Sound field construction method and device, active noise reduction method and device to be reduced

Abstract

The application provides a sound field construction method and device, an active noise reduction method and a device to be reduced, the sound field construction method comprising: determining an initial basis coefficient complex vector of a sound field based on sound pressure values of at least one first observation point in the sound field collected by at least one microphone; moving the at least one microphone to at least one Lth observation point position along a preset track, wherein L is greater than or equal to 2; determining an updated basis coefficient complex vector based on sound pressure values of different observation points in the at least one first, second,..., L-1th and Lth observation points collected by the at least one microphone; and constructing a three-dimensional sound field based on the updated basis coefficient complex vector. The technical scheme of the application can restore the global sound distribution of the entire three-dimensional sound field through a small number of microphones.

Description

Technical Field

[0001] This application relates to the field of acoustic technology, specifically to a sound field construction method and apparatus, an active noise reduction method, and a noise reduction device. Background Technology

[0002] Currently, active noise cancellation technology is commonly used in numerous scenarios requiring noise reduction. The basic principle of active noise cancellation is to minimize the noise level in a three-dimensional sound field by playing a noise-reducing signal with the same amplitude but opposite phase as the noise signal. Therefore, active noise cancellation first requires reconstructing the sound information of the entire three-dimensional sound field to obtain specific information about the noise signal.

[0003] However, the distribution of noise information in a three-dimensional sound field is quite complex. Therefore, when reconstructing the global sound distribution of the entire three-dimensional sound field, the cost of deploying (collecting) microphones is usually increased. That is, multiple microphones are added throughout the three-dimensional sound field to collect sound signals and construct the three-dimensional sound field. In addition, although noise information in a three-dimensional sound field can be estimated using Fourier transform, Fourier transform obtains high-quality spectral estimation results by increasing the window length, thus resulting in a large time delay, which does not meet the real-time requirements of active noise reduction technology. Summary of the Invention

[0004] In view of this, embodiments of this application provide a sound field construction method and apparatus, an active noise reduction method and a noise reduction device, which can restore the global sound distribution of the entire three-dimensional sound field using a very small number of microphones.

[0005] In a first aspect, embodiments of this application provide a sound field construction method, comprising: determining an initial basis coefficient complex vector of the sound field based on sound pressure values ​​collected by at least one microphone at at least one first observation point in the sound field; moving at least one microphone along a preset trajectory to at least one Lth observation point, where L≥2; determining an updated basis coefficient complex vector based on sound pressure values ​​of different observation points among at least one 1st, 2nd, ..., L-1th, Lth observation points collected by at least one microphone; and constructing a three-dimensional sound field based on the updated basis coefficient complex vector.

[0006] In some embodiments of this application, determining the updated basis coefficient complex vector based on the sound pressure values ​​of at least one of the first, second, ..., L-1, L observation points collected by at least one microphone includes: determining the complex sound pressure corresponding to each L observation point based on the sound pressure value of at least one L observation point collected by at least one microphone; and determining the updated basis coefficient complex vector based on the complex sound pressure corresponding to each of the at least one first to at least one L observation points collected by at least one microphone and the sound field basis vector.

[0007] In some embodiments of this application, determining the complex sound pressure corresponding to each Lth observation point based on the sound pressure value of at least one Lth observation point collected by at least one microphone includes: determining the complex sound pressure estimation error of at least one Lth observation point based on the sound pressure value of at least one Lth observation point and the initial complex sound pressure; determining the adjusted complex sound pressure based on the complex sound pressure estimation error to obtain the adjusted complex sound pressure estimation error of at least one Lth observation point; when the adjusted complex sound pressure estimation error meets the preset convergence condition, determining the adjusted complex sound pressure as the complex sound pressure corresponding to each Lth observation point; otherwise, performing the step of determining the adjusted complex sound pressure based on the complex sound pressure estimation error to obtain the adjusted complex sound pressure estimation error.

[0008] In some embodiments of this application, determining the adjusted complex sound pressure based on the complex sound pressure estimation error to obtain the adjusted complex sound pressure estimation error for at least the Lth observation point includes: continuously adjusting the real part and the imaginary part of the complex sound pressure based on the complex sound pressure estimation error for at least the Lth observation point to obtain the adjusted complex sound pressure and the adjusted complex sound pressure estimation error.

[0009] In some embodiments of this application, determining the updated basis coefficient complex vector based on the complex sound pressure and sound field basis vector corresponding to each different observation point from at least the first observation point to at least the Lth observation point collected by at least one microphone includes: determining the basis coefficient complex vector after the LN-1th update based on the complex sound pressure and sound field basis vector corresponding to each different observation point from at least the first observation point to at least the LNth observation point collected by at least one microphone, where 0≤N≤L-2.

[0010] In some embodiments of this application, at least one microphone includes a single microphone.

[0011] In some embodiments of this application, constructing a three-dimensional sound field based on the updated basis coefficient complex vector includes: when each basis coefficient element in the updated basis coefficient complex vector converges, determining the currently updated basis coefficient complex vector as the basis coefficient complex vector of the sound field, and ending the movement of at least one microphone; replacing the sound pressure value collected by at least one microphone with the basis coefficient complex vector of the sound field to restore the global sound distribution of the three-dimensional sound field.

[0012] Secondly, embodiments of this application provide an active noise reduction method, comprising: obtaining a basis coefficient complex vector based on the sound field construction method of the first aspect described above; and using the basis coefficient complex vector as a feedback quantity to adjust the noise reduction parameters of the active noise reduction filter.

[0013] Thirdly, embodiments of this application provide a noise reduction device, including: an active noise reduction device for performing the active noise reduction method of the second aspect described above; and a housing, wherein the active noise reduction device is located inside the housing.

[0014] Fourthly, embodiments of this application provide a sound field construction apparatus, comprising: a first determining module, configured to determine an initial basis coefficient complex vector of the sound field based on sound pressure values ​​collected by at least one microphone at at least one first observation point in the sound field; a moving module, configured to move at least one microphone along a preset trajectory to at least one Lth observation point, where L≥2; a second determining module, configured to determine an updated basis coefficient complex vector based on sound pressure values ​​at different observation points among at least one 1st, 2nd, ..., L-1th, Lth observation points collected by at least one microphone; and a construction module, configured to construct a three-dimensional sound field based on the updated basis coefficient complex vector.

