CN111272274A - Closed space low-frequency sound field reproduction method based on microphone random sampling - Google Patents
Closed space low-frequency sound field reproduction method based on microphone random sampling Download PDFInfo
- Publication number
- CN111272274A CN111272274A CN202010109592.6A CN202010109592A CN111272274A CN 111272274 A CN111272274 A CN 111272274A CN 202010109592 A CN202010109592 A CN 202010109592A CN 111272274 A CN111272274 A CN 111272274A
- Authority
- CN
- China
- Prior art keywords
- sound field
- closed space
- microphone
- plane wave
- random sampling
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 54
- 238000005070 sampling Methods 0.000 title claims abstract description 33
- 239000013598 vector Substances 0.000 claims description 27
- 239000011159 matrix material Substances 0.000 claims description 12
- 230000008569 process Effects 0.000 claims description 7
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 claims description 5
- 238000002790 cross-validation Methods 0.000 claims description 4
- 238000012217 deletion Methods 0.000 claims description 4
- 230000037430 deletion Effects 0.000 claims description 4
- 238000000638 solvent extraction Methods 0.000 claims description 3
- 102100031262 Deleted in malignant brain tumors 1 protein Human genes 0.000 claims 2
- 101000844721 Homo sapiens Deleted in malignant brain tumors 1 protein Proteins 0.000 claims 2
- ZINJLDJMHCUBIP-UHFFFAOYSA-N ethametsulfuron-methyl Chemical compound CCOC1=NC(NC)=NC(NC(=O)NS(=O)(=O)C=2C(=CC=CC=2)C(=O)OC)=N1 ZINJLDJMHCUBIP-UHFFFAOYSA-N 0.000 claims 2
- 238000004364 calculation method Methods 0.000 abstract description 11
- 238000004422 calculation algorithm Methods 0.000 abstract description 10
- 238000000354 decomposition reaction Methods 0.000 abstract description 10
- 238000003491 array Methods 0.000 abstract description 3
- 230000003190 augmentative effect Effects 0.000 abstract description 3
- 238000012360 testing method Methods 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000011156 evaluation Methods 0.000 description 3
- 238000006386 neutralization reaction Methods 0.000 description 3
- 238000011084 recovery Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 239000006185 dispersion Substances 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000013213 extrapolation Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000005404 monopole Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 230000017105 transposition Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Computational Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Computing Systems (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Circuit For Audible Band Transducer (AREA)
Abstract
The invention provides a reproduction method of a closed space low-frequency sound field based on microphone random sampling, which utilizes a plurality of microphones to sample at random positions in a closed space without utilizing microphone arrays such as a spherical array and the like, ensures the reproduction precision, and has simpler, more convenient and flexible operation and low test cost; then, recording and reproducing the sound field by a space domain decomposition method of the plane wave; aiming at the problem of the calculation speed caused by the large base number of the plane wave model, the method adopts the SALSA (split augmented Lagrange shrinkage Algorithm), and compared with a complex coordinate descent algorithm, the calculation speed is greatly increased, so that the plane wave model can be suitable for the reproduction of a total-space unsteady sound field.
Description
Technical Field
The invention belongs to the technical field of space sound field recording and reproduction, and particularly relates to a microphone random sampling-based closed space low-frequency sound field reproduction method.
Background
In many engineering applications, it is very important to acquire prior information such as spatial sound field distribution characteristics, time-frequency characteristics of field points, and acoustic transfer functions from speakers to microphones (referred to as impulse responses in time domain). For example, when active control is performed on low-frequency noise in a closed space, the first confusion is that the temporal and spatial distribution characteristics of the noise are unknown, so that it is difficult to provide an effective evaluation and solution. Therefore, scholars at home and abroad carry out deep research on the aspect of high-precision restoration of real sound field characteristics, and a plurality of Sound Field Reconstruction (SFR) methods are provided.
The SFR technique is a technique of numerically calculating or predicting a target sound field parameter by using a specific algorithm by creating a mathematical model, and presenting the target sound field in the form of data, an image, audio, or the like (i.e., visualizing or audilizing the sound field). More sophisticated SFR techniques include finite element, boundary element, statistical energy analysis, etc. although these methods are more sophisticated and have commercial software offering powerful computational power, the computational results are still far from the actual sound field distribution due to the difficulty in simulating the real load and boundary conditions. Therefore, the reproduction method based on actual measurement is still the most reliable and efficient method.
Due to the limitation of Shannon-Nyquist sampling theorem, great test cost is consumed for directly measuring the space-time characteristics of the whole sound field point by point in a three-dimensional closed space. An alternative approach is to interpolate and extrapolate the sound field, i.e. estimate its sound pressure at spatially unmeasured locations. The method is based on the basis of the basis function decomposition of the sound field, and the sound pressure of the sound field is reproduced by linear superposition of the product of the basis function and the expansion coefficient of the basis function through solving the expansion coefficient. The method is more suitable for engineering application because the method does not need to predict the geometrical shape and boundary conditions of the closed space and the decomposition form is flexible and various. Common base functions in practice are plane waves, cylindrical harmonics, spherical harmonics, etc.
The SFR method based on the basis function decomposition generally comprises the steps of ① firstly utilizing a microphone array (in various forms such as a circular array, a plane array, a spherical array and the like) to collect a field sound field, ② secondly constructing a least square inverse problem or a sparse recovery problem to solve a basis function (plane wave or spherical harmonic function) expansion coefficient, and ③ later estimating the sound pressure of any field point through a sound field interpolation or extrapolation formula by the expansion coefficient.
In the field of sound field sampling, the spherical array is the first choice due to the advantages of various forms, simple and efficient signal processing method, capability of effectively collecting three-dimensional sound fields from all directions and the like. However, different types of spherical arrays have advantages and disadvantages, for example, a hollow spherical array has a problem of 'Bessel zero point', a rigid spherical array is not suitable for being measured close to the wall surface of a cabin, a heart-shaped spherical array is easy to introduce phase mismatch errors, and the like, so that the spherical array is not good in universality in practical application, and a proper type and configuration need to be selected according to specific conditions. In addition, purchasing a ball array also requires additional capital cost.
In the aspect of a reproduction algorithm, due to the sparsity of the acoustic mode of the closed space, a compressed sensing theory is applied to construct L1The sparse recovery problem of norm relaxation is solved, and the precision of SFR can be ensured while the number of sampling points is reduced. Solving method generally will be L described above1The sparse recovery problem of norm relaxation is equivalent to L in the form of Lagrangian1The norm-constrained least squares problem, also known as the Lasso problem, is solved using a complex coordinate descent algorithm.
In addition, the basis functions are typically selected to be plane waves or spherical harmonics. The spherical harmonic model has small dimension and high calculation speed, generally adopts a spherical array, can effectively reproduce a sound field in an area near the spherical array, but can generate sound field distortion at a position far away from the spherical array, so the method is more suitable for SFR (small frequency response) of local space steady state and unsteady state; the plane wave model can adopt various array forms, is suitable for steady state SFR of a full space, but has large dimension and slow calculation speed.
Disclosure of Invention
In order to save the acquisition cost of spherical array equipment and simplify the optimization selection work of spherical array types and configuration, the invention provides the method for sampling at random positions in a closed space by using a plurality of microphones, so that the operation is simpler, more convenient and more flexible, and sound field recording and reproduction are carried out by a space domain decomposition method of plane waves.
In addition, aiming at the problem of the calculation speed caused by the large base number of the plane wave model, the invention adopts a Split Augmented Lagrange Shrinkage Algorithm (SALSA) and has a calculation speed which is greatly accelerated compared with a complex coordinate reduction algorithm, so that the plane wave model can be suitable for the reproduction of a total-space unsteady sound field.
The technical scheme of the invention is as follows:
the method for reproducing the low-frequency sound field in the closed space based on the random sampling of the microphone is characterized by comprising the following steps of: the method comprises the following steps:
step 1: establishing a rectangular coordinate system in a closed space V;
step 2: in the rectangular coordinate system established in the step 1, Q measuring point positions are randomly selected, a microphone is placed, and the position vector of the Q measuring point is recorded as rq=[xq,yq,zq]T,q=1,2,...,Q,(·)TRepresenting a transpose; sampling a sound field in a closed space V by using the Q microphones to obtain complex sound pressure vectors at the positions of the Q microphones under the frequency fWhereinRepresenting a complex set;
In the formula sn=[sinθncosφn,sinθnsinφn,cosθn]TDenotes the propagation direction of the nth plane wave, N being 1,2nIs the angle of orientation, θnAt a pitch angle of αnIs a set plane wave discrete correlation constant, e represents a natural base number,represents an imaginary unit; k is 2 pi f/c is wave number, c is sound velocity;
and 4, step 4: according toThe Lasso problem is constructed, whereinIs a complex amplitude vector of the plane wave to be solved, and N represents the number of discrete plane waves; lambda belongs to (0, | | H)Hp||∞]Is a sparsity adjustment parameter (.)HRepresenting the conjugate transpose, | · | | non-conducting phosphor∞Represents infinite norm, | ·| non-conducting phosphor2Represents L2Norm, | · | luminance1Represents L1A norm;
and 5: solving the Lasso problem constructed in the step 4 by using SALSA to obtain a plane wave complex amplitude vector w;
Further, in step 4, the larger the value of the sparsity adjusting parameter λ is, the more sparse or compressible w is.
