CN117831495A - Audio sensing signal optimization method and device based on constant power constraint - Google Patents
Audio sensing signal optimization method and device based on constant power constraint Download PDFInfo
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Abstract
The application discloses an audio sensing signal optimization method and device based on constant power constraint, wherein the method comprises the steps of circularly filtering an audio sensing signal by using a filter with constant power as constraint to obtain a filtered audio sensing signal; calculating an error loss function of the filter based on the audio actual signal and the filtered audio sensing signal; performing block-type iterative adjustment on the filter coefficient according to the error loss function until the error loss function converges; the filter after the error loss function is converged is used for processing the audio sensing signal, so that the iterative solution of the filter coefficient is realized on the basis of constant power, the amplitude recovery of outliers in the audio sensing signal can be ensured as much as possible during filtering, the noise of the audio sensing signal after filtering can be effectively reduced, and the accuracy and the effectiveness of optical fiber sensing are improved.
Description
Technical Field
The application relates to the technical field of optical fiber sensing, in particular to an audio sensing signal optimization method and device based on constant power constraint.
Background
The optical fiber sensing and communication integrated technology is a technology integrating optical fiber communication and optical fiber sensing functions, and utilizes the same optical fiber channel to simultaneously transmit communication signals and sensing signals, so that environment physical quantity can be sensed, measured and transmitted while ensuring normal communication requirements. The optical fiber sensing technology can be used for monitoring the vibration, damage, stretching and deformation of the optical fiber, the temperature, humidity, sound, pressure and the like of the external environment, and is widely applied to numerous application scenes such as seismic exploration, oil and gas pipeline monitoring, engineering structure monitoring, engineering control, engineering environment monitoring and the like. However, the optical fiber sensing has a conversion flow from other physical quantities to optical signals and then to electrical signals, and because other non-detection signals, design errors of a coherent receiver and systematic errors of a digital signal processing algorithm exist in an optical fiber transmission channel, other noise signals can be detected in sensing signals received by the coherent receiver, which makes the design of a receiving end sensing optimization algorithm of optical fiber communication an important task.
Compared with other signals, the distortion of a sensing system in which the sensing signal is an audio signal is more serious, because the change of the sound amplitude in the audio signal is more important than the amplitude, the abrupt change of the audio signal caused by sensing noise can cause the audio signal to be severely distorted, so the influence of the analog sensing channel on the signal and the filtering occupy an important position on the audio optical fiber sensing.
At present, less audio optical fiber sensing research is carried out, and the sound change is rapid, so that strict requirements are imposed on the sensing technology. In the optical fiber transmission process, a player is used for playing audio signals, even song signals, outside the optical fiber bare fiber, the audio signals can be extracted from the receiving end, and melodies and lyrics of songs can be obviously heard. However, the complex nature of the sensing channel causes serious distortion and noise in the final received result, and therefore, it is necessary to process it, simulate the actual channel and perform channel filtering.
The least mean squares method (Least Mean Square, LMS) is a classical adaptive filtering algorithm that enables the signal recovered at the receiving end to be brought closer to the desired signal by iteration. Conventional LMS algorithms approach the desired signal gradually through iterative optimization, but this approach tends to have a small overall mean square error, possibly ignoring some outliers, which are actually key points of the audio signal, whose trend of variation with neighboring signals is very important in the audio signal. By using the traditional minimum average method, the filtering error of the outliers is larger than that of other points, and the prediction error is generated, so that the variation trend of the outliers and the front and back data in the whole recovered audio signal is changed, the data cannot be accurately recovered, and new noise is generated.
Therefore, how to overcome the influence of estimation errors of outliers in the sensed audio signal on the sensed audio recovery is a technical problem to be solved.
Disclosure of Invention
The application provides an audio sensing signal optimization method and device based on constant power constraint, which can solve the technical problems that in the prior art, an adaptive filtering algorithm through a minimum average method can cause estimation errors of outliers in an audio sensing signal, the audio sensing signal cannot be accurately recovered, and new noise can be caused.
In a first aspect, embodiments of the present application provide a method for optimizing an audio sensing signal based on a constant power constraint, the method for optimizing an audio sensing signal based on a constant power constraint including:
circularly filtering the audio sensing signal by using the filter with constant power as constraint to obtain a filtered audio sensing signal;
calculating an error loss function of the filter based on the audio actual signal and the filtered audio sensing signal;
performing block-type iterative adjustment on the filter coefficient according to the error loss function until the error loss function converges;
and processing the audio sensing signal by using a filter after the error loss function is converged.
With reference to the first aspect, in one implementation manner, filtering, by a filter, the audio sensing signal with constant power as a constraint, to obtain a filtered audio sensing signal includes:
and carrying out bidirectional prediction on the audio sensing signal based on the length of the filter to obtain an audio sensing signal to be processed:
wherein, among them,inputting an audio sensing signal to be processed for a filter of the ith filtered audio sensing signal, wherein L is the length of the filter;
inputting the audio sensing signal to be processed into a filter, and filtering the audio sensing signal to be processed by the filter with constant power as constraint:
W(n)＝[w n (1)，w n (2)，w n (3)…w n (L)] T
obtaining a filtered audio sensing signal:
wherein,m is the length of the audio sensing signal block, y n (i) For the ith filtered audio sensor signal in the nth iteration, W (n) is the filter coefficient at the nth filtering, Y (n) is the nth filtered audio sensor signal block, pn is constant power>d (i) is the audio actual signal corresponding to the i-th point.
In some embodiments, calculating an error loss function of the filter based on the audio actual signal and the filtered audio sensor signal includes:
calculating an error loss function of the filter based on a minimum mean square error of the audio actual signal and the filtered audio sensing signal:
J(n)＝E((y n (i)-d(i)) 2 )
where J (n) is the error loss function of the nth iteration.
In some embodiments, performing block-wise iterative adjustment on the filter coefficients according to the error loss function until the error loss function converges, including:
the lagrangian function is established using the lagrangian algorithm based on the error loss function:
wherein J is lagrange (n) is a Lagrangian function, λ is the Lagrangian multiplier;
determining an iterative gradient of the filter according to the Lagrangian function;
determining the iteration step length of the filter according to the error loss function;
and performing block-type iterative adjustment on the filter coefficients based on the iterative gradient and the iterative step length until the error loss function converges.
In some embodiments, the creating a lagrangian function using a lagrangian algorithm based on the error loss function further comprises:
and performing partial derivative on the initial filter coefficient through the Lagrangian function to obