Optimization method, system and equipment of active noise reduction system, medium and product

Abstract

The invention provides an optimization method and system of an active noise reduction system, equipment, a medium and a product. The optimization method of the active noise reduction system comprises the following steps: acquiring a current feedback path and a preset feedback path of the active noise reduction system; calculating a first deviation between the current feedback path and a preset feedback path; optimizing a controller of the active noise reduction system according to the first deviation, and / or optimizing a preset feedback path according to the first deviation; the optimized controller and / or the optimized preset feedback path are / is used for active noise reduction. The first deviation between the current feedback path and the preset feedback path is calculated, the first deviation reflects the difference between the preset feedback path and the current feedback path, the difference between the preset feedback path and the current feedback path is caused by the fact that different acoustic devices cannot be matched, and the controller and / or the preset feedback path are / is optimized according to the first deviation. The controller and / or the feedback path can adapt to different acoustic devices, and the stability and the noise reduction effect of the active noise reduction system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of active noise reduction, and in particular to an optimization method, system, device, medium and product of an active noise reduction system. Background Art

[0002] On the one hand, before the active noise reduction system is put into use, the coefficients of the active noise reduction system (including at least one of the following coefficients: the coefficient of the primary path, the coefficient of the secondary path, the coefficient of the feedback path, and the coefficient of the controller) are generally calibrated. During the process of the active noise reduction system being put into use, noise reduction is performed based on the pre-calibrated coefficients of the active noise reduction system. Since the coefficient calibration of the active noise reduction system is based on at least one set of microphones and speakers, and the microphones and speakers actually put into use are different from the microphones and speakers used in the calibration process, the sensitivities of different microphones may not be completely matched, and the frequency response curves of different speakers may not be completely matched, which affects the noise reduction effect of the active noise reduction system. On the other hand, during the process of the active noise reduction system being put into use, the microphones and speakers may produce changes in acoustic characteristics due to contamination, resulting in the microphones and speakers being unable to match the microphones and speakers used during calibration, thereby affecting the noise reduction effect of the active noise reduction system.

[0003] Since the acoustic devices actually used in the active noise reduction system (such as microphones and speakers, etc.) do not match the acoustic devices used during calibration, sound waves of certain frequencies in the feedback path are over-amplified or weakened. The controller and feedback path of the active noise reduction system cannot adapt to different acoustic devices. If noise reduction is performed based on the original feedback path and controller, the noise reduction effect cannot be guaranteed, which may cause noise of certain frequencies to not be effectively identified or eliminated, thereby affecting the noise reduction effect. The speaker may even produce howling and abnormal sounds, affecting the user experience. Summary of the invention

[0004] The technical problem to be solved by the present disclosure is to overcome the defect that the controller or feedback path in the active noise reduction system in the prior art cannot adapt to different acoustic devices, resulting in poor noise reduction effect, and to provide an optimization method, system, device, medium and product for an active noise reduction system.

[0005] The present invention solves the above technical problems through the following technical solutions:

[0006] In a first aspect, a method for optimizing an active noise reduction system is provided, the method comprising:

[0007] Obtain the current feedback path and preset feedback path of the active noise reduction system;

[0008] Calculating a first deviation between the current feedback path and the preset feedback path;

[0009] The controller of the active noise reduction system is optimized according to the first deviation, and / or the preset feedback path is optimized according to the first deviation; the optimized controller and / or the optimized preset feedback path are used for active noise reduction.

[0010] Optionally, the step of calculating a first deviation between the current feedback path and the preset feedback path includes:

[0011] Adopting a target optimization method, using the first deviation to compensate the preset feedback path, and determining the first deviation when the target function is minimum or when the target function is less than a target threshold;

[0012] The objective function represents a second deviation between the preset feedback path and the current feedback path after compensation.

[0013] Optionally, the step of determining the first deviation when the objective function is minimized includes:

[0014] Calculating the first deviation when the objective function is minimum by using the least square method;

[0015] Alternatively, an iterative method is used to iteratively adjust the first deviation until the objective function is smaller than a target threshold.

[0016] Optionally, the step of optimizing the preset feedback path according to the first deviation includes:

[0017] The first deviation is multiplied by the preset feedback path.

[0018] Optionally, the controller comprises: a FIR filter, and the step of optimizing the controller according to the first deviation comprises:

[0019] The FIR filter is multiplied by the first deviation.

