Audio sensing signal optimization method and apparatus based on improved least mean method

Abstract

The application discloses an audio sensing signal optimization method and device based on an improved least mean method, which comprises the following steps: calculating an error loss function of a filter based on a least mean square error and a square term least mean square error of an audio actual signal and an audio sensing signal filtered through the filter by circulation, so as to increase the proportion of outliers in the audio sensing signal in the total error loss function; iteratively adjusting filter coefficients according to the error loss function until the error loss function converges; and processing the audio sensing signal by using the filter after the error loss function converges, so that the outliers in the audio sensing signal are better estimated, the noise of the audio sensing signal after filtering is reduced, and the accuracy and effectiveness of fiber sensing are improved.

Description

Technical Field

[0001] This application relates to the field of fiber optic sensing, specifically to an audio sensing signal optimization method and apparatus based on an improved minimum mean method. Background Technology

[0002] Fiber optic sensing technology can be used to monitor fiber optic jitter, damage, tension, and deformation, as well as external environmental factors such as temperature, humidity, sound, and pressure. It is widely used in numerous applications including earthquake detection, oil and gas pipeline monitoring, engineering structure monitoring, engineering control, and engineering environmental monitoring. However, fiber optic sensing involves a conversion process from other physical quantities to optical signals and then to electrical signals. Furthermore, due to the presence of other non-detection signals in the fiber optic transmission channel, design errors in the coherent receiver, and system errors in the digital signal processing algorithm, other noise signals will be detected in the sensing signal received by the coherent receiver. This makes the design of optimized sensing algorithms at the receiver end of fiber optic communication a crucial task.

[0003] Compared to other signals, the distortion of sensing systems that use audio signals is more severe because the change in sound amplitude is more important than the magnitude of the amplitude. Sudden changes in the audio signal caused by sensing noise will cause serious distortion. Therefore, simulating the influence of the sensing channel on the signal and filtering it plays an important role in audio fiber optic sensing.

[0004] Currently, research on fiber optic audio sensing is relatively limited, mainly because sound changes rapidly, thus placing stringent requirements on sensing technology. During fiber optic transmission, an audio signal, even a song signal, can be played outside the bare fiber optic cable. The audio signal can be extracted at the receiving end, and the melody and lyrics can be clearly heard. However, the complexity of the sensing channel can lead to severe distortion and noise in the final received result. Therefore, it is necessary to process the signal, simulate the actual channel, and perform channel filtering.

[0005] The Least Mean Square (LMS) method is a classic adaptive filtering algorithm that iteratively approaches the desired signal at the receiver. Traditional LMS algorithms optimize by gradually reconstructing the signal, but this method tends to minimize the overall mean square error, potentially ignoring outliers. However, these outliers are crucial to the audio signal, and their trends relative to adjacent signals are very important. Using the traditional LMS method, the filtering error at these outliers is larger than at other points, leading to misprediction. Consequently, the trends of these outliers relative to preceding and following data are altered in the recovered audio signal, resulting in inaccurate data reconstruction and the introduction of new noise.

[0006] Therefore, overcoming the impact of outlier estimation errors in the sensor audio signal on sensor audio recovery is a technical problem that needs to be solved. Summary of the Invention

[0007] This application provides an audio sensing signal optimization method and apparatus based on an improved minimum mean method, which can solve the technical problem in the prior art that the adaptive filtering algorithm using the minimum mean method will cause estimation errors of outliers in the audio sensing signal, making it impossible to accurately recover the audio sensing signal and leading to new noise.

[0008] In a first aspect, embodiments of this application provide an audio sensing signal optimization method based on an improved minimum mean method, the audio sensing signal optimization method based on the improved minimum mean method comprising:

[0009] The loop calculates the filter's error loss function based on the minimum mean square error and the minimum mean square error of the squared term of the actual audio signal and the filtered audio sensor signal, in order to increase the proportion of outliers in the audio sensor signal in the total error loss function.

[0010] The filter coefficients are iteratively adjusted according to the error loss function until the error loss function converges.

[0011] The audio sensor signal is processed using a filter that has converged using the error loss function.

[0012] In conjunction with the first aspect, in one implementation, the error loss function of the filter is calculated based on the minimum mean square error and the minimum mean square error of the squared term of the actual audio signal and the audio sensing signal after filtering, including:

[0013] The audio sensing signal to be processed is obtained by bidirectional prediction of the audio sensing signal based on the filter length:

[0014]

[0015] Where n is the number of iterations and For the first The audio sensor signal to be processed in the next iteration, where L is the length of the filter;

[0016] The audio sensor signal to be processed is input into the filter to obtain the filtered audio sensor signal:

[0017]

[0018] W(n)=[w1(n) w2(n) w3(n) … w L (n)] T

[0019] in, y(n) is the nth The filtered sensing signal at the nth iteration, W(n) is the... Filter coefficients during secondary filtering;

[0020] The error loss function of the filter is calculated based on the minimum mean square error and the minimum mean square error of the squared term of the actual audio signal and the filtered audio signal:

[0021] J(n)=ε*E((y(n)-d(n)) 2 )+(1-ε)*E(((y(n) 2 -d(n) 2 ) 2 ))

[0022] Where J(n) is the nth The error loss function of the next iteration, where ε is the coefficient of the minimum mean square error and 1-ε is the coefficient of the minimum mean square error of the squared term.

[0023] In some embodiments, the filter coefficients are iteratively adjusted according to the error loss function until the error loss function converges, and the method further includes:

[0024] The iteration gradient and iteration step size of the filter are determined based on the error loss function;

[0025] The filter coefficients are iteratively adjusted based on the iterative gradient and iterative step size until the error loss function converges.

[0026] In some embodiments, determining the iterative gradient of the filter based on the error loss function includes:

[0027] The iterative gradient is obtained by taking the partial derivative of the error loss function with respect to the initial filter coefficients:

[0028]

[0029] in, Let be the gradient of the nth iteration.

[0030] In some embodiments, determining the iteration step size of the filter based on the error loss function includes:

[0031] If the error loss function after this iteration is greater than or equal to a times the minimum iteration error or the number of iterations is less than or equal to b times the maximum number of iterations, then the preset initial step size is used as the iteration step size.

[0032] If the error loss function after this iteration is less than a times the minimum iteration error or the number of iterations is greater than b times the maximum number of iterations, then the initial step size is multiplied by a preset shrinkage constant to obtain the iteration step size.

[0033] In this case, the value of a is greater than 1, and the value of b is less than 1.

[0034] In some embodiments, the filter coefficients are iteratively adjusted based on the iterative gradient and iterative step size until the error loss function converges, including:

[0035] Subtracting the product of the iteration gradient and the iteration step size from the current filter coefficients yields the iterative filter coefficients:

[0036]

[0037] Where W(n+1) are the filter coefficients of the (n+1)th iteration, W(n) are the filter coefficients of the nth iteration, and μ is the iteration step size. For the iterative gradient.

[0038] In some embodiments, the filter coefficients are iteratively adjusted according to the error loss function until the error loss function converges, and the method further includes:

[0039] The instantaneous gradient of the objective function is used as the average gradient, and the filter coefficients are iteratively adjusted in conjunction with the iteration step size.

[0040]

[0041] Where W(n+1) are the filter coefficients of the (n+1)th iteration, W(n) are the filter coefficients of the nth iteration, and μ is the iteration step size. For the iterative gradient.

[0042] In some embodiments, before iteratively calculating the filter's error loss function based on the minimum mean square error and the minimum mean square error of the squared term of the actual audio signal and the filtered audio sensing signal, the method further includes:

[0043] The actual audio signal is sent to the fiber optic sensing monitoring section, and the polarization change of the received signal is tracked at the receiving end to obtain the audio sensing signal.

[0044] In some embodiments, after the audio sensing signal is extracted at the receiving end, the method further includes:

[0045] Adjust the magnitude of the audio sensing signal to make it consistent with the magnitude of the actual audio signal.

[0046] Secondly, embodiments of this application provide an audio sensing signal optimization device based on an improved minimum average method, the audio sensing signal optimization device based on the improved minimum average method comprising:

[0047] The calculation module is used to iteratively calculate the error loss function of the filter based on the minimum mean square error and the minimum mean square error of the squared term of the actual audio signal and the audio sensing signal after filtering, so as to increase the proportion of outliers in the audio sensing signal in the total error loss function.

[0048] The adjustment module is used to iteratively adjust the filter coefficients according to the error loss function until the error loss function converges.

[0049] The processing module is used to process the audio sensor signal using a filter that has converged after applying an error loss function.

[0050] This application provides an audio sensing signal optimization method and apparatus based on an improved minimum mean square method. The method calculates the filter's error loss function iteratively based on the minimum mean square error and the minimum mean square error of the squared term of the actual audio signal and the filtered audio sensing signal, thereby increasing the proportion of outliers in the total error loss function. The filter coefficients are iteratively adjusted according to the error loss function until it converges. The audio sensing signal is then processed using the filter after the error loss function convergence. This achieves better estimation of outliers in the audio sensing signal, reduces noise in the filtered audio sensing signal, and improves the accuracy and effectiveness of fiber optic sensing. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating an embodiment of the audio sensing signal optimization method based on the improved minimum mean method of this application.

[0052] Figure 2 This is a flowchart illustrating another embodiment of the audio sensing signal optimization method based on the improved minimum mean method of this application;

[0053] Figure 3 This is a functional block diagram of an embodiment of the audio sensing signal optimization device based on the improved minimum mean method of this application. Detailed Implementation

[0054] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0055] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0056] In a first aspect, embodiments of this application provide an audio sensing signal optimization method based on an improved minimum mean method.

[0057] In one embodiment, reference is made to Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the audio sensing signal optimization method based on the improved minimum mean method of this application. Figure 1 As shown, the overall idea of ​​the audio sensing signal optimization method based on the improved minimum mean method includes:

[0058] Step S101: Calculate the error loss function of the filter based on the minimum mean square error and the minimum mean square error of the squared term of the actual audio signal and the audio sensing signal after filtering, so as to increase the proportion of outliers in the total error loss function of the audio sensing signal.

[0059] Step S102: Iteratively adjust the filter coefficients according to the error loss function until the error loss function converges.

[0060] Step S103: Process the audio sensor signal using the filter that has converged after the error loss function.

[0061] It is worth noting that in this embodiment, the linear combination of the mean square error and the mean square error of the squared term to establish the error loss function can increase the proportion of outliers in the total error loss function, thereby better ensuring the estimation of outliers, reducing the noise in the recovered audio signal, and improving the accuracy and reliability of fiber optic sensing.

[0062] In one embodiment, before calculating the error loss function of the filter based on the minimum mean square error and the minimum mean square error of the squared term of the actual audio signal and the filtered audio sensing signal, the method further includes: transmitting the actual audio signal in the optical fiber sensing monitoring section and tracking the polarization change of the received signal at the receiving end to obtain the audio sensing signal.

[0063] Specifically, such as Figure 2 As shown, audio is played in the fiber optic sensing monitoring section to transmit the actual audio signal. Then, the polarization fast tracking algorithm is used to track the change of optical polarization state in the fiber to recover the audio sensing signal from the transmission matrix. Finally, this actual audio signal and the audio sensing signal are used to estimate the channel and design the filter.

[0064] It is worth noting that a conventional pair of orthogonally polarized light signals can recover four sensing signals. However, in a channel using polarization multiplexing, there are more polarization states, the transmission matrix is ​​more complex, and more sensing signals are recovered. This embodiment utilizes the correlation between these sensing signals and the actual signal to select the sensing signal with the strongest correlation for sensing channel estimation.

[0065] In this embodiment, the audio sensing signal selected according to the maximum correlation coefficient criterion is:

[0066] S = [s(1)s(2)s(3)…s(M)] T

[0067] The actual audio signal is:

[0068] D = [d(1)d(2)d(3)…d(M)] T

[0069] Where M is the length of the audio sensing signal.

[0070] It is worth noting that the magnitudes of the audio sensing signal calculated using the polarization fast tracking algorithm and the actual audio signal may not be consistent. This can lead to large errors in the initial iteration, making adaptive filtering convergence more difficult. Therefore, before performing adaptive filtering, it is necessary to adjust the magnitude of the audio sensing signal to eliminate the influence of signal magnitude and ensure that the magnitudes of the audio sensing signal and the actual audio signal are consistent. Adjusting the magnitude of the audio sensing signal includes:

[0071]

[0072] Where (i = 1, 2, 3…M), the adjusted audio sensing signal is:

[0073]

[0074] in, M is the adjusted audio sensing signal, where M is the length of the audio sensing signal.

[0075] Furthermore, the error loss function of the filter is calculated based on the minimum mean square error and the minimum mean square error of the squared term of the actual audio signal and the audio sensing signal after filtering, including:

[0076] The audio sensing signal to be processed is obtained by bidirectional prediction of the audio sensing signal based on the filter length:

[0077]

[0078] Where n is the number of iterations and For the first The audio sensor signal to be processed in the next iteration, where L is the length of the filter.

[0079] The audio sensor signal to be processed is input into the filter to obtain the filtered audio sensor signal:

[0080]

[0081] W(n)=[w1(n) w2(n) w3(n) … w L (n)] T

[0082] in, y(n) is the nth The filtered sensing signal at the nth iteration, W(n) is the... The filter coefficients during the second filtering.

[0083] The error loss function of the filter is calculated based on the minimum mean square error and the minimum mean square error of the squared term of the actual audio signal and the filtered audio signal:

[0084]

[0085] Where J(n) is the nth The error loss function of the next iteration, where ε is the coefficient of the minimum mean square error and 1-ε is the coefficient of the minimum mean square error of the squared term.

[0086] It is worth noting that in this embodiment, the error loss function is designed by using an adaptive algorithm that combines the minimum mean square error and the minimum mean square error of the squared term with the adjusted audio sensing signal and the actual audio signal. Furthermore, a bidirectional predictive adaptive filter design is adopted, which can increase the proportion of outliers in the audio sensing signal in the total error loss function, thereby better preserving the changing trend of the audio signal.

[0087] Preferably, the step of iteratively adjusting the filter coefficients according to the error loss function until the error loss function converges further includes: determining the iterative gradient and iterative step size of the filter according to the error loss function; and iteratively adjusting the filter coefficients based on the iterative gradient and iterative step size until the error loss function converges.

[0088] Specifically, determining the iterative gradient of the filter based on the error loss function includes: obtaining the iterative gradient by taking the partial derivative of the error loss function with respect to the initial filter coefficients.

[0089]

[0090] in, Let ε be the gradient of the nth iteration, ε be the coefficient of the minimum mean square error, and 1-ε be the coefficient of the minimum mean square error of the squared term. A larger ε indicates a greater proportion of the minimum mean square error, while a smaller ε indicates a greater proportion of the squared term mean square error. 0 < ε < 1. ε can be chosen according to specific needs.

[0091] Furthermore, determining the filter's iteration step size based on the error loss function includes:

[0092] If the error loss function after this iteration is greater than or equal to a times the minimum iteration error or the number of iterations is less than or equal to b times the maximum number of iterations, then the preset initial step size is used as the iteration step size; if the error loss function after this iteration is less than a times the minimum iteration error or the number of iterations is greater than b times the maximum number of iterations, then the initial step size is multiplied by a preset shrinkage constant to obtain the iteration step size.

[0093] As an example, in this embodiment, the value of 'a' is 2, and the value of 'b' is... The iteration step size is then calculated as follows:

[0094]

[0095] Where μ is the iteration step size, μ0 is the initial step size, and Gen is the number of iterations. max J is the maximum number of iterations. min To minimize the iteration error, m is a shrinkage constant, and its value can be chosen as needed. It's worth noting that when setting μ0, its value cannot be too large; it must meet the constraints. Choosing an excessively large value will lead to non-convergence. This can be addressed by... The value is determined by the size of the variable.

[0096] Explained, in this embodiment, adaptive iteration is performed using a variable step size μ. First, a large iteration step size is used to iterate to the vicinity of the filtering optimum, and then a small iteration step size is used to further optimize the data. This method can find the vicinity of the possible optimum as quickly as possible in the early stage of iteration, and can optimize the data more accurately in the later stage of iteration, while also avoiding getting trapped in local optima.

[0097] Furthermore, the filter coefficients are iteratively adjusted based on the iterative gradient and iterative step size until the error loss function converges, including:

[0098] Subtracting the product of the iteration gradient and the iteration step size from the current filter coefficients yields the iterative filter coefficients:

[0099]

[0100] Where W(n+1) are the filter coefficients of the (n+1)th iteration, W(n) are the filter coefficients of the nth iteration, and μ is the iteration step size. For the iterative gradient.

[0101] In another embodiment, iteratively adjusting the filter coefficients according to the error loss function until the error loss function converges further includes:

[0102] The instantaneous gradient of the objective function is used as the average gradient, and the filter coefficients are iteratively adjusted in conjunction with the iteration step size.

[0103]

[0104] Where W(n+1) are the filter coefficients of the (n+1)th iteration, W(n) are the filter coefficients of the nth iteration, and μ is the iteration step size. For the iterative gradient.

[0105] It is worth noting that this embodiment uses the instantaneous gradient of the objective function instead of the true gradient, which avoids calculating the mathematical expectation and obtains an improved iterative formula for the adaptive filter of the minimum mean method, thus reducing the computational complexity.

[0106] Repeat the above steps to obtain W(n), which satisfies the iterative convergence requirement. This is the coefficient of the estimated signal filter, which is used to process the subsequent sensing data to obtain the filtered audio sensing data.

[0107] In some embodiments, the idea of ​​introducing squared error can be extended to other adaptive filtering algorithms, such as Wiener filtering and Kalman filtering, thereby achieving faster iteration speed and filtering accuracy.

[0108] The audio sensing signal optimization method based on the improved minimum mean square method provided in this application transforms the error loss function into a linear combination of the traditional mean square error and the mean square error of the squared term during the iterative solution process. This amplifies the error of outliers by introducing the squared error, thereby increasing the weight of outliers in the overall optimization and making these outliers more predictable. This preserves the audio trend of amplitude changes before and after these outliers, leading to better estimation of the sensing signal. Furthermore, bidirectional prediction better preserves the changing trend of the audio signal and reduces noise in the recovered audio signal. The variable iteration step size enables faster and more accurate adaptive filtering, thereby improving the accuracy and reliability of fiber optic sensing.

[0109] Secondly, embodiments of this application also provide an audio sensing signal optimization device based on an improved minimum mean method.

[0110] In one embodiment, reference is made to Figure 3 , Figure 3 This is a functional block diagram of an embodiment of the audio sensing signal optimization device based on the improved minimum mean method of this application. Figure 3 As shown, the audio sensing signal optimization device based on the improved minimum mean method includes:

[0111] The calculation module is used to iteratively calculate the error loss function of the filter based on the minimum mean square error and the minimum mean square error of the squared term of the actual audio signal and the audio sensing signal after filtering, so as to increase the proportion of outliers in the audio sensing signal in the total error loss function.

[0112] The adjustment module is used to iteratively adjust the filter coefficients according to the error loss function until the error loss function converges.

[0113] The processing module is used to process the audio sensor signal using a filter that has converged after applying an error loss function.

[0114] Furthermore, in one embodiment, the computing module is also used for:

[0115] The audio sensing signal to be processed is obtained by bidirectional prediction of the audio sensing signal based on the filter length:

[0116]

[0117] Where n is the number of iterations and For the first The audio sensor signal to be processed in the next iteration, where L is the length of the filter;

[0118] The audio sensor signal to be processed is input into the filter to obtain the filtered audio sensor signal:

[0119]

[0120] W(n)=[w1(n) w2(n) w3(n) … w L (n)] T

[0121] in, For the first The filtered sensing signal at the nth iteration, W(n) is the... Filter coefficients during secondary filtering;

[0122] The error loss function of the filter is calculated based on the minimum mean square error and the minimum mean square error of the squared term of the actual audio signal and the filtered audio signal:

[0123]

[0124] Where J(n) is the nth The error loss function of the next iteration, where ε is the coefficient of the minimum mean square error and 1-ε is the coefficient of the minimum mean square error of the squared term.

[0125] Furthermore, in one embodiment, the adjustment module is used to:

[0126] The iteration gradient and iteration step size of the filter are determined based on the error loss function;

[0127] The filter coefficients are iteratively adjusted based on the iterative gradient and iterative step size until the error loss function converges.

[0128] Furthermore, in one embodiment, the adjustment module is used to:

[0129] The iterative gradient is obtained by taking the partial derivative of the error loss function with respect to the initial filter coefficients:

[0130]

[0131] in, Let be the gradient of the nth iteration.

[0132] Furthermore, in one embodiment, the adjustment module is used to:

[0133] If the error loss function after this iteration is greater than or equal to a times the minimum iteration error or the number of iterations is less than or equal to b times the maximum number of iterations, then the preset initial step size is used as the iteration step size.

[0134] If the error loss function after this iteration is less than a times the minimum iteration error or the number of iterations is greater than b times the maximum number of iterations, then the initial step size is multiplied by a preset shrinkage constant to obtain the iteration step size.

[0135] In this case, the value of a is greater than 1, and the value of b is less than 1.

[0136] Furthermore, in one embodiment, the adjustment module is used to:

[0137] Subtracting the product of the iteration gradient and the iteration step size from the current filter coefficients yields the iterative filter coefficients:

[0138]

[0139] Where W(n+1) are the filter coefficients of the (n+1)th iteration, W(n) are the filter coefficients of the nth iteration, and μ is the iteration step size. For the iterative gradient.

[0140] Furthermore, in one embodiment, the adjustment module is used to:

[0141] The instantaneous gradient of the objective function is used as the average gradient, and the filter coefficients are iteratively adjusted in conjunction with the iteration step size.

[0142]

[0143] Where W(n+1) are the filter coefficients of the (n+1)th iteration, W(n) are the filter coefficients of the nth iteration, and μ is the iteration step size. For the iterative gradient.

[0144] Furthermore, in one embodiment, the device is also used for:

[0145] The actual audio signal is sent to the fiber optic sensing monitoring section, and the polarization change of the received signal is tracked at the receiving end to obtain the audio sensing signal.

[0146] Furthermore, in one embodiment, the device is also used for:

[0147] Adjust the magnitude of the audio sensing signal to make it consistent with the magnitude of the actual audio signal.

[0148] The functions of each module in the audio sensor signal optimization device based on the improved minimum mean method correspond to the steps in the embodiment of the audio sensor signal optimization method based on the improved minimum mean method. Their functions and implementation processes will not be described in detail here.

[0149] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0150] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0151] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0152] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0153] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0154] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0155] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. An audio sensing signal optimization method based on an improved minimum mean method, characterized in that, The audio sensing signal optimization method based on the improved minimum mean method includes: The loop calculates the filter's error loss function based on the minimum mean square error and the minimum mean square error of the squared term of the actual audio signal and the filtered audio sensor signal, in order to increase the proportion of outliers in the audio sensor signal in the total error loss function. The filter coefficients are iteratively adjusted according to the error loss function until the error loss function converges. The audio sensor signal is processed using a filter that has converged using the error loss function; The error loss function of the filter is calculated based on the minimum mean square error and the minimum mean square error of the squared term of the actual audio signal and the audio sensor signal after filtering. This includes: The audio sensing signal to be processed is obtained by bidirectional prediction of the audio sensing signal based on the filter length: Where n is the number of iterations and , For the first The audio sensor signal to be processed in the next iteration The length of the filter; The audio sensor signal to be processed is input into the filter to obtain the filtered audio sensor signal: in, y(n) is the nth The filtered sensor signal at the next iteration. For the first Filter coefficients during secondary filtering; Calculate the filter's error loss function based on the minimum mean square error and the minimum mean square error of the squared term of the actual audio signal and the filtered audio signal: Where J(n) is the nth Error loss function for each iteration The coefficient representing the minimum mean square error. It is the coefficient of the minimum mean square error of the squared term.

2. The audio sensing signal optimization method based on the improved minimum mean method as described in claim 1, characterized in that, Iteratively adjusting the filter coefficients based on the error loss function until the error loss function converges also includes: The iteration gradient and iteration step size of the filter are determined based on the error loss function; The filter coefficients are iteratively adjusted based on the iterative gradient and iterative step size until the error loss function converges.

3. The audio sensing signal optimization method based on the improved minimum mean method as described in claim 2, characterized in that, The iterative gradient of the filter is determined based on the error loss function, including: The iterative gradient is obtained by taking the partial derivative of the error loss function with respect to the initial filter coefficients: in, Let be the gradient of the nth iteration.

4. The audio sensing signal optimization method based on the improved minimum mean method as described in claim 3, characterized in that, Determining the iteration step size of the filter based on the error loss function includes: If the error loss function after this iteration is greater than or equal to a times the minimum iteration error or the number of iterations is less than or equal to b times the maximum number of iterations, then the preset initial step size is used as the iteration step size. If the error loss function after this iteration is less than a times the minimum iteration error or the number of iterations is greater than b times the maximum number of iterations, then the initial step size is multiplied by a preset shrinkage constant to obtain the iteration step size. In this case, the value of a is greater than 1, and the value of b is less than 1.

5. The audio sensing signal optimization method based on the improved minimum mean method as described in claim 4, characterized in that, The filter coefficients are iteratively adjusted based on the iterative gradient and iterative step size until the error loss function converges, including: Subtracting the product of the iteration gradient and the iteration step size from the current filter coefficients yields the iterative filter coefficients: in, These are the filter coefficients for the (n+1)th iteration. These are the coefficients of the filter in the nth iteration. The iteration step size, For the iterative gradient.

6. The audio sensing signal optimization method based on the improved minimum mean method as described in claim 1, characterized in that, Iteratively adjusting the filter coefficients based on the error loss function until the error loss function converges also includes: The instantaneous gradient of the objective function is used as the average gradient, and the filter coefficients are iteratively adjusted in conjunction with the iteration step size. in, These are the filter coefficients for the (n+1)th iteration. These are the coefficients of the filter in the nth iteration. The iteration step size, For the iterative gradient.

7. The audio sensing signal optimization method based on the improved minimum mean method as described in claim 1, characterized in that, Before calculating the filter's error loss function using the loop based on the minimum mean square error and the minimum mean square error of the squared term of the actual audio signal and the filtered audio sensor signal, the following steps are also included: The actual audio signal is sent to the fiber optic sensing monitoring section, and the polarization change of the received signal is tracked at the receiving end to obtain the audio sensing signal.

8. The audio sensing signal optimization method based on the improved minimum mean method as described in claim 7, characterized in that, After the audio sensor signal is extracted at the receiving end, the process also includes: Adjust the magnitude of the audio sensing signal to make it consistent with the magnitude of the actual audio signal.

9. An audio sensing signal optimization device based on an improved minimum mean method, characterized in that, The audio sensing signal optimization device based on the improved minimum mean method includes: The calculation module is used to iteratively calculate the error loss function of the filter based on the minimum mean square error and the minimum mean square error of the squared term of the actual audio signal and the audio sensing signal after filtering, so as to increase the proportion of outliers in the audio sensing signal in the total error loss function. The adjustment module is used to iteratively adjust the filter coefficients according to the error loss function until the error loss function converges. The processing module is used to process the audio sensor signal using a filter that has converged after applying an error loss function. The calculation module is further used for: The audio sensing signal to be processed is obtained by bidirectional prediction of the audio sensing signal based on the filter length: Where n is the number of iterations and , For the first The audio sensor signal to be processed in the next iteration The length of the filter; The audio sensor signal to be processed is input into the filter to obtain the filtered audio sensor signal: in, y(n) is the nth The filtered sensor signal at the next iteration. For the first Filter coefficients during secondary filtering; Calculate the filter's error loss function based on the minimum mean square error and the minimum mean square error of the squared term of the actual audio signal and the filtered audio signal: Where J(n) is the nth Error loss function for each iteration The coefficient representing the minimum mean square error. It is the coefficient of the minimum mean square error of the squared term.

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