CN114677997A - Real vehicle active noise reduction method and system based on acceleration working condition - Google Patents
Real vehicle active noise reduction method and system based on acceleration working condition Download PDFInfo
- Publication number
- CN114677997A CN114677997A CN202210131846.3A CN202210131846A CN114677997A CN 114677997 A CN114677997 A CN 114677997A CN 202210131846 A CN202210131846 A CN 202210131846A CN 114677997 A CN114677997 A CN 114677997A
- Authority
- CN
- China
- Prior art keywords
- signal
- secondary channel
- error
- noise reduction
- filter
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 230000009467 reduction Effects 0.000 title claims abstract description 56
- 238000000034 method Methods 0.000 title claims abstract description 49
- 230000001133 acceleration Effects 0.000 title claims abstract description 24
- 238000012546 transfer Methods 0.000 claims abstract description 24
- 230000000694 effects Effects 0.000 claims abstract description 17
- 239000013598 vector Substances 0.000 claims description 40
- 230000006870 function Effects 0.000 claims description 31
- 238000004422 calculation algorithm Methods 0.000 claims description 30
- 230000003044 adaptive effect Effects 0.000 claims description 25
- 239000011159 matrix material Substances 0.000 claims description 22
- 230000004044 response Effects 0.000 claims description 13
- 230000008569 process Effects 0.000 claims description 12
- 238000004590 computer program Methods 0.000 claims description 10
- 238000001914 filtration Methods 0.000 claims description 9
- 230000005284 excitation Effects 0.000 claims description 8
- 230000008859 change Effects 0.000 claims description 7
- 230000010354 integration Effects 0.000 claims description 7
- 230000005236 sound signal Effects 0.000 claims description 7
- 238000012360 testing method Methods 0.000 claims description 7
- 238000005316 response function Methods 0.000 claims description 5
- 238000003860 storage Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000009795 derivation Methods 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 claims description 3
- 230000010363 phase shift Effects 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 230000003595 spectral effect Effects 0.000 claims description 3
- 238000010408 sweeping Methods 0.000 claims description 3
- 238000005457 optimization Methods 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 6
- 238000011160 research Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 210000005069 ears Anatomy 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000011810 insulating material Substances 0.000 description 1
- 239000012774 insulation material Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000007935 neutral effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000003908 quality control method Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
Images
Classifications
-
- 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
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
- H04R3/00—Circuits for transducers, loudspeakers or microphones
- H04R3/02—Circuits for transducers, loudspeakers or microphones for preventing acoustic reaction, i.e. acoustic oscillatory feedback
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Acoustics & Sound (AREA)
- Multimedia (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Otolaryngology (AREA)
- Signal Processing (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
The invention discloses a real vehicle active noise reduction method and system based on an acceleration working condition. Based on the arrangement in the vehicle, the sweep frequency signal played by the ANC controller is played through a secondary loudspeaker and then transmitted to the ANC controller again through the acquisition of an error microphone; based on the frequency sweep signal and the error microphone acquisition signal, identifying the output frequency sweep signal and the signal acquired by the error microphone to obtain a secondary channel transfer function; based on the frequency sweep signal and the error microphone acquisition signal, identifying the output frequency sweep signal and the signal acquired by the error microphone to obtain a secondary channel transfer function for calculating a convergence coefficient stability curve; and integrating the secondary channel transfer function and the convergence coefficient stability curve into an FxLMS algorithm program, and executing a noise reduction program to reduce the noise in the vehicle. The invention solves the problems of system debugging and effect optimization of the active noise reduction system under the acceleration working condition.
Description
Technical Field
The invention belongs to the field of automobile manufacturing; in particular to a real vehicle active noise reduction method and a system based on an acceleration working condition.
Background
With the improvement of living standard and quality of life, people pay more attention to the comfort of riding in vehicles and the silent degree of acoustic environment, even the sound quality control of sound field in vehicles, the sound field reproduction of entertainment sound and the like. The engine order noise is one of the main sources of noise in the conventional fuel vehicle. Currently, the automobile noise control methods are mainly classified into a passive noise control method and an active noise control method. The passive noise control has a good effect of suppressing middle and high frequency noise by adding a sound insulating material, but the passive control method has a poor effect of controlling low frequency noise due to the physical characteristics of low frequency noise. The Active Noise reduction in the vehicle is an application scene of Active Noise Control (Active Noise Control), secondary sound signals with the same frequency as the original Noise and 180-degree phase difference are emitted through secondary speakers (generally a vehicle door speaker and a subwoofer speaker), and are superposed at an error microphone to generate a silent area at human ears, so that the Noise reduction effect is achieved. Due to the physical characteristics of the system, the active noise reduction method has a good control effect on low-frequency noise, is not easy to control high-frequency noise, is just a supplement of a passive noise control method, does not need to add additional sound insulation materials, has high integration degree, and accords with the development trend of light weight, so that the research and application of the active noise reduction technology on automobiles are more and more.
Researchers have made more researches on the active control of a stationary narrowband noise signal, wherein a representative method is to apply an adaptive notch filter based on the FxLMS algorithm to the active control of the stationary narrowband noise signal. Elliot and Boucher et al, Southampton university, uk, studied the behavior of a multi-channel adaptive feedforward control system for adaptive notch filters based on the FxLMS algorithm. Young-Sup Lee et al, national university of korea, renchuang, have studied the effect of impulse response functions of different lengths on active noise control systems by theory and experiments. In recent years, domestic scholars pay more attention to active control of engine noise in vehicles, and Liujian of the university of Harbin industry performs detailed and deep performance analysis on a narrow-band ANC system based on FxLMS algorithm on the basis of LMS theory. An adaptive notch filter algorithm is adopted as an adaptive control algorithm in Sun Jidong, Jilin university, an in-vehicle noise adaptive controller principle prototype is developed based on Digital Signal Processor (DSP) design, a single-channel in-vehicle noise adaptive control system is established, and in-vehicle low-frequency peak noise active control test under the conditions of transmission neutral gear and different engine rotating speeds is carried out on an autonomous brand car.
The active noise reduction technology based on the self-adaptive notch filter has simple and convenient algorithm and small calculated amount, has better control effect on narrow-band noise such as engine order noise and the like, and is also the most widely applied active noise control method aiming at the engine order noise at present. At present, most of researches on the engine noise active noise reduction system based on the self-adaptive notch filter discuss the noise reduction effect and the system stability of a single-channel or multi-channel active noise reduction system under a steady-state working condition, and relatively few researches on system debugging and effect optimization of the active noise reduction system under an acceleration working condition are carried out.
Disclosure of Invention
The present invention is directed to solving, at least in part, one of the technical problems in the related art.
Therefore, one object of the present invention is to provide an active noise reduction method for a real vehicle based on an acceleration condition, which achieves the purposes of system debugging and effect optimization of an active noise reduction system under the acceleration condition.
The second purpose of the invention is to provide an active noise reduction system of a real vehicle based on an acceleration working condition.
A third object of the invention is to propose a computer device.
A fourth object of the invention is to propose a non-transitory computer-readable storage medium.
The invention is realized by the following technical scheme:
an active noise reduction method for a real vehicle based on an acceleration working condition comprises the following steps:
s01: arranging an error microphone, a secondary loudspeaker, a CAN rotating speed signal and an ANC controller in the vehicle;
s02: based on the arrangement in the vehicle of S01, the frequency sweeping signal played by the ANC controller is played through the secondary loudspeaker and then transmitted to the ANC controller again through the collection of the error microphone;
s03: based on the S02 frequency sweep signal and the error microphone acquisition signal, an FIR filter with the filter length of 128 is adopted to identify the output frequency sweep signal and the signal acquired by the error microphone to obtain a secondary channel transfer function;
s04: based on the S02 frequency sweep signal and the error microphone collected signal, adopting an FIR filter with the filter length of 1024 to identify the output frequency sweep signal and the signal collected by the error microphone to obtain a secondary channel transfer function for calculating a convergence coefficient stability curve;
s05: the secondary channel transfer function obtained at S03 and the convergence coefficient stability curve obtained at S04 are integrated into the FxLMS algorithm program, and a noise reduction program is executed to reduce noise in the vehicle.
Further, in the step S01, specifically, the error microphone is disposed on the frame; placing a secondary loudspeaker in a vehicle door, connecting a CAN rotating speed signal to an OBD interface, and monitoring the rotating speed of an engine; the ANC controller is installed in the vehicle.
Further, in S02, it is specifically assumed that J secondary speakers and K error microphones are used in the multi-channel active noise reduction system, one secondary channel exists between each secondary speaker and each error microphone, and a secondary channel transfer function of the whole system is represented by hs (z);
x (n) is a reference signal formed according to the rotation speed signal,
secondary communication of presentation systemEstimating, wherein the total number of the secondary channels is J multiplied by K, and the filtering reference signal R is obtained after the reference signal x (n) and the 90-degree phase shift signal thereof are respectively convolved with the secondary channel estimation0(n)、R1(n) is a J × K dimensional matrix, i.e. having:
in a multi-channel adaptive notch filtering system, a filter weight vector W1And W2The residual error signal vector e (n) is K × 1 dimensional vector, y (n) is J × 1 dimensional vector, and the iterative formula of two adaptive weight vectors obtained by the FxLMS algorithm is:
therefore, obtaining the controller output secondary sound signal has:
Y(n)＝x0(n)W0+x1(n)W1 (3)。
Further, the step S03 of identifying the output frequency sweep signal and the signal collected by the error microphone to obtain the secondary channel transfer function specifically includes,
the identifying the secondary channel transfer function of the secondary channel is specifically that in the secondary channel identification model, x (n) represents noise excitation in the secondary channel testing process, namely an excitation signal output by a controller; d (n) represents that the microphone received by the controller acquires a voltage signal or a sound pressure signal in the test process; y (n) represents the adaptive filter output, i.e., the response of the noise excitation after passing through the filter; e (n) represents a residual error signal after the voltage signal received by the controller and the output response of the filter are superposed; in the process of identifying the secondary path, continuously updating and iterating the weight vector of the adaptive filter according to an LMS algorithm, so that a residual error signal e (n) is continuously close to 0, namely the output response y (n) of the filter is continuously close to a voltage signal received by a controller; when the system converges and the residual error signal is close to 0, the adaptive filter weight vector coefficients can be equivalent to the secondary path impulse response function.
Further, the S04 is to calculate the stability curve of the convergence coefficient, that is, assuming that there are M secondary sound sources and L error microphones, assuming that the complex component of the ith error signal at the nth harmonic is denoted as E l(ωn) The complex component of the mth secondary signal at this harmonic is denoted as Wm(ωn) Then the error signal is
Wherein Dl(ωn) Is the first complex error signal, C, caused by the primary sourcelm(ωn) Is the complex response of the mth secondary source to the lth error sensor at that frequency, in vector form having
E(ωn)＝D(ωn)+C(ωn)W(ωn) (5)
Wherein
E(ωn)＝[E1(ωn),E2(ωn),...,EL(ωn)]T
D(ωn)＝[D1(ωn),D2(ωn),...,DL(ωn)]T
W(ωn)＝[W1(ωn),W2(ωn),...,WM(ωn)]T
For single frequency noise, the objective function is written as
J＝EHAE+WHBW (6)
Where superscript H represents the Hermite transpose of the vector or matrix; e and W represent the complex error signal of L × 1 and the complex secondary sound signal of M × 1, respectively, A and B are positive definite weighting matrices of L × L and M × M, respectively; equation (6) can also be written as the modular squared sum of the unweighted error signals plus the modular squared sum of the weighted secondary signals:
J＝EHE+βWHW (7)
the objective function of conjunctive equation (5) can be written in the form of a quadratic form of the variable W:
J＝DHD+WHCHD+DHCW+WH[CHC+βI]W (8)
target function for W real part (W)R) And an imaginary part (W)I) Are all real, so the complex gradient vector can be defined as:
since the real and imaginary parts of g are independent of each other, let g equal to 0 set J for WRAnd WIIs equal to 0, resulting in an optimal control signal vector:
Wopt＝-[CHC+βI]-1CHD (10)
in conjunction with equation (5), the complex gradient vector can be written as:
g＝2[CHE+βW] (11)
adjusting the real part and the imaginary part of the complex secondary signal in a direction inversely proportional to the gradient vector to obtain a steepest descent algorithm:
W(k+1)＝(1-αβ)W(k)-αCHE(k) (12)
Where α represents a convergence coefficient. Combining equations (5) and (10) iterative equation (12) is written as:
(W(k+1)-Wopt)＝[I-α(CHC+βI)](W(k)-Wopt) (13) assuming that W (0) is 0, repeating application of formula (13) yields
W(k)-Wopt＝-[I-α(CHC+βI)]kWopt (14)
If the complex sea plug matrix is written into the form of complex unitary matrix, standardizing characteristic vector Q and characteristic value diagonal matrix, and changing Λ into the form of complex unitary matrixdiag(λ1,λ2,...,λM) Wherein the eigenvalues are all real numbers, so
CHC+βI＝QΛQH (15)
Defining the primary coordinates of the control system as
V(k)＝QH(W(k)-Wopt) (16)
Therefore, formula (14) is written as
V(k)＝[1-αΛ]kV(0) (17)
Since Λ is a diagonal matrix, the convergence of the principal coordinates of the control system is independent, and the m-th component of V (k) is written as
Wherein the above equation is valid by ensuring-1 < 1-alpha lambdam< 1, stability condition based on convergence coefficient is obtained: for all m, 0 < alpha < 2/lambdam。
By combining the above derivation process with equations (15) and (18), it can be seen that the convergence coefficient stability boundary curve can be calculated by the secondary channel transfer function matrix when determining the value of the convergence coefficient α. Since the order noise frequency also changes when the engine speed of the automobile changes, the convergence coefficient limit value satisfying the system stability also differs at the engine speed, that is, the convergence coefficient stability limit value changes with the change in the engine speed.
Further, the step S05 is specifically to verify that the FIR filters with different filter lengths recognize the secondary channel effect, specifically, recognize that the secondary channel is used for FIR filter length selection 128 when the algorithm integration is performed;
The frequency resolution based on the convergence coefficient stability boundary curve is related to the selection of the FIR filter length. The longer the length of the secondary channel used for calculating the stability boundary curve, the more spectral lines of the stability boundary curve in the same frequency bandwidth range, and the finer the frequency resolution, the relationship can be written as:
df＝fs/Length_of_FIR (19)
where df is the frequency resolution of the stability boundary curve, fs is the sampling rate used for the identification of the secondary channel, and Length _ of _ FIR is the Length of the FIR filter used for the identification of the secondary channel. Different from the method for algorithm integration, the secondary channel used for calculating the stability boundary curve of the convergence coefficient does not relate to the algorithm operation amount, relatively speaking, the longer filter length is greatly helpful for improving the accuracy of the stability boundary curve, and if the filter length is shorter, the phenomenon that the system is unstable in some frequency ranges due to the resolution factor is caused. The method uses an FIR filter with the filter length of 1024 to identify a secondary channel for calculating a convergence coefficient stability boundary curve;
the curve convergence coefficient change is plotted as 1/5 for the stability boundary curve based on the system steady state error.
Further, the noise reduction system comprises
Error microphone: the device is used for acquiring error signals and giving the error signals to the ANC controller for algorithm calculation;
a secondary speaker: the ANC controller calculates operation to obtain an output signal, and the output signal is sent to a secondary loudspeaker through a power amplifier;
CAN bus: collecting the rotating speed of an engine to construct a reference signal of an active noise reduction system;
an ANC controller: the ANC controller is used to execute the FxLMS algorithm program.
A computer device comprising a memory storing a computer program and a processor implementing the steps of any of the methods described above when the processor executes the computer program.
A non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of any of the above.
The invention has the beneficial effects that:
a convergence coefficient stability boundary curve (a curve of the upper limit of the convergence coefficient along with the change of frequency on the premise of ensuring the system stability) is calculated through a secondary channel transfer function matrix, so that the system can achieve a good noise reduction effect when the system is accelerated in a wider rotating speed range of an engine, and the application scene and range of the active noise reduction system are widened.
Identifying twice during identifying the secondary channel, respectively adopting two different filter lengths, and using the result of identifying the secondary channel by the FIR filter with the length of 128 for system algorithm integration to reduce the calculated amount of the algorithm as much as possible;
The result of identifying the secondary channel by the FIR filter with the length of 1024 is used for calculating the stability boundary curve of the convergence coefficient, so that the calculated stability boundary curve has higher resolution in a frequency domain, and the condition of system divergence in certain engine rotating speed ranges under the acceleration working condition is avoided.
Drawings
FIG. 1 is a schematic structural diagram of an active noise reduction system of the present invention.
Fig. 2 is a block diagram of a secondary channel of the multi-channel active noise reduction system of the present invention.
FIG. 3 is a block diagram of the multi-channel adaptive notch filtering of the present invention.
FIG. 4 is a diagram of a secondary path identification model of the present invention.
Fig. 5 is a graph of the stability boundary of the convergence coefficient calculated by the FIR filter of 128 and 1024 lengths, respectively, identifying the secondary channel.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
An active noise reduction method for a real vehicle based on an acceleration working condition comprises the following steps:
s01: arranging an error microphone, a secondary loudspeaker, a CAN rotating speed signal and an ANC controller in the vehicle;
s02: based on the arrangement in the vehicle of S01, the frequency sweeping signal played by the ANC controller is played through the secondary loudspeaker and then transmitted to the ANC controller again through the collection of the error microphone;
s03: based on the S02 frequency sweep signal and the error microphone acquisition signal, an FIR filter with the filter length of 128 is adopted to identify the output frequency sweep signal and the signal acquired by the error microphone to obtain a secondary channel transfer function;
s04: based on the S02 frequency sweep signal and the error microphone collected signal, adopting an FIR filter with the filter length of 1024 to identify the output frequency sweep signal and the signal collected by the error microphone to obtain a secondary channel transfer function for calculating a convergence coefficient stability curve;
s05: the secondary channel transfer function obtained at S03 and the convergence coefficient stability curve obtained at S04 are integrated into the FxLMS algorithm program, and a noise reduction program is executed to reduce noise in the vehicle.
Example 2
An actual vehicle active noise reduction method based on an acceleration working condition is disclosed, wherein S01 specifically comprises arranging an error microphone on a vehicle frame; placing a secondary speaker in a car door, connecting a CAN rotating speed signal at an OBD interface, and monitoring the rotating speed of an engine; an ANC controller is installed in a vehicle.
Example 3
Specifically, S02 discloses that the active noise reduction system is based on a multi-channel adaptive notch filter, i.e., a plurality of secondary speakers are used to control the noise level at the positions of a plurality of error microphones, and the block diagram of the secondary channels of the multi-channel active noise reduction system is shown in fig. 2. Assuming that J secondary speakers and K error microphones are used in the multi-channel active noise reduction system, one secondary channel exists between each secondary speaker and each error microphone, Hjk in fig. 2 represents the secondary channel from the jth secondary speaker to the kth error sensor, and the transfer function of the secondary channel of the whole system is represented by hs (z); a block diagram of the multi-channel adaptive notch filtering principle is shown in fig. 3.
x (n) is formed according to the rotation speed signalThe reference signal of (a) is set,
representing the estimation of the secondary channel of the system, having J × K secondary channels, respectively convolving the reference signal x (n) and its 90 ° phase shift signal with the estimation of the secondary channel to obtain the filtering reference signal R0(n)、R1(n) is a J × K dimensional matrix, i.e. having:
in a multi-channel adaptive notch filtering system, a filter weight vector W1And W2The residual error signal vector e (n) is K × 1 dimensional vector, y (n) is J × 1 dimensional vector, and the iterative formula of two adaptive weight vectors obtained by the FxLMS algorithm is:
Therefore, obtaining the controller output secondary sound signal has:
Y(n)＝x0(n)W0+x1(n)W1 (3)。
example 4
An active noise reduction method for a real vehicle based on an acceleration condition is disclosed, wherein S03 identifies an output sweep frequency signal and a signal collected by an error microphone to obtain a secondary channel transfer function,
the effect of the secondary channel on the control system needs to be considered in an active noise reduction system, and therefore the secondary channel needs to be identified. The sub-path recognition model is shown in fig. 4. In the secondary path identification model, x (n) represents noise excitation in the secondary path test process, namely an excitation signal output by the controller;
d (n) represents a voltage signal or a sound pressure signal acquired by a microphone received by the controller in the test process; y (n) represents the adaptive filter output, i.e., the response of the noise excitation after passing through the filter; e (n) represents a residual error signal after the voltage signal received by the controller and the output response of the filter are superposed; in the process of identifying the secondary path, continuously updating and iterating the weight vector of the adaptive filter according to an LMS algorithm, so that a residual error signal e (n) is continuously close to 0, namely the output response y (n) of the filter is continuously close to a voltage signal received by a controller; when the system converges and the residual error signal is close to 0, the adaptive filter weight vector coefficients can be equivalent to the secondary path impulse response function.
Example 5
An actual vehicle active noise reduction method based on an acceleration working condition is characterized in that S04 calculates a convergence coefficient stability curve specifically, in an actual vehicle active noise reduction system, M secondary sound sources and L error microphones are assumed, and then active control of narrow-band harmonic noise is analyzed from a frequency domain angle; let the complex component of the ith error signal at the nth harmonic be denoted as El(ωn) The complex component of the mth secondary signal at this harmonic is denoted as Wm(ωn) Then the error signal is
Wherein Dl(ωn) Is the first complex error signal, C, caused by the primary sourcelm(ωn) Is the complex response of the mth secondary source to the lth error sensor at that frequency, in vector form having
E(ωn)＝D(ωn)+C(ωn)W(ωn) (5)
Wherein
E(ωn)＝[E1(ωn),E2(ωn),...,EL(ωn)]T
D(ωn)＝[D1(ωn),D2(ωn),...,DL(ωn)]T
W(ωn)＝[W1(ωn),W2(ωn),...,WM(ωn)]T
Writing of an objective function for single frequency noise
J＝EHAE+WHBW (6)
Where the superscript H represents the hermitian transpose (conjugate transpose) of the vector or matrix; e and W represent the complex error signal of L × 1 and the complex secondary sound signal of M × 1, respectively, A and B are positive definite weighting matrices of L × L and M × M, respectively; equation (6) can also be written as the modular squared sum of the unweighted error signals plus the modular squared sum of the weighted secondary signals:
J＝EHE+βWHW (7)
the objective function of conjunctive equation (5) can be written in the form of a quadratic form of the variable W:
J＝DHD+WHCHD+DHCW+WH[CHC+βI]W (8)
target function for W real part (W) R) And imaginary component (W)I) Are all real, so the complex gradient vector can be defined as:
since the real and imaginary parts of g are independent of each other, let g equal to 0 set J for WRAnd WIIs equal to 0, resulting in an optimal control signal vector:
Wopt＝-[CHC+βI]-1CHD (10)
in conjunction with equation (5), the complex gradient vector can be written as:
g＝2[CHE+βW] (11)
adjusting the real part and the imaginary part of the complex secondary signal in a direction inversely proportional to the gradient vector to obtain a steepest descent algorithm:
W(k+1)＝(1-αβ)W(k)-αCHE(k) (12)
where α represents a convergence coefficient. Iterative equation (12) is written in conjunction with equations (5) and (10):
(W(k+1)-Wopt)＝[I-α(CHC+βI)](W(k)-Wopt) (13) assuming that W (0) is 0, repeating the application of formula (13) to obtain
W(k)-Wopt＝-[I-α(CHC+βI)]kWopt (14)
If the complex sea-plug matrix is written in the form of a complex unitary matrix, the eigenvector Q and the eigenvalue diagonal matrix are normalized, Λ ═ diag (λ)1,λ2,...,λM) Wherein the eigenvalues are all real numbers, so
CHC+βI＝QΛQH (15)
Defining the principal coordinates of the control system as
V(k)＝QH(W(k)-Wopt) (16)
Therefore, formula (14) is written as
V(k)＝[1-αΛ]kV(0) (17)
Since Λ is a diagonal matrix, the convergence of the principal coordinates of the control system is independent, and the m-th component of V (k) is written as
Wherein the above equation is valid by ensuring-1 < 1-alpha lambdam< 1, stability condition based on convergence coefficient is obtained: for all m, 0 < alpha < 2/lambdam。
By combining the above derivation process with equations (15) and (18), it can be seen that the convergence coefficient stability boundary curve can be calculated by the secondary channel transfer function matrix when determining the value of the convergence coefficient α. Since the order noise frequency also changes when the engine speed of the automobile changes, the convergence coefficient limit value satisfying the system stability also differs at the engine speed, that is, the convergence coefficient stability limit value changes with the change in the engine speed.