[0020] Optionally, the preset feedback path is obtained by calibrating a standard microphone and a standard speaker;

[0021] The sensitivity deviation of the standard microphone is less than a sensitivity threshold, and the frequency response curve deviation of the standard speaker is less than a frequency response threshold.

[0022] In a second aspect, an optimization system for an active noise reduction system is provided, the optimization system for the active noise reduction system comprising:

[0023] An acquisition module, used to acquire a current feedback path and a preset feedback path of an active noise reduction system;

[0024] A calculation module, used for calculating a first deviation between the current feedback path and the preset feedback path;

[0025] An optimization module is used to optimize the controller of the active noise reduction system according to the first deviation, and / or optimize the current feedback path according to the first deviation; the optimized controller and / or the optimized preset feedback path are used for active noise reduction.

[0026] Optionally, the calculation module is specifically configured to adopt a target optimization method, use the first deviation to compensate the preset feedback path, and determine the first deviation when the target function is minimum or when the target function is less than a target threshold;

[0027] The objective function represents a second deviation between the preset feedback path and the current feedback path after compensation.

[0028] Optionally, the calculation module includes:

[0029] A first calculation unit, used for calculating a first deviation when the objective function is minimum by using a least square method;

[0030] Or, the second computing unit is used to adopt an iterative method to iteratively adjust the first deviation until the objective function is less than a target threshold.

[0031] Optionally, the optimization module includes:

[0032] A feedback path optimization unit is used to multiply the first deviation by the preset feedback path.

[0033] Optionally, the controller includes: a FIR filter, and the optimization module includes:

[0034] A controller optimization unit is used to multiply the FIR filter by the first deviation.

[0035] Optionally, the preset feedback path is obtained by calibrating a standard microphone and a standard speaker;

[0036] The sensitivity deviation of the standard microphone is less than a sensitivity threshold, and the frequency response curve deviation of the standard speaker is less than a frequency response threshold.

[0037] In a third aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and used to run on the processor, wherein the processor implements any of the above-mentioned methods for optimizing the active noise reduction system when executing the computer program.

[0038] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that when the computer program is executed by a processor, the optimization method of the active noise reduction system described in any one of the above items is implemented.

[0039] In a fifth aspect, a computer program product is provided, including a computer program, wherein when the computer program is executed by a processor, the optimization method of the active noise reduction system described in any one of the above items is implemented.

[0040] On the basis of being in accordance with the common sense in the art, the above-mentioned preferred conditions can be arbitrarily combined to obtain the preferred embodiments of the present disclosure.

[0041] The positive progressive effect of the present disclosure lies in that the present disclosure calculates a first deviation between a current feedback path and a preset feedback path, the first deviation reflects the difference between the preset feedback path and the current feedback path, and the difference between the preset feedback path and the current feedback path is caused by the inability to match different acoustic components. The controller and / or the preset feedback path are optimized according to the first deviation, so that the controller and / or the feedback path can adapt to different acoustic components, thereby improving the stability of the active noise reduction system and enhancing the noise reduction effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A flowchart of an optimization method for an active noise reduction system provided by an exemplary embodiment of the present disclosure;

[0043] Figure 2 A schematic diagram of a module of an optimization system for an active noise reduction system provided by an exemplary embodiment of the present disclosure;

[0044] Figure 3 The present invention provides a schematic structural diagram of an electronic device according to an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0045] The present disclosure is further described below by way of examples, but the present disclosure is not limited to the scope of the examples.

[0046] Prefixes such as "first" and "second" are used in the embodiments of the present disclosure only to distinguish different description objects, and have no limiting effect on the position, order, priority, quantity or content of the described objects. The use of prefixes such as ordinal numbers to distinguish description objects in the embodiments of the present disclosure does not constitute a limitation on the described objects. For the statement of the described objects, please refer to the description in the context of the claims or embodiments, and no unnecessary limitation should be constituted due to the use of such prefixes. In addition, in the description of the present embodiment, unless otherwise specified, the meaning of "plurality" is two or more.

[0047] Example 1

[0048] In the field of active noise reduction, controller optimization for small devices (such as headphones) is relatively simple, but controller optimization for large devices (such as range hoods) is more complicated. Specifically, controller optimization refers to enabling the controller to adapt to the acoustic devices in the active noise reduction system and to be applicable to the current feedback path, thereby ensuring a high noise reduction effect.