[0015] This application provides a sound field construction method and apparatus, an active noise reduction method, and a noise reduction device. By dynamically acquiring sound pressure values ​​at multiple observation points within the sound field using a very small number of microphones, the fundamental coefficient complex vector corresponding to the sound field is obtained, replacing the signal acquired by the microphone array. This allows the technical solution of this application to further reduce the cost of microphone placement while accurately restoring the entire three-dimensional sound field. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a sound field construction method provided in an exemplary embodiment of this application.

[0017] Figure 2 This is a flowchart illustrating a sound field construction method provided in another exemplary embodiment of this application.

[0018] Figure 3 This is a flowchart illustrating a sound field construction method provided in another exemplary embodiment of this application.

[0019] Figure 4 This is a flowchart illustrating an exemplary embodiment of the active noise reduction method provided in this application.

[0020] Figure 5 This is a schematic diagram of the structure of the noise reduction device provided in an exemplary embodiment of this application.

[0021] Figure 6 This is a schematic diagram of the structure of a sound field construction device provided in an exemplary embodiment of this application.

[0022] Figure 7 This is a block diagram of an electronic device for sound field construction provided in an exemplary embodiment of this application. Detailed Implementation

[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] The construction process of a three-dimensional sound field can be understood as follows: Based on the sound pressure levels measured by the microphone, estimate the complex sound pressure levels at each moment; based on the estimated complex sound pressure levels, determine the basis coefficient complex vector in real time; replace the signals collected by the microphone in real time with the determined basis coefficient complex vector to characterize the information of the sound signals in the entire three-dimensional sound field. The complex sound pressure level includes a real part and an imaginary part, which together determine the amplitude and phase information of the sound signal at each observation point.

[0025] However, since the sound pressure level measured by the microphone is a real value, the amplitude and phase information of the sound signal cannot be determined solely from the real sound pressure level collected by the microphone. Therefore, existing technologies typically use Fourier transform spectrum estimation algorithms to calculate complex sound pressure levels. However, to obtain high-quality spectrum estimation results, the window length of the Fourier transform needs to be increased. However, the longer the selected window length of the Fourier transform, the greater the time delay generated by the algorithm, making it increasingly difficult to meet real-time requirements.

[0026] In order to accurately reproduce the global sound distribution within the sound field, existing technologies typically employ microphone arrays to simultaneously collect sound pressure levels by placing multiple microphones in the (three-dimensional) sound field. However, this method further increases the cost of microphone placement.

[0027] To address the aforementioned technical problems, this application provides a sound field construction method that can reproduce the global sound distribution of the entire three-dimensional sound field using a very small number of microphones.

[0028] Figure 1 This is a flowchart illustrating a sound field construction method provided in an exemplary embodiment of this application. Figure 1 The method is executed by a computing device, such as a server. Figure 1 As shown, the sound field construction method includes the following:

[0029] 110: Determine the initial basis coefficient complex vector of the sound field by acquiring the sound pressure value of at least one first observation point in the sound field based on at least one microphone.

[0030] Specifically, at least one microphone can collect the sound pressure value of each first observation point at each moment in real time at at least one first observation point location. The sound pressure value is a real number, that is, the sound pressure value collected by at least one microphone at at least one first observation point is a real sound pressure.

[0031] The sound field can include multiple observation points, and the sound field can be a holographic, steady-state three-dimensional sound field. The number of at least one microphone determines the number of at least one primary observation point among the multiple observation points. For example, if the number of at least one microphone is 3, then the number of primary observation points in a single acquisition is also 3. Furthermore, the number of multiple observation points is greater than the number of at least one microphone.

[0032] In one example, each observation point in a three-dimensional sound field can be described by the following formula:

[0033]

[0034] Where p(n) is the instantaneous complex sound pressure, and n is the discrete time point; Let m be the m-th order sound field basis (complex value). c is the sound field basis (row) vector; m (n) represents the basis coefficients (complex values) corresponding to the m-th order sound field basis. It is a complex vector of basis coefficients, and it is also a column vector of basis coefficients.

[0035] Initial complex sound pressure levels (SPLs) can be set for each of the at least one first observation point, where the initial complex SPL is a complex number. The real-valued form of each initial complex SPL is compared with the real-valued SPL values ​​collected in real time by each microphone at the first observation point to obtain the complex SPL estimation error for each first observation point. The complex SPL estimation error vector for at least one first observation point can be constructed based on the complex SPL estimation errors for each first observation point.

[0036] In one embodiment, the complex sound pressure estimation error vector can be calculated using the following formula (2):

[0037]

[0038] in, Let be the error vector for complex sound pressure estimation, which is the sum of the errors for each step. and The error obtained by adaptive estimation; is the sound pressure value (real number) collected in real time by the microphone at at least one first observation point; cos2πf0n is the reference cosine signal and sin2πf0n is the reference sine signal (where the frequency f0 is the center frequency of the narrow-frequency sound signal in the known three-dimensional sound field); It is the real part of the complex sound pressure vector corresponding to at least one first observation point; It is the imaginary part of the complex sound pressure vector corresponding to at least the first observation point.

[0039] The obtained complex sound pressure estimation error corresponding to each first observation point is used as feedback to adjust each initial complex sound pressure, thus obtaining the adjusted complex sound pressure at each first observation point. Based on the adjusted complex sound pressure and the real sound pressure value collected in real time by each microphone at the next moment, the adjusted complex sound pressure estimation error corresponding to each first observation point is determined.

[0040] To obtain the converged complex sound pressure level (CPL) for each first observation point, the CPL at each first observation point can be repeatedly adjusted, and the adjusted CPL estimation error can be recalculated after each adjustment to obtain an adjusted CPL estimation error vector for at least one first observation point. Adjustment of the CPL can be stopped when the square of the magnitude of the adjusted CPL estimation error vector finally converges to its minimum value. The CPL after the last adjustment at each first observation point is taken as the final CPL. The CPL of each first observation point constitutes the CPL vector for at least one first observation point.

[0041] The process of obtaining the adjusted complex sound pressure level can be based on an adaptive algorithm, such as the Least Mean Square (LMS) algorithm. This application does not specify the adaptive algorithm used.

[0042] Based on the three-dimensional sound field, at least one initially preset complex vector of basis coefficients corresponding to the first observation point is pre-set, wherein the initially preset complex vector of basis coefficients can be set as a zero vector. Based on the aforementioned complex sound pressure vector, sound field basis matrix, and initially preset complex vector of basis coefficients corresponding to at least one first observation point, the basis coefficient estimation error vector corresponding to at least one first observation point is obtained.

