CN115116423A - Self-adaptive feedback active noise reduction method based on frequency band constraint - Google Patents
Self-adaptive feedback active noise reduction method based on frequency band constraint Download PDFInfo
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- 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
- G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
- 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
- G10K11/1785—Methods, e.g. algorithms; Devices
- G10K11/17853—Methods, e.g. algorithms; Devices of the filter
- G10K11/17854—Methods, e.g. algorithms; Devices of the filter the filter being an adaptive filter
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- 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
- G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
- 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
- G10K11/17817—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 between the output signals and the error signals, i.e. secondary path
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- 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
- G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
- 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
- G10K11/1787—General system configurations
- G10K11/17875—General system configurations using an error signal without a reference signal, e.g. pure feedback
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- 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
- H04R3/04—Circuits for transducers, loudspeakers or microphones for correcting frequency response
Abstract
The invention discloses a self-adaptive feedback active noise reduction method capable of realizing frequency band noise reduction amount constraint, which is characterized by comprising the following steps: in the adaptive feedback of the wide frequency band, the noise reduction amount generated by the frequency band with low noise reduction demand is restrained by analyzing the noise reduction demand of the specific frequency band, and the noise reduction amount of the target noise reduction frequency band is promoted. The method mainly comprises the following steps: 1) estimating a secondary path, namely determining a target noise reduction frequency band and a constraint frequency band according to the secondary path and the noise reduction requirement; 2) designing a weight filter according to the target noise reduction frequency band and the constraint frequency band; 3) constructing a reference signal and a filtering reference signal based on an adaptive feedback algorithm controlled by an internal model; 4) constructing a penalty term and a control filter coefficient iteration formula; 5) the filter coefficients are iteratively controlled to converge. The invention realizes the adjustment of the noise reduction amount of different frequency bands and the system stability performance in the adaptive feedback algorithm, and can be effectively applied to scenes with higher noise reduction requirements on specific frequency bands.
Description
Technical Field
The invention belongs to the field of adaptive feedback active noise reduction, and provides an adaptive feedback noise reduction method capable of restricting the noise reduction amount of a non-target frequency band so as to improve the noise reduction amount of the target frequency band aiming at the noise reduction requirement of a specific frequency band.
Background
Modern active noise control theory begins in 1936 and its basic process is: extracting primary noise information by a microphone, generating a secondary signal after real-time analysis by a controller, and playing the secondary signal in real time by using a loudspeaker as a secondary sound source; the secondary signal destructively interferes with the primary noise, thereby reducing noise in the region, effectively reducing noise for low frequencies.
The active noise control system is divided into a feedforward control system, a feedback control system and a feedforward hybrid control system. The performance of a feed forward system depends on the coherence between the reference signal and the primary noise signal and is limited by causality and the like. The feedback system no longer needs the microphone to receive the reference signal, but outputs the signal of the error signal after being filtered by the feedback controller to the secondary source, and the performance of the feedback system depends on the sensitivity transfer function of the system.
The feedback system has a simple structure, and is suitable for noise with a narrow frequency band, but the stability requirement required by the system can influence the noise reduction performance. In addition, the feedback system also has a water bed effect, and noise in other frequency bands can be amplified when noise in a specific frequency band is reduced. The feedback active noise control can be divided into two categories of self-adaption and non-self-adaption, and the self-adaption algorithm can effectively adjust the amplitude and the phase of offset noise generated by an electronic system aiming at the time-varying characteristics of the sound field environment and the propagation of a noise source so as to obtain satisfactory performance.
In the adaptive algorithm, the filtering-x least mean square (FxLMS) algorithm has the advantages of simple calculation, strong robustness and the like, and quickly becomes a widely-applied controller updating algorithm. The FxLMS adaptive feedback algorithm controls the feedback ANC system based on the internal model principle, and the adaptive feedback active noise control is realized by subtracting a signal synthesis reference signal generated by a secondary source at an error point from an error signal. For The suppression of water bed effect, The filter amplitude can be controlled in full band constraint to reduce The magnitude of The full band output signal by adding a penalty proportional to The sum of The squares of The control filter amplitudes in The cost function, thereby obtaining a leaky FxLMS algorithm (Qiu X, Handsen C H.A student of time-domain FXLMS algorithms with control output constraint [ J ] The Journal of The acoustic Society of America,2001,109(6): 2815) -2823). In addition, in order to realize the purpose of restricting the amplitudes of the filters in different frequency bands by different weights so as to realize water bed suppression, researchers have proposed a generalized leakage FxLMS algorithm (Wu L, Qiu X, Guo Y.A generated left FxLMS algorithm for tuning the water floor effect of feedback active noise control Systems [ J ]. Mechanical Systems & Signal Processing,2018,106:13-23) and a dual-gradient FxLMS algorithm (Wu etifu, Chen crystal, Gu Nai applied to a dual-gradient active noise control adaptive algorithm [ J ]. applied acoustics 2020,39(04): 632-637). In addition, in order to constrain the water bed in the specific frequency band and reduce the computational complexity, a method for suppressing the water bed effect of the adaptive feedback active control system (Zhou dynasty, Zhoushan, Qilittle army, etc.. method for suppressing the water bed effect of the adaptive feedback active control system, CN112233643A [ P ] 2021) is also proposed.
At present, the research of adaptive feedback control mainly focuses on the aspect of water bed effect suppression, and in practical application, there is often a specific demand for noise reduction amount of a specific frequency band. Therefore, the restriction and adjustment of the noise reduction amount in a specific frequency range in the frequency range where the noise reduction effect is generated are problems to be solved.
Disclosure of Invention
The purpose of the invention is as follows: the method is used for restraining the noise reduction amount of a non-target noise reduction frequency band and improving the noise reduction amount of a target noise reduction frequency band, so that different required noise reduction effects can be obtained in different noise reduction frequency bands. In order to achieve the purpose, the invention adopts the technical scheme that:
the invention relates to a self-adaptive feedback control method based on frequency band constraint, which defines a frequency band with higher noise reduction requirement in primary noise d (n) as a target noise reduction frequency band F 1 Defining the non-target noise reduction frequency band in the primary noise as the constraint frequency band F 2 . The sampling frequency of the self-adaptive feedback noise reduction system is f s The control filter length is L. The method comprises the following steps:
constructing a penalty term by using Gaussian white noise and a weight filter K;
constructing a reference signal x (n) and a filtering reference signal x' (n) based on an FxLMS adaptive feedback algorithm of an internal model control structure;
and constructing an iterative formula for controlling the filter coefficient w by using the error signal e (n), the filtering reference signal x' (n) and the penalty term, iteratively controlling the filter coefficient w until convergence, and obtaining a self-adaptive feedback active noise reduction result after the frequency band noise reduction is restrained at the moment.
Further, in step 1, unit impulse response of the secondary path is estimated, and a target noise reduction frequency band F is determined 1 And constrained band F 2 The method specifically comprises the following steps: the unit impulse response of the secondary path is estimated by adopting an LMS algorithm
Where L is the length of the unit impulse response. To pair
Carrying out spectrum analysis, selecting a target frequency band near a spectrum shape peak value, and determining a target noise reduction frequency band F by combining target noise reduction requirements of practical application 1 And constrained band F 2 。
Further, in step 2 according to F 1 And F 2 Designing a weight filter K, specifically: the weighting filter K is realized by a band-pass filter with a pass-band in the frequency range F 2 The passband has an amplitude of 10dB and the stopband has a frequency range including F 1 But does not contain F 2 The stopband amplitude is-30 dB.
Further, in step 2, a penalty term is constructed by using gaussian white noise and a weight filter K, specifically: penalty signal q (n) ═ q (n), q (n-1),.. q (n-L +1)] T T is transposed symbol, q (n) is Gaussian white noise signal p (n) and is filtered by weightThe signal filtered by the K filter;
w(n)＝[w 0 ,w 1 ,...,w L-1 ] T is a control filter coefficient of length L;
q (n) and w (n) form a penalty term r (n) w T (n)q(n)q(n)。
Further, in step 2, a reference signal x (n) and a filtered reference signal x' (n) are constructed, specifically: the coefficients of the control filter are initialized to w (0) ([ 0, 0., 0 ])] T The output signal of the control filter y (n) ([ y (n), y (n-1),. -%, y (n-L + 1))] T By passing
Subtracting the error signal e (n) to obtain a reference signal vector x (n) ([ x (n), x (n-1).., x (n-L + 1))] T Wherein
x (n) is
Obtaining a filtered reference signal x '(n) ═ x' (n), x '(n-1),.., x' (n-L +1)] T In which
And x (n) is obtained by controlling the filter to obtain y (n), y (n) ═ w (n) T x(n)。
Further, in step 2, an iterative formula for controlling the filter coefficient w is constructed by using the error signal e (n), the filtering reference signal x' (n) and the penalty term, and the filter coefficient w is iteratively controlled until convergence, specifically:
the iterative formula for controlling the filter coefficients w (n) is w (n +1) ═ w (n) - μ e (n) x' (n) - μ r (n), where μ is the iteration step size;
if e (N) of the current N time discrete points satisfies
The adaptive iteration ends and the converged control filter coefficients w (N) are obtained, where N 0 Representing the current time instant, e represents the mean of e (N) for the current N time discrete points, and σ represents a smaller value, typically 0.5% of the variance of d (N).
The invention has the following beneficial effects: (1) the method provided by the invention is based on an online self-adaptive feedback design, and can be used for a real-time self-adaptive system compared with a non-adaptive filter design method.
(2) Aiming at the noise reduction requirement of a specific frequency band, the invention realizes the adjustment of the noise reduction amount of different frequency bands based on the frequency band constraint, and improves the noise reduction amount of a target noise reduction frequency band.
(3) The invention can effectively optimize the stability of the feedback system and adjust the stability of the feedback system aiming at different frequency bands.
Drawings
FIG. 1 is a block diagram of a frequency band constrained adaptive feedback noise reduction system;
FIG. 2 is a headphone secondary path frequency response;
FIG. 3 is a weight filter;
FIG. 4 is a time domain noise signal;
FIG. 5 is a noise reduction comparison between the classic FxLMS algorithm and the frequency band constraint algorithm, i.e. the noise reduction comparison before and after the frequency band constraint;
FIG. 6 is a Nyquist plot before and after band constraints;
fig. 7 is an enlarged view of a low frequency portion of fig. 6.
Fig. 8 is a flow chart of the method of the present invention.
Detailed Description
Embodiments of the invention are further described below with reference to the accompanying drawings:
taking an active noise reduction earphone as an example for testing, the primary noise signal is a band-pass white noise signal with cut-off frequencies of 50Hz to 1300Hz respectively. Setting the length L of the control filter to 300 and the sampling frequency f s 65536Hz and 8 seconds time, fig. 1 is a block diagram of a frequency band constrained adaptive feedback noise reduction system, and fig. 8 is a flow chart of the method of the present invention.
The method comprises the following steps:
(1) the unit impulse response of the secondary path of the earphone is estimated by adopting an LMS algorithm
And performing spectrum analysis, determining a target noise reduction frequency band F according to the resonance frequency characteristics and the target noise reduction frequency band requirement of the earphone, wherein the frequency response of the spectrum analysis is shown in figure 2 1 In the range of 50Hz to 600Hz, and a constrained frequency band F 2 In the range of 600Hz to 1300Hz,
(2) the weighting filter K is designed as shown in fig. 3, the upper and lower cut-off frequencies are 1300Hz and 600Hz, respectively, the transition band bandwidth is 100Hz, and the pass band and stop band amplitudes are 10dB and-30 dB, respectively.
(3) Obtaining a penalty signal q (n) by normally distributed white Gaussian noise through a weight filter K, wherein the penalty signal vector q (n) and a control filter coefficient vector w (n) with the length of 300 form a penalty term r (n) ═ w T (n)q(n)q(n)。
(4) The coefficients of the control filter are initialized to w (0) ([ 0, 0., 0 ])] T The output of the control filter, y (n) ([ y (n)), y (n-1),. -, y (n-L +1)] T By passing
Subtracting the error signal e (n) to obtain a reference signal vector x (n) ([ x (n), x (n-1).., x (n-L + 1))] T Wherein
x (n) is
Obtaining a filtered reference signal x '(n) ═ x' (n), x '(n-1),.., x' (n-L +1)] T Wherein
And x (n) is obtained by controlling the filter to obtain y (n), y (n) ═ w (n) T x(n)。
(5) The iterative formula for controlling the filter coefficient vector w (n) is w (n +1) ═ w (n) - μ e (n) x' (n) - μ r (n), where the iteration step size μ is 0.00001. The time domain convergence of the noise signal is shown in fig. 4, where N is 2000 and σ is 9 × 10 -4 After 5s
Reduced to 9X 10 -4 Hereinafter, it is assumed that the adaptive iteration is completed and the converged control filter coefficient w (n) is obtained. And comparing the time domain signals before and after the noise reduction in the interval of 7s to 8s to obtain the noise reduction amount.
For the same test noise, a control filter with the same length is adopted to carry out a classic FxLMS (frequency band constraint-free) adaptive feedback noise reduction test. The noise reduction comparison before and after the band constraint is shown in fig. 5, and table 1 shows the noise reduction comparison results at the partial frequency points.
TABLE 1 noise reduction quantity comparison of partial frequency points
| frequency/Hz | 200 | 300 | 500 | 950 | 1250 |
| FxLMS adaptive feedback noise reduction/dB | 5.60 | 5.80 | 3.32 | 13.34 | 12.2 |
| Frequency band constraint self-adaptive feedback noise reduction/dB | 8.01 | 9.41 | 10.22 | 7.08 | 5.05 |
By comparing the noise reduction amount in fig. 5 with that in table 1, it can be seen that the noise reduction amount above 600Hz is greatly constrained, and the noise reduction amount below the target noise reduction frequency band, i.e. below 500Hz, is improved. The classic FxLMS algorithm has a noise reduction effect of about 20dB at the maximum above 1000Hz, the noise reduction amount above 600Hz is restricted to be below 10dB after the frequency band is restricted, the noise reduction amount below 500Hz is improved to a certain extent, the maximum improvement amount reaches 8dB, and the maximum noise reduction amount reaches 11.39dB of the full frequency band at 350 Hz.
Taking the control filter coefficient w after the convergence of the adaptive algorithm 0 And analyzing the stability of the system before and after the frequency band constraint. For a feedback control system, the open loop transfer function is the product of the secondary path transfer function and the control filter transfer function. A nyquist plot is plotted based on the open-loop transfer function, fig. 6 is a nyquist plot before and after band constraint, and fig. 7 is an enlarged view of the low frequency part thereof: before the frequency band constraint, the distance from a Nyquist curve at a low frequency to a Nyquist point is short, so that a feedback system is easy to be unstable, and the distance of a high frequency part is long, so that a large adjusting space is provided; after the frequency band is constrained, the distance between the low-frequency Nyquist curve and the Nyquist point is increased, and the robustness of the feedback system at a low frequency is improved.
Claims (7)
1. A self-adaptive feedback active noise reduction method based on frequency band constraint is characterized by comprising the following steps:
step 1, estimating unit impulse response of a secondary path, and determining a target noise reduction frequency band F 1 And constrained band F 2 (ii) a Wherein the target noise reduction frequency band F 1 For the frequency band with higher noise reduction requirement in the primary noise d (n), the constraint frequency band F 2 Non-target noise reduction frequency bands in the primary noise d (n);
step 2, according to F 1 And F 2 Designing a weight filter K;
constructing a penalty term by using Gaussian white noise and a weight filter K;
constructing a reference signal x (n) and a filtering reference signal x' (n) based on an FxLMS adaptive feedback algorithm of an internal model control structure;
and constructing an iterative formula for controlling the filter coefficient w by using the error signal e (n), the filtering reference signal x' (n) and the penalty term, iteratively controlling the filter coefficient w until convergence, and obtaining a self-adaptive feedback active noise reduction result after the frequency band noise reduction is restrained at the moment.
2. The adaptive feedback active noise reduction method based on frequency band constraint of claim 1, wherein the unit impulse response of the secondary path is estimated in step 1 to determine the target noise reduction frequency band F 1 And constrained band F 2 The method specifically comprises the following steps:
the unit impulse response of the secondary path is estimated by adopting an LMS algorithm
Wherein L is the length of the unit impulse response; to pair
Carrying out spectrum analysis, selecting a target frequency band near a spectrum shape peak value, and determining a target noise reduction frequency band F by combining target noise reduction requirements of practical application 1 And constrained band F 2 。
3. The adaptive feedback active noise reduction method based on frequency band constraint of claim 1, wherein the step 2 is based on F 1 And F 2 Designing a weight filter K, specifically: the weighting filter K is realized by a band-pass filter with a pass-band in the frequency range F 2 The pass band has an amplitude of 10dB and the stop band has a frequency range including F 1 But does not contain F 2 The stopband amplitude is-30 dB。
4. The adaptive feedback active noise reduction method based on the frequency band constraint of claim 1, wherein in the step 2, a penalty term is constructed by using white gaussian noise and a weight filter K, specifically:
penalty signal q (n) ═ q (n), q (n-1),.. q (n-L +1)] T T is the transposed symbol, q (n) is a Gaussian white noise signal p (n) after being filtered by a weight filter K;
w(n)＝[w 0 ,w 1 ,...,w L-1 ] T is a control filter coefficient of length L,
q (n) and w (n) form a penalty term r (n) w T (n)q(n)q(n)。
5. The adaptive feedback active noise reduction method according to claim 1, wherein the reference signal x (n) and the filtered reference signal x' (n) are constructed in step 2, and specifically:
the coefficients of the control filter are initialized to w (0) ([ 0, 0., 0 ])] T The output signal of the control filter y (n) ([ y (n), y (n-1),. -%, y (n-L + 1))] T By passing
Subtracting the error signal e (n) to obtain a reference signal vector x (n) ([ x (n), x (n-1).., x (n-L + 1))] T Wherein
x (n) is
Obtaining a filtered reference signal x '(n) ═ x' (n), x '(n-1),.., x' (n-L +1)] T Wherein
And x (n) is obtained by controlling the filter to obtain y (n), y (n) ═ w (n) T x(n)。
6. The adaptive feedback active noise reduction method based on frequency band constraint according to claim 1, wherein in step 2, an iterative formula for controlling a filter coefficient w is constructed by using an error signal e (n), a filtering reference signal x' (n) and a penalty term, and the iterative control filter coefficient w is iterated until convergence, specifically:
the iterative formula for controlling the filter coefficients w (n) is w (n +1) ═ w (n) - μ e (n) x' (n) - μ r (n), where μ is the iteration step size;
if e (N) of the current N time discrete points satisfies
The adaptive iteration ends and the converged control filter coefficients w (N) are obtained, where N 0 Which represents the current time of day,
represents the mean of e (N) for the current N time-discrete points, and σ represents a smaller value.
7. The adaptive feedback active noise reduction method according to claim 6, wherein σ has a value of d (n) variance of 0.5%.
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