms, adaptive adjustment
Sampling period T span, sef-adapting filter exponent number N span and convergence factor μ scope, further according to the receipts
Hold back factor mu, path transfer function H [n], a series of incoherent reference acoustic mode S [n], residual noise e [n] and main noises
Source noise x [n] generates optimal noise control mode.
3. it is applied to the active noise reduction seat of high ferro business class according to claim 2, it is characterised in that described to refer to acoustics
Schema extraction unit, including：
Subelement is extracted with reference to acoustic mode, it is described to be used to receive main noise Sound source noise x with reference to acoustic mode extraction subelement
[n], and a series of with reference to acoustic mode S'[n according to main noise source noise x [n] extractions]；
Subelement is separated with reference to acoustic mode, it is described with reference to acoustic mode separation subelement and reference acoustic mode extraction
It is unit, a series of with reference to acoustic mode S'[n for receiving], and acoustic mode S'[n will be referred to] between mutual information be adjusted to
Minimum, obtain a series of incoherent with reference to acoustic mode S [n].
4. it is applied to the active noise reduction seat of high ferro business class according to claim 3, it is characterised in that described to be made an uproar according to master
Sound source noise x [n] extractions are a series of with reference to acoustic mode S'[n] process, including：
Function will be extractedIt is applied in main noise source noise x [n]：
5. it is applied to the active noise reduction seat of high ferro business class according to claim 3, it is characterised in that one system of the acquisition
The incoherent process with reference to acoustic mode S [n] is arranged, including：
Contrast function α is acted on a series of with reference to acoustic mode S'[n] in：
α (S'[n])=I [S'[1] ... S'[k]],
Function is extracted using adjust automatically algorithm adjust automaticallyA series of with reference to acoustic mode S'[n] between mutual information I
[S'[1] ... S'[k]] it is minimum when, the acoustic mode that is exported is a series of incoherent with reference to acoustic mode S [n].
6. it is applied to the active noise reduction seat of high ferro business class according to claim 2, it is characterised in that the Noise measarement
Model prediction unit, including：
Path transfer function analyzes subelement, and the path transfer function analysis subelement is used to analyze echo signal from loudspeaker
To the path transfer function of error microphone, and obtain the path transfer function H [n] for noise control mode prediction；
Sef-adapting filter, the sef-adapting filter respectively with the path transfer function analysis subelement, error microphone,
It is connected with reference to microphone with reference to acoustic mode extraction unit, letter is transmitted in the path that noise control mode prediction is used for for receiving
Number H [n], residual noise e [n] and a series of incoherent reference acoustic mode S [n], and FX-LMS algorithms are based on, it is adaptive to adjust
Whole sampling period T span, sef-adapting filter exponent number N span and convergence factor μ scope, further according to filtering
Device weight coefficient calculation formula ω [n+1]=ω [n]+μ (e [n] H [n] x [n]), in the range of the convergence factor μ
Adjust μ value, update weight coefficient w, until residual noise e (n) convergence when convergence factor μ minimums when, generate optimal make an uproar
Acoustic control pattern Y [n]=wS [n].
7. it is applied to the active noise reduction seat of high ferro business class according to claim 6, it is characterised in that described obtain is used for
The path transfer function H [n] of noise control mode prediction process, including：
Judge j-th of path transfer function H [j] whether so that residual noise e (n) restrains in n sampling number；
If convergence, noise control mode prediction is carried out using H [j]；
If not restraining, judge whether (j+1) individual path transfer function H [j+1] causes residual noise e (n) in n sampled point
Restrained in number.
8. it is applied to the active noise reduction seat of high ferro business class according to claim 6, it is characterised in that described to be based on FX-
LMS algorithm, the process of the adaptive span for adjusting sampling period T, including：
Sampling period T is adjusted, makes T=1/fs≤1/2f0, wherein, fs is sample frequency, f0For the optimal Noise measarement mould of output
The frequency range upper limit of formula.
9. it is applied to the active noise reduction seat of high ferro business class according to claim 6, it is characterised in that described to be based on FX-
LMS algorithm, the process of the adaptive scope for adjusting sef-adapting filter exponent number N, including：
In reference noise frequency discontinuity, N values are raised；
In reference noise frequency stabilization, N values are reduced.