Further, in step 4, the sparsity adjusting parameter λ is determined by a cross validation method, and the specific process is as follows:
step 4.1: arbitrary partitioning of a vector p into NgNon-overlapping subsets to obtainp(g)The g-th subset representing p;
step 4.2: in the interval (0, lambda)nom]To uniformly select NλValues as possible values of λ:wherein λnomIs a set preselected value upper limit;
Wherein H(g)Is the g-th subset p in H(g)A matrix of corresponding columns; w is a(-g)Is λ ═ λnIn time, the Lasso problem is solved by using SALSAThe solution obtained, p(-g)Indicates p deletion p(g)Vector obtained after neutralization of element, H(-g)Indicates H deletes H(g)The matrix obtained after the column(s) in (1);
Further, the specific process for solving the Lasso problem by using the SALSA is as follows:
step 5.1: initializing parameters, and enabling w to be 0, d to be 0, v to be 0 and mu to be more than 0;
step 5.2: are respectively in accordance with
v=soft(w-d,λ/μ)+d
d=v-w
Updating w, v, and d, wherein soft (·,) represents a soft threshold function;
step 5.3: if the termination condition is reached: maximum number of iterations or threshold ΔwE, w is the final plane waveA complex amplitude vector; otherwise, repeating the step 5.2; deltawIs | | | w(i)-w(i-1)||∞/||w(i)||∞I denotes the current number of iterations, ∈
Is a set, very small positive number.
Further, in step 4.1, NgThe value is 4 or 5.
Further, in step 4.2, λnomTake | | | HHp||∞/50,NλThe value range of (A) is 10-50.
Further, in step 3, the direction angle phinE is 0,360 degree, pitch angle thetan∈[0,180°]。
Further, in step 3, the determination of N directions is obtained by approximately uniformly sampling a spherical surface based on the Thomson problem.
Further, in step 3, α is taken1=α2=…=αN=4π/N。
Advantageous effects
(1) The invention adopts the microphones which are randomly distributed in the closed space to sample the field sound field, does not need to utilize the microphone arrays such as a spherical array and the like, ensures the reproduction precision, has simpler and more flexible operation and low test cost.
(2) The invention solves the Lasso problem of plane wave decomposition by using SALSA, greatly accelerates the calculation speed, and ensures that the plane wave model can be suitable for the reproduction of unsteady sound fields.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of a method for reproducing a low-frequency sound field in a closed space based on microphone random sampling according to the invention;
FIG. 2 is a schematic diagram of a desired sound field in a rectangular enclosure with rigid walls;
FIG. 3 is a schematic diagram of a randomly sampled microphone distribution within an enclosed space;
FIG. 4 is a schematic representation of a sound field reproduced using the method of the present invention;
fig. 5 is a schematic diagram of the spatial relative error of a reproduced sound field using the method of the present invention.
Detailed Description
The invention provides a method for sampling at random positions in a closed space by using a plurality of microphones, which is simpler and more flexible to operate, and records and reproduces a sound field by a space domain decomposition method of plane waves; aiming at the problem of the calculation speed caused by the large base number of the plane wave model, a Split Augmented Lagrangian Shrinkage Algorithm (SALSA) is adopted, and compared with a complex coordinate reduction algorithm, the calculation speed is greatly increased, so that the plane wave model can be suitable for the reproduction of a total-space unsteady sound field.
The method comprises the following specific steps:
step 1: in a specific closed space V, a rectangular coordinate system is established according to actual conditions.
Step 2: in the established rectangular coordinate system, Q measuring point positions are randomly selected, a microphone is placed, and the position vector of the Q measuring point is recorded as rq=[xq,yq,zq]T(Q ═ 1,2,.., Q), where (·)TIndicating transposition. Sampling a sound field in a closed space V by using the Q microphones to obtain complex sound pressure vectors at the positions of the Q microphones under the frequency fWhere k is 2 pi f/c is the wave number, where c is the speed of sound,a complex set is represented. (frequency (k) is omitted for simplicity).
And step 3: constructing a 'plane wave space domain transformation matrix'The specific elements are as follows
In the formula, sn=[sinθncosφn,sinθnsinφn,cosθn]T(N ═ 1, 2.., N) denotes the propagation direction (Φ) of the nth plane waven,θn) Wherein phinE [0,360 deg. ] is direction angle thetan∈[0,180°]Determination of N directions for pitch angle by approximately uniform sampling of the sphere based on Thomson's problem αn(N1, 2.., N) is a constant related to plane wave dispersion, and for the approximately uniform dispersion strategy employed by the present invention, there is approximately α1=α2=…=αN4 pi/N; e represents a natural base number of the image,representing imaginary units.