Example 6
An active noise reduction method for a real vehicle based on an acceleration working condition is disclosed, wherein S05 specifically includes that before algorithm debugging and running, a secondary channel identification result is integrated into an algorithm program to carry out filtering processing on a reference signal, and an equivalent secondary channel impulse response function of an FIR filter is generally used. In this application scenario, the filter length is selected in consideration of noise reduction, system stability, and algorithm computation. Theoretically, the higher the order of the filter is, the more accurate the identification result of the secondary channel is, and the frequency response from the secondary loudspeaker to the error microphone can be reflected more; however, the longer the filter length is, the more the arithmetic operation amount is increased, and when the filter length is greater than a certain value, the longer the FIR filter length is, the less the noise reduction effect is improved; in order to give consideration to the accuracy of the arithmetic operation amount and the identification result, the FIR filter length selection 128 is selected when the identification secondary channel is used for arithmetic integration;
the frequency resolution (or resolution as a function of engine speed) based on the convergence coefficient stability boundary curve is related to the selection of the FIR filter length. The longer the length of the secondary channel used for calculating the stability boundary curve, the more spectral lines of the stability boundary curve in the same frequency bandwidth range, and the finer the frequency resolution, the relationship can be written as:
df＝fs/Length_of_FIR (19)
And df is the frequency resolution of the stability boundary curve, fs is the sampling rate used for identifying the secondary channel, and Length _ of _ FIR is the FIR filter Length adopted for identifying the secondary channel. Different from the algorithm integration, the secondary channel used for calculating the stability boundary curve of the convergence coefficient does not relate to the algorithm operation amount, relatively speaking, the use of the longer filter length has a great help for improving the accuracy of the stability boundary curve, and if the filter length is shorter, the phenomenon that the system is unstable in some frequency ranges due to the resolution factor can be caused. The method uses an FIR filter with the filter length of 1024 to identify a secondary channel for calculating a stability boundary curve of a convergence coefficient;
the convergence coefficient not only affects the convergence rate of the active noise reduction system, but also affects the steady-state error during system convergence, and when the convergence coefficient is too large, although the convergence rate is increased, the corresponding steady-state error is increased, and the noise reduction effect is affected to a certain extent. Therefore, based on the steady-state error of the system, when the frequency-convergence coefficient curve is integrated, the curve convergence coefficient change curve is 1/5 of the stability boundary curve, and the factors such as the convergence speed of the system, the noise reduction effect and the steady-state error are considered.
Example 7
An active noise reduction system for real vehicles based on acceleration working conditions comprises
Error microphone: the ANC controller is used for acquiring an error signal and giving the error signal to the ANC controller for algorithm calculation;
a secondary speaker: the ANC controller calculates operation to obtain an output signal, and the output signal is sent to a secondary loudspeaker through a power amplifier; four door speakers are used as the secondary speakers,
CAN bus: collecting the rotating speed of an engine to construct a reference signal of an active noise reduction system;
an ANC controller: the ANC controller is used to execute the FxLMS algorithm program.
Example 8
A computer device comprising a memory storing a computer program and a processor implementing the steps of any of the methods described above when the processor executes the computer program.
Example 9
A non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of any of the above.
Claims (9)
1. An actual vehicle active noise reduction method based on an acceleration working condition is characterized by comprising the following steps:
s01: arranging an error microphone, a secondary loudspeaker, a CAN rotating speed signal and an ANC controller in the vehicle;
S02: based on the arrangement in the vehicle of S01, the frequency sweeping signals played by the ANC controller are played through the secondary loudspeaker and then transmitted to the ANC controller again through the acquisition of the error microphone;
s03: based on the S02 frequency sweep signal and the error microphone acquisition signal, an FIR filter with the length of the filter being 128 is adopted to identify the output frequency sweep signal and the signal acquired by the error microphone to obtain a secondary channel transfer function;
s04: based on the S02 frequency sweep signal and the error microphone collected signal, adopting an FIR filter with the length of the filter being 1024, identifying the output frequency sweep signal and the signal collected by the error microphone to obtain a secondary channel transfer function for calculating a convergence coefficient stability curve;
s05: the secondary channel transfer function obtained at S03 and the convergence coefficient stability curve obtained at S04 are integrated into the FxLMS algorithm program, and a noise reduction program is executed to reduce noise in the vehicle.
2. The active noise reduction method for the real vehicle based on the acceleration working condition according to claim 1, wherein in step S01, an error microphone is disposed on a vehicle frame; placing a secondary speaker in a car door, connecting a CAN rotating speed signal at an OBD interface, and monitoring the rotating speed of an engine; an ANC controller is installed in a vehicle.