[0049] It is usually time-consuming to optimize the controller of each active noise reduction system separately. Since controller optimization requires the installation of additional devices, such as error microphones, in order to optimize the controller, the equipment needs to be installed and disassembled, which takes a long time and greatly affects the efficiency of the production line.

[0050] To optimize the controller of each active noise reduction system uniformly, it is necessary to build a database based on a large amount of sample data and train a neural network to match acoustic devices and controllers. However, for large-scale equipment such as range hoods where active noise reduction has not yet been maturely applied, it is impossible to collect enough sample data. Furthermore, even if enough sample data is collected for training the neural network, since the optimized controller can only be used for the same model, the amortized cost of the controller optimization process is high when the shipment volume is not large.

[0051] The acoustic devices in the active noise reduction system include but are not limited to microphones and speakers. The number of microphones and speakers can be set according to actual conditions and is not particularly limited here.

[0052] The input of the controller of the active noise reduction system is the reference microphone signal for removing the acoustic feedback effect, and the output is the speaker control signal. Specifically, the historical speaker control signal is dot-multiplied by the feedback path to obtain the acoustic feedback amount, the acquired reference microphone signal is subtracted from the acoustic feedback amount to obtain the reference microphone signal for removing the acoustic feedback effect, and the reference microphone signal for removing the acoustic feedback effect is input into the controller to obtain the speaker control signal. If the active noise reduction system includes multiple speakers, it is necessary to respectively dot-multiply the multiple speaker control signals by the feedback path, calculate multiple acoustic feedback amounts, superimpose the multiple acoustic feedback amounts, subtract the superimposed acoustic feedback amount from the acquired reference microphone signal, and obtain the reference microphone signal for removing the acoustic feedback effect. The above-mentioned specific process can be found in the relevant technical description, which will not be repeated here.

[0053] Figure 1 A flowchart of an optimization method for an active noise reduction system provided by an exemplary embodiment of the present disclosure, the optimization method for the active noise reduction system comprising:

[0054] Step S101: Acquire a current feedback path and a preset feedback path of an active noise reduction system.

[0055] The current feedback path is the feedback path at the current moment obtained by using the microphone and the speaker. The preset feedback path can be pre-stored in the storage device of the active noise reduction system, or it can be the preset feedback path after the last optimization. The specific selection can be based on actual conditions and is not particularly limited here.

[0056] After the speaker control signal is sent, the reference microphone collects white noise, and the white noise emitted by the speaker and the white noise collected by the reference microphone are calculated using the LMS algorithm or the Wiener filter algorithm to obtain the current feedback path. The current feedback path and the preset feedback path can be expressed as a transfer function in the frequency domain or as a time domain model (such as an FIR filter), which can be selected according to actual conditions and is not particularly limited here.

[0057] Step S102: Calculate a first deviation between the current feedback path and the preset feedback path.

[0058] The first deviation indicates the difference between the current feedback path and the preset feedback path. Since different microphones have different sensitivity deviations and different speakers have different frequency response curve deviations, there are differences in the feedback paths from different microphones to speakers, that is, there are differences between the current feedback path and the preset feedback path.

[0059] The larger the first deviation is, the larger the difference between the current feedback path and the preset feedback path is. Conversely, the smaller the first deviation is, the smaller the difference between the current feedback path and the preset feedback path is.

[0060] Step S103: optimizing a controller of the active noise reduction system according to the first deviation, and / or optimizing a preset feedback path according to the first deviation; the optimized controller and / or the optimized preset feedback path are used for active noise reduction.

[0061] It can be understood that since the first deviation is calculated based on the current feedback path and the preset feedback path, it can reflect the differences in feedback paths caused by different acoustic devices, that is, the differences between the current feedback path and the preset feedback path. Therefore, the controller optimized according to the first deviation and / or the optimized preset feedback path can adapt to the currently used acoustic devices and adapt to environmental changes.

[0062] In this embodiment, by calculating the first deviation between the current feedback path and the preset feedback path, the first deviation reflects the difference between the preset feedback path and the current feedback path, and the difference between the preset feedback path and the current feedback path is caused by the inability to match different acoustic components. The controller and / or the preset feedback path are optimized according to the first deviation, so that the controller and / or the feedback path can adapt to different acoustic components, thereby improving the stability of the active noise reduction system and enhancing the noise reduction effect.

[0063] In one embodiment, step S103 may only optimize the controller, and optimize the controller of the active noise reduction system according to the first deviation, and the optimized controller is used for active noise reduction.

