CN116013239A - Active noise reduction algorithm and device for air duct - Google Patents
Active noise reduction algorithm and device for air duct Download PDFInfo
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Abstract
The application belongs to the technical field of air duct noise reduction, and discloses an air duct active noise reduction algorithm and an air duct active noise reduction device. Collecting reference noise signals through arranging a first non-uniform microphone array in an air inlet area of the air duct, and transmitting the reference noise signals to a noise reduction controller; the noise reduction controller generates a reverse noise driving signal based on the reference noise signal and transmits the reverse noise driving signal to a loudspeaker array arranged in the central area of the air duct, and drives the loudspeaker array to generate a reverse noise signal, and the reverse noise signal is overlapped with the reference noise signal to generate an error noise signal; and setting a second non-uniform microphone array in an air outlet area of the air duct to collect error noise signals, feeding the error noise signals back to a noise reduction controller, adjusting a filter coefficient by the noise reduction controller based on the error noise signals, updating the reverse noise driving signals, and driving a loudspeaker array to generate new reverse noise signals. The noise signal is accurately collected, the more accurate reverse noise signal is output, and the noise reduction capability is improved.
Description
Technical Field
The application relates to the technical field of air duct noise reduction, in particular to an air duct active noise reduction algorithm and an air duct active noise reduction device.
Background
The existing air duct noise reduction device mainly adopts a single microphone active noise reduction control technology, a microphone and a filter are arranged at the front end of a pipeline to generate reverse noise signals, the reverse noise is played through a loudspeaker to eliminate pipeline noise, and the filter is updated through detection and feedback of error noise to adaptively generate the reverse noise so as to effectively control the pipeline noise. However, since the single microphone has no directivity, when the air inlet collects reference noise, and when the pipeline noise is formed by aliasing of a plurality of noise sources, the single microphone cannot accurately reflect the noise distribution and the intensity of the pipeline interface, the external environment noise is easily collected together when the reference noise is collected, and error reverse noise is easily generated under the condition of high environment noise intensity, and finally the effect of effectively reducing noise is not obtained, and the noise intensity of the air outlet is increased, so that the noise reduction capability is poor.
Disclosure of Invention
Therefore, the embodiment of the application provides an active noise reduction algorithm for the air duct, which can comprehensively and accurately collect noise signals, generate more accurate reverse noise signals and improve noise reduction capability.
In a first aspect, the present application provides an active noise reduction algorithm for an air duct.
The application is realized by the following technical scheme:
an active noise reduction algorithm for an air duct, the algorithm comprising:
a first non-uniform microphone array is arranged in an air inlet area of the air duct, a reference noise signal of the air inlet area is collected by the first non-uniform microphone array, and the reference noise signal is transmitted to a noise reduction controller;
the noise reduction controller generates a reverse noise driving signal based on the reference noise signal, transmits the reverse noise driving signal to a loudspeaker array arranged in the central area of the air duct, drives the loudspeaker array to generate a reverse noise signal, and superimposes the reverse noise signal and the reference noise signal to generate an error noise signal, wherein the noise reduction controller is a multi-input multi-output noise reduction controller;
and setting a second non-uniform microphone array in an air outlet area of the air duct, collecting the error noise signals by using the second non-uniform microphone array, feeding the error noise signals back to the noise reduction controller, adjusting a filter coefficient based on the error noise signals, updating the reverse noise driving signals, and driving the speaker array to generate new reverse noise signals.
In a preferred example of the present application, the step of disposing a first non-uniform microphone array in the air inlet area of the air duct and disposing a second non-uniform microphone array in the air outlet area of the air duct each includes:
and calculating and obtaining a setting scheme of the non-uniform microphone array by using a Bayesian optimization method.
In a preferred example of the present application, the step of calculating the setting scheme for obtaining the non-uniform microphone array by using a bayesian optimization method may further include:
setting a group of initial array deployment vectors in a feasible region of an air duct, calculating noise control performance corresponding to the initial array deployment vectors, and generating an initial data set based on the initial array deployment vectors and the noise control performance;
establishing a Gaussian process regression model based on the initial data set, and calculating posterior estimation of the initial data set according to the Gaussian process regression model;
setting a sample acquisition function based on the posterior estimation, and solving an optimal solution of the sample acquisition function to obtain a new sample point; deployment of a non-uniform microphone array is performed based on the new sample points.
In a preferred example of the present application, it may be further configured to establish a gaussian process regression model based on the initial data set, and the step of calculating the posterior estimate of the initial data set according to the gaussian process regression model further includes:
calculating mathematical expectations of the initial data set, and selecting a radial basis function as a kernel function of the initial data set;
according to the Bayes theorem, calculating a super-parameter posterior of the kernel function;
and establishing a probability distribution function of noise control performance of the noise control system under any array layout based on the initial data set obeying a Gaussian process regression model, and obtaining posterior estimation of the initial data set based on the probability distribution function.
In a preferred example of the present application, it may be further configured that the error noise signal adjusts a filter coefficient, and the step of updating the inverse noise driving signal includes:
and based on a time-varying step length updating method, adjusting the filter coefficient and updating the inverse noise driving signal.
In a preferred example of the present application, the step of adjusting the filter coefficient based on the time-varying step length updating method may be further configured as follows:
and (3) setting up a signal model of the error noise signal, designing an optimization problem of a filter based on the signal model, and adjusting a filter coefficient by adopting an adaptive gradient method based on a transfer function of the filter and a loudspeaker so as to update the inverse noise driving signal.
In a preferred example of the present application, it may be further configured that the signal model of the error noise signal is:
wherein e (n) represents a residual signal of the air outlet area processed by the noise reduction controller, d (n) represents a noise signal of the air outlet area not processed by the noise reduction controller, h 2,i (n) represents the transfer function between the filter and the outlet microphone,
representing the filter coefficients, x i (n) represents the reference noise signal acquired by the ith microphone of the intake area.
In a second aspect, the present application provides an active noise reduction device for an air duct.
The application is realized by the following technical scheme:
wind channel initiative noise reduction device, the device includes:
the first non-uniform microphone array is arranged in an air inlet area of the air duct and is used for collecting reference noise signals of the air inlet area;
a noise reduction controller for receiving the reference noise signal and generating a reverse noise driving signal based on the reference noise signal;
the loudspeaker array is arranged in the central area of the air duct and is used for receiving the reverse noise driving signal output by the noise reduction controller and generating a reverse noise signal;
the second non-uniform microphone array is arranged in the air outlet area of the air duct and is used for collecting error noise signals generated after the reverse noise signals and the reference noise signals are overlapped and transmitting the error noise signals to the noise reduction controller;
the noise reduction controller also comprises a filter module, wherein the filter module is used for adjusting a filter coefficient according to the error noise signal, updating the inverse noise driving signal and driving the loudspeaker array to generate a new inverse noise signal.
In a third aspect, the present application provides a computer device.
The application is realized by the following technical scheme:
a computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of any of the wind tunnel active noise reduction algorithms described above when the computer program is executed by the processor.
In a fourth aspect, the present application provides a computer-readable storage medium.
The application is realized by the following technical scheme:
a computer readable storage medium storing a computer program which when executed by a processor performs the steps of any of the duct active noise reduction algorithms described above.
In summary, compared with the prior art, the technical scheme provided by the embodiment of the application has the beneficial effects that at least: the first non-uniform microphone array is arranged in the air inlet area of the air duct, the acquired reference noise signals have directivity, the acquired reference noise signals of the air inlet area are more comprehensive, the distribution and change information of noise in the air duct can be accurately captured, and the interference of external environment noise can be avoided; the multi-input multi-output noise reduction controller can realize the distributed generation of reverse noise signals by a filter group with a shorter length, and carries out parallel operation, thereby improving the operation efficiency; the loudspeaker array has directivity when playing the reverse noise signal, can reduce the reflection times of sound waves in the air duct, and meanwhile, avoids the reverse noise signal from being transmitted back to the air outlet area and being overlapped with the reference noise signal, so that the noise reduction is more efficient; when the second non-uniform microphone array is arranged in the air outlet area and is used for collecting error noise signals, the error noise signals in the air duct can be accurately obtained, the environmental noise interference is restrained, and the fed-back error noise signals are favorable for accurately correcting the noise reduction controller, so that the noise reduction capability is improved.
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Fig. 1 is a flowchart of an active noise reduction algorithm for an air duct according to an exemplary embodiment of the present application.
Detailed Description
The present embodiment is merely illustrative of the present application and is not intended to be limiting, and those skilled in the art, after having read the present specification, may make modifications to the present embodiment without creative contribution as required, but is protected by patent laws within the scope of the claims of the present application.