3. The active noise reduction method for real vehicles based on acceleration conditions of claim 1, wherein S02 is specifically that, assuming that J secondary speakers and K error microphones are adopted in the multi-channel active noise reduction system, there is a secondary channel between each secondary speaker and each error microphone, and the secondary channel transfer function of the whole system is represented by hs (z);
x (n) is a reference signal formed according to the rotation speed signal,
representing the estimation of the secondary channel of the system, J multiplied by K secondary channels are totally arranged, and the filtering reference signal R is obtained after the convolution of the reference signal x (n) and the 90-degree phase shift signal thereof with the estimation of the secondary channel respectively0(n)、R1(n) is a matrix of dimension J x K,namely, the method comprises the following steps:
in a multi-channel adaptive notch filtering system, a filter weight vector W1And W2The residual error signal vector e (n) is K × 1 dimensional vector, y (n) is J × 1 dimensional vector, and the iterative formula of two adaptive weight vectors obtained by the FxLMS algorithm is:
therefore, obtaining the controller output secondary sound signal has:
Y(n)＝x0(n)W0+x1(n)W1 (3)。
4. the active noise reduction method for real vehicles based on the acceleration condition as claimed in claim 3, wherein the S03 is implemented by identifying the output sweep frequency signal and the signal collected by the error microphone to obtain the secondary channel transfer function,
The secondary channel transfer function for identifying the secondary channel is specifically that in the secondary channel identification model, x (n) represents noise excitation in the secondary channel test process, namely an excitation signal output by the controller; d (n) represents that the microphone received by the controller acquires a voltage signal or a sound pressure signal in the test process; y (n) represents the adaptive filter output, i.e., the response of the noise stimulus after passing through the filter; e (n) represents a residual error signal after the voltage signal received by the controller and the output response of the filter are superposed; in the process of identifying the secondary path, continuously updating and iterating the weight vector of the adaptive filter according to an LMS algorithm, so that a residual error signal e (n) is continuously close to 0, namely the output response y (n) of the filter is continuously close to a voltage signal received by a controller; when the system converges and the residual error signal is close to 0, the adaptive filter weight vector coefficients can be equivalent to the secondary path impulse response function.
5. The active noise reduction method for real vehicles based on the acceleration condition of claim 1, wherein the S04 is used to calculate the stability curve of the convergence coefficient, specifically, assuming that there are M secondary sound sources and L error microphones, and assuming that the complex component of the ith error signal at the nth harmonic is denoted as E l(ωn) The complex component of the mth secondary signal at this harmonic is denoted as Wm(ωn) Then the error signal is
Wherein Dl(ωn) Is the first complex error signal, C, caused by the primary sourcelm(ωn) Is the complex response of the mth secondary source to the lth error sensor at that frequency, in vector form having
E(ωn)＝D(ωn)+C(ωn)W(ωn) (5)
Wherein
E(ωn)＝[E1(ωn),E2(ωn),...,EL(ωn)]T
D(ωn)＝[D1(ωn),D2(ωn),...,DL(ωn)]T
W(ωn)＝[W1(ωn),W2(ωn),...,WM(ωn)]T
Writing of an objective function for single frequency noise
J＝EHAE+WHBW (6)
Where superscript H represents the Hermite transpose of the vector or matrix; e and W represent the complex error signal of L × 1 and the complex secondary sound signal of M × 1, respectively, A and B are positive definite weighting matrices of L × L and M × M, respectively; equation (6) can also be written as the modular squared sum of the unweighted error signals plus the modular squared sum of the weighted secondary signals:
J＝EHE+βWHW (7)
the objective function of conjunctive equation (5) can be written in the form of a quadratic form of the variable W:
J＝DHD+WHCHD+DHCW+WH[CHC+βI]W (8)
target function for W real part (W)R) And an imaginary part (W)I) Are all real, so the complex gradient vector can be defined as:
since the real and imaginary parts of g are independent of each other, let g equal to 0 set J for WRAnd WIIs equal to 0, resulting in an optimal control signal vector:
Wopt＝-[CHC+βI]-1CHD (10)
in conjunction with equation (5), the complex gradient vector can be written as:
g＝2[CHE+βW] (11)
adjusting the real part and the imaginary part of the complex secondary signal in a direction inversely proportional to the gradient vector to obtain a steepest descent algorithm:
W(k+1)＝(1-αβ)W(k)-αCHE(k) (12)
wherein α represents a convergence coefficient; iterative equation (12) is written in conjunction with equations (5) and (10):
(W(k+1)-Wopt)＝[I-α(CHC+βI)](W(k)-Wopt) (13)
Assuming that W (0) is equal to 0, repeating the application of equation (13) yields
W(k)-Wopt＝-[I-α(CHC+βI)]kWopt (14)
If the complex sea plug matrix is written in the form of complex unitary matrix, standardizing the eigenvector Q and eigenvalue diagonal matrix, and changing lambda to diag (lambda)1,λ2,...,λM) Where the eigenvalues are all real numbers, so
CHC+βI＝QΛQH (15)
Defining the primary coordinates of the control system as
V(k)＝QH(W(k)-Wopt) (16)
Therefore, equation (14) is written as
V(k)＝[1-αΛ]kV(0) (17)
Since Λ is a diagonal matrix and the convergence of the principal coordinates of the control system is independent, the m-th component of V (k) is written as
Wherein the above equation is valid with the assurance that-1 < 1-alpha lambdam< 1, obtaining stability conditions based on convergence coefficients: for all m, 0 < alpha < 2/lambdam；
Through the above derivation process, it can be seen by combining equation (15) and equation (18) that, in determining the value of the convergence coefficient α, the convergence coefficient stability boundary curve can be calculated by the secondary channel transfer function matrix; since the order noise frequency also changes when the engine speed of the automobile changes, the convergence coefficient limit value satisfying the system stability also differs at the engine speed, that is, the convergence coefficient stability limit value changes with the change in the engine speed.
