CN111862925A - Adaptive active noise control system based on inertia learning and method thereof - Google Patents
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Abstract
The invention relates to a self-adaptive active noise control system based on inertia learning and a method thereof, wherein the system comprises a reference microphone, a controller, an actuator and an error microphone; the reference microphone and the error microphone are respectively arranged at the position close to the sound source and the control point, respectively collect a reference signal and a feedback signal to be controlled, convert the collected signals into electric signals and input the electric signals to the input end of the controller; the controller processes the reference signal and the feedback signal to generate a control signal which has the same amplitude and opposite phase with the noise signal to be controlled, and inputs the control signal to the input end of the actuator; the actuator converts the control signal into a control sound wave, and the control sound wave is superposed with the noise to be controlled at the control point to eliminate the noise. The invention has reasonable design, combines the k-NN method and the adaptive LMS algorithm, reduces the convergence time in the control process, accelerates the convergence speed, and effectively meets the requirement of rapid convergence of the noise reduction system under special working conditions.
Description
Technical Field
The invention belongs to the technical field of active noise control, and particularly relates to an inertia learning-based adaptive active noise control system and method.
Background
The low-frequency line spectrum noise is a common noise in life and industrial production at present, has great influence on normal life and work of people, even influences physical and mental health of residents and workers when being exposed to a noise environment for a long time seriously, and the high-efficiency noise control technology is an important problem in the field of acoustic research for a long time.
Generally, common noise reduction means mainly include both passive noise elimination and active noise elimination. Passive sound attenuation is generally achieved by providing sound attenuation materials to block the sound wave propagation path or attenuating the echo and transmitted waves of the noise by using an acoustic covering layer. The working frequency of the passive noise elimination technology is mainly medium-high frequency, and especially has a good effect on high-frequency noise, but the effect on low-frequency noise is limited. Therefore, currently, suppression using active noise control technology is often considered for low-frequency noise.
The active noise control technology utilizes an acoustic superposition principle, namely, noise can be offset by generating a control sound wave with the same amplitude and opposite phases through a secondary sound source, so that the purpose of noise suppression is achieved. An active noise control system generally consists of a microphone for picking up acoustic signals, a controller for processing the noise signals, and an actuator for emitting control sound waves. A common control system generally controls the output of the secondary sound source through a self-adaptive filtering algorithm, so that the system can have functions of automatically detecting noise, automatically calculating filter parameters and eliminating noise, and a function of combining active noise elimination and active control is realized.
In an active noise control system, how to reasonably optimize a control algorithm is always a focus and hot point of research in the field. At present, a Least Mean Square (LMS) algorithm and an improved algorithm thereof are the most commonly applied methods in the adaptive control process. The algorithm is an optimization extension of a wiener filtering theory and a steepest descent method, does not need prior knowledge of target signal statistical characteristics, and updates a filter coefficient by adding a proportional term of a negative mean square error gradient to the filter coefficient at the previous moment. The algorithm has the advantages of low calculation complexity and good convergence on stable signals. On the basis, Morgan provides an Fx-LMS algorithm, a secondary channel transfer function generated by delay and error of an electronic component is taken into consideration, the fact that an actual error signal is not a simple combination of noise and filter output is pointed out, filtering is carried out according to the secondary channel transfer function before filter parameters are updated, the purpose of eliminating errors is achieved, and the adaptive algorithm is the adaptive algorithm which is best in stability and most widely applied in the field of active noise control.
Generally, the convergence speed and the control accuracy of the controller are two most important criteria for evaluating a control system. Although the expectation value of the minimum mean square error algorithm can be converged to the wiener solution without bias, because the algorithm directly takes the derivative of the square instantaneous value of an error signal as the estimation value of the mean square error gradient in order to reduce the calculation complexity in the process of using gradient descent, the optimal solution can not be converged smoothly in the iterative process, residual errors are inevitably generated, and the convergence speed of the algorithm is influenced.
In an actual working environment, many situations require a system to have a faster response speed. For example, when the active noise control technology is used for submarine underwater noise elimination, whether the system can quickly converge determines the sound stealth performance of a naval vessel, and the defect that the traditional minimum mean square error algorithm converges too slowly increases the submarine exposure probability, so that the target of sound stealth cannot be well realized by using the active noise reduction technology.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a self-adaptive active noise control system based on inertia learning and a method thereof, and solves the problem that a noise reduction system of the traditional minimum mean square error algorithm cannot meet the requirement of quick convergence under special working conditions.
The technical problem to be solved by the invention is realized by adopting the following technical scheme:
an adaptive active noise control system based on inertia learning comprises a reference microphone, a controller, an actuator and an error microphone;
the reference microphone and the error microphone are respectively arranged at the position close to the sound source and the control point, respectively collect a reference signal and a feedback signal to be controlled, convert the collected signals into electric signals and input the electric signals to the input end of the controller;
The controller processes the reference signal and the feedback signal to generate a control signal which has the same amplitude and opposite phase with the noise signal to be controlled, and inputs the control signal to the input end of the actuator;
the actuator converts the control signal into a control sound wave, and the control sound wave is superposed with the noise to be controlled at a control point to eliminate the noise.
Further, the controller comprises an FFT circuit, an outlier detection circuit, a priori data set, a k-NN regressor circuit, an LMS filter circuit and a driving circuit;
the FFT circuit carries out fast Fourier transform on the reference signal and inputs the calculated frequency spectrum into the outlier detection circuit;
the outlier detection circuit extracts outliers in the frequency spectrum through a dbscan algorithm and inputs the outliers into the k-NN regressor circuit as the frequency of a line spectrum;
the frequency input k-NN regressor circuit searches a priori data set and samples points adjacent to the frequency to perform weighted average to obtain an iterative initial value of the LMS algorithm;
the LMS filter circuit carries out iteration again on the LMS module tap weight vector according to the error signal and the reference signal, and calculates a control signal;
the drive circuit outputs a control signal to the actuator.
Further, the controller is an integrated circuit chip.
An adaptive active noise control method based on inertia learning comprises the following steps:
step 4, the outlier detection circuit performs clustering analysis on the frequency spectrum and extracts n outliers o in the frequency spectrum1，o2，…，onWherein o isjRepresenting line spectrum signals of different frequencies constituting the control band noise, in fjRepresents the signal frequency, j ═ 1,2, …, n;
ωj＝(fn-fk)ωk+(fk+1-fn)ωk+1
according to the frequency synthesis method, the tap weight vector omegapredictExpressed as:
step 6, carrying out tapping weight vector omega predictThe input signal vector is taken as an iteration initial value and input into an LMS filter circuit, and the input signal vector is taken as:
X(t)＝[x(t)，x(t-1)，…，x(t-l+1)]
the output signal of the LMS filter circuit is then:
step 7, the output signal of the LMS filter circuit is output as a control sound wave by an actuator through a driving circuit, the control sound wave is superposed with noise at a control point, and an error signal e (t) is acquired through an error microphone; inputting an LMS filter circuit to iterate a tap weight vector omega, wherein the iteration formula is as follows:
ω′＝′+2μe(t)x(t)
where ω' is the new tap weight vector after iteration.
Further, the amplitude and phase of the signal in step 1 do not affect the tap weight vector after the filter convergence.
Further, the outlier detection circuit in the step 4 performs cluster analysis and extraction on the frequency spectrum by adopting a dbscan algorithm.
The invention has the advantages and positive effects that:
1. as an active noise control method, the invention combines a k-nearest neighbor inertia learning algorithm (k-NN algorithm) and a minimum mean square error algorithm (self-adaptive LMS algorithm), thereby greatly reducing the convergence time in the control process and effectively meeting the requirement of rapid convergence of a noise reduction system under special working conditions.
2. The invention is based on the LMS algorithm, calculates and obtains the optimal filter tap vector corresponding to each frequency line spectrum signal before the control is started, and avoids the repeated calculation in the control process. The method has the advantages that the algorithm can start to converge from a better iteration initial value, the obvious control effect is achieved on line spectrum signals with higher energy in noise, the subsequent control effect of the LMS algorithm on other broadband noises is not affected, and the convergence speed is greatly increased.
3. The algorithm used by the invention can be used together with optimization methods such as a classical Fx-LMS algorithm, a variable step length LMS algorithm and the like, and the requirements under different working conditions are met.
4. The invention utilizes the computing power provided by modern high-performance computing chips, and can utilize the statistical characteristics of noise in the control process to provide basis for other signal processing means.
Drawings
FIG. 1 is a connection diagram of an adaptive active noise control system of the present invention;
FIG. 2 is a graph comparing simulation results of noise reduction performance of the present invention with a conventional LMS algorithm;
in the figure, 1-reference microphone, 2-FFT circuit, 3-outlier detection circuit, 4-prior data set and k-NN regressor circuit, 5-LMS filter circuit, 6-drive circuit, 7-controller, 8-actuator and 9-error microphone.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The design idea of the invention is as follows:
the traditional minimum mean square error algorithm is widely used, has the advantage of accurate convergence, is beneficial to low calculation complexity of the algorithm, and is suitable for meeting the real-time requirement of noise reduction under the condition of limited calculation power. With continuous breakthrough of high-performance computing chips in recent years, a new idea is provided for the development of an active noise control technology, so that some efficient machine learning algorithms can participate in the control process.
The Nearest neighbor regression (k-NN) algorithm is a classical inert learning algorithm. The algorithm needs a certain scale of training samples, but the data set is not analyzed before the data to be predicted is received, k sample points adjacent to the point in the parameter space are found after the data is obtained, and the k sample points are weighted and averaged to serve as the predicted value of the data point. The minimum mean square error algorithm is optimized by utilizing nearest neighbor regression, filter parameters corresponding to single-frequency signals with different frequencies are firstly calculated to serve as a prior data set according to conditions such as given sampling frequency, filter tap number and the like before denoising is started, acoustic parameters of the single-frequency signals are extracted according to collected noise in the denoising process, proper filter parameters are calculated by utilizing the nearest neighbor algorithm and a frequency synthesis method, the parameters serve as iteration initial values of the minimum mean square error algorithm, the iteration process of the minimum mean square error algorithm can be greatly reduced, and therefore the effect of accelerating system convergence is achieved.
The invention filters noise based on Finite Impulse Response (FIR) filter, solves FIR filter tap weight vector corresponding to controlled noise by using nearest neighbor regression algorithm in combination with minimum mean square error algorithm, controls secondary sound source to actively send out control sound wave with same amplitude and opposite phase with the noise to be controlled, and superposes with the noise to be controlled at control point, thereby achieving the purpose of eliminating noise.
Based on the above design concept, the present invention provides an adaptive active noise control system based on lazy learning, as shown in fig. 1, comprising at least one set of reference microphone 1, controller 7, actuator 8 and error microphone 9.
The reference microphone 1 is arranged near a sound source, the error microphone 9 is arranged at a control point, and is used for respectively acquiring a reference signal to be controlled and a feedback signal, converting the acquired signals into electric signals and inputting the electric signals to the input end of the controller 7.
The controller 7 generates a control signal with the same amplitude and opposite phase of the noise signal to be controlled, and inputs the control signal to the input end of the actuator 8.
The actuator 8 converts the control signal into a control sound wave, and the control sound wave is superposed with the noise to be controlled at the control point to play a role in silencing.
The controller 7 includes an FFT circuit 2, an outlier detection circuit 3, a prior data set and k-NN regressor circuit 4, an LMS filter circuit 5, and a drive circuit 6.
The FFT circuit 2 carries out fast Fourier transform on a reference signal with a certain length and inputs a frequency spectrum obtained by calculation into an outlier detection circuit 3;
the outlier detection circuit 3 extracts outliers in a frequency spectrum through a dbscan algorithm and inputs the outliers into the k-NN regressor circuit 4 as the frequency of a line spectrum;
The frequency input k-NN regressor circuit 4 searches a priori data set and samples points adjacent to the frequency to perform weighted average to obtain an iterative initial value of the LMS algorithm; the error signal is collected by the error microphone 9 and output to the LMS filter circuit 5.
And the LMS filter circuit 5 carries out iteration again on the LMS module tap weight vector according to the error signal and the reference signal, and calculates the control signal.
The drive circuit 6 outputs a control signal to the actuator 8.
The controller 7 may be integrated into a hardware chip in actual use.
Based on the above adaptive active noise control system based on the lazy learning, the present invention further provides an adaptive active noise control method based on the lazy learning, which comprises the following steps:
Step 4, the outlier detection circuit 3 utilizes the dbscan algorithm to perform cluster analysis on the frequency spectrum and extract n outliers o in the frequency spectrum1，o2，…，onWherein o isj(j-1, 2, …, n) represents line spectrum signals of different frequencies constituting the control band noise, denoted by fjRepresenting the signal frequency.
ωj＝(fn-fk)ωk+(fk+1-fn)ωk+1#(1)
The tap weight vector omega can be adjusted according to the frequency synthesis methodpredictIs shown as
Step 6, carrying out tapping weight vector omegapredictAsThe iterative initial value is input into an LMS filter circuit 5, and the input signal vector is taken as
X(t)＝[x(t)x(t-1)，…，x(t-l+1)]#(3)
The output signal of the LMS filter circuit 5 is
Step 7, the output signal of the LMS filter circuit 5 is output as a control sound wave by an actuator 8 through a driving circuit 6, is superposed with noise at a control point, and an error signal e (t) is acquired through an error microphone 9; the input LMS filter circuit 5 iterates the tap weight vector omega with the iterative formula
ω′＝ω+2μe(t)x(t)#(5)
Where ω' is the new tap weight vector after iteration.
A comparative example of noise control performed by the present invention and the conventional LMS algorithm is given below, and the intelligent active noise reduction adaptive active noise control system is simulated by using the Simulink module of MATLAB, and the simulation result is divided into three parts from top to bottom as shown in fig. 2: the top is a waveform diagram of a reference signal, the middle is a waveform diagram of an error signal for noise control by using a traditional LMS algorithm, and the bottom is a waveform diagram of an error signal for noise control by using the method. Wherein, the noise to be controlled is set as a single-frequency signal with the frequency of 600.2Hz, the sampling rate of the system is 4000Hz, and the control is carried out for 2sec at times; the horizontal axis of the coordinate is a time axis, the vertical axis is the amplitude of the error signal, and the smaller the vibration amplitude is, the more obvious the control effect is. As can be seen from the three graphs, compared with the traditional algorithm, the new algorithm has obvious advantages in convergence speed and control precision, and is more suitable for research and application in special acoustic environments such as underwater acoustic countermeasure.
It should be emphasized that the embodiments described herein are illustrative rather than restrictive, and thus the present invention is not limited to the embodiments described in the detailed description, but also includes other embodiments that can be derived from the technical solutions of the present invention by those skilled in the art.
Claims (6)
1. An adaptive active noise control system based on lazy learning, characterized by: the device comprises a reference microphone, a controller, an actuator and an error microphone;
the reference microphone and the error microphone are respectively arranged at the position close to the sound source and the control point, respectively collect a reference signal and a feedback signal to be controlled, convert the collected signals into electric signals and input the electric signals to the input end of the controller;
the controller processes the reference signal and the feedback signal to generate a control signal which has the same amplitude and opposite phase with the noise signal to be controlled, and inputs the control signal to the input end of the actuator;
the actuator converts the control signal into a control sound wave, and the control sound wave is superposed with the noise to be controlled at a control point to eliminate the noise.
2. The adaptive active noise control system based on lazy learning according to claim 1, characterized in that: the controller comprises an FFT circuit, an outlier detection circuit, a priori data set, a k-NN regressor circuit, an LMS filter circuit and a driving circuit;
The FFT circuit carries out fast Fourier transform on the reference signal and inputs the calculated frequency spectrum into the outlier detection circuit;
the outlier detection circuit extracts outliers in the frequency spectrum through a dbscan algorithm and inputs the outliers into the k-NN regressor circuit as the frequency of a line spectrum;
the frequency input k-NN regressor circuit searches a priori data set and samples points adjacent to the frequency to perform weighted average to obtain an iterative initial value of the LMS algorithm;
the LMS filter circuit carries out iteration again on the LMS module tap weight vector according to the error signal and the reference signal, and calculates a control signal;
the drive circuit outputs a control signal to the actuator.
3. The adaptive active noise control system based on lazy learning according to claim 2, characterized in that: the controller is an integrated circuit chip.
4. A control method of an adaptive active noise control system based on lazy learning according to any of claims 1 to 3, characterized by comprising the steps of:
step 1, taking the sampling rate as Fs, and respectively comparing the frequency fi＝1，2，……，fmaxEstablishing a single frequency signal
Step 2, taking the iteration step length of the adaptive filtering algorithm as mu and the FIR filter length as l, and respectively iterating and solving each f by utilizing the LMS algorithm iCorresponding tap weight vector omegaiUntil convergence, an a priori data set W ═ ω (ω) is established1，ω2，…，ωmax)T；
Step 3, transmitting the noise x (t) and the Gaussian window to be controlled to the input end of an FFT circuit, obtaining the frequency spectrum F (t) of the moment through fast Fourier transform, and transmitting the obtained frequency spectrum F (t) to the input end of an outlier detection circuit;
step 4, the outlier detection circuit performs clustering analysis on the frequency spectrum and extracts n outliers o in the frequency spectrum1，o2，…，onWherein o isjRepresenting line spectrum signals of different frequencies constituting the control band noise, in fjRepresents the signal frequency, j ═ 1,2, …, n;
step 5, mixing fjInputting the data into the input end of the k-NN regressor circuit, and searching two sample points W (f) adjacent to the point in the prior data setk)，W(fk+1) Let fk＜fj＜fk+1According to it and fjWeighted average of the distances to obtain an estimation filter:
ωj＝(fn-fk)ωk+(fk+1-fn)ωk+1
according to the frequency synthesis method, the tap weight vector omegapredictExpressed as:
step 6, carrying out tapping weight vector omegapredictThe input signal vector is taken as an iteration initial value and input into an LMS filter circuit, and the input signal vector is taken as:
X(t)＝[x(t)，x(t-1)，…，x(t-l+1)]
the output signal of the LMS filter circuit is then:
step 7, the output signal of the LMS filter circuit is output as a control sound wave by an actuator through a driving circuit, the control sound wave is superposed with noise at a control point, and an error signal e (t) is acquired through an error microphone; inputting an LMS filter circuit to iterate a tap weight vector omega, wherein the iteration formula is as follows:
ω′＝ω+2μe(t)x(t)
Where ω' is the new tap weight vector after iteration.
5. The method of claim 4, wherein the adaptive active noise control system comprises: the amplitude and phase of the signal in the step 1 do not influence the tap weight vector after the filter convergence.
6. The method of claim 4, wherein the adaptive active noise control system comprises: and the outlier detection circuit in the step 4 performs cluster analysis and extraction on the frequency spectrum by adopting a dbscan algorithm.
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