10. it is applied to the active noise reduction seat of high ferro business class according to claim 6, it is characterised in that described to be based on FX-
LMS algorithm, the adaptive process for adjusting convergence factor μ scopes, including：
The adaptive adjustment convergence factor μ of adjustment so that μ meets stability of a system condition and system convergence condition.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107995566A (en) * | 2018-01-10 | 2018-05-04 | 湘潭大学 | An active control device for vehicle noise |
| CN108597489A (en) * | 2018-04-21 | 2018-09-28 | 中车青岛四方机车车辆股份有限公司 | A kind of bullet train car Active noise control system |
| CN109151668A (en) * | 2018-09-28 | 2019-01-04 | 天津职业技术师范大学 | One kind multiplying auto use chair sound quality active control system |
| CN110085208A (en) * | 2018-01-26 | 2019-08-02 | 优酷网络技术（北京）有限公司 | A kind of noise-reduction method, computer storage medium, auxiliary bedroom equipment and server |
| CN112810648A (en) * | 2021-03-23 | 2021-05-18 | 刘玉梅 | Freely-bendable active noise reduction headrest |
| CN113066467A (en) * | 2021-03-15 | 2021-07-02 | 南京大学 | An IoT-based lounge chair suitable for use in noisy environments |
| CN115294953A (en) * | 2022-08-15 | 2022-11-04 | 浙江大学 | A method for active control of vehicle cabin noise based on multi-channel independent order filters |
| WO2023124441A1 (en) * | 2021-12-29 | 2023-07-06 | 华为技术有限公司 | Noise reduction method, active noise control (anc) headrest system, and electronic device |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120288110A1 (en) * | 2011-05-11 | 2012-11-15 | Daniel Cherkassky | Device, System and Method of Noise Control |
| CN103208281A (en) * | 2013-03-12 | 2013-07-17 | 武汉大学 | Transformer noise active control method adopting variable step size adjustment |
| CN104616667A (en) * | 2014-12-02 | 2015-05-13 | 清华大学 | Active noise reduction method for automobile |
| CN104935293A (en) * | 2015-06-25 | 2015-09-23 | 国网山东省电力公司莱芜供电公司 | High Power Transformer Adaptive Active Noise Reduction Control Method and Control System |
| CN106128449A (en) * | 2016-08-16 | 2016-11-16 | 青岛歌尔声学科技有限公司 | A kind of automobile active denoising method |
| CN207328464U (en) * | 2017-06-28 | 2018-05-08 | 邢优胜 | A kind of active noise reduction seat suitable for high ferro business class |
-
2017
- 2017-06-28 CN CN201710506465.8A patent/CN107351853A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120288110A1 (en) * | 2011-05-11 | 2012-11-15 | Daniel Cherkassky | Device, System and Method of Noise Control |
| CN103208281A (en) * | 2013-03-12 | 2013-07-17 | 武汉大学 | Transformer noise active control method adopting variable step size adjustment |
| CN104616667A (en) * | 2014-12-02 | 2015-05-13 | 清华大学 | Active noise reduction method for automobile |
| CN104935293A (en) * | 2015-06-25 | 2015-09-23 | 国网山东省电力公司莱芜供电公司 | High Power Transformer Adaptive Active Noise Reduction Control Method and Control System |
| CN106128449A (en) * | 2016-08-16 | 2016-11-16 | 青岛歌尔声学科技有限公司 | A kind of automobile active denoising method |
| CN207328464U (en) * | 2017-06-28 | 2018-05-08 | 邢优胜 | A kind of active noise reduction seat suitable for high ferro business class |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107995566A (en) * | 2018-01-10 | 2018-05-04 | 湘潭大学 | An active control device for vehicle noise |
| CN110085208A (en) * | 2018-01-26 | 2019-08-02 | 优酷网络技术（北京）有限公司 | A kind of noise-reduction method, computer storage medium, auxiliary bedroom equipment and server |
| CN108597489A (en) * | 2018-04-21 | 2018-09-28 | 中车青岛四方机车车辆股份有限公司 | A kind of bullet train car Active noise control system |
| CN109151668A (en) * | 2018-09-28 | 2019-01-04 | 天津职业技术师范大学 | One kind multiplying auto use chair sound quality active control system |
| CN109151668B (en) * | 2018-09-28 | 2023-01-24 | 天津职业技术师范大学 | A sound quality active control system for passenger seat |
| CN113066467A (en) * | 2021-03-15 | 2021-07-02 | 南京大学 | An IoT-based lounge chair suitable for use in noisy environments |
| CN112810648A (en) * | 2021-03-23 | 2021-05-18 | 刘玉梅 | Freely-bendable active noise reduction headrest |
| WO2023124441A1 (en) * | 2021-12-29 | 2023-07-06 | 华为技术有限公司 | Noise reduction method, active noise control (anc) headrest system, and electronic device |
| CN115294953A (en) * | 2022-08-15 | 2022-11-04 | 浙江大学 | A method for active control of vehicle cabin noise based on multi-channel independent order filters |
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