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
In addition, the term "and/or" in this application is merely an association relationship describing an association object, and indicates that three relationships may exist, for example, a and/or B may indicate: a exists alone, A and B exist together, and B exists alone. In this application, unless otherwise specified, the term "/" generally indicates that the associated object is an "or" relationship.
The terms "first," "second," and the like in this application are used to distinguish between identical or similar items that have substantially the same function and function, and it should be understood that there is no logical or chronological dependency between the "first," "second," and "nth" terms, nor is it limited to the number or order of execution.
It should be noted that, the principle of active noise reduction is that in the process of acoustic wave propagation in space, the acoustic wave propagated by each acoustic source in space will not change its propagation rule due to the existence of other acoustic waves, but when two acoustic waves with the same amplitude and frequency in space and opposite phases and the same propagation direction are overlapped, the opposite phases refer to 180 degrees of phase difference, which will cancel each other to zero amplitude, thus realizing the purpose of noise elimination.
Embodiments of the present application are described in further detail below with reference to the drawings attached hereto.
In one embodiment of the present application, an active noise reduction algorithm for an air duct is provided, as shown in fig. 1, and the main steps are described as follows:
s10, arranging a first non-uniform microphone array in an air inlet area of the air duct, collecting a reference noise signal of the air inlet area by using the first non-uniform microphone array, and transmitting the reference noise signal to a noise reduction controller.
Specifically, a first non-uniform microphone array is arranged in an air inlet area of the air duct and used for detecting and collecting a reference noise signal in the air inlet area, performing analog-to-digital conversion on the reference noise signal, converting the reference noise signal from an acoustic signal form into an electric signal form and transmitting the electric signal form to the noise reduction controller; the first non-uniform microphone array is disposed on the same duct section of the air intake area of the air duct and includes at least one microphone. The non-uniform microphone array is adopted to acquire noise in the air inlet area, directivity is achieved, and when the pipeline noise is formed by overlapping a plurality of noise sources, the non-uniform microphone array can accurately reflect the noise distribution condition and the intensity condition of the pipeline section.
Preferably, the setting scheme of the first non-uniform microphone array is obtained by calculating by using a Bayesian optimization method.
Preferably, a group of initial array deployment vectors are arranged in a feasible region of the air duct, noise control performance corresponding to the initial array deployment vectors is calculated, and an initial data set is generated based on the initial array deployment vectors and the noise control performance;
establishing a Gaussian process regression model based on the initial data set, and calculating posterior estimation of the initial data set according to the Gaussian process regression model;
setting a sample acquisition function based on the posterior estimation, and solving an optimal solution of the sample acquisition function to obtain a new sample point; deployment of the non-uniform microphone array is performed based on the new sample point.
Preferably, the step of calculating a posterior estimate of the initial dataset comprises:
calculating the mathematical expectation of the initial data set, and selecting a radial basis function as a kernel function of the initial data set;
according to the Bayes theorem, calculating the super-parameter posterior of the kernel function;
and establishing a probability distribution function of noise control performance of the noise control system under any array layout based on the initial data set obeying a Gaussian process regression model, and obtaining posterior estimation of the initial data set based on the probability distribution function.
Specifically, a set of initial array deployment vectors X are arranged in a certain section of an air inlet area of the air duct, the initial array deployment vectors X can be uniformly generated, randomly generated or determined according to the actual size of a pipeline system, and noise control performance Y under the initial array deployment vectors X is further calculated to generate an initial data set D= { X, Y }; establishing a gaussian process regression model based on an initial dataset d= { X, Y }
Here it is shown that the initial dataset d= { X, Y } obeys a gaussian distribution, and the mean vector and autocorrelation matrix of the initial dataset d= { X, Y } are then used to calculate a posterior estimate of the initial dataset.
Specifically, a mathematical expectation mu of an initial data set is calculated, a radial basis function is selected to define a kernel function, and the expression of the kernel function is as follows:
wherein x is 1 ，x 2 E, X, θ= { γ, l } is a hyper-parameter of the kernel function; gamma, l is the super-parameter of the radial basis function, where gamma represents the bandwidth and l represents the characteristic length.
According to the Bayes theorem, obtaining the super-parameter posterior of the kernel function:
where p (y|x, θ) is an edge likelihood distribution function, p (θ) is a prior distribution function of the super-parameters, and p (yx) represents a likelihood distribution function of the marginalized θ.