And 4, step 4: according toThe Lasso problem is constructed, whereinIs a complex amplitude vector of the plane wave to be solved, and N represents the number of discrete plane waves; lambda belongs to (0, | | H)Hp||∞]Is a sparsity adjustment parameter (.)HRepresenting the conjugate transpose, | · | | non-conducting phosphor∞Representing an infinite norm, the larger the value of λ, the more sparse or compressible w; i | · | purple wind2Represents L2Norm, | · | luminance1Represents L1And (4) norm.
And 5: and solving the Lasso problem constructed in the fourth step by using the SALSA to obtain a plane wave complex amplitude vector w.
The sparsity adjusting parameter lambda can be determined by adopting a cross validation method, and the specific process is as follows:
step 4.1: arbitrary partitioning of a vector p into NgNon-overlapping subsets, i.e.Where N isgThe value can be 4 or 5;
step 4.2: in the interval (0, lambda)nom]To uniformly select NλThe value is taken as a possible value of λ, i.e.Wherein λnomSuggested as | | HHp||∞/50,NλThe value range of (1) is 10-50;
Wherein p is(g)Denotes the g-th subset of p, H(g)Is the g-th subset p in H(g)Matrix of corresponding columns, p(-g)Indicates p deletion p(g)Vector obtained after neutralization of element, H(-g)Indicates H deletes H(g)The matrix obtained after the column(s) in (1); w is a(-g)Is λ ═ λnIn time, the Lasso problem is solved by using SALSAThe solution obtained;
step 5.1: initializing parameters, and enabling w to be 0, d to be 0, v to be 0 and mu to be more than 0 (an empirical value is lambda/10);
step 5.2: are respectively in accordance with
v=soft(w-d,λ/μ)+d
d=v-w
Updating w, v, and d, wherein soft (·,) represents a soft threshold function;
step 5.3: if the end condition is reached, the maximum number of iterations I or the threshold value DeltawIf not more than epsilon, w is the final plane wave complex amplitude vector; otherwise, repeating the step 5.2; the value range of I is 500-1000; deltawIs | | | w(i)-w(i-1)||∞/||w(i)||∞I represents the current number of iterations, epsilon is a small positive number, and can be taken to be 10-4。
The present invention will be further described with reference to the following drawings and simulation examples, which include, but are not limited to, the following examples.
The basic flow of the method for reproducing the low-frequency sound field of the closed space based on the random sampling of the microphone is shown in figure 1, and the specific process is as follows:
1. given a rectangular enclosure of rigid walls with dimensions 3.7m × 1.8m × 1.2m, the origin of coordinates is chosen to be a few in the enclosureAnd a center of the line. A monopole point source is applied to the position (1.83,0.88,0.58) with the intensity of 0.0001+0.0001i m3And/s, producing a desired sound field to be reproduced. The frequencies under examination of the examples were chosen to be 100Hz and 195Hz, which are the non-resonant frequency and the approximate resonant frequency of the enclosed space, respectively. Fig. 2 shows the expected sound field of the closed space at 100Hz and 195Hz, respectively, by plotting the real part of sound pressure at the field point. The field points are 8000 observation points which are evenly distributed in space.
2. In a closed space, the Q is randomly distributed to 36 measuring points, and microphones are placed, as shown in figure 3. An expected sound field vector consisting of sound pressure at the position of a sampling point is obtained by sampling the expected sound field
3. Approximately and uniformly sampling the spherical surface based on Thomson problem, wherein the number N of sampling points is 400, and the direction (phi) of the sampling pointsn,θn) The (n is 1 to 400) is the direction of the discrete plane wave. Obtaining a 'plane wave space domain transformation matrix' according to the following formula "
Wherein e represents a natural base number,denotes an imaginary unit, k 2 pi f/c is a wave number, and c is a sound velocity, (. DEG)TRepresenting a transpose; sn=[sinθncosφn,sinθnsinφn,cosθn]T(n is 1 to 400) represents the propagation direction (phi) of the nth plane waven,θn) Wherein phinE [0,360 deg. ] is direction angle thetan∈[0,180°]Is a pitch angle.
4. The structural Lasso problem is shown by the following formula
WhereinIs a complex amplitude vector of plane wave to be solved, | · | | luminance2Represents L2Norm, | · | luminance1Represents L1And (4) norm. Lambda > 0 is a sparsity adjusting parameter which directly controls the sparsity of the solution w, the larger the lambda value is, the more sparse the solution is, but when lambda is larger than HHp||∞When the temperature of the water is higher than the set temperature,
since the calculation result w is 0, it is meaningless, and therefore, it may be in the interval (0, | H)Hp||∞/50]A suitable lambda value is determined. The invention adopts a cross-validation method to determine the parameter lambda, namely dividing the vector p into Ng5 non-overlapping subsets in the interval (0, | HHp||∞/50]To uniformly select Nλ20 possible valuesAccording toCalculating the error of the g subsetWherein, w(-g)Is λ ═ λnSolving the Lasso problem by using the SALSAThe solution obtained; p is a radical of(g)Denotes the g-th subset of p, H(g)Is the g-th subset p in H(g)Matrix of corresponding columns, p(-g)Indicates p deletion p(g)Vector obtained after neutralization of element, H(-g)Indicates H deletes H(g)The matrix obtained after the column(s) in (1); then according toCalculating to obtain lambdanCorresponding error En(ii) a Finally, theAccording to λ ═ λn′Determining a final parameter λ, whereinIn this embodiment, the parameters λ are determined to be 1.24 × 10 by cross-validation-4(100Hz) and 0.004(195 Hz).
