Active noise reduction, step optimization method, device and system of active noise reduction parameters

Abstract

The application discloses an active noise reduction parameter optimization method, characterized in that the method is applied to a household appliance, and the household appliance is provided with a reference microphone and an error microphone; the optimization method comprises the following steps: in the process of active noise reduction, a first noise signal collected by the reference microphone and a second noise signal collected by the error microphone are acquired; a first optimization step is determined according to the second noise signal; and the first optimization step is optimized according to the first noise signal and the second noise signal, so that a second optimization step of the active noise reduction parameter is obtained. The application can avoid the instability of active noise reduction caused by the excessively large optimization step in the optimization process of the active noise reduction parameter, can avoid the failure to track the noise change in real time caused by the excessively small optimization step, and can affect the optimal control effect, so that the active noise reduction effect on the household appliance can be greatly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of household appliance control, and in particular to an active noise reduction, a step optimization method, device and system for active noise reduction parameters. BACKGROUND

[0002] Active noise reduction is a noise control technology that can effectively reduce the noise generated by household appliances such as range hoods and dishwashers. Taking a range hood as an example, in order to improve the oil smoke absorption effect, the air volume / speed of the range hood is continuously increased, which brings the problem of increasing noise, affecting user experience and physical and mental health. Currently, the method of arranging sound-absorbing materials inside the air duct of the range hood is usually used to reduce noise, but this method has poor control effect on low-frequency noise; another method is to use active noise reduction method, that is, to arrange microphones and loudspeakers in the air duct of the range hood for identifying and canceling noise. However, the current active noise reduction control effect is not good, and there is a loss of stability phenomenon in the active noise reduction process, which is not good for user experience. SUMMARY

[0003] The technical problem to be solved by the present application is to overcome the above-mentioned defects in the prior art, and to provide an active noise reduction, a step optimization method, device and system for active noise reduction parameters.

[0004] In a first aspect, a step optimization method for active noise reduction parameters is provided, applied to a household appliance, and the household appliance is deployed with a reference microphone and an error microphone; the optimization method comprises:

[0005] In the process of active noise reduction, a first noise signal collected by the reference microphone and a second noise signal collected by the error microphone are obtained;

[0006] A first optimization step is determined according to the second noise signal;

[0007] The first optimization step is optimized according to the first noise signal and the second noise signal to obtain a second optimization step; wherein the second optimization step is used to optimize the active noise reduction parameter.

[0008] Optionally, the first optimization step is determined according to the second noise signal, comprising:

[0009] The first optimization step is calculated according to the second noise signal and based on the hyperbolic sine formula.

[0010] Optionally, the first optimization step is optimized according to the first noise signal and the second noise signal, comprising:

[0011] The first energy of the first noise signal and the second energy of the second noise signal are determined;

[0012] determine a step optimization coefficient according to the first energy and the second energy;

[0013] optimize the first optimization step according to the step optimization coefficient.

[0014] Optionally, the step of determining a step optimization coefficient according to the first energy and the second energy comprises:

[0015] determine a distance influence factor when the gains of the reference microphone and the error microphone are different; wherein the distance influence factor is negatively correlated with a first distance between a noise source of the household appliance and the error microphone; the first distance is smaller than a second distance between the noise source and the reference microphone;

[0016] determine a deviation of the first energy and the second energy;

[0017] adjust the deviation according to the distance influence factor, and determine the step optimization coefficient according to an adjustment result.

[0018] Optionally, the step of determining a first energy of the first noise signal and a second energy of the second noise signal comprises:

[0019] a two-stage sliding window algorithm is used to calculate the first energy of the first noise signal and the second energy of the second noise signal.

[0020] Optionally, the step of optimizing the first optimization step according to the step optimization coefficient comprises:

[0021] multiply the step optimization coefficient by the first optimization step;

[0022] or, multiply the step optimization coefficient by a comparison result; wherein the comparison result is the minimum value of the first optimization step and a preset optimization step.

[0023] In a second aspect, a method for optimizing an active noise reduction parameter is provided, comprising:

[0024] determining a second optimization step according to the step optimization method for the active noise reduction parameter of the first aspect;

[0025] optimizing the active noise reduction parameter according to the second optimization step;

[0026] using the optimized active noise reduction parameter to perform active noise reduction on the household appliance.

[0027] In a third aspect, a step optimization device for an active noise reduction parameter is provided, which is applied to a household appliance, and the household appliance is provided with a reference microphone and an error microphone; the optimization device comprises:

[0028] The acquisition module is configured to acquire a first noise signal collected by a reference microphone and a second noise signal collected by an error microphone during the process of the loudspeaker;

[0029] The determination module is configured to determine a first optimization step length according to the second noise signal;

[0030] The step length optimization module is configured to optimize the first optimization step length according to the first noise signal and the second noise signal to obtain a second optimization step length, wherein the second optimization step length is used to optimize the active noise reduction parameter.

[0031] In a fourth aspect, an active noise reduction system is provided, which comprises a noise reduction device and the step length optimization device of the active noise reduction parameter according to the third aspect.

[0032] The step length optimization device is configured to determine a second optimization step length.

[0033] The noise reduction device is configured to optimize the active noise reduction parameter according to the second optimization step length, and to perform active noise reduction on the household appliance by using the optimized active noise reduction parameter.

[0034] In a fifth aspect, a household appliance is provided, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method according to any one of the preceding aspects when executing the computer program.

[0035] On the basis of common knowledge in the art, the above-mentioned preferred conditions can be combined arbitrarily to obtain preferred examples of the present application.

[0036] The positive progress effect of the present application is that the present application adaptively determines the optimization step length (second optimization step length) of the adaptive noise reduction parameter according to the noise signals collected by the reference microphone and the error microphone, and optimizes the active noise reduction parameter according to the optimization step length, which can avoid instability of the active noise reduction caused by an excessively large optimization step length in the optimization process of the active noise reduction parameter, and can avoid the failure to track noise changes in real time and affect the optimal control effect caused by an excessively small optimization step length, thereby greatly improving the active noise reduction effect on the household appliance, i.e., the adaptive step length optimization algorithm of the present application can dynamically optimize the active noise reduction parameter in real time, so that the noise volume presents a trend of gradually decreasing from large to small and gradually stabilizing, or the noise volume can be timely controlled to rapidly decrease after a sudden increase at a certain time node / working condition. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 A flowchart of a step length optimization method of an active noise reduction parameter is provided for an example embodiment of the present application.

[0038] Figure 2 For Figure 1The flow chart of step 103;

[0039] Figure 3 The flow chart of an active noise reduction method provided for an example embodiment of the present application;

[0040] Figure 4 The module schematic diagram of a step length optimization device for an active noise reduction parameter provided for an example embodiment of the present application. DETAILED DESCRIPTION

[0041] The present application will be further described below by way of examples, but the present application is not limited to the examples.

[0042] At present, in order to improve the active noise reduction effect of a household appliance, an adaptive active noise reduction algorithm is generally adopted, that is, the active noise reduction parameter is optimized in the active noise reduction process of the household appliance, so that the active noise reduction parameter is adapted to the use environment or scene of the household appliance. In the optimization process of the active noise reduction parameter, the optimization step length of the active noise reduction parameter will affect the active noise reduction effect. If the optimization step length is too large, the active noise reduction based on the active noise reduction parameter obtained based on the too large optimization step length will appear unstable; if the optimization step length is too small, the optimization time length of the active noise reduction parameter becomes long, and the active noise reduction parameter cannot track the noise change of the household appliance in time, and it is difficult to achieve the optimal control effect.

[0043] Based on the above problems, an embodiment of the present application provides a step length optimization method for an active noise reduction parameter, which is applied to a household appliance. The household appliance can be, for example, an extractor hood containing a noise source such as a fan, or a dishwasher containing a noise source such as a motor and a transmission. Figure 1 The step length optimization method includes the following steps:

[0044] Step 101, in the process of active noise reduction, a first noise signal collected by a reference microphone and a second noise signal collected by an error microphone are acquired.

[0045] The household appliance is provided with a reference microphone and an error microphone. In addition to the reference microphone and the error microphone, the household appliance is also provided with a loudspeaker. Taking the household appliance as an example of an extractor hood, the loudspeaker, the reference microphone and the error microphone are generally arranged in the air duct of the household appliance. It should be noted that the number of loudspeakers can be one or multiple; similarly, the number of reference microphones and error microphones can be one or multiple. The number of loudspeakers, reference microphones and error microphones is not particularly limited in the embodiment of the present application.

[0046] In the process of active noise reduction, a speaker is started to play a noise reduction signal to cancel the noise signal generated by the noise source of the household appliance. However, in practice, the noise signal cannot be completely eliminated, and there is a residual signal, which may contain two parts of signals: one part is the residual noise signal after cancellation, and the other part is the residual noise reduction signal. The first noise signal collected by the reference microphone is the residual signal at the position where the reference microphone is arranged. The second noise signal collected by the error microphone is the residual signal at the position where the error microphone is arranged. It can be understood that the energy of the first noise signal collected by the reference microphone and the energy of the second noise signal collected by the error microphone can evaluate the effect of active noise reduction.

[0047] The reference microphone is used to provide input for the active noise reduction system, that is, the reference microphone is the input of the controller of the active noise reduction system; and the error microphone is used to evaluate the noise reduction effect of the active noise reduction system, the smaller the noise signal collected by the error microphone, the better the control effect, and the active noise reduction parameters are optimized based on this. The active noise reduction parameters are the coefficients or filter coefficients of the controller. The active noise reduction system includes digital-to-analog conversion module, signal amplification circuit, analog-to-digital conversion module, filter circuit, speaker, reference microphone and error microphone and other components.

[0048] Step 102, determining a first optimization step length according to the second noise signal.

[0049] In one embodiment, the first optimization step length is calculated according to the second noise signal and based on the hyperbolic sine formula.

[0050] In an implementation, the calculation formula of the first optimization step length is as follows:

[0051]

[0052] Wherein, step1 represents the first optimization step length; e k represents the amplitude of the second noise signal collected by the kth error microphone; a and b are coefficients. a and b can be determined according to actual needs, for example, a = 2; b = 0.1.

[0053] The second noise signal e k is substituted into formula (1) to obtain the first optimization step length step1.

[0054] In the embodiment of the present application, the hyperbolic sine formula is used to calculate the first optimization step, and the hyperbolic sine formula can determine the first optimization step based on the error of the noise reduction system; when the error of the noise reduction system is large, the first optimization step calculated is large, so as to improve the efficiency of the optimization of the active noise reduction parameters, and make the active noise reduction parameters converge as soon as possible in the process of iterative optimization; when the error of the active noise reduction system is small, the stability of the active noise reduction system is more concerned, and the first optimization step calculated is small, so as to ensure the stability of the active noise reduction system. That is, the active noise reduction parameters can be dynamically optimized in real time, so that the noise volume presents a trend of gradually decreasing from large to small and gradually stabilizing as a whole, or the noise volume can be timely controlled after a sudden increase at a certain time node / working condition, so that the noise volume quickly decreases.

[0055] Step 103, optimizing the first optimization step according to the first noise signal and the second noise signal to obtain a second optimization step for optimizing the active noise reduction parameters.

[0056] In one embodiment, referring to Figure 2 , step 103 includes:

[0057] Step 103-1, determining a first energy of the first noise signal and a second energy of the second noise signal.

[0058] An implementation of calculating the first energy and the second energy is given as follows:

[0059] Supposing that the sampling time window of the two-stage sliding window algorithm is within 0.2-1 seconds (the time window is usually taken), and the subscript of the corresponding discrete sequence is 1-n, the energy formula is calculated as follows:

[0060]

[0061] wherein, E ek represents the energy of the second noise signal e k collected by the kth error microphone; e k (i) represents the amplitude of the second noise signal collected at the ith sampling moment; E fik represents the energy of the first noise signal f ik collected by the kth reference microphone; f ik (i) represents the amplitude of the second noise signal collected at the ith sampling moment; and n represents the sampling number. It should be noted that the first noise signal in formula (2) can be the noise signal collected by the reference microphone directly, or the noise signal after the reference microphone is preprocessed.

[0062] An implementation of preprocessing the reference microphone is introduced as follows:

[0063] Supposing that the noise signal collected by the reference microphone directly is x iBy using a feedback channel model to eliminate the influence of the loudspeaker's sound on the reference signal, r is obtained. i Then calculate r i The signal f after passing through the secondary channel from the j-th speaker to the k-th error microphone ijk The signal f ijk It was identified as the second noise signal.

[0064] The formula for the feedback channel model can be, but is not limited to, expressed as follows:

[0065]

[0066] Where J represents the total number of loudspeakers; y j F represents the noise reduction signal output by the j-th speaker; ji This represents the coefficients of the feedback channel filter from the j-th loudspeaker to the i-th reference microphone, which can be, but are not limited to, pass vector representation.

[0067] The formula for secondary channel filtering can be, but is not limited to, expressed as follows:

[0068] f ijk =r i *S jk (4)

[0069] Among them, S jk The coefficients represent the secondary channel filters of the j-th loudspeaker to the k-th error microphone, and these coefficients can be, but are not limited to, pass vector representations.

[0070] It should be noted that the asterisk (*) in the above formula represents convolution.

[0071] The following is another way to calculate the first and second energies:

[0072]

[0073] In one embodiment, a two-stage sliding window algorithm is used to calculate the first energy of the first noise signal and the second energy of the second noise signal, respectively.

[0074] Considering that directly calculating the energy within the sliding window requires a large storage space to store historical energy, this embodiment uses a two-level sliding window algorithm for approximation, such as letting n = m1 * m2. This can significantly reduce space usage without sacrificing real-time performance.

[0075] The following is another way to calculate the first and second energies:

[0076]

[0077] Wherein, m1 represents the window width of the first sliding window; and m2 represents the window width of the second sliding window.

[0078] The purpose of the first sliding window (upper layer) is to quickly calculate the sum of squares in the target time interval, and the second sliding window (bottom layer) is to compress the stored data. The buffer is filled with data at each sampling time, and the interval sum of squares is calculated each time the buffer is full. Then the buffer is emptied, and the result is updated to the first sliding window, and the interval corresponding to the first sliding window is adjusted. Thus, the first aspect can avoid large fluctuations caused by too short sampling intervals, the second aspect can reduce the memory occupation of data storage, and the third aspect can ensure a certain update real-time performance.

[0079] It should be noted that, in the subsequent processing, E ek and E fik will be divided, and a dimensionless number will be obtained. Therefore, the right side of formula (6) is not divided by n.

[0080] In one embodiment, if the noise level fluctuation is not large in the application scenario, a filtering strategy can be used for approximation to further reduce the storage occupation. At this time, there is:

[0081]

[0082] Step 103-2, determining the step size optimization coefficient according to the first energy and the second energy.

[0083] In one embodiment, when the gains of the reference microphone and the error microphone are the same, the deviation of the first energy and the second energy is determined as the step size optimization coefficient; wherein the deviation can be the ratio of the first energy and the second energy, or the deviation is the square root operation result of the ratio.

[0084] In one embodiment, when the gains of the reference microphone and the error microphone are different, the deviation of the first energy and the second energy needs to be adjusted according to the distance influence factor, so that the gains of the reference microphone and the error microphone are the same. Specifically: determining the distance influence factor, determining the deviation of the first energy and the second energy, adjusting the deviation according to the distance influence factor, and determining the step size optimization coefficient according to the adjustment result.

[0085] Wherein, the distance influence factor is negatively correlated with the first distance between the noise source of the household appliance and the error microphone; and the first distance is less than the second distance between the noise source and the reference microphone.

[0086] The calculation formula of the step size optimization coefficient is as follows:

[0087]

[0088] Wherein, ε represents the step size optimization coefficient; represents the deviation of the first energy and the second energy; β represents a distance influence factor.

[0089] In one embodiment, taking the deployment of the reference microphone and the error microphone in the air duct of the range hood as an example, one way of providing β is provided, and the formula is as follows:

[0090]

[0091] wherein, l is the distance from the noise source to the reference microphone, R is the radius of the air duct of the range hood, and γ is the degree of the noise source position deviating from the axis of the air duct (0 represents the position at the axis, and 1 represents the position at the edge of the air duct).

[0092] It should be noted that the distance between the noise source and the error microphone is usually large, and generally satisfies the following relationship: L > (1 + γ)R, and l < (1 + γ)R. L represents the distance between the noise source and the error microphone.

[0093] β is used to correct the amplitude attenuation in the process of changing the main low-frequency noise component from a spherical wave to a plane wave, that is, to correct the different signal amplitudes caused by the different distances between the reference microphone, the error microphone and the loudspeaker. The distance between the noise source and the error microphone is usually large, and when the noise generated by the noise source propagates to the error microphone, it is initially a spherical wave, the intensity decreases with the increase of the distance, and then it becomes a plane wave, the intensity no longer changes. The noise signal intensity at the reference microphone attenuates according to the spherical wave; the attenuation coefficient of the error microphone depends on the maximum distance of the spherical wave propagation, and the subsequent plane wave is not attenuated, so it is necessary to correct the amplitude attenuation in the process of changing the main low-frequency noise component from a spherical wave to a plane wave. Since the distance influence factor represents the amplitude ratio, the formula (8) needs to be operated by square root to convert the energy ratio into the amplitude ratio.

[0094] It should be noted that if the distance influence factor is calculated in other ways, the deviation of the first energy and the second energy can also be calculated in other ways.

[0095] The purpose of formula (8) is to avoid too large optimization step, the distance influence factor β is greater than 1, and the ratio of the second energy to the first energy represents the ratio of the energy of the error microphone to the energy of the filtered reference microphone, which is converted into amplitude dimension by square root. Generally speaking, this value is close to 1 in the early stage, and the energy of the error microphone becomes smaller later, and the value will be less than 1.

[0096] In one embodiment, the size of the step optimization coefficient is also limited, and the calculation formula of the step optimization coefficient is as follows:

[0097]

[0098] The product of β and represents the signal amplitude relationship after correction based on the distance influence factor.

[0099] The purpose of formula (10) is to avoid the optimization step being too large, and thus avoid the active noise reduction system from being unstable.

[0100] Step 103-3: optimizing the first optimization step according to the step optimization coefficient.

[0101] In one embodiment, the step optimization coefficient is multiplied by the first optimization step to optimize the first optimization step. The optimization formula is expressed as follows:

[0102] μ = step1 * ε; (11)

[0103] Wherein, μ is the optimized step.

[0104] In one embodiment, the step optimization coefficient is multiplied by the comparison result to optimize the first optimization step; wherein the comparison result is the minimum value between the first optimization step and the preset optimization step. The optimization formula is expressed as follows:

[0105] μ = min(MaxStep, step1) * ε; (12)

[0106] Wherein, MaxStep represents the preset optimization step.

[0107] MaxStep can be an empirical value; by limiting the maximum value of the optimization step through MaxStep, the active noise reduction system can be prevented from being unstable.

[0108] The optimized active noise reduction parameter is used for active noise reduction. The optimization algorithm of the active noise reduction parameter can be but is not limited to the FxLMS algorithm.

[0109] The formula for optimizing the active noise reduction parameter according to the optimization step is expressed as follows:

[0110] w = w' + 2μ * e k *f ijk ;

[0111] Wherein, w' represents the initial active noise reduction parameter or the active noise reduction parameter obtained by the last round of iteration optimization.

[0112] In the embodiment of the present application, the adaptive determination of the optimization step length of the adaptive noise reduction parameter according to the noise signals collected by the reference microphone and the error microphone realizes dynamic learning of the optimization step length of the adaptive noise reduction parameter. The optimization of the adaptive noise reduction parameter according to the optimization step length can avoid instability of the adaptive noise reduction caused by an excessively large optimization step length in the optimization process of the adaptive noise reduction parameter, and can avoid the failure to track noise changes in real time caused by an excessively small optimization step length, thereby affecting the optimal control effect, so that the effect of the active noise reduction of the household appliance can be greatly improved. Moreover, the optimization of the adaptive noise reduction parameter based on the optimization step length can effectively accelerate the convergence of the noise parameter algorithm, timely track the noise changes of the household appliance, quickly obtain the optimal adaptive noise reduction parameter, and further ensure the stability of the noise reduction effect. The embodiment of the present application also has the advantage of consuming less computing power.

[0113] The embodiment of the present application also provides an active noise reduction method, which is described below with reference to Figure 3 The active noise reduction method comprises the following steps.

[0114] Step 301: determining a second optimization step length.

[0115] The second optimization step length in step 301 is determined according to the step length optimization method of the adaptive noise reduction parameter provided in any of the above embodiments.

[0116] Step 302: optimizing the adaptive noise reduction parameter according to the second optimization step length.

[0117] Step 303: performing active noise reduction on the household appliance by using the optimized adaptive noise reduction parameter.

[0118] The specific implementation modes of steps 302 and 303 are described in the related art, and will not be described here.

[0119] In the embodiment of the present application, the adaptive step length optimization algorithm of the present application can be used to dynamically optimize the adaptive noise reduction parameter in real time, so that the noise volume presents a trend of gradually decreasing from large to small and gradually stabilizing, or the noise volume can be timely controlled after a sudden increase at a certain time node / working condition, so that the noise volume can be quickly reduced.

[0120] Figure 4 A module schematic diagram of an optimization device of an adaptive noise reduction parameter is provided for an exemplary embodiment of the present application. The optimization device is applied to a household appliance, and the household appliance is deployed with a reference microphone and an error microphone. The optimization device comprises:

[0121] An acquisition module 41 is configured to acquire a first noise signal collected by the reference microphone and a second noise signal collected by the error microphone in the process of active noise reduction.

[0122] A determination module 42 is configured to determine a first optimization step length according to the second noise signal.

[0123] The step size optimization module 43 is configured to optimize the first optimization step size according to the first noise signal and the second noise signal to obtain a second optimization step size, wherein the second optimization step size is used to optimize the active noise reduction parameter.

[0124] Optionally, the determining module 42 is specifically configured to:

[0125] The first optimization step size is calculated according to the second noise signal and based on a hyperbolic sine formula.

[0126] The step size optimization module 43 comprises:

[0127] The first determining unit is configured to determine a first energy of the first noise signal and a second energy of the second noise signal.

[0128] The second determining unit is configured to determine a step size optimization coefficient according to the first energy and the second energy.

[0129] The optimization unit is configured to optimize the first optimization step size according to the step size optimization coefficient.

[0130] The second determining unit is specifically configured to:

[0131] When the gains of the reference microphone and the error microphone are different, a distance influence factor is determined, wherein the distance influence factor is negatively correlated with a first distance between a noise source of the household appliance and the error microphone, and the first distance is smaller than a second distance between the noise source and the reference microphone.

[0132] A deviation of the first energy and the second energy is determined.

[0133] The deviation is adjusted according to the distance influence factor, and a step size optimization coefficient is determined according to an adjustment result.

[0134] Optionally, the first determining unit is specifically configured to:

[0135] A two-stage sliding window algorithm is used to calculate the first energy of the first noise signal and the second energy of the second noise signal respectively.

[0136] Optionally, the step size optimization module 43 is specifically configured to:

[0137] The step size optimization coefficient is multiplied by the first optimization step size to optimize the first optimization step size.

[0138] Or, the step size optimization coefficient is multiplied by a comparison result to optimize the first optimization step size, wherein the comparison result is a minimum value of the first optimization step size and a preset optimization step size.

[0139] The embodiment of the present application further provides an active noise reduction system, comprising the noise reduction device and the step optimization device of the active noise reduction parameter provided by any of the above embodiments.

[0140] The step optimization device is used for determining a second optimization step;

[0141] The noise reduction device is used for optimizing the active noise reduction parameter according to the second optimization step, and performing active noise reduction on the household appliance by using the optimized active noise reduction parameter.

[0142] The embodiment of the present application further provides a household appliance, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method provided by any of the above embodiments when executing the computer program.

[0143] Although the specific embodiments of the present application are described above, those skilled in the art should understand that this is only an example, and the protection scope of the present application is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principles and essence of the present application, and these changes and modifications all fall within the protection scope of the present application.

Claims

1. A method for optimizing the step size of active noise reduction parameters, characterized in that, Applied to a household appliance, the household appliance being equipped with a reference microphone and an error microphone; the optimization method includes: During active noise reduction, a first noise signal acquired by a reference microphone and a second noise signal acquired by an error microphone are obtained. The first optimization step size is determined based on the second noise signal; The first optimization step size is optimized based on the first noise signal and the second noise signal to obtain a second optimization step size; wherein, the second optimization step size is used to optimize the active noise reduction parameters; Determining the first optimization step size based on the second noise signal includes: The first optimization step size is calculated based on the second noise signal and the hyperbolic sine formula. Optimizing the first optimization step size based on the first noise signal and the second noise signal includes: Determine the first energy of the first noise signal and the second energy of the second noise signal; The step size optimization coefficient is determined based on the first energy and the second energy; The first optimization step size is optimized based on the step size optimization coefficient.

2. The method for optimizing the step size of active noise reduction parameters according to claim 1, characterized in that, The step size optimization coefficient determined based on the first energy and the second energy includes: When the gain of the reference microphone and the error microphone are different, a distance influence factor is determined; wherein, the distance influence factor is negatively correlated with a first distance between the noise source of the household appliance and the error microphone; the first distance is less than a second distance between the noise source and the reference microphone; Determine the deviation between the first energy and the second energy; The deviation is adjusted according to the distance influence factor, and the step size optimization coefficient is determined based on the adjustment result.

3. The method for optimizing the step size of active noise reduction parameters according to claim 1, characterized in that, Determining the first energy of the first noise signal and the second energy of the second noise signal includes: A two-stage sliding window algorithm is used to calculate the first energy of the first noise signal and the second energy of the second noise signal, respectively.

4. The method for optimizing the step size of active noise reduction parameters according to claim 1, characterized in that, The first optimization step size is optimized according to the step size optimization coefficient, including: Multiply the step size optimization coefficient by the first optimization step size; Alternatively, the step size optimization coefficient can be multiplied by the comparison result; wherein the comparison result is the minimum value between the first optimization step size and the preset optimization step size.

5. A method for optimizing active noise reduction parameters, characterized in that, include: The second optimization step size is determined by the step size optimization method for active noise reduction parameters according to any one of claims 1-4; The active noise reduction parameters are optimized according to the second optimization step size; Optimized active noise cancellation parameters are used to actively reduce noise in home appliances.

6. A step size optimization device for active noise reduction parameters, characterized in that, The method is applied to household appliances, which are equipped with a reference microphone and an error microphone; the optimization device is used to implement the step size optimization method for active noise reduction parameters according to any one of claims 1-4; the optimization device includes: The acquisition module is used to acquire a first noise signal collected by a reference microphone and a second noise signal collected by an error microphone during the loudspeaker operation. The determination module is used to determine the first optimization step size based on the second noise signal; The step size optimization module is used to optimize the first optimization step size based on the first noise signal and the second noise signal to obtain a second optimization step size; wherein the second optimization step size is used to optimize the active noise reduction parameters.

7. An active noise reduction system, characterized in that, include: The noise reduction device and the step size optimization device for the active noise reduction parameters as described in claim 6; The step size optimization device is used to determine the second optimized step size; The noise reduction device is used to optimize the active noise reduction parameters according to the second optimization step size, and to perform active noise reduction on household appliances using the optimized active noise reduction parameters.

8. A household appliance, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method according to any one of claims 1 to 5.

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