Further solving the super parameter theta= { gamma, l } by adopting a maximum likelihood estimation method;
based on assumption that initial data set obeys Gaussian process regression model, building x under arbitrary array layout * Noise control performance y of the noise control system * Probability distribution function, i.e. posterior estimation:
wherein mu GP (x) A posterior mean value representing a posterior estimate:
μ GP (x)＝μ(X)+κ(x，X)(κ(X，X)+vI)-1(Y-μ(X)1)；
wherein I represents an identity matrix, v represents variance of noise, k (X, X) represents the kernel function, and k (X, X) represent kernel function vectors, and k (X, X) represents a kernel function matrix, specifically, the number is as follows:
posterior mean μ based on posterior estimation GP (x) Sum of the post-test square difference
To define a sample collection function, in particular, a sample collection function may be defined by a confidence upper bound function (Upper Confidence Bound, UCB):
where α (x) represents a sample acquisition function, λ represents a factor balancing the expected and variance, λ=1.2 in this embodiment;
solving the optimal solution of the sample acquisition function alpha (x) to obtain a new sample point x based on the optimal solution new Based on the new sample point x new A layout of a first non-uniform microphone array is performed.
Further, calculate the point x according to the new sample new Performing noise control performance under deployment of a first non-uniform microphone array, in particular, deployment of a first non-uniform microphone arrayThen, designing a filter bank, establishing a noise control system, and testing noise control performance, namely noise attenuation rate:
wherein P (d (n)) represents noise energy before the noise control system, and P (e (n)) represents noise energy after the noise control system;
comparing the calculated noise attenuation rate with a preset threshold value, and if the noise attenuation rate reaches the preset threshold value, according to the current new sample point x new Deploying a first non-uniform microphone array;
if the noise attenuation rate does not reach the preset threshold value, the data set D= { [ Xx ] is updated new ]，[Yy new ]And (3) repeating the steps until the noise attenuation rate reaches a preset threshold value, outputting an optimal array deployment vector, and ensuring that a better non-uniform microphone array can be obtained. In practical applications, the non-uniform array has better beamforming performance.
And S20, the noise reduction controller generates a reverse noise driving signal based on the reference noise signal, transmits the reverse noise driving signal to a loudspeaker array arranged in the central area of the air duct, drives the loudspeaker array to generate a reverse noise signal, and superimposes the reverse noise signal and the reference noise signal to generate an error noise signal, wherein the noise reduction controller is a multi-input multi-output noise reduction controller.
Specifically, the noise reduction controller adopts a multi-input multi-output noise reduction controller, and the multi-input multi-output noise reduction controller can realize the generation of reverse noise signals in a distributed mode of a filter group with a shorter length, and performs parallel operation, so that the operation efficiency is improved. The noise reduction controller generates a corresponding inverse noise driving signal based on the received reference noise signal in the form of an electric signal corresponding to each microphone in the first non-uniform microphone array, transmits the inverse noise driving signal to a loudspeaker array arranged in a central area between an air inlet and an air outlet of the air duct, drives the loudspeaker array to generate an inverse noise signal with opposite noise sound wave phases and same amplitude as the reference noise signal, and the inverse noise signal is overlapped with the reference noise signal transmitted to the loudspeaker array area, so that the amplitude of the inverse noise signal is mutually offset to generate an error noise signal.
And S30, setting a second non-uniform microphone array in the air outlet area of the air duct, collecting error noise signals by using the second non-uniform microphone array, feeding the error noise signals back to the noise reduction controller, adjusting the filter coefficient based on the error noise signals, updating the reverse noise driving signals, and driving the loudspeaker array to generate new reverse noise signals.
Specifically, a second non-uniform microphone array is arranged on a certain section of the air outlet area of the air duct, and the calculation mode of the deployment scheme of the second non-uniform microphone array is the same as that of the deployment scheme of the first non-uniform microphone array. When the second non-uniform microphone array is arranged in the air outlet area and is used for collecting error noise signals, the error noise signals in the air duct can be accurately obtained, the environmental noise interference is restrained, and the fed-back error noise signals are favorable for accurately correcting the noise reduction controller, so that the noise reduction capability is improved. And the second non-uniform microphone array is used for collecting an error noise signal generated after the superposition of the reverse noise signal and the reference noise signal, the error noise signal is transmitted to the noise reduction controller, the noise reduction controller adjusts the coefficient of the filter module based on the error noise signal, so that the noise reduction controller outputs a new reverse noise driving signal, and further the loudspeaker array is driven to generate a new reverse noise signal, so that the error noise signal generated after the superposition of the reverse noise signal and the reference noise signal is smaller and smaller, and a better noise reduction effect is achieved. The loudspeaker array has directivity when playing the reverse noise signal, can reduce the reflection times of sound waves in the air duct, and simultaneously avoids the reverse noise signal from being transmitted back to the air outlet area and being overlapped with the reference noise signal, so that the noise reduction performance is better.
Preferably, the filter coefficients are adjusted based on a time-varying step update method to update the inverse noise drive signal.
Preferably, the filter coefficients are adjusted by an adaptive gradient method based on the transfer function of the filter and the speaker to update the inverse noise drive signal by creating a signal model of the error noise signal, designing an optimization problem of the filter based on the signal model.
Specifically, the signal model of the error noise signal is:
wherein e (n) represents a residual signal of the air outlet area processed by the noise reduction controller, d (n) represents a noise signal of the air outlet area not processed by the noise reduction controller, h 2,i (n) represents the transfer function between the filter and the outlet microphone,
representing the filter coefficients, x i (n) represents the reference noise signal acquired by the ith microphone of the intake area.
Establishing an optimization problem of the filter:
wherein N represents the number of non-uniform microphone arrays and L represents the length of the filter coefficients behind each microphone;
based on the transfer function of the filter and the loudspeaker, the adaptive gradient method is adopted to update the filter:
w i (n+1)＝w i (n)-μe(n)h 2，i (n)x i (n)，
wherein x is i (n) reference noise signal obtained by ith microphone of nth iteration of air inlet area, w i (n) represents the ith filter coefficient, w, of the nth iteration i (n+1) represents the ith filter coefficient of n+1 iterations, e (n) represents the error noise signal processed by the noise reduction control system in the air outlet area, h 2,i (n) represents the filter to air outlet microphoneTransfer function between winds.
To generate an adaptive inverse noise signal:
thereby realizing the self-adaptive adjustment and noise reduction of the time-varying noise signals.
The application also provides an active noise reduction device for an air duct, which comprises:
the first non-uniform microphone array is arranged in an air inlet area of the air duct and is used for collecting reference noise signals of the air inlet area;
the noise reduction controller is used for receiving a reference noise signal transmitted by the first non-uniform microphone array and generating an inverse noise driving signal based on the reference noise signal;
the loudspeaker array is arranged in the central area of the air duct and is used for receiving the reverse noise driving signal output by the noise reduction controller and generating a reverse noise signal;
the second non-uniform microphone array is arranged in the air outlet area of the air duct and is used for collecting error noise signals generated after the reverse noise signals and the reference noise signals are overlapped and transmitting the error noise signals to the noise reduction controller;
the noise reduction controller further includes a filter module for adjusting the filter coefficients based on the error noise signal, updating the inverse noise drive signal, and driving the speaker array to generate a new inverse noise signal.
In one embodiment, a computer device is provided, which may be a server.
The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium has an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements any of the wind tunnel active noise reduction algorithms described above.
In one embodiment, a computer readable storage medium is provided that includes a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor executing the computer program to implement any of the wind tunnel active noise reduction algorithms described above.
Those skilled in the art will appreciate that implementing all or part of the above described embodiments may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink), DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the system described in the present application is divided into different functional units or modules to perform all or part of the above-described functions.
Claims (10)
1. The active noise reduction algorithm for the air duct is characterized by comprising the following steps of:
a first non-uniform microphone array is arranged in an air inlet area of the air duct, a reference noise signal of the air inlet area is collected by the first non-uniform microphone array, and the reference noise signal is transmitted to a noise reduction controller;
the noise reduction controller generates a reverse noise driving signal based on the reference noise signal, transmits the reverse noise driving signal to a loudspeaker array arranged in the central area of the air duct, drives the loudspeaker array to generate a reverse noise signal, and superimposes the reverse noise signal and the reference noise signal to generate an error noise signal, wherein the noise reduction controller is a multi-input multi-output noise reduction controller;
and setting a second non-uniform microphone array in an air outlet area of the air duct, collecting the error noise signals by using the second non-uniform microphone array, feeding the error noise signals back to the noise reduction controller, adjusting a filter coefficient based on the error noise signals, updating the reverse noise driving signals, and driving the speaker array to generate new reverse noise signals.
2. The active noise reduction algorithm of claim 1, wherein the steps of disposing a first non-uniform microphone array in an air intake area of the air duct and disposing a second non-uniform microphone array in an air outlet area of the air duct each comprise:
and calculating and obtaining a setting scheme of the non-uniform microphone array by using a Bayesian optimization method.
3. The active noise reduction algorithm for air duct according to claim 2, wherein the step of calculating the setting scheme for obtaining the non-uniform microphone array by using a bayesian optimization method comprises:
setting a group of initial array deployment vectors in a feasible region of an air duct, calculating noise control performance corresponding to the initial array deployment vectors, and generating an initial data set based on the initial array deployment vectors and the noise control performance;
establishing a Gaussian process regression model based on the initial data set, and calculating posterior estimation of the initial data set according to the Gaussian process regression model;
setting a sample acquisition function based on the posterior estimation, and solving an optimal solution of the sample acquisition function to obtain a new sample point; deployment of a non-uniform microphone array is performed based on the new sample points.
4. The wind tunnel active noise reduction algorithm of claim 3, wherein establishing a gaussian process regression model based on the initial dataset, the step of calculating a posterior estimate of the initial dataset from the gaussian process regression model further comprises:
calculating mathematical expectations of the initial data set, and selecting a radial basis function as a kernel function of the initial data set;
according to the Bayes theorem, calculating a super-parameter posterior of the kernel function;
and establishing a probability distribution function of noise control performance of the noise control system under any array layout based on the initial data set obeying a Gaussian process regression model, and obtaining posterior estimation of the initial data set based on the probability distribution function.
5. The active noise reduction algorithm of claim 1, wherein the error noise signal adjusts a filter coefficient, and the step of updating the inverse noise driving signal comprises:
and based on a time-varying step length updating method, adjusting the filter coefficient and updating the inverse noise driving signal.
6. The active noise reduction algorithm for air ducts according to claim 5, wherein the step of adjusting the filter coefficients based on the time-varying step size update method comprises:
and (3) setting up a signal model of the error noise signal, designing an optimization problem of a filter based on the signal model, and adjusting a filter coefficient by adopting an adaptive gradient method based on a transfer function of the filter and a loudspeaker so as to update the inverse noise driving signal.
7. The active noise reduction algorithm of claim 6, wherein the signal model of the error noise signal is:
wherein e (n) represents a residual signal of the air outlet area processed by the noise reduction controller, d (n) represents a noise signal of the air outlet area not processed by the noise reduction controller, h 2,i (n) represents the transfer function between the filter and the outlet microphone,
representing the filter coefficients, x i (n) represents the reference noise signal acquired by the ith microphone of the intake area.
8. The device of making an uproar falls in wind channel initiative, its characterized in that, the device includes:
the first non-uniform microphone array is arranged in an air inlet area of the air duct and is used for collecting reference noise signals of the air inlet area;
a noise reduction controller for receiving the reference noise signal and generating a reverse noise driving signal based on the reference noise signal;
the loudspeaker array is arranged in the central area of the air duct and is used for receiving the reverse noise driving signal output by the noise reduction controller and generating a reverse noise signal;
the second non-uniform microphone array is arranged in the air outlet area of the air duct and is used for collecting error noise signals generated after the reverse noise signals and the reference noise signals are overlapped and transmitting the error noise signals to the noise reduction controller;
the noise reduction controller also comprises a filter module, wherein the filter module is used for adjusting a filter coefficient according to the error noise signal, updating the inverse noise driving signal and driving the loudspeaker array to generate a new inverse noise signal.
9. A computer device comprising a memory, a processor and a computer program stored on the memory, the processor executing the computer program to perform the steps of the algorithm of any one of claims 1 to 7.
10. A computer readable storage medium, characterized in that it stores a computer program which, when executed by a processor, implements the steps of the algorithm of any one of claims 1 to 7.
Priority Applications (1)
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| CN112562629A (en) * | 2020-12-10 | 2021-03-26 | 南京汉得利智能科技有限公司 | Device and method for reducing wind noise of automobile air conditioner pipeline |
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