6. The active noise reduction method for the real vehicle based on the acceleration working condition of claim 1, wherein the step S05 is to verify that the FIR filters with different filter lengths identify the secondary channel effect, specifically, identify that the secondary channel is used for FIR filter length selection 128 when algorithm integration;
The frequency resolution based on the convergence coefficient stability boundary curve is related to the selection of the FIR filter length; the longer the length of the secondary channel used for calculating the stability boundary curve, the more spectral lines of the stability boundary curve in the same frequency bandwidth range, and the finer the frequency resolution, the relationship is written as:
df＝fs/Length_of_FIR (19)
wherein df is the frequency resolution of the stability boundary curve, fs is the sampling rate used for identifying the secondary channel, and Length _ of _ FIR is the Length of the FIR filter used for identifying the secondary channel; different from the method for integrating the algorithm, the secondary channel used for calculating the stability boundary curve of the convergence coefficient does not relate to the algorithm operation amount, relatively speaking, the longer filter length is used for greatly helping to improve the accuracy of the stability boundary curve, and if the filter length is shorter, the system in some frequency ranges is unstable due to the resolution; the method uses an FIR filter with the filter length of 1024 to identify a secondary channel for calculating a convergence coefficient stability boundary curve;
the curve convergence coefficient change is plotted as 1/5 for the stability boundary curve based on the system steady state error.
7. The active noise reduction system for real vehicles based on acceleration conditions of claim 1, wherein the noise reduction system comprises
Error microphone: the ANC controller is used for acquiring an error signal and giving the error signal to the ANC controller for algorithm calculation;
a secondary speaker: the ANC controller calculates and operates to obtain an output signal, and the output signal is sent to a secondary loudspeaker through a power amplifier;
CAN bus: collecting the rotating speed of an engine to construct a reference signal of an active noise reduction system;
an ANC controller: the ANC controller is used to execute the FxLMS algorithm program.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor realizes the steps of the method according to any of claims 1-6 when executing the computer program.
9. A non-transitory computer readable storage medium, having stored thereon a computer program, characterized in that the computer program, when being executed by a processor, realizes the steps of the method of any one of claims 1-6.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210131846.3A CN114677997B (en) | 2022-02-14 | 2022-02-14 | Real vehicle active noise reduction method and system based on acceleration working condition |
| PCT/CN2022/105938 WO2023151229A1 (en) | 2022-02-14 | 2022-07-15 | Real vehicle active noise reduction method and system based on acceleration conditions |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210131846.3A CN114677997B (en) | 2022-02-14 | 2022-02-14 | Real vehicle active noise reduction method and system based on acceleration working condition |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114677997A true CN114677997A (en) | 2022-06-28 |
| CN114677997B CN114677997B (en) | 2024-08-06 |
Family
ID=82072298
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210131846.3A Active CN114677997B (en) | 2022-02-14 | 2022-02-14 | Real vehicle active noise reduction method and system based on acceleration working condition |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN114677997B (en) |
| WO (1) | WO2023151229A1 (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115294953A (en) * | 2022-08-15 | 2022-11-04 | 浙江大学 | Automobile carriage noise active control method of multi-channel independent order filter |
| WO2023151229A1 (en) * | 2022-02-14 | 2023-08-17 | 中国第一汽车股份有限公司 | Real vehicle active noise reduction method and system based on acceleration conditions |
| CN117690404A (en) * | 2024-02-04 | 2024-03-12 | 清华大学苏州汽车研究院（相城） | Active control method and device for engine noise of automobile |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5384853A (en) * | 1992-03-19 | 1995-01-24 | Nissan Motor Co., Ltd. | Active noise reduction apparatus |
| JPH08249075A (en) * | 1995-03-10 | 1996-09-27 | Mazda Motor Corp | Method and device for oscillation control of vehicle |
| US20050207585A1 (en) * | 2004-03-17 | 2005-09-22 | Markus Christoph | Active noise tuning system |
| JP2008015046A (en) * | 2006-07-03 | 2008-01-24 | Masaaki Okuma | Signal processing method at the time of online identification in active noise elimination device |
| CN102280102A (en) * | 2010-06-14 | 2011-12-14 | 哈曼贝克自动系统股份有限公司 | Adaptive noise control |
| CN104038181A (en) * | 2014-06-05 | 2014-09-10 | 北京航空航天大学 | Self-adapting filter construction method based on NLMS algorithm |
| CN109448686A (en) * | 2018-12-13 | 2019-03-08 | 重庆邮电大学 | Intersected based on secondary channel on-line identification new algorithm and updates active noise control system |
| CN109859733A (en) * | 2019-01-02 | 2019-06-07 | 哈尔滨理工大学 | Engine noise control method based on FXLMS algorithm |
| CN111564151A (en) * | 2020-05-13 | 2020-08-21 | 吉林大学 | Narrow-band active noise reduction optimization system for engine order noise in vehicle |
| CN113257214A (en) * | 2021-02-04 | 2021-08-13 | 南京汉得利智能科技有限公司 | Active noise reduction method for fan pipeline system |
| CN113291248A (en) * | 2021-05-25 | 2021-08-24 | 浙江大学 | Multi-channel decoupling and splitting active control method and system for automobile compartment noise |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180190258A1 (en) * | 2016-12-30 | 2018-07-05 | Qualcomm Incorporated | Adaptations for active noise cancellation inside a vehicle |
| CN108189724B (en) * | 2018-01-03 | 2021-05-11 | 上海工程技术大学 | Automobile feedback control active noise reduction neck pillow |