5. Solving the Lasso problem using the SALSAObtaining a complex amplitude w of the plane wave, which comprises the following specific steps:
(1) initializing parameters, and enabling w to be 0, d to be 0, v to be 0 and mu to be lambda/10;
(2) are respectively in accordance with
v=soft(w-d,λ/μ)+d
d=v-w
Updating w, v, and d, wherein soft (·,) represents a soft threshold function;
(3) if the end condition is reached, the maximum number of iterations I or the threshold value DeltawIf not more than epsilon, w is the final plane wave complex amplitude vector; otherwise, repeating the step (2); in this embodiment, I is 1000; deltawIs | | | w(i)-w(i-1)||∞/||w(i)||∞I denotes the current number of iterations, ε is taken to be 10-4。
6. By usingEstimating an arbitrary field point rmAcoustic pressure ofWhereinAs shown in fig. 4, the sound field is reproduced in full space at 100Hz and 195Hz respectively (estimated real part of sound pressure at 8000 field points). As can be seen from the observation, the utilization of the air is36 microphones which are randomly distributed in the middle are sampled, and the distribution of a full-space sound field can be well reproduced. To further quantify the accuracy of the evaluation SFR, the evaluation index at frequency f is given as follows:
① field point relative error er
② Total spatial average relative error Er
Wherein M is the number of the field points.
③ mode assurance criterion MAC
Wherein,to evaluate the desired sound pressure vector at the site,to evaluate the reproduced sound pressure vector at the site. The MAC value range is usually 0-1, and the closer to 1, the more similar the spatial distribution of the reproduced sound field and the expected sound field is.
FIG. 5 shows the relative error for 8000 field points at 100Hz and 195Hz, and it can be seen that the majority of the relative errors are below 20%. Full space average relative error E at two frequenciesrAnd the mode assurance criterion MAC is respectively 7.33 percent and 0.9947(100Hz) and 7.91 percent and 0.9941(195Hz), which shows that the method provided by the invention can more accurately reproduce the sound pressure and the full-space sound field distribution of the unmeasured field points.
Table 1 compares the CPU time required for plane wave decomposition using the complex coordinate descent algorithm and the SALSA when the number Q of random sampling points is 36 and the number N of discrete plane waves is 200, 400, and 700, respectively. It can be seen that the SALSA adopted by the invention greatly improves the calculation speed of plane wave decomposition and is convenient for data processing and analysis. The method is suitable for reproduction of a total-space unsteady sound field when the number N of the discrete plane waves is small.
TABLE 1 plane wave decomposition computation time
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made in the above embodiments by those of ordinary skill in the art without departing from the principle and spirit of the present invention.
Claims (9)
1. A reproduction method of a low-frequency sound field in a closed space based on microphone random sampling is characterized by comprising the following steps: the method comprises the following steps:
step 1: establishing a rectangular coordinate system in a closed space V;
step 2: in the rectangular coordinate system established in the step 1, Q measuring point positions are randomly selected, a microphone is placed, and the position vector of the Q measuring point is recorded as rq=[xq,yq,zq]T,q=1,2,...,Q,(·)TRepresenting a transpose; sampling a sound field in a closed space V by using the Q microphones to obtain complex sound pressure vectors at the positions of the Q microphones under the frequency fWhereinRepresenting a complex set;
In the formula sn=[sinθncosφn,sinθnsinφn,cosθn]TDenotes the propagation direction of the nth plane wave, N being 1,2nIs the angle of orientation, θnAt a pitch angle of αnIs a set plane wave discrete correlation constant, e represents a natural base number,represents an imaginary unit; k is 2 pi f/c is wave number, c is sound velocity;
and 4, step 4: according toThe Lasso problem is constructed, whereinIs a complex amplitude vector of the plane wave to be solved, and N represents the number of discrete plane waves; lambda belongs to (0, | | H)Hp||∞]Is a sparsity adjustment parameter (.)HRepresenting the conjugate transpose, | · | | non-conducting phosphor∞Represents infinite norm, | ·| non-conducting phosphor2Represents L2Norm, | · | luminance1Represents L1A norm;
and 5: solving the Lasso problem constructed in the step 4 by using SALSA to obtain a plane wave complex amplitude vector w;
2. The method for reproducing the low-frequency sound field in the closed space based on the random sampling of the microphone as claimed in claim 1, wherein: in step 4, the larger the value of the sparsity adjusting parameter lambda is, the more sparse or compressible w is.