[0064] In this embodiment, the controller is optimized according to the first deviation, so that the controller can be adjusted according to environmental changes, thereby being able to adapt to different acoustic components, improving the stability of the active noise reduction system, and enhancing the noise reduction effect.

[0065] In one embodiment, step S103 may optimize only the preset feedback path, optimize the preset feedback path according to the first deviation, and the optimized preset feedback path is used for active noise reduction.

[0066] In this embodiment, the preset feedback path is optimized according to the first deviation, so that the preset feedback path can be adjusted according to environmental changes, thereby being able to adapt to different acoustic components, improving the stability of the active noise reduction system, and enhancing the noise reduction effect.

[0067] In a specific implementation, the controller and the preset feedback path can be optimized at the same time, that is, the controller of the active noise reduction system is optimized according to the first deviation, and the preset feedback path can be optimized according to the first deviation, thereby improving the noise reduction effect of the active noise reduction system. The optimization object can be the controller or the preset feedback path, which can be selected according to the actual situation and is not particularly limited here.

[0068] In one embodiment, the preset feedback path is pre-stored in a storage device of the active noise reduction system.

[0069] Before the active noise reduction system is put into use, a standard microphone and a standard speaker are generally used to calibrate a preset feedback path, and the preset feedback path is stored and recorded. The specific process can be found in the relevant technical description of obtaining the feedback path, which will not be repeated here. It should be noted that after the active noise reduction system is put into use, the layout of the microphone and speaker in actual use should be the same as the layout of the standard microphone and the standard speaker. At this time, the preset feedback path obtained by calibration can be used for the subsequent calculation of the first deviation.

[0070] In this embodiment, the preset feedback path is pre-stored in a storage device of the active noise reduction system and can be directly obtained when needed, thus saving computing resources.

[0071] In one embodiment, the preset feedback path is the preset feedback path optimized last time.

[0072] Specifically, each time the preset feedback path is optimized according to the first deviation, an optimized preset feedback path is obtained. When the preset feedback path is optimized according to the first deviation next time, the preset feedback path optimized last time is used as the original feedback path, and the subsequent optimization of the preset feedback path is determined based on the first deviation between the preset feedback path optimized last time and the current feedback path.

[0073] In this embodiment, the preset feedback path is the preset feedback path after the last optimization, and the first deviation can accurately reflect the degree of change between the preset feedback path after the last optimization and the current feedback path, thereby facilitating accurate optimization of the preset feedback path, thereby improving the noise reduction effect of the active noise reduction system.

[0074] In one embodiment, step S102 includes: using a target optimization method, using a first deviation to compensate a preset feedback path, and determining the first deviation when the target function is minimum or when the target function is less than a target threshold.

[0075] The target threshold can be set according to actual conditions and is not specifically limited here.

[0076] It should be noted that each controller corresponds to a microphone and a speaker, and each feedback path also corresponds to a microphone and a speaker. Assuming that there are M microphones and N speakers in the active noise reduction system, the number of controllers is M*N, the number of feedback paths is also M*N, and each feedback path corresponds to a controller.

[0077] In this embodiment, the target optimization method is used to calculate the first deviation when the target function is minimum, so as to optimize the controller and / or preset feedback path according to the first deviation, thereby being able to adapt to different acoustic devices and improve the noise reduction effect.

[0078] In one embodiment, the step of determining the first deviation when the objective function is minimized includes: calculating the first deviation when the objective function is minimized using a least squares method.

[0079] The target optimization method in this embodiment includes the least squares method.

[0080] For the i-th microphone and j-th speaker, the objective function is expressed as: , where FB2 i,j Indicates the preset feedback path, FB1 i,j represents the current feedback path, i represents the number of the microphone, j represents the number of the speaker, k i,j Indicates the first deviation.

[0081] The objective function represents the second deviation between the preset feedback path after compensation and the current feedback path. Specifically, the objective function represents k i,j FB2i,j With FB1 i,j The larger the value of the objective function, the greater the k i,j FB2 i,j With FB1 i,j The greater the approximate difference, the smaller the value of the objective function is, indicating that k i,j FB2 i,j With FB1 i,j The smaller the approximate difference.

[0082] If the controllers of multiple active noise reduction systems need to be optimized, the numbers of the acoustic components at the same position on the multiple active noise reduction systems need to be consistent. The acoustic components of the active noise reduction system include but are not limited to microphones and speakers. The specific number and position can be set according to actual conditions and are not specifically limited here.