[0043] The basis coefficient estimation error vector is used as feedback to update the initially preset basis coefficient complex vector, resulting in the updated basis coefficient complex vector. Based on the updated basis coefficient complex vector and the adjusted complex sound pressure at the next time step, the adjusted basis coefficient estimation error vector is obtained.

[0044] To obtain a convergent basis coefficient complex vector, the basis coefficient complex vector at at least one first observation point can be repeatedly adjusted, and the updated basis coefficient complex vector can be recalculated after each adjustment to obtain the adjusted basis coefficient estimation error vector. The adjustment of the basis coefficient complex vector can be stopped when the square of the magnitude of the adjusted basis coefficient estimation error vector finally converges to its minimum value. The last updated basis coefficient complex vector is then used as the final basis coefficient complex vector for at least one first observation point (i.e., the initial basis coefficient complex vector described in step 110).

[0045] The process of obtaining the updated basis coefficient complex vector can be based on an adaptive algorithm, such as the least mean square algorithm. This application does not specifically limit the adaptive algorithm used.

[0046] In one embodiment, the complex vector of basis coefficients can be calculated using the following formula (3):

[0047]

[0048] in, This is the error vector for estimating the basic coefficients; It is a complex sound pressure vector; Here is the basis matrix of the sound field; is a complex vector of basis coefficients.

[0049] It should be noted that the embodiments of this application include two adaptive steps, one for obtaining complex sound pressure and the other for obtaining the complex vector of basis coefficients. The two adaptive steps can iterate simultaneously, or the adaptive step for obtaining complex sound pressure can be executed first, followed by the adaptive step for obtaining the complex vector of basis coefficients. The embodiments of this application do not impose specific limitations on the iteration order of the two adaptive steps.

[0050] 120: Move at least one microphone along a preset trajectory to at least one Lth observation point location.

[0051] In one embodiment, L ≥ 2.

[0052] Specifically, after at least one microphone obtains converged complex sound pressure and initial basis coefficient complex vector at at least one first observation point, at least one microphone is moved to the next Lth observation point position in a plurality of observation points, wherein the plurality of observation points can form a preset trajectory according to a preset arrangement. In particular, if only a single microphone moves along the preset trajectory, it moves to the next Lth observation point position, where L≥2. It should be noted that when the number of at least one microphone is greater than or equal to 2, at least one microphone can move along the preset trajectory simultaneously. The embodiments of this application do not specifically limit the way the microphones are moved.

[0053] The shape of the preset trajectory can be a number of parallel lines or any curve. In this embodiment, the shape of the preset trajectory is not specifically limited.

[0054] In one example, when the preset trajectory is in the form of two parallel lines and the number of at least two microphones is two, a microphone can be placed at the same end of each of the two parallel lines to collect sound pressure levels. Alternatively, a microphone can be placed at each end of the two parallel lines, i.e., at the left end of one parallel line and the right end of the other, so that at least one microphone faces each other to collect sound pressure levels. This application does not specifically limit the placement of the at least one microphone in its embodiments and can be flexibly set according to actual conditions.

[0055] 130: Determine the updated basis coefficient complex vector based on the sound pressure values ​​of different observation points in at least one of the 1st, 2nd, ..., L-1th, Lth observation points collected by at least one microphone.

[0056] Specifically, based on the sound pressure value of at least one Lth observation point collected by at least one microphone, the complex sound pressure corresponding to each Lth observation point is determined. And based on the complex sound pressure values ​​of K different observation points from at least one 1st observation point to at least one Lth observation point collected by at least one microphone, a complex sound pressure vector is constructed, for example...

[0057] It should be noted that since at least one microphone may repeatedly capture at least one observation point from the first observation point to at least one observation point L, the number of rows of the sound field basis matrix is ​​copied, resulting in unnecessary computational redundancy. Therefore, when constructing the complex sound pressure vector, the repeatedly captured observation points are removed, meaning that the constructed complex sound pressure vector includes the complex sound pressure corresponding to each different observation point from at least the first observation point to at least the Lth observation point.

[0058] Based on the complex sound pressure vectors and sound field basis matrices corresponding to each different observation point from at least the first observation point to at least the Lth observation point collected by at least one microphone, the updated basis coefficient complex vectors are determined.

[0059] The sound field basis matrix is ​​constructed based on the sound field basis vectors corresponding to at least one first observation point, at least one second observation point, ..., at least one (L-1)th observation point, and at least one Lth observation point, which are all collected by at least one microphone.

[0060] It should be noted that for a detailed description of this step, please refer to [link / reference needed]. Figure 2 The descriptions of the following embodiments will not be repeated here to avoid repetition.

[0061] 140: Construct a three-dimensional sound field based on the updated basis coefficient complex vector.

[0062] Specifically, when all the basis coefficient elements in the updated basis coefficient complex vector converge, the currently updated basis coefficient complex vector is determined to be the final basis coefficient complex vector of the sound field. And terminate the movement of at least one microphone. Simultaneously, convert the final basis coefficient complex vector of the sound field... It replaces the sound pressure level collected by at least one microphone to reconstruct the global sound distribution of the three-dimensional sound field.

[0063] It should be noted that the sound field construction method provided in this application constructs sub-sound field information of corresponding frequencies in a three-dimensional sound field. Furthermore, the sound field construction method provided in this application can be applied to active noise reduction technology, as well as other related technologies requiring sound field restoration. This application does not specifically limit the specific application scenarios of the sound field construction method.

[0064] Furthermore, the embodiments of this application mainly focus on constructing narrowband acoustic signals in a three-dimensional sound field. The narrowband signal model can be expressed as:

[0065]

[0066] Where f0 is the signal frequency; p(t) represents the initial phase of the signal; p(t) represents the signal amplitude, which is usually a variable that fluctuates slowly around a certain constant value.

[0067] In some embodiments, the present application may employ a single microphone or two microphones that move in the sound field based on a preset trajectory, thereby determining the complex sound pressure at one or two observation positions each time. Similarly, other numbers of microphones may also be used; the present application does not specifically limit the number of microphones.

[0068] Therefore, it can be seen that the embodiments of this application dynamically collect sound pressure values ​​at multiple observation points within the sound field using a very small number of microphones, and obtain the basis coefficient complex vector corresponding to the sound field, in order to replace the signal collected by the microphone array. This allows the technical solution of this application to further reduce the cost of microphone placement while accurately restoring the entire three-dimensional sound field.