3. A reproduction method of a closed space low-frequency sound field based on microphone random sampling according to claim 1 or 2, characterized in that: in the step 4, the sparsity adjusting parameter lambda is determined by adopting a cross validation method, and the specific process is as follows:
step 4.1: arbitrary partitioning of a vector p into NgNon-overlapping subsets to obtainp(g)The g-th subset representing p;
step 4.2: in the interval (0, lambda)nom]To uniformly select NλValues as possible values of λ:wherein λnomIs a set preselected value upper limit;
Wherein H(g)Is the g-th subset p in H(g)A matrix of corresponding columns; w is a(-g)Is λ ═ λnIn time, the Lasso problem is solved by using SALSAThe solution obtained, p(-g)Indicates p deletion p(g)After the element is neutralized to obtainVector of (a), H(-g)Indicates H deletes H(g)The matrix obtained after the column(s) in (1);
4. The method for reproducing the low-frequency sound field in the closed space based on the random sampling of the microphone as claimed in claim 1, wherein: the specific process for solving the Lasso problem by using the SALSA is as follows:
step 5.1: initializing parameters, and enabling w to be 0, d to be 0, v to be 0 and mu to be more than 0;
step 5.2: are respectively in accordance with
v=soft(w-d,λ/μ)+d
d=v-w
Updating w, v, and d, wherein soft (·,) represents a soft threshold function;
step 5.3: if the termination condition is reached: maximum number of iterations or threshold ΔwIf not more than epsilon, w is the final plane wave complex amplitude vector; otherwise, repeating the step 5.2; deltawIs | | | w(i)-w(i-1)||∞/||w(i)||∞I denotes the current number of iterations and epsilon is a set small positive number.
5. The method for reproducing the low-frequency sound field in the closed space based on the random sampling of the microphone as claimed in claim 3, wherein: in step 4.1, NgThe value is 4 or 5.
6. The method for reproducing the low-frequency sound field in the closed space based on the random sampling of the microphone as claimed in claim 3, wherein: in step 4.2, λnomTake | | | HHp||∞/50,NλThe value range of (A) is 10-50.
7. The method for reproducing the low-frequency sound field in the closed space based on the random sampling of the microphone as claimed in claim 1, wherein: in step 3, the direction angle phinE is 0,360 degree, pitch angle thetan∈[0,180°]。
8. The method for reproducing the low-frequency sound field in the closed space based on the random sampling of the microphone as claimed in claim 1, wherein: in step 3, the determination of the N directions is obtained by approximately uniform sampling of a spherical surface based on the Thomson problem.
9. The method for reproducing the low-frequency sound field in the closed space based on the random sampling of the microphone as claimed in claim 1, wherein in the step 3, α is taken1=α2=…=αN=4π/N。
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010109592.6A CN111272274B (en) | 2020-02-22 | 2020-02-22 | Closed space low-frequency sound field reproduction method based on microphone random sampling |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010109592.6A CN111272274B (en) | 2020-02-22 | 2020-02-22 | Closed space low-frequency sound field reproduction method based on microphone random sampling |
Publications (2)
Publication Number | Publication Date |
---|---|
CN111272274A true CN111272274A (en) | 2020-06-12 |
CN111272274B CN111272274B (en) | 2022-07-19 |
Family
ID=70997201
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202010109592.6A Active CN111272274B (en) | 2020-02-22 | 2020-02-22 | Closed space low-frequency sound field reproduction method based on microphone random sampling |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN111272274B (en) |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112525338A (en) * | 2020-11-30 | 2021-03-19 | 合肥工业大学 | Method for eliminating Doppler effect of rotary sound source based on compressed sensing theory |
CN113239573A (en) * | 2021-06-05 | 2021-08-10 | 西北工业大学 | Closed space sound field reconstruction method based on grid fluctuation-free modeling |
Citations (17)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20110135102A1 (en) * | 2009-12-04 | 2011-06-09 | Hsin-Chieh Huang | Method, computer readable storage medium and system for localizing acoustic source |
US20130006624A1 (en) * | 2006-09-01 | 2013-01-03 | Audiozoom Ltd | Sound sources separation and monitoring using directional coherent electromagnetic waves |