[0083] Specifically, the least squares method is used to calculate the first deviation k when the objective function is minimum. i,j If the preset feedback path and the current feedback path are expressed as two FIR filters of the same order, FB1 i,j and FB2 i,j It is a vector composed of the coefficients of two FIR filters of the same order. The first deviation can be solved by the dot product of the vectors.

[0084] First deviation k i,j The calculation formula is:

[0085]

[0086] where • represents the dot product of vectors, is the current feedback path FB1 i,j With standard feedback path FB2 i,j The dot product of For standard feedback path FB2 i,j The dot product of .

[0087] After obtaining the current feedback path and the preset feedback path of the active noise reduction system, the current feedback path and the preset feedback path are substituted into the first deviation k i,j The calculation formula of the first deviation can be used to calculate the first deviation. The derivation process of the calculation formula of the first deviation is further explained below.

[0088] Assume FB1 i,j and FB2 i,j Both are n-dimensional vectors, FB1 i,j and FB2 i,j They can be expressed as:

[0089] FB1 i,j =[FB11, FB12, ..., FB1n ]

[0090] FB2 i,j =[FB21, FB22, ..., FB2 n ]

[0091] Calculate the objective function The first deviation k at the minimum i,j , can be calculated by The first deviation k at the minimum i,j In which, the superscript 2 represents the square, and the subscript 2 represents the Euclidean norm. yes The square of the Euclidean norm of The square of the second-order modulus of . The specific calculation can be calculated by The first deviation k at the minimum i,j , you can also calculate The first deviation k at the minimum i,j , which can be selected according to actual conditions and is not particularly limited here.

[0092] Can be expanded to:

[0093]

[0094] To minimize , for k i,j Take the derivative and set it to zero:

[0095]

[0096] After expansion, we get:

[0097]

[0098] Rearrange the above equation and solve for k i,j :

[0099]

[0100]

[0101] It can be obtained that the gain k i,j The calculation formula is:

[0102]

[0103] In this embodiment, the least squares method is used to calculate the first deviation when the objective function is minimum. The least squares method is relatively simple and easy to implement, with low computational cost. The controller can be adjusted according to the first deviation, so that different acoustic devices can be adapted to improve the noise reduction effect.

[0104] In one embodiment, the step of determining the first deviation when the objective function is minimum includes: using an iterative method to iteratively adjust the first deviation until the objective function is less than a target threshold.

[0105] The target optimization method in this embodiment includes an iterative method. The purpose of setting the target threshold is to stop the iteration when the target function is minimum and obtain the first deviation when the target function is minimum. The target threshold can be set according to the actual situation and is not specifically limited here. The target function is less than the target threshold, indicating that k i,j FB2 i,j With FB1 i,j The approximate difference is small. On the contrary, the objective function is greater than or equal to the target threshold, indicating that k i,j FB2 i,j With FB1 i,j The approximate difference is large.

[0106] In this embodiment, an iterative method is used to iteratively adjust the first deviation until the objective function is less than the target threshold. The iterative method has high flexibility and can be designed to be updated online or in real time. It is suitable for systems that require fast response and has a fast local convergence speed, thereby achieving accurate adjustment of the first deviation and improving the stability of the active noise reduction system.

[0107] In one embodiment, the step of optimizing the preset feedback path according to the first deviation includes: multiplying the first deviation by the preset feedback path.

[0108] In this embodiment, the preset feedback path is optimized according to the first deviation so that the preset feedback path is closer to the current feedback path. The preset feedback path can be adjusted according to environmental changes so that it can adapt to different acoustic devices. Active noise reduction is performed based on the optimized preset feedback path to improve the noise reduction effect.

[0109] In one embodiment, the optimized preset feedback path is the same as the current feedback path.

[0110] In this embodiment, the preset feedback path is replaced with the current feedback path to obtain an optimized preset feedback path. The optimized preset feedback path is the same as the current feedback path, can be adapted to the acoustic device currently used, and the optimized preset feedback path is used for active noise reduction, thereby improving the noise reduction effect of the active noise reduction system.

[0111] In one embodiment, the controller includes: a FIR filter, and step S103 includes: multiplying the FIR filter by the first deviation.

[0112] Specifically, each coefficient of the FIR filter is multiplied by the first deviation k through numerical multiplication.

[0113] In this embodiment, the FIR filter is multiplied by the first deviation, so that the controller can be adjusted according to the change of the feedback path, thereby being able to adapt to different acoustic devices, improving the stability of the active noise reduction system, and enhancing the noise reduction effect.