[0069] In one embodiment of this application, determining the updated basis coefficient complex vector based on the sound pressure values ​​of at least one observation point (at least the first, second, ..., L-1, L) collected by at least one microphone includes: determining the complex sound pressure corresponding to each observation point (at least the Lth observation point) based on the sound pressure value collected by at least one microphone; and determining the updated basis coefficient complex vector based on the complex sound pressure corresponding to each observation point (at least the first to at least the Lth observation point) and the sound field basis vector collected by at least one microphone.

[0070] Specifically, based on at least one microphone, the sound pressure level (SPL) value at at least one Lth observation point and an initial complex SPL are collected. The complex SPL estimation error corresponding to each Lth observation point is determined, where the complex SPL estimation errors corresponding to each Lth observation point form the complex SPL estimation error vector corresponding to at least one Lth observation point. Based on the complex SPL estimation errors of each Lth observation point, the adjusted complex SPL of each Lth observation point is determined to obtain the adjusted complex SPL estimation error vector for at least one Lth observation point. When the square of the magnitude of the adjusted complex SPL estimation error vector finally converges to its minimum value, the adjusted complex SPL is determined as the complex SPL corresponding to each Lth observation point.

[0071] In one embodiment, if the square of the adjusted complex sound pressure estimation error of each Lth observation point does not converge to the minimum value, the step of determining the adjusted complex sound pressure of each Lth observation point based on the complex sound pressure estimation error of each Lth observation point is continued to obtain the adjusted complex sound pressure estimation error of each Lth observation point.

[0072] Based on the complex sound pressure vectors and sound field basis matrices corresponding to at least one observation point from at least one microphone to at least one observation point from one observation point ...

[0073] It should be noted that the process of obtaining the adjusted complex sound pressure and the updated basis coefficient complex vector can be based on an adaptive algorithm, such as the least mean square algorithm. The embodiments of this application do not specifically limit the adaptive algorithm used.

[0074] Therefore, this embodiment obtains convergent complex sound pressure and fundamental coefficient complex vectors by iterating through two adaptive stages, thereby improving accuracy while constructing a three-dimensional sound field in real time. Furthermore, this embodiment eliminates the need for Fourier transform on the sound signal acquired by the microphone, enabling real-time tracking of amplitude fluctuations in narrowband sound signals, thus meeting the real-time requirements of active noise reduction.

[0075] Figure 2 This is a flowchart illustrating a sound field construction method provided in another exemplary embodiment of this application. Figure 2 The example is as follows Figure 1 The similarities in the examples of the embodiments will not be repeated here; the focus is on describing the differences. For example... Figure 2The sound field construction method includes the following:

[0076] 210: Based on the sound pressure value of at least one Lth observation point and the initial complex sound pressure, determine the complex sound pressure estimation error of at least one Lth observation point.

[0077] Specifically, after the converged complex sound pressure and fundamental coefficient complex vector are obtained at at least one L-1 observation point, at least one microphone is moved to at least one L-1 observation point based on a preset trajectory.

[0078] An initial complex sound pressure level (SPL) can be set for each Lth observation point based on at least one Lth observation point, where the initial complex SPL is a complex number. The difference between the real-valued form of each initial complex SPL and the real-valued SPL collected in real time by each microphone at the Lth observation point is compared to obtain the complex SPL estimation error for each Lth observation point. The complex SPL estimation error vector for at least one Lth observation point can be constructed based on the complex SPL estimation errors for each Lth observation point.

[0079] For example, when L=2 and at least one microphone is two microphones, that is, after the two first observation points have converged, the two microphones move to the two second observation points based on a preset trajectory, and estimate the complex sound pressure vector corresponding to each time point based on the measured sound pressure value.

[0080] It should be noted that the number of at least Lth observation points can be 1, 2, or 3, and this application embodiment does not specifically limit this. Furthermore, the number of at least Lth observation points is the same as the number of at least one microphone collecting sound pressure values, where L≥2.

[0081] It should also be noted that when the number of Lth observation points is a single point, the calculated result is the complex sound pressure estimation error. When the number of Lth observation points is greater than or equal to 2, the result calculated from multiple Lth observation points is the complex sound pressure estimation error vector.

[0082] 220: Determine the adjusted complex sound pressure based on the complex sound pressure estimation error to obtain the adjusted complex sound pressure estimation error for at least the Lth observation point.

[0083] Specifically, the calculated complex sound pressure estimation error at each Lth observation point is used as a feedback quantity to adjust each initial complex sound pressure level, thereby obtaining the adjusted complex sound pressure level corresponding to each Lth observation point. The process of obtaining the adjusted complex sound pressure level can be implemented based on an adaptive algorithm; however, this embodiment does not specifically limit the adaptive algorithm used.

[0084] Based on the adjusted complex sound pressure at each Lth observation point and the real sound pressure value at the Lth observation point acquired in real time by each microphone, the adjusted complex sound pressure estimation error corresponding to each Lth observation point is determined, so as to obtain at least one complex sound pressure estimation error vector corresponding to the Lth observation point.

[0085] 230: When the adjusted complex sound pressure estimation error meets the preset convergence condition, the adjusted complex sound pressure is determined as the complex sound pressure corresponding to each Lth observation point.

[0086] Specifically, to obtain the converged complex sound pressure level (CSU) for each Lth observation point, the CSU at each Lth observation point can be repeatedly adjusted, and the adjusted CSU estimation error can be recalculated after each adjustment to obtain an adjusted CSU estimation error vector for at least one Lth observation point. Adjustment of the CSU can be stopped when the adjusted CSU estimation error for each Lth observation point meets a preset convergence condition, or when the adjusted CSU estimation error vector for at least one Lth observation point meets a preset convergence condition. The final adjusted CSU at each Lth observation point is then taken as the final CSU. The CSU at each Lth observation point constitutes the CSU vector for at least one Lth observation point.

[0087] Among them, the preset convergence condition can be the optimal condition, such as the square of the complex sound pressure estimation error converges to the minimum value, or the square of the magnitude of the complex sound pressure estimation error vector converges to the minimum value.

[0088] In one embodiment, if the adjusted complex sound pressure estimation error of each Lth observation point does not meet the preset convergence condition, or if the adjusted complex sound pressure estimation error vector of at least one Lth observation point does not meet the preset convergence condition, then step 220 continues to be executed until the complex sound pressure corresponding to the Lth observation point is converged.

[0089] Therefore, the embodiments of this application convert the sound pressure value (real number) with less characterization information collected by the microphone into the complex sound pressure value (complex number) with more characterization information, so as to obtain the amplitude and phase information of each observation point at each time, and then use it to perform active noise reduction and other operations on the three-dimensional sound field in real time.