CN103453980A (en) * | 2013-08-08 | 2013-12-18 | 大连理工大学 | Sound field parameter obtaining method based on compressed sensing |
CN103945308A (en) * | 2013-01-23 | 2014-07-23 | 中国科学院声学研究所 | Sound reproduction method and system based on wave field synthesis and wave field analysis |
CN105241666A (en) * | 2015-09-21 | 2016-01-13 | 华南理工大学 | Rolling bearing fault feature extraction method based on signal sparse representation theory |
CN105531934A (en) * | 2013-09-12 | 2016-04-27 | 高通股份有限公司 | Method for compressed sensing of streaming data and apparatus for performing the same |
US20160161325A1 (en) * | 2013-07-22 | 2016-06-09 | Brüel & Kjær Sound & Vibration Measurement A/S | Wide-band acoustic holography |
CN106165444A (en) * | 2014-04-16 | 2016-11-23 | 索尼公司 | Sound field reproduction apparatus, methods and procedures |
US20170064478A1 (en) * | 2015-08-31 | 2017-03-02 | University Of Maryland | Simultaneous solution for sparsity and filter responses for a microphone network |
CN106772220A (en) * | 2016-12-28 | 2017-05-31 | 西北工业大学 | Single microphone sound localization method based on the modeling of closing space geological information |
CN107389184A (en) * | 2017-06-29 | 2017-11-24 | 江苏理工学院 | A kind of method of two-dimensional space window processing acoustical signal error |
CN108012214A (en) * | 2017-11-08 | 2018-05-08 | 西北工业大学 | Reconstruction of Sound Field method based on the recessed penalty function of broad sense minimax |
JP2018077139A (en) * | 2016-11-09 | 2018-05-17 | 日本電信電話株式会社 | Sound field estimation device, sound field estimation method and program |
CN108562871A (en) * | 2018-04-27 | 2018-09-21 | 国网陕西省电力公司电力科学研究院 | Low Frequency Noise Generator high-precision locating method based on vector microphone array |
CN109507640A (en) * | 2018-12-18 | 2019-03-22 | 重庆大学 | A kind of comprehensive equivalent source identification of sound source method based on solid ball array |
CN109752710A (en) * | 2019-01-07 | 2019-05-14 | 中国人民解放军国防科技大学 | Rapid target angle estimation method based on sparse Bayesian learning |
CN109884192A (en) * | 2019-02-28 | 2019-06-14 | 江苏大学 | Sparse representation method for characteristics of weld seam guided wave flaw echoes feature extraction |
-
2020
- 2020-02-22 CN CN202010109592.6A patent/CN111272274B/en active Active
Patent Citations (17)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20130006624A1 (en) * | 2006-09-01 | 2013-01-03 | Audiozoom Ltd | Sound sources separation and monitoring using directional coherent electromagnetic waves |
US20110135102A1 (en) * | 2009-12-04 | 2011-06-09 | Hsin-Chieh Huang | Method, computer readable storage medium and system for localizing acoustic source |
CN103945308A (en) * | 2013-01-23 | 2014-07-23 | 中国科学院声学研究所 | Sound reproduction method and system based on wave field synthesis and wave field analysis |
US20160161325A1 (en) * | 2013-07-22 | 2016-06-09 | Brüel & Kjær Sound & Vibration Measurement A/S | Wide-band acoustic holography |
CN103453980A (en) * | 2013-08-08 | 2013-12-18 | 大连理工大学 | Sound field parameter obtaining method based on compressed sensing |
CN105531934A (en) * | 2013-09-12 | 2016-04-27 | 高通股份有限公司 | Method for compressed sensing of streaming data and apparatus for performing the same |
CN106165444A (en) * | 2014-04-16 | 2016-11-23 | 索尼公司 | Sound field reproduction apparatus, methods and procedures |
US20170064478A1 (en) * | 2015-08-31 | 2017-03-02 | University Of Maryland | Simultaneous solution for sparsity and filter responses for a microphone network |
CN105241666A (en) * | 2015-09-21 | 2016-01-13 | 华南理工大学 | Rolling bearing fault feature extraction method based on signal sparse representation theory |
JP2018077139A (en) * | 2016-11-09 | 2018-05-17 | 日本電信電話株式会社 | Sound field estimation device, sound field estimation method and program |
CN106772220A (en) * | 2016-12-28 | 2017-05-31 | 西北工业大学 | Single microphone sound localization method based on the modeling of closing space geological information |
CN107389184A (en) * | 2017-06-29 | 2017-11-24 | 江苏理工学院 | A kind of method of two-dimensional space window processing acoustical signal error |
CN108012214A (en) * | 2017-11-08 | 2018-05-08 | 西北工业大学 | Reconstruction of Sound Field method based on the recessed penalty function of broad sense minimax |
CN108562871A (en) * | 2018-04-27 | 2018-09-21 | 国网陕西省电力公司电力科学研究院 | Low Frequency Noise Generator high-precision locating method based on vector microphone array |
CN109507640A (en) * | 2018-12-18 | 2019-03-22 | 重庆大学 | A kind of comprehensive equivalent source identification of sound source method based on solid ball array |