[0114] In one embodiment, the preset feedback path is obtained by calibrating a standard microphone and a standard speaker.

[0115] Among them, the sensitivity deviation of the standard microphone is less than the sensitivity threshold, and the frequency response curve deviation of the standard speaker is less than the frequency response threshold.

[0116] Since the amplitude-frequency characteristics and phase-frequency characteristics of acoustic devices are coupled, the amplitude-frequency characteristics specifically refer to the sensitivity of the microphone and the frequency response curve of the speaker. If the amplitude-frequency characteristics deviate too much, that is, the sensitivity deviation and the frequency response curve deviation are too large, it will cause the phase-frequency characteristics of the acoustic device to deviate too much. The phase-frequency characteristics deviation cannot be compensated by the algorithm, affecting the noise reduction effect and stability of the active noise reduction system.

[0117] The sensitivity threshold is usually set to ±1.5dBA, and the frequency response threshold is usually set to ±1dBA. The above values ​​are only used for illustration and can be set according to actual conditions and are not particularly limited here.

[0118] The number of standard microphones and standard speakers can be selected according to actual conditions and is not particularly limited here. Before the active noise reduction system is put into use, a standard microphone and a standard speaker are generally used to calibrate a preset feedback path. The specific process can be found in the relevant technical description of obtaining the feedback path, which will not be repeated here. It should be noted that after the active noise reduction system is put into use, the layout of the microphones and speakers used in actual applications should be the same as the layout of the standard microphones and standard speakers. At this point, the preset feedback path obtained by calibration can be used for the subsequent calculation of the first deviation.

[0119] In this embodiment, the preset feedback path is a feedback path between a standard microphone and a standard speaker. A standard microphone whose sensitivity deviation is less than a sensitivity threshold and a standard speaker whose frequency response curve deviation is less than a frequency response threshold are selected, so that the preset feedback path obtained based on the standard microphone and the standard speaker is more accurate, and the preset feedback path to be optimized is more referenceable, thereby improving the accuracy of feedback path optimization and controller optimization.

[0120] In one embodiment, the amplitude-frequency characteristics of the acoustic device may be measured and the measurement data may be analyzed. The standard microphone and the standard speaker are both acoustic devices with amplitude-frequency characteristics close to the corresponding average values.

[0121] Acoustic devices include but are not limited to microphones and speakers. Specifically, for speakers, the average frequency response within a preset frequency band or the frequency response at a preset frequency can be used as a judgment index to compare with the corresponding average value. For microphones, the average sensitivity within a preset frequency band or the sensitivity at a preset frequency can be used as a judgment index to compare with the corresponding average value.

[0122] Typically, the preset frequency band can be set to 100-1000 Hz, and the preset frequency can be set to 1 kHz. The above values ​​are only used for illustration and can be selected according to actual conditions, and are not particularly limited here.

[0123] In this embodiment, a standard microphone and a standard speaker whose amplitude-frequency characteristics are close to the corresponding average values ​​are selected, so that the preset feedback path obtained based on the standard microphone and the standard speaker is more accurate, and the preset feedback path to be optimized is more referenceable, thereby improving the accuracy of feedback path optimization and controller optimization.

[0124] Example 2

[0125] Corresponding to the aforementioned optimization method embodiment of the active noise reduction system, the present disclosure also provides an embodiment of an optimization system of the active noise reduction system.

[0126] Figure 2 A schematic diagram of a module of an optimization system of an active noise reduction system provided by an exemplary embodiment of the present disclosure, wherein the optimization system of the active noise reduction system comprises:

[0127] The acquisition module 21 is used to acquire the current feedback path and the preset feedback path of the active noise reduction system.

[0128] The speaker emits white noise, the reference microphone collects white noise, and the white noise emitted by the speaker and the white noise collected by the reference microphone are calculated using the LMS algorithm or the Wiener filter algorithm to obtain the current feedback path. The current feedback path and the preset feedback path can be expressed as a transfer function in the frequency domain or as a time domain model (such as an FIR filter), which can be selected according to actual conditions and is not particularly limited here.

[0129] The calculation module 22 is used to calculate a first deviation between the current feedback path and the preset feedback path.

[0130] The first deviation indicates the difference between the current feedback path and the preset feedback path. Since different microphones have different sensitivity deviations and different speakers have different frequency response curve deviations, there are differences in the feedback paths from different microphones to speakers, that is, there are differences between the current feedback path and the preset feedback path.

[0131] The larger the difference between the first deviation and the standard deviation, the larger the difference between the current feedback path and the preset feedback path. Conversely, the smaller the difference between the first deviation and the standard deviation, the smaller the difference between the current feedback path and the preset feedback path, that is, the current feedback path is closest to the preset feedback path. Assuming that the standard deviation is 1, when the first deviation is equal to the standard deviation, the current feedback path is the same as the preset feedback path. The above values ​​are for example only, and the standard deviation can be selected according to actual conditions and is not particularly limited here.

[0132] The optimization module 23 is used to optimize the controller of the active noise reduction system according to the first deviation, and / or optimize the current feedback path according to the first deviation; the optimized controller and / or the optimized preset feedback path are used for active noise reduction.

[0133] It can be understood that since the first deviation is calculated based on the current feedback path and the preset feedback path, it can reflect the differences in feedback paths caused by different acoustic devices, that is, the differences between the current feedback path and the preset feedback path. Therefore, the controller optimized according to the first deviation and / or the optimized preset feedback path can adapt to the currently used acoustic devices and adapt to environmental changes.

[0134] In this embodiment, by calculating the first deviation between the current feedback path and the preset feedback path, the controller and / or the preset feedback path is optimized according to the first deviation, so that the controller and / or the preset feedback path can be adjusted according to environmental changes, thereby being able to adapt to different acoustic devices, thereby improving the stability of the active noise reduction system and enhancing the noise reduction effect.

[0135] In one embodiment, the calculation module 22 is specifically used to adopt a target optimization method, use the first deviation to compensate the preset feedback path, and determine the first deviation when the target function is minimum or when the target function is less than the target threshold;

[0136] The objective function represents a second deviation between the compensated preset feedback path and the current feedback path.

[0137] In this embodiment, the target optimization method is used to calculate the first deviation when the target function is minimum, so as to optimize the controller and / or preset feedback path according to the first deviation, thereby being able to adapt to different acoustic devices and improve the noise reduction effect.

[0138] In one embodiment, the calculation module 22 includes:

[0139] A first calculation unit, used for calculating a first deviation when the objective function is minimum by using a least square method;

[0140] Or, the second computing unit is used to adopt an iterative method to iteratively adjust the first deviation until the objective function is less than a target threshold.

[0141] In this embodiment, the least squares method is relatively simple and easy to implement, has low computational cost, and can adjust the controller according to the first deviation. The iterative method can be designed to be updated online or in real time, which is suitable for systems that require fast response and has fast local convergence speed, thereby achieving accurate adjustment of the first deviation and improving the stability of the active noise reduction system.

[0142] In one embodiment, the optimization module 23 includes:

[0143] The feedback path optimization unit is used to multiply the first deviation by a preset feedback path.

[0144] In this embodiment, the preset feedback path is optimized according to the first deviation so that the preset feedback path is closer to the current feedback path. The preset feedback path can be adjusted according to environmental changes so that it can adapt to different acoustic devices. Active noise reduction is performed based on the optimized preset feedback path to improve the noise reduction effect.

[0145] In one embodiment, the controller includes: a FIR filter, and the optimization module 23 includes:

[0146] A controller optimization unit is configured to multiply the FIR filter with the first deviation.

[0147] In this embodiment, the FIR filter is multiplied by the first deviation, so that the controller can be adjusted according to the change of the feedback path, thereby being able to adapt to different acoustic devices, improving the stability of the active noise reduction system, and enhancing the noise reduction effect.

[0148] As for the system embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The system embodiment described above is only illustrative, wherein the units described as separate components may or may not be physically separated, and the components as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the disclosed solution.

[0149] Example 3

[0150] Figure 3 This is a structural schematic diagram of an electronic device shown in an exemplary embodiment of the present disclosure, the electronic device includes a memory, a processor, and a computer program stored in the memory and used to run on the processor, and when the processor executes the computer program, the optimization method of the active noise reduction system described in any of the above embodiments is implemented. Figure 3 The electronic device 30 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0151] like Figure 3 As shown, the electronic device 30 may be in the form of a general-purpose computing device, for example, it may be a server device. The components of the electronic device 30 may include, but are not limited to: at least one processor 31, at least one memory 32, and a bus 33 connecting different system components (including the memory 32 and the processor 31).

[0152] The bus 33 includes a data bus, an address bus, and a control bus.

[0153] The memory 32 may include a volatile memory, such as a random access memory (RAM) 321 and / or a cache memory 322 , and may further include a read only memory (ROM) 323 .

[0154] The memory 32 may also include a program tool 325 (or utility) having a set (at least one) of program modules 324, such program modules 324 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0155] The processor 31 executes various functional applications and data processing by running the computer program stored in the memory 32, such as the optimization method of the active noise reduction system provided in any of the above embodiments.

[0156] The electronic device 30 may also communicate with one or more external devices 34 (e.g., keyboards, pointing devices, etc.). Such communication may be performed via an input / output (I / O) interface 35. Furthermore, the electronic device 30 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 36. As shown, the network adapter 36 communicates with other modules of the electronic device 30 via a bus 33. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 30, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.

[0157] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided into multiple units / modules to be embodied.

[0158] Example 4

[0159] The embodiments of the present disclosure further provide a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the optimization method of the active noise reduction system provided in any of the above embodiments is implemented.

[0160] The readable storage medium may include but is not limited to: a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device or any suitable combination of the above.

[0161] Example 5

[0162] The embodiment of the present disclosure further provides a computer program product, including a computer program, wherein when the computer program is executed by a processor, the optimization method of the active noise reduction system described in any one of the above items is implemented.

[0163] Among them, the program code for executing the computer program product of the present disclosure can be written in any combination of one or more programming languages, and the program code can be executed completely on the user device, partially on the user device, as an independent software package, partially on the user device and partially on a remote device, or completely on the remote device.

[0164] Although the specific embodiments of the present disclosure are described above, those skilled in the art should understand that this is only an example, and the protection scope of the present disclosure is defined by the appended claims. Those skilled in the art may make various changes or modifications to these embodiments without departing from the principles and essence of the present disclosure, but these changes and modifications all fall within the protection scope of the present disclosure.

Claims

1. An optimization method for an active noise reduction system, characterized in that: The optimization method of the active noise reduction system comprises: Obtain the current feedback path and preset feedback path of the active noise reduction system; Calculating a first deviation between the current feedback path and the preset feedback path; The controller of the active noise reduction system is optimized according to the first deviation, and / or the preset feedback path is optimized according to the first deviation; the optimized controller and / or the optimized preset feedback path are used for active noise reduction.

2. The optimization method of the active noise reduction system according to claim 1, characterized in that: The step of calculating a first deviation between the current feedback path and the preset feedback path comprises: Adopting a target optimization method, using the first deviation to compensate the preset feedback path, and determining the first deviation when the target function is minimum or when the target function is less than a target threshold; The objective function represents a second deviation between the preset feedback path and the current feedback path after compensation.

3. The optimization method of the active noise reduction system according to claim 2, characterized in that: The step of determining the first deviation when the objective function is minimized comprises: Calculating the first deviation when the objective function is minimum by using the least square method; Alternatively, an iterative method is used to iteratively adjust the first deviation until the objective function is smaller than the target threshold.

4. The optimization method of the active noise reduction system according to claim 1, characterized in that: The step of optimizing the preset feedback path according to the first deviation comprises: The first deviation is multiplied by the preset feedback path.

5. The optimization method of the active noise reduction system according to claim 1, characterized in that: The controller includes: a FIR filter, and the step of optimizing the controller according to the first deviation includes: The FIR filter is multiplied by the first deviation.

6. The optimization method of the active noise reduction system according to any one of claims 1 to 5, characterized in that: The preset feedback path is obtained by calibrating a standard microphone and a standard speaker; The sensitivity deviation of the standard microphone is less than a sensitivity threshold, and the frequency response curve deviation of the standard speaker is less than a frequency response threshold.

7. An optimization system for an active noise reduction system, characterized in that: The optimization system of the active noise reduction system comprises: An acquisition module, used to acquire a current feedback path and a preset feedback path of an active noise reduction system; A calculation module, used for calculating a first deviation between the current feedback path and the preset feedback path; An optimization module is used to optimize the controller of the active noise reduction system according to the first deviation, and / or optimize the current feedback path according to the first deviation; the optimized controller and / or the optimized preset feedback path are used for active noise reduction.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and used to run on the processor, characterized in that: When the processor executes the computer program, the optimization method of the active noise reduction system according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the optimization method of the active noise reduction system according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the optimization method of the active noise reduction system according to any one of claims 1 to 6 is implemented.

Images