[0090] In one embodiment of this application, determining the adjusted complex sound pressure based on the complex sound pressure estimation error to obtain the adjusted complex sound pressure estimation error for at least the Lth observation point includes: continuously adjusting the real part and the imaginary part of the complex sound pressure based on the complex sound pressure estimation error for at least the Lth observation point to obtain the adjusted complex sound pressure and the adjusted complex sound pressure estimation error.

[0091] Specifically, the adjusted complex sound pressure level can be obtained based on an adaptive algorithm, where the objective function of the adaptive algorithm can be the following formula (10). Since the complex sound pressure level is a complex number, it can be divided into two adaptive iterations: one adjusting the real part of the complex sound pressure level, and the other adjusting the imaginary part. Based on the adjusted complex sound pressure level and the real sound pressure level value acquired by the microphone at the next moment, the adjusted complex sound pressure level estimation error corresponding to the Lth observation point is determined.

[0092] It should be noted that the process of adjusting the real part and the imaginary part of the complex sound pressure in this application is detailed in the following embodiments and the description of formulas (10) to (14). To avoid repetition, it will not be repeated here.

[0093] Therefore, it can be seen that the embodiments of this application continuously adjust the real and imaginary parts of the complex sound pressure to transform the sound pressure value with less representational information collected by the microphone into a complex sound pressure with more representational information, so as to obtain the amplitude and phase information of each observation point at each moment, thereby realizing operations such as active noise reduction.

[0094] In one embodiment of this application, determining the updated basis coefficient complex vector based on the complex sound pressure and sound field basis vector corresponding to each different observation point from at least the first observation point to at least the Lth observation point collected by at least one microphone includes: determining the basis coefficient complex vector after the LN-1th update based on the complex sound pressure and sound field basis vector corresponding to each different observation point from at least the first observation point to at least the LNth observation point collected by at least one microphone, where 0≤N≤L-2.

[0095] Specifically, based on at least one first observation point to at least one second observation point (i.e., N = L - 2) acquired by at least one microphone, the complex vector of the basis coefficients after the first update is determined. Based on at least one first observation point to at least one third observation point (i.e., N = L - 3) acquired by at least one microphone, the complex vector of the basis coefficients after the second update is determined. ... Based on at least one first observation point to at least one (L - 1)th observation point (i.e., N = 1) acquired by at least one microphone, the complex vector of the basis coefficients after the (L - 1)th update is determined. Based on at least one first observation point to at least one (L)th observation point (i.e., N = 0) acquired by at least one microphone, the complex vector of the basis coefficients after the (L - 1)th update is determined.

[0096] Based on the sound field basis vectors corresponding to at least one first observation point and at least one LNth observation point, the sound field basis vectors corresponding to duplicate observation points are removed to form a sound field basis matrix. The sound field basis vectors corresponding to each observation point are pre-set and determined by the sound field wave equation, the sound field structure boundary conditions, and the coordinates of the observation points, which are known quantities.

[0097] For ease of description, the following example uses a single microphone to collect data at the second observation point.

[0098] The complex sound pressure vector is composed of the complex sound pressure p1(n) estimated at the first observation point and the complex sound pressure p2(n) estimated at the second observation point, for example, [p1(n), p2(n)]. T Recalculate the complex vector of basis coefficients This yields the complex vector of the basis coefficients from the first update. At this point (for the two observation points), the acoustic holographic model that can be established is:

[0099]

[0100] in, It is a complex sound pressure vector. Here is the basis matrix of the sound field; The fundamental coefficients are complex vectors. It should be noted that the dimension M of the sound field fundamental coefficients obtained from this decomposition can be much larger than the number of observation points K in the sound field, thus reconstructing more sound field information with fewer collected signals. Compared to the limited acquisition of sound signals from local points in the sound field by a microphone array, the above-described method in this application can obtain explosive, global sound field information.

[0101] At this point, the formula for calculating the basic coefficient estimation error vector corresponding to the two observation points is as follows (6):

[0102]

[0103] in, This is the error vector for estimating the basic coefficients; [ψ(n)] is the complex sound pressure vector; [ψ(n)] is the sound field basis matrix; is a complex vector of basis coefficients.

[0104] In one embodiment, the following formula (7) can be used as the objective function for subsequent iterative operations:

[0105]

[0106] Where J2(n) is the objective function of the adaptive algorithm; E[x] is the expected value. This is the error vector for estimating the basic coefficients; This is the transpose of the basis coefficient estimation error vector.

[0107] Based on the above formula (7), the adaptive iterative formula is obtained as follows:

[0108]

[0109] in, Let be the complex vector of basis coefficients; μ2 be the step size; [ψ(n)] T This is the transpose of the acoustic field basis matrices corresponding to the two observation points; This is the error vector for estimating the basic coefficients.

[0110] Therefore, it can be seen that the embodiments of this application use an adaptive algorithm to update and iterate the basis coefficient complex vector, which can construct the full picture of the three-dimensional sound field in real time.

[0111] In one embodiment of this application, at least one microphone includes a single microphone.

[0112] Specifically, a single microphone is placed at the first observation point, and the measured sound pressure value p is used as the basis for the measurement. r，1 (n) Estimate the complex sound pressure p1(n) at each time point, which is equivalent to estimating q. r，1 (n) and q i，1 (n). Based on the estimated complex sound pressure p1(n) = q r，1 (n)+jq i，1 (n), the initial complex vector of basis coefficients is determined in real time.

[0113] An initial complex sound pressure level (SPL) is pre-set based on the three-dimensional sound field, where the initial complex SPL is a complex number. The corresponding real-valued form of the initial complex SPL is then compared with the real-valued sound pressure level p collected in real time by a single microphone at the first observation point. r，1 (n) Perform difference comparison to obtain the complex sound pressure estimation error corresponding to the first observation point.

[0114] In one embodiment, the complex sound pressure estimation error can be calculated using the following formula (9):

[0115] e1(n)=p r，1 (n)-(cos2πf0n·q r，1 (n)+sin2πf0n·q i，1 (n)) Equation (9)

[0116] Where e1(n) is the complex sound pressure estimation error, which is the error of each step on p r，1 (n) and q r，1 (n) The error obtained by adaptive estimation; p r，1 (n) represents the sound pressure level (real number) collected in real time by the microphone at the first observation point; cos2πf0n is the reference cosine signal, and sin2πf0n is the reference sine signal (where frequency f0 is the center frequency of the narrow-band sound signal in the known three-dimensional sound field); q r，1 (n) represents the real part of the complex sound pressure corresponding to the first observation point; q i，1 (n) is the imaginary part of the complex sound pressure corresponding to the first observation point.

[0117] The calculated complex sound pressure estimation error is used as feedback to adjust the initial complex sound pressure to obtain the adjusted complex sound pressure. The process of obtaining the adjusted complex sound pressure can be based on an adaptive algorithm, such as the least mean square algorithm; however, this embodiment does not specifically limit the adaptive algorithm used. Based on the adjusted complex sound pressure and the real sound pressure value collected by a single microphone at the next moment, the adjusted complex sound pressure estimation error corresponding to the first observation point is determined according to formula (9).

[0118] In one embodiment, the adjusted complex sound pressure level can be obtained based on an adaptive algorithm. Since the complex sound pressure level is a complex number, it can be divided into two adaptive iterations: one updating the real part of the complex sound pressure level, and the other updating the imaginary part.

[0119] In one example, the following formula (10) can be used as the objective function of the adaptive algorithm.

[0120]

[0121] Where J1(n) is the objective function of the adaptive algorithm; E[x] is the mathematical expectation value; and e1(n) is the complex sound pressure estimation error.

[0122] The adaptive iterative formula for the real part of the complex sound pressure is as follows:

[0123]

[0124] Substituting the objective function (i.e., formula (10)) into the equation, we get:

[0125] q r，1 (n+1)=q r，1 (n)+2μ1cos2πf0n·e1(n) Formula (12)

[0126] In equations (11) and (12), J1(n) is the objective function of the adaptive algorithm; q r，1 (n) is the real part of the complex sound pressure corresponding to the first observation point; μ1 is the step size; cos2πf0n is the reference cosine signal (where the frequency f0 is the center frequency of the narrow-frequency sound signal in the known three-dimensional sound field); e1(n) is the complex sound pressure estimation error; To obtain the partial derivative.

[0127] The adaptive iterative formula for the imaginary part of the complex sound pressure level is as follows:

[0128]

[0129] Substituting the objective function (i.e., formula (10)) into the equation, we get:

[0130] q i，1 (n+1)=qi，1 (n)+2μ1sin2πf0n·e1(n) Formula (14)

[0131] In equations (13) and (14), J1(n) is the objective function of the adaptive algorithm; e1(n) is the complex sound pressure estimation error; q i，1 (n) is the imaginary part of the complex sound pressure corresponding to the first observation point; μ1 is the step size; sin2πf0n is the reference sine signal (where the frequency f0 is the center frequency of the narrow-frequency sound signal in the known three-dimensional sound field); To obtain the partial derivative.

[0132] When the square of the adjusted complex sound pressure estimation error finally converges to a minimum value (e.g., approaches 0), the adjustment of the complex sound pressure can be stopped, and the adjusted complex sound pressure obtained from the last adjustment can be taken as the final complex sound pressure.

[0133] Based on the first observation point (i.e., an observation point where the single microphone collects data at the corresponding location), the acoustic holographic model can be established as follows (15):

[0134]

[0135] Where p1(n) is the complex sound pressure at the first observation point; This is the acoustic field basis vector corresponding to the first observation point; is a complex vector of basis coefficients.

[0136] Based on the three-dimensional sound field, multiple pre-set basis coefficient complex vectors are configured for each observation point, and these pre-set basis coefficient complex vectors can be set to a zero vector. Based on the complex sound pressure at the first observation point and the pre-set basis coefficient complex vectors, an adaptive algorithm is used to determine the basis coefficient estimation error corresponding to the first observation point.

[0137] In one embodiment, the basic coefficient estimation error can be calculated using the following formula (16), where e2(n) is the value for each step of the estimation. The error obtained by adaptive estimation:

[0138]

[0139] Where e2(n) is the basic coefficient estimation error; q r (n)+jq i (n) represents the complex sound pressure corresponding to the first observation point; This is the acoustic field basis vector corresponding to the first observation point; is a complex vector of basis coefficients.

[0140] The obtained basis coefficient error vector is used as feedback to adjust the initially preset basis coefficient complex vector, thereby obtaining the updated basis coefficient complex vector. The updated basis coefficient complex vector can be obtained based on an adaptive algorithm, such as the least mean square algorithm. This application embodiment does not specify the adaptive algorithm used. Based on the obtained updated basis coefficient complex vector and the complex sound pressure at the next moment, the adjusted complex sound pressure estimation error corresponding to the first observation point is determined according to formula (16).

[0141] In one embodiment, the objective function of the adaptive algorithm in the following formula (17) can be used for subsequent update iteration operations:

[0142]

[0143] Where J2(n) is the objective function of the adaptive algorithm; e2(n) is the basic coefficient estimation error; and E[x] is the mathematical expectation value.

[0144] The adaptive iterative formula for the complex vector of the basis coefficients of the three-dimensional sound field is:

[0145]

[0146] Substituting the objective function (i.e., formula (17)) into the equation, we get:

[0147]

[0148] In formulas (18) and (19), The vector is the complex vector of basis coefficients; μ2 is the step size; e2(n) is the transpose of the acoustic field basis vector corresponding to the first observation point; e2(n) is the basis coefficient estimation error. To obtain the partial derivative.

[0149] When the square of the basis coefficient estimation error e²(n) in the iterative cycle finally converges to its minimum value, the updating of the basis coefficient complex vector can be stopped, and the basis coefficient complex vector updated last is used as the initial basis coefficient complex vector. Based on the sound pressure values ​​of each observation point in the first and second observation points collected by a single microphone, the updated basis coefficient complex vector is determined. For details of the specific implementation process, please refer to the description in the above embodiment; to avoid repetition, it will not be repeated here. Based on the updated basis coefficient complex vector, a three-dimensional sound field is constructed.

[0150] Therefore, it can be seen that the embodiments of this application obtain the basis coefficient complex vector of the sound field by dynamically acquiring the sound pressure value of the observation point in the sound field through a single microphone, which replaces the method of acquiring by a microphone array and can significantly save hardware costs.

[0151] Figure 3This is a flowchart illustrating a sound field construction method provided in another exemplary embodiment of this application. Figure 3 The example is as follows Figure 1 The examples in the embodiments are similar and will not be repeated here; the focus is on describing the differences. This sound field construction method includes the following.

[0152] 310: When all the basic coefficient elements in the updated basic coefficient complex vector converge, determine that the currently updated basic coefficient complex vector is the basic coefficient complex vector of the sound field, and end the movement of at least one microphone.

[0153] 320: Replace the sound pressure values ​​collected by at least one microphone with the basis coefficient complex vector of the sound field to restore the global sound distribution of the three-dimensional sound field.

[0154] Specifically, the calculated final convergent basis coefficient complex vector is used to replace the sound pressure value collected by at least one microphone to construct a three-dimensional sound field, that is, to restore the global sound distribution of the three-dimensional sound field.

[0155] Therefore, this embodiment of the application, by using dynamically configured microphones to monitor the progress of sound field construction in real time, can stop the calculation promptly once the sound field construction accuracy for a given area is achieved, thus saving computational resources. Furthermore, this embodiment of the application eliminates the need for a microphone array, significantly reducing hardware and real-time computation costs.

[0156] Figure 4 This is a flowchart illustrating an exemplary embodiment of the active noise reduction method provided in this application. Figure 4 The method is executed by the computing device. For example... Figure 4 As shown, the active noise reduction method includes the following components.

[0157] 410: Obtain the complex vector of the basis coefficients.

[0158] In one embodiment, the basis coefficient complex vector is based on the above. Figures 1 to 3 The sound field was obtained using the method described in the embodiments; please refer to the above description for details.

[0159] 420: The basis coefficient complex vector is used as a feedback quantity to adjust the noise reduction parameters of the active down-conversion filter.

[0160] Specifically, the obtained basis coefficient complex vector replaces the signal collected by the error microphone in the three-dimensional sound field as the feedback quantity, and the noise reduction parameters in the active noise canceller can be adjusted through an adaptive algorithm to achieve optimal active noise cancellation. The error microphone is placed in the three-dimensional sound field to collect the sound signal in the noise-reduced three-dimensional sound field, thereby dynamically adjusting the noise level in the three-dimensional sound field.

[0161] It should be noted that the active noise reduction technology provided in this application embodiment is for targeted noise reduction of narrow-frequency signals in a three-dimensional sound field. It overcomes the inability of conventional broadband filtering to reduce narrow-frequency noise, and can minimize the narrow-frequency noise of the entire three-dimensional sound field.

[0162] Therefore, it can be seen that the obtained basis coefficient complex vector in this embodiment is used as a feedback quantity, which enables real-time holographic global narrowband sound field information, rather than just local sound field information collected by the microphone. Thus, this embodiment can also be configured with a corresponding active noise reduction system to output corresponding narrowband noise reduction, thereby achieving global minimization of narrowband noise energy in the entire three-dimensional sound field.

[0163] Figure 5 This is a schematic diagram of the structure of a noise reduction device provided in an exemplary embodiment of this application. For example... Figure 5 As shown, the noise reduction device includes an active noise cancellation device 510 and a housing 520. The active noise cancellation device 510 is used to perform... Figure 4 The active noise cancellation method described in the embodiment. Furthermore, the active noise cancellation device 510 is located inside the housing 520.

[0164] Therefore, the embodiments of this application can also be configured with a corresponding active noise reduction system to output corresponding narrow-band noise reduction, so as to achieve global minimization of narrow-band noise energy in the entire three-dimensional sound field.

[0165] Figure 6 This is a schematic diagram of the structure of a sound field construction device provided in an exemplary embodiment of this application. Figure 6 As shown, the sound field construction device 600 includes: a first determining module 610, a moving module 620, a second determining module 630, and a construction module 640.

[0166] The first determining module 610 is used to determine the initial basis coefficient complex vector of the sound field based on the sound pressure value of at least one first observation point in the sound field acquired by at least one microphone. The moving module 620 is used to move at least one microphone along a preset trajectory to at least one Lth observation point, where L≥2. The second determining module 630 is used to determine the updated basis coefficient complex vector based on the sound pressure values ​​of different observation points among at least one of the at least 1st, 2nd, ..., L-1th, Lth observation points acquired by at least one microphone. And the construction module 640 is used to construct a three-dimensional sound field based on the updated basis coefficient complex vector.

[0167] This application provides a sound field construction device that dynamically collects sound pressure values ​​at multiple observation points within the sound field using a very small number of microphones, replacing the microphone array method. This allows the technical solution of this application to accurately reproduce the entire three-dimensional sound field while further reducing the cost of microphone placement.

[0168] According to one embodiment of this application, the second determining module 630 is used to determine the complex sound pressure corresponding to each Lth observation point based on the sound pressure value of at least one Lth observation point collected by at least one microphone; and to determine the updated basis coefficient complex vector based on the complex sound pressure corresponding to each different observation point from at least one 1st observation point to at least one Lth observation point collected by at least one microphone and the sound field basis vector.

[0169] According to an embodiment of this application, the second determining module 630 is used to determine the complex sound pressure estimation error of at least one Lth observation point based on the sound pressure value of at least one Lth observation point and the initial complex sound pressure; determine the adjusted complex sound pressure based on the complex sound pressure estimation error to obtain the adjusted complex sound pressure estimation error of at least one Lth observation point; when the adjusted complex sound pressure estimation error meets the preset convergence condition, the adjusted complex sound pressure is determined as the complex sound pressure corresponding to each Lth observation point; otherwise, the step of determining the adjusted complex sound pressure based on the complex sound pressure estimation error to obtain the adjusted complex sound pressure estimation error is performed.

[0170] According to one embodiment of this application, the second determining module 630 is used to continuously adjust the real part and the imaginary part of the complex sound pressure based on the complex sound pressure estimation error of at least one Lth observation point, so as to obtain the adjusted complex sound pressure and the adjusted complex sound pressure estimation error.

[0171] According to one embodiment of this application, the second determining module 630 is further configured to determine the basis coefficient complex vector after the LN-1th update based on the complex sound pressure and sound field basis vector corresponding to each different observation point from at least one first observation point to at least one LNth observation point collected by at least one microphone, where 0≤N≤L-2.

[0172] According to one embodiment of this application, at least one microphone includes a single microphone.

[0173] According to one embodiment of this application, the construction module 640 is used to determine that the currently updated basis coefficient complex vector is the basis coefficient complex vector of the sound field when each basis coefficient element in the updated basis coefficient complex vector converges, and to end the movement of at least one microphone; and to replace the sound pressure value collected by at least one microphone with the basis coefficient complex vector of the sound field to restore the global sound distribution of the three-dimensional sound field.

[0174] It should be understood that the specific working process and functions of the first determining module 610, the moving module 620, the second determining module 630, and the constructing module 640 in the above embodiments can be referred to the above description. Figures 1 to 3 The description of the sound field construction method provided in the embodiments will not be repeated here to avoid repetition.

[0175] Figure 7 This is a block diagram of an electronic device for sound field construction provided in an exemplary embodiment of this application.

[0176] Reference Figure 7 The electronic device 700 includes a processing component 710, which further includes one or more processors, and memory resources represented by memory 720 for storing instructions, such as application programs, that can be executed by the processing component 710. The application programs stored in memory 720 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 710 is configured to execute instructions to perform the aforementioned sound field construction method.

[0177] Electronic device 700 may also include a power supply component configured to perform power management of electronic device 700, a wired or wireless network interface configured to connect electronic device 700 to a network, and an input / output (I / O) interface. Electronic device 700 can be operated based on an operating system stored in memory 720, such as Windows Server. TM Mac OSX TM Unix TM Linux TM FreeBSD TM Or similar.

[0178] A non-transitory computer-readable storage medium, when the instructions in the storage medium are executed by the processor of the aforementioned electronic device 700, enables the electronic device 700 to perform a sound field construction method, comprising: determining an initial basis coefficient complex vector of the sound field based on the sound pressure value of at least one first observation point in the sound field acquired by at least one microphone; moving at least one microphone along a preset trajectory to at least one Lth observation point, where L≥2; determining an updated basis coefficient complex vector based on the sound pressure values ​​of different observation points among at least one 1st, 2nd, ..., L-1th, Lth observation points acquired by at least one microphone; and constructing a three-dimensional sound field based on the updated basis coefficient complex vector.

[0179] All of the above-mentioned optional technical solutions can be combined in any way to form optional embodiments of this application, and will not be described in detail here.

[0180] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0181] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0182] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0183] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0184] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0185] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program verification codes, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0186] It should be noted that in the description of this application, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0187] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications or equivalent substitutions made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A sound field construction method, characterized by, The method comprises: determining an initial basis coefficient complex vector of a sound field based on sound pressure values of at least one first observation point in the sound field collected by at least one microphone; moving the at least one microphone to at least one Lth observation point along a preset trajectory simultaneously, wherein L≥2; determining an updated basis coefficient complex vector based on sound pressure values of different observation points among the at least one first, second,..., L-1th and Lth observation points collected by the at least one microphone, comprising: determining a complex sound pressure corresponding to each Lth observation point based on the sound pressure value of the at least one Lth observation point collected by the at least one microphone; determining an updated basis coefficient complex vector based on the complex sound pressure corresponding to each different observation point among the at least one first observation point to the at least one Lth observation point collected by the at least one microphone and a sound field basis vector, comprising: determining an Lth-N-1th updated basis coefficient complex vector based on the complex sound pressure corresponding to each different observation point among the at least one first observation point to the at least one L-Nth observation point collected by the at least one microphone and a sound field basis vector, wherein 0≤N≤L-2; and constructing a three-dimensional sound field based on the updated basis coefficient complex vector.

2. The sound field construction method of claim 1, wherein, The determination of the complex sound pressure corresponding to each Lth observation point based on the sound pressure value of the at least one Lth observation point collected by the at least one microphone comprises: determining a complex sound pressure estimation error of the at least one Lth observation point based on the sound pressure value of the at least one Lth observation point and an initial complex sound pressure; determining an adjusted complex sound pressure based on the complex sound pressure estimation error to obtain an adjusted complex sound pressure estimation error of the at least one Lth observation point; when the adjusted complex sound pressure estimation error meets a preset convergence condition, determining the adjusted complex sound pressure as the complex sound pressure corresponding to each Lth observation point; otherwise, performing the step of determining an adjusted complex sound pressure based on the complex sound pressure estimation error to obtain an adjusted complex sound pressure estimation error.

3. The sound field construction method of claim 2, wherein, The determination of the adjusted complex sound pressure based on the complex sound pressure estimation error to obtain an adjusted complex sound pressure estimation error of the at least one Lth observation point comprises: continuously adjusting a real part of the complex sound pressure and an imaginary part of the complex sound pressure based on the complex sound pressure estimation error of the at least one Lth observation point to obtain the adjusted complex sound pressure and the adjusted complex sound pressure estimation error.

4. The sound field construction method of claim 1, wherein, The at least one microphone comprises a single microphone.

5. The sound field construction method according to any one of claims 1 to 4, characterized in that, The construction of the three-dimensional sound field based on the updated basis coefficient complex vector comprises: when each basis coefficient element in the updated basis coefficient complex vector converges, determining a currently updated basis coefficient complex vector as a basis coefficient complex vector of the sound field, and ending the movement of the at least one microphone; replacing the basis coefficient complex vector of the sound field with the sound pressure value collected by the at least one microphone to restore a global sound distribution of the three-dimensional sound field.

6. An active noise reduction method, characterized by, The method comprises: obtaining the basis coefficient complex vector based on the sound field construction method in any one of claims 1 to 5; using the basis coefficient complex vector as a feedback quantity to adjust a noise reduction parameter of an active noise reduction filter.

7. An apparatus to be noise reduced, characterized by The method comprises: An active noise reduction device for performing the active noise reduction method of claim 6; A housing, wherein the active noise reduction device is located inside the housing.

8. A sound field construction apparatus characterized by comprising: Comprising: A first determining module configured to determine an initial basis coefficient complex vector of a sound field based on sound pressure values of at least one first observation point in the sound field collected by at least one microphone; A moving module configured to move the at least one microphone to at least one L-th observation point simultaneously along a preset track, wherein L≥2; A second determining module configured to determine an updated basis coefficient complex vector based on sound pressure values of different observation points among at least one first, second,..., L-1-th and L-th observation points collected by the at least one microphone, comprising: determining a complex sound pressure corresponding to each L-th observation point based on the sound pressure value of the at least one L-th observation point collected by the at least one microphone; determining an updated basis coefficient complex vector based on the complex sound pressure corresponding to each different observation point among the at least one first observation point to the at least one L-th observation point collected by the at least one microphone and a sound field basis vector, comprising: determining an L-N-1-th updated basis coefficient complex vector based on the complex sound pressure corresponding to each different observation point among the at least one first observation point to the at least one L-N-th observation point collected by the at least one microphone and a sound field basis vector, wherein 0≤N≤L-2; and A constructing module configured to construct a three-dimensional sound field based on the updated basis coefficient complex vector.

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