CN109752710A (en) * | 2019-01-07 | 2019-05-14 | 中国人民解放军国防科技大学 | Rapid target angle estimation method based on sparse Bayesian learning |
CN109884192A (en) * | 2019-02-28 | 2019-06-14 | 江苏大学 | Sparse representation method for characteristics of weld seam guided wave flaw echoes feature extraction |
Non-Patent Citations (6)
Title |
---|
ANGELOSANTE: "Sound Field Reproduction using the Lasso", 《IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING》 * |
CHEN, KEAN: "Low frequency sound spatial encoding within an enclosure using spherical microphone arrays", 《JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA》 * |
KEAN CHEN: "A DOA Estimation Algorithm of Acoustic Sources based on Wavefield Decomposition using UCAs", 《2007 SECOND INTERNATIONAL CONFERENCE ON COMMUNICATIONS AND NETWORKING IN CHINA》 * |
杨真真: "压缩感知重构技术及其在图像融合中的应用研究", 《中国博士学位论文全文数据库 信息科技辑》 * |
王岩等: "圆柱腔低频声场的球谐函数分解及声场再现", 《声学学报》 * |
陈克安等: "基于声场复现的有源噪声控制支撑技术", 《应用声学》 * |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112525338A (en) * | 2020-11-30 | 2021-03-19 | 合肥工业大学 | Method for eliminating Doppler effect of rotary sound source based on compressed sensing theory |
CN112525338B (en) * | 2020-11-30 | 2022-10-04 | 合肥工业大学 | Method for eliminating Doppler effect of rotary sound source based on compressed sensing theory |
CN113239573A (en) * | 2021-06-05 | 2021-08-10 | 西北工业大学 | Closed space sound field reconstruction method based on grid fluctuation-free modeling |
CN113239573B (en) * | 2021-06-05 | 2024-05-07 | 西北工业大学 | Closed space sound field reconstruction method based on gridless fluctuation modeling |
Also Published As
Publication number | Publication date |
---|---|
CN111272274B (en) | 2022-07-19 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN107247251B (en) | Three-dimensional sound source positioning method based on compressed sensing | |
CN111272274B (en) | Closed space low-frequency sound field reproduction method based on microphone random sampling | |
CN103453980B (en) | A kind of sound field parameters acquisition methods based on compressed sensing | |
KR20070072518A (en) | Farfield analysis of noise sources | |
JP6386556B2 (en) | Wide frequency band acoustic holography | |
CN110837791B (en) | Sound velocity profile inversion method based on over-complete dictionary | |
CN105403878B (en) | A kind of Ocean Acoustic Tomography method based on delay sensitive core | |
CN109884592B (en) | Sound source positioning simulation method facing low-frequency Gaussian noise source | |
CN109489796A (en) | A kind of underwater complex structural radiation noise source fixation and recognition based on unit radiation method and acoustic radiation forecasting procedure | |
Hamilton et al. | On finite difference schemes for the 3-D wave equation using non-Cartesian grids | |
Yu et al. | Achieving the sparse acoustical holography via the sparse bayesian learning | |
CN111781639B (en) | Shot-geophone reciprocal elastic wave full waveform inversion method for OBS multi-component data | |
CN111812581B (en) | Spherical array sound source direction-of-arrival estimation method based on atomic norms | |
Zhongming et al. | A monotonic two-step iterative shrinkage/thresholding algorithm for sound source identification based on equivalent source method | |
CN114036745A (en) | Anisotropic medium three-dimensional magnetotelluric forward modeling method and system | |
Damiano et al. | Soundfield reconstruction in reverberant rooms based on compressive sensing and image-source models of early reflections | |
CN111707353A (en) | Near-field acoustic holography technology-based revolution surface sound field reconstruction method | |
CN109798975A (en) | A kind of real-time restoring method of free field of the unstable state flat sound source measured using acoustic pressure and particle acceleration | |
Koyama et al. | Sound field decomposition in reverberant environment using sparse and low-rank signal models | |
CN114252148B (en) | Sound field reconstruction method based on prolate ellipsoid wave superposition | |
CN115267673B (en) | Sparse sound source imaging method and system considering reconstruction grid offset | |
CN114373477B (en) | Active source lean secondary path modeling method based on sound field reconstruction | |
CN111339651A (en) | Secondary sound source layout optimization method for decoupling error sensor layout information | |
Chien et al. | Cubic-spline expansion for a two-dimensional periodic conductor in free space | |
Lobato et al. | Near-field acoustic holography using far-field measurements |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |