CN110335582B - Active noise reduction method suitable for impulse noise active control - Google Patents
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- G—PHYSICS
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- 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
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- G10K11/178—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
- G10K11/1781—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase characterised by the analysis of input or output signals, e.g. frequency range, modes, transfer functions
- G10K11/17813—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase characterised by the analysis of input or output signals, e.g. frequency range, modes, transfer functions characterised by the analysis of the acoustic paths, e.g. estimating, calibrating or testing of transfer functions or cross-terms
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- G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
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- G10K11/1787—General system configurations
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- G—PHYSICS
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- G10K2210/3028—Filtering, e.g. Kalman filters or special analogue or digital filters
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Abstract
The invention provides an active noise reduction method suitable for impulse noise active control, which comprises the following steps: step one, adopting a reference microphone to collect impulse noise emitted by a noise source as a reference signal, and adjusting step length parameters by using a Gaussian distribution function with the reference signal as an independent variable; collecting noise after cancellation by an error microphone as an error signal, and updating a prediction filter weight coefficient according to a continuously updated reference signal, the error signal and the adjusted step length parameter; and thirdly, calculating a counteracting signal through the updated weight coefficient of the prediction filter, and sending the counteracting signal through a loudspeaker to counteract the reference signal so as to perform active noise reduction.
Description
Technical Field
The invention relates to the field of active noise control, and simultaneously relates to an active noise control method, in particular to an active noise reduction method suitable for impulse noise active control.
Background
With the deep development of the fields of modern industry, building industry, transportation industry and the like, the noise pollution problem is increasingly prominent, and the study, work and life of residents are seriously affected.
Existing noise control techniques are mainly divided into passive noise reduction (Passive Noise Control, PNC) and active noise reduction (Active Noise Control, ANC). The passive noise reduction technology adopts traditional sound absorption, sound insulation and noise elimination to realize the target noise inhibition, and the passive noise reduction technology can obtain ideal effects in the aspect of medium-high frequency noise control, but is not satisfactory in the aspect of low frequency noise control. In contrast, the active noise reduction technique (also called active noise control technique) adopts the principle of destructive interference of sound waves, and the secondary sound source emits a cancellation signal with approximately the same phase and opposite amplitude as the target noise through an adaptive control algorithm. The active noise control system is composed of a controller, a loudspeaker and a microphone, has smaller body size and stronger universality, and is flexible to arrange in a specific application scene, so that the active noise control system is widely applied to the fields of industry, traffic, military and the like.
With the deep development of the self-adaptive filtering technology and the signal processing system, the active noise reduction technology is rapidly developed in nearly 30 years, and research teams at home and abroad have made a plurality of breakthrough progress in the aspects of active noise reduction core control algorithms and hardware design. However, some of the challenges still existing in active noise reduction technology directly limit the practical application of active noise control systems, where active impulse noise control (Active impulse noise control) is one of the core challenges.
Classical active noise reduction algorithms have a more desirable control effect on noise signals that follow gaussian distributions. However, impulse noise generally follows an α steady-state distribution rather than a gaussian distribution, and therefore, conventional algorithms such as FxLMS are used to control impulse noise, which often fails and diverges from the system. Impulse noise is typically modeled using an alpha steady state distribution, the characteristic function of which isWhere α is a characteristic index of impulse noise, and the smaller the α value, the more remarkable the impulse characteristic of the signal, and the more intense the impulse intensity.
In order to realize effective control of impulse noise, many researchers have conducted related exploration and proposed a series of active impulse noise reduction algorithms. Typical representatives of these algorithms are: fxLMP (Filtered-x least mean p-norm) algorithm that achieves active control of impulse noise by minimizing the p-moment of the error signal. Compared with the classical FxLMS algorithm, the FxLMP algorithm has better pulse suppression performance, but has the defects that the algorithm depends on priori acquisition of the pulse noise characteristic index alpha, so that the universality of the algorithm against pulse noise with different intensities is poor; akhtar et al propose Th-FxLMS (threshold-based FxLMS) algorithm that employs a clipping function to suppress the pulse sample amplitude in the reference signal and the error signal, thereby improving the pulse noise reduction performance of the system, however, the necessary estimation of the clipping threshold in the clipping function is a significant disadvantage of the algorithm because the clipping threshold needs to be re-estimated for different intensities of pulse noise; wu Lifu et al propose an fxlog lms (Filtered-xlogarithmic error LMS) algorithm that adaptively updates the weight coefficients of the filter with the mean square value of the logarithmic transformation of the error signal as a cost function, thereby achieving effective control of impulse noise, the fxlog lms algorithm does not require a priori acquisition of the characteristic index α, and does not require the introduction of a clipping threshold, while the algorithm declares a related patent and is authorized (active control method of impulse noise based on logarithmic transformation, ZL 201010017642.4). However, when the amplitude of the error signal is smaller than 1, a dead zone exists in the updating of the filter coefficient of the algorithm, so that the noise reduction performance and the convergence speed of the algorithm are slightly deteriorated.
Therefore, the impulse noise active control method with strong self-adaptive capacity, excellent noise reduction performance and high convergence speed is provided, and is very important for effectively suppressing impulse noise in an actual scene.
Disclosure of Invention
The invention designs and develops an active noise reduction method suitable for impulse noise active control, and the method does not need to acquire a characteristic index priori when impulse noise active control is carried out; the clipping threshold is not required to be introduced, so that more complicated threshold estimation is avoided; meanwhile, dead zones do not exist in the prediction filter weight iteration when the amplitude of the error signal is smaller than 1.
The technical scheme provided by the invention is as follows:
an active noise reduction method suitable for impulse noise active control comprises the following steps:
step one, adopting a reference microphone to collect impulse noise emitted by a noise source as a reference signal, and adjusting step length parameters by using a Gaussian distribution function with the reference signal as an independent variable;
collecting noise after cancellation by an error microphone as an error signal, and updating a prediction filter weight coefficient according to the continuously updated reference signal, the error signal and the adjusted step size parameter;
and thirdly, calculating a counteracting signal through the updated weight coefficient of the prediction filter, and sending the counteracting signal through a loudspeaker to counteract the reference signal so as to perform active noise reduction.
Preferably, in the first step, the adjustment formula of the step size parameter is:
wherein n is a time series, x (n) is a reference signal, and μ [ x (n) ]]For a step size parameter with the reference signal as an argument,as a base step size parameter, σ is the standard deviation of the distribution function.
Preferably, in the second step, the weight coefficient of the prediction filter is updated by a weight adaptive iterative formula.
Preferably, the weight adaptive iteration formula is expressed as:
w(n+1)＝w(n)-μ·▽J(n)；
wherein w (n) is the weight coefficient of the n-time prediction filter, w (n+1) is the weight coefficient of the n+1-time prediction filter, J (n) is the cost function of the adaptive iteration formula, and J (n) is the gradient of J (n).
Preferably, the cost function of the adaptive iteration formula is expressed as:
J(n)＝E{f 2 [e(n)]}≈f 2 [e(n)]；
where e (n) is the error signal and f [ e (n) ] is the nonlinear transformation function of the error signal.
Preferably, the error signal nonlinear transformation function is expressed as:
preferably, the cost function gradient is expressed as:
wherein x is f And (n) is the reference signal after passing through the secondary path.
Preferably, the deriving of the cancellation signal from the weighting coefficients of the prediction filter is expressed as:
y(n)＝w T (n)·x(n)；
where y (n) is a cancellation signal at time n and T is a matrix transpose.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention provides an active noise reduction method suitable for impulse noise active control, which does not need to introduce a limiting threshold value, thereby avoiding more complicated threshold value estimation; the prior acquisition of the characteristic index of the impulse noise is not needed; and meanwhile, dead zones do not exist in the prediction filter weight iteration.
2. The invention adopts the nonlinear transformation function of the error signal to carry out robust processing on the error signal which participates in the weight iteration of the prediction filter, thereby attenuating the influence of the pulse sample in the error signal on the noise reduction performance and the stability of the system; meanwhile, the Gaussian distribution function taking the reference signal as an independent variable is adopted to adaptively adjust the step length parameter, so that the influence of a pulse sample in the reference signal on the noise reduction performance and stability of the system is weakened, the pulse suppression performance of the control system is further improved, and the method has more ideal comprehensive performance than the traditional pulse noise reduction method.
Drawings
Fig. 1 is a schematic diagram of an active noise reduction method suitable for impulse noise active control according to the present invention.
Fig. 2 is a schematic diagram of a high-intensity impulse noise signal according to an embodiment of the invention.
Fig. 3 is a schematic diagram of a low-intensity impulse noise signal according to an embodiment of the invention.
FIG. 4 is a graph showing the amplitude-frequency response of the primary channel and the secondary channel according to the embodiment of the present invention.
Fig. 5 is a schematic diagram of a phase frequency response curve of a primary channel and a secondary channel according to an embodiment of the present invention.
Fig. 6 is a schematic diagram showing the comparison of noise reduction effect under the high-intensity impulse noise signal according to the embodiment of the invention.
Fig. 7 is a schematic diagram showing the comparison of noise reduction effect under low-intensity impulse noise signals according to an embodiment of the invention.
Detailed Description
The present invention is described in further detail below with reference to the drawings to enable those skilled in the art to practice the invention by referring to the description.
When the invention actively reduces noise aiming at impulse noise, the noise reduction process is as follows:
step one, adopting a reference microphone to collect impulse noise emitted by a noise source as a reference signal x (n), and adjusting step length parameters by using a Gaussian distribution function with the reference signal x (n) as an independent variable;
in another embodiment, as shown in fig. 1, the step size parameter is adjusted by a step size parameter adaptive adjustment submodule, where the adjustment method is:
wherein n is a time series, mu [ x (n)]For a step size parameter with the reference signal as an argument,as a base step size parameter, σ is the standard deviation of the distribution function.
Collecting noise after cancellation by an error microphone as an error signal e (n), and adaptively updating a prediction filter coefficient according to the continuously updated reference signal, the error signal and the adjusted step length parameter;
in another embodiment, the weight updating submodule updates the prediction filter coefficient in real time by adopting a weight adaptive iteration formula according to the step-size parameter, the reference signal and the error signal, and the weight adaptive iteration formula is expressed as follows based on the steepest descent principle:
w(n+1)＝w(n)-μ·▽J(n)；
wherein w (n) is the weight coefficient of the n-time prediction filter, w (n+1) is the weight coefficient of the n+1-time prediction filter, J (n) is the cost function of the adaptive iteration formula, and J (n) is the gradient of J (n).
In another embodiment, the cost function of the adaptive iteration formula is expressed as:
J(n)＝E{f 2 [e(n)]}≈f 2 [e(n)]；
where f [ e (n) ] is the nonlinear transformation function of the error signal.
In another embodiment, the representation of the error signal nonlinear transformation function is:
in another embodiment, the gradient of the cost function is expressed as:
furthermore, the weight adaptive iteration formula is:
wherein x is f (n) is the estimated secondary path filtered reference signal.
And thirdly, calculating a cancellation signal through a weight coefficient of the prediction filter, sending the cancellation signal through a loudspeaker, and actively canceling the reference signal.
In another embodiment, the cancellation signal is calculated and sent by the prediction filter submodule, the cancellation signal and the reference signal have approximately equal amplitude and opposite phases, and in practical application, the cancellation signal is sent by the loudspeaker, and the cancellation signal is expressed as:
y(n)＝w T (n)·x(n)；
where y (n) is the cancellation signal and T is the matrix transpose.
In another embodiment, the reference signal x (n) is a signal d (n) after the primary path P (z), and the reference signal x (n) is an estimated secondary pathPost signal x f (n) the calculation is as follows:
d(n)＝P(z)*x(n)；
wherein, is convolution operation, P (z) is the primary path between the reference microphone and the error microphone,to estimate the resulting secondary path.
In another embodiment, the signal of the cancellation signal y (n) sent by the prediction filter submodule after passing through the secondary path is calculated as follows:
y s (n)＝S(z)*y(n)；
wherein S (z) is the secondary path between the error microphone and the speaker, where S (z) andthe results were consistent.
In another embodiment, the error signal may also be expressed as:
e(n)＝d(n)+y s (n)；
wherein y is s And (n) is a signal obtained by canceling the signal y (n) through the secondary path.
As a preference, σ takes a value of 2 in this example.
As a preferable mode, the weight updating sub-module continuously repeats the updating process, so that the impulse noise in the target scene can be effectively controlled.
In order to test the impulse noise reduction performance of the method provided by the invention, the following experiment is carried out:
impulse noise generally follows an alpha steady state distribution, and a random signal is used to generate target impulse noise according to the alpha steady state distribution characteristics.
Examples
As shown in fig. 2 and 3, in order to fully check the effectiveness of the present invention, the method according to the present invention is adopted to actively reduce noise for the high-intensity impulse noise signal (with a characteristic index of 1.4) and the low-intensity impulse noise signal (with a characteristic index of 1.8), respectively. Meanwhile, a Th-FxLMS algorithm, an FxLMP algorithm and an FxlogLMS algorithm in a classical impulse noise control algorithm are selected for noise reduction comparison.
The primary path P (z) and the secondary path S (z) adopted in the present embodiment are each represented by an FIR filter with 300 steps, and the amplitude-frequency response curves and the phase-frequency response curves of the primary path and the secondary path are shown in fig. 4 and fig. 5, respectively.
As shown in fig. 6 and 7, the experimental results of this example are shown. Fig. 6 shows the noise reduction effect of four algorithms on the high-intensity impulse noise signal (α=1.4), fig. 7 shows the noise reduction effect of four algorithms on the low-intensity impulse noise signal (α=1.8), and compared with fig. 6 and fig. 7, it can be seen that compared with the classical impulse noise control algorithm, the noise reduction method provided by the invention has better noise reduction amount and faster convergence speed.
Although embodiments of the present invention have been disclosed above, it is not limited to the details and embodiments shown and described, it is well suited to various fields of use for which the invention would be readily apparent to those skilled in the art, and accordingly, the invention is not limited to the specific details and illustrations shown and described herein, without departing from the general concepts defined in the claims and their equivalents.
Claims (3)
1. An active noise reduction method suitable for impulse noise active control is characterized by comprising the following steps:
step one, adopting a reference microphone to collect impulse noise emitted by a noise source as a reference signal, and adjusting step length parameters by using a Gaussian distribution function with the reference signal as an independent variable;
collecting noise after cancellation by an error microphone as an error signal, and updating a prediction filter weight coefficient according to the continuously updated reference signal, the error signal and the adjusted step size parameter;
step three, calculating a counteracting signal through the updated weight coefficient of the prediction filter, and sending the counteracting signal through a loudspeaker to counteract the reference signal so as to perform active noise reduction;
in the first step, the adjustment formula of the step size parameter is:
wherein n is a time series, x (n) is a reference signal, and μ [ x (n) ]]For a step size parameter with the reference signal as an argument,as a basic step size parameter, sigma is the standard deviation of a distribution function;
in the second step, the weight coefficient of the prediction filter is updated through a weight self-adaptive iterative formula;
the weight adaptive iteration formula is expressed as:
wherein w (n) is the weight coefficient of the n-moment prediction filter, w (n+1) is the weight coefficient of the n+1-moment prediction filter, J (n) is the cost function of the adaptive iteration formula,a gradient of J (n);
the cost function of the adaptive iteration formula is expressed as:
J(n)＝E{f 2 [e(n)]}≈f 2 [e(n)]；
wherein e (n) is an error signal, and f [ e (n) ] is a nonlinear transformation function of the error signal;
the nonlinear transformation function of the error signal is expressed as:
2. active noise reduction method suitable for impulse noise active control according to claim 1, characterized in that the cost function gradient is expressed as:
wherein x is f (n) is the estimated secondary path filtered reference signal.
3. Active noise reduction method suitable for impulse noise active control according to claim 1 or 2, characterized in that deriving the cancellation signal from the weighting coefficients of the prediction filter is expressed as:
y(n)＝w T (n)·x(n)；
where y (n) is a cancellation signal at time n and T is a matrix transpose.
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| CN113470607B (en) * | 2020-03-31 | 2024-04-16 | 本田技研工业株式会社 | Active vibration noise reduction system |
| CN111540372B (en) * | 2020-04-28 | 2023-09-12 | 北京声智科技有限公司 | Method and device for noise reduction processing of multi-microphone array |
| CN111554263B (en) * | 2020-04-30 | 2023-03-24 | 华南理工大学 | Active noise distributed control system and method for open space |
| CN111564151B (en) * | 2020-05-13 | 2022-09-23 | 吉林大学 | Narrow-band active noise reduction optimization system for engine order noise in vehicle |
| CN112309362A (en) * | 2020-10-30 | 2021-02-02 | 上海联影医疗科技股份有限公司 | Active acoustic noise reduction method and device of MR system and computer equipment |
| CN112562624B (en) * | 2020-11-30 | 2021-08-17 | 深圳百灵声学有限公司 | Active noise reduction filter design method, noise reduction method, system and electronic equipment |
| CN113299261B (en) * | 2021-05-21 | 2023-10-20 | 北京安声浩朗科技有限公司 | Active noise reduction method and device, earphone, electronic equipment and readable storage medium |
| CN117254782B (en) * | 2023-11-13 | 2024-02-23 | 电子科技大学 | Multi-channel active noise control method of unequal-order noise control filter |
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