| CN111613202A (en) * | 2020-06-28 | 2020-09-01 | 清华大学苏州汽车研究院（相城） | Active control system for in-vehicle noise reduction and control method thereof |
| CN112509546B (en) * | 2020-10-22 | 2023-06-20 | 上海迪彼电子科技有限公司 | Active noise reduction system for dynamic change sound field of automobile |
| CN113593517A (en) * | 2021-08-03 | 2021-11-02 | 深圳丽的声学科技有限公司 | Intelligent partition active noise reduction control system and method for automobile |
| CN114677997B (en) * | 2022-02-14 | 2024-08-06 | 中国第一汽车股份有限公司 | Real vehicle active noise reduction method and system based on acceleration working condition |
-
2022
- 2022-02-14 CN CN202210131846.3A patent/CN114677997B/en active Active
- 2022-07-15 WO PCT/CN2022/105938 patent/WO2023151229A1/en unknown
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5384853A (en) * | 1992-03-19 | 1995-01-24 | Nissan Motor Co., Ltd. | Active noise reduction apparatus |
| JPH08249075A (en) * | 1995-03-10 | 1996-09-27 | Mazda Motor Corp | Method and device for oscillation control of vehicle |
| US20050207585A1 (en) * | 2004-03-17 | 2005-09-22 | Markus Christoph | Active noise tuning system |
| JP2008015046A (en) * | 2006-07-03 | 2008-01-24 | Masaaki Okuma | Signal processing method at the time of online identification in active noise elimination device |
| CN102280102A (en) * | 2010-06-14 | 2011-12-14 | 哈曼贝克自动系统股份有限公司 | Adaptive noise control |
| CN104038181A (en) * | 2014-06-05 | 2014-09-10 | 北京航空航天大学 | Self-adapting filter construction method based on NLMS algorithm |
| CN109448686A (en) * | 2018-12-13 | 2019-03-08 | 重庆邮电大学 | Intersected based on secondary channel on-line identification new algorithm and updates active noise control system |
| CN109859733A (en) * | 2019-01-02 | 2019-06-07 | 哈尔滨理工大学 | Engine noise control method based on FXLMS algorithm |
| CN111564151A (en) * | 2020-05-13 | 2020-08-21 | 吉林大学 | Narrow-band active noise reduction optimization system for engine order noise in vehicle |
| CN113257214A (en) * | 2021-02-04 | 2021-08-13 | 南京汉得利智能科技有限公司 | Active noise reduction method for fan pipeline system |
| CN113291248A (en) * | 2021-05-25 | 2021-08-24 | 浙江大学 | Multi-channel decoupling and splitting active control method and system for automobile compartment noise |
Non-Patent Citations (5)
| Title |
|---|
| CHAN, YUM-JI ET, AL.: "《Effects of secondary loudspeaker properties on broadband feedforward active duct noise control》", 《 JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA》, vol. 134, no. 1, 21 August 2013 (2013-08-21), pages 257 - 263 * |
| ITO, HAYATO ET, AL.: "《SPATIAL ACTIVE NOISE CONTROL BASED ON KERNEL INTERPOLATION WITH DIRECTIONAL WEIGHTING》", 《 2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING》, 2 March 2021 (2021-03-02), pages 8404 - 8408 * |
| 刘斌 等: "《 汽车运行降噪优化控制仿真研究》", 《计算机仿真》, vol. 35, no. 06, 15 June 2018 (2018-06-15), pages 151 - 155 * |
| 张士强: "《乘用车内发动机噪声的主动控制》", 《中国优秀硕士学位论文全文数据库 工程科技II辑, 1 July 2021 (2021-07-01), pages 19 - 60 * |
| 李涛;肖罡;李状;沈召源;罗竹辉;吴斌;: "基于FxLMS定收敛因子的自适应前馈主动噪声控制", 中国新技术新产品, no. 18, 20 September 2018 (2018-09-20) * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023151229A1 (en) * | 2022-02-14 | 2023-08-17 | 中国第一汽车股份有限公司 | Real vehicle active noise reduction method and system based on acceleration conditions |
| CN115294953A (en) * | 2022-08-15 | 2022-11-04 | 浙江大学 | Automobile carriage noise active control method of multi-channel independent order filter |
| CN117690404A (en) * | 2024-02-04 | 2024-03-12 | 清华大学苏州汽车研究院（相城） | Active control method and device for engine noise of automobile |
| CN117690404B (en) * | 2024-02-04 | 2024-05-03 | 清华大学苏州汽车研究院（相城） | Active control method and device for engine noise of automobile |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114677997B (en) | 2024-08-06 |
| WO2023151229A1 (en) | 2023-08-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114677997A (en) | Real vehicle active noise reduction method and system based on acceleration working condition | |
| EP3367378B1 (en) | Active noise control using variable step-size adaptation | |
| CN108900943B (en) | A kind of scene adaptive active denoising method and earphone | |
| CN111161751A (en) | Distributed microphone pickup system and method under complex scene | |
| CN100524465C (en) | A method and device for noise elimination | |
| EP1488661B1 (en) | Reducing noise in audio systems | |
| CN111402853B (en) | Wide-band and narrow-band hybrid active noise reduction algorithm suitable for interior of vehicle | |
| JPH05265468A (en) | Active type noise controller | |
| CN109346052B (en) | Device and method for optimizing sound quality in vehicle by utilizing active noise reduction | |
| Ekpo et al. | Regulated-element frost beamformer for vehicular multimedia sound enhancement and noise reduction applications | |
| CN114582312B (en) | Active control method and system for anti-interference adaptive road noise in vehicle | |
| EP4439550A1 (en) | Active noise reduction method and device for vehicle and storage medium | |
| CN112331226B (en) | Voice enhancement system and method for active noise reduction system | |
| Albu | The constrained stability least mean square algorithm for active noise control | |
| CN110838303B (en) | Voice sound source positioning method using microphone array | |
| CN108039179B (en) | Efficient self-adaptive algorithm for microphone array generalized sidelobe canceller | |
| CN113655440B (en) | Self-adaptive compromise pre-whitened sound source positioning method | |
| CN113345457B (en) | Acoustic echo cancellation adaptive filter based on Bayes theory and filtering method | |
| Yu et al. | Speech enhancement based on the generalized sidelobe cancellation and spectral subtraction for a microphone array | |
| CN114220450A (en) | Method for restraining strong noise of space-based finger-controlled environment | |
| CN113763984A (en) | Parameterized noise elimination system for distributed multiple speakers | |
| CN117594030B (en) | Active noise reduction method and related device | |
| CN116074697B (en) | Vehicle-mounted acoustic equalizer compensation method and system based on deep neural network | |
| CN118280332A (en) | Distributed active noise control system and method for wind noise suppression | |
| CN112687285B (en) | Echo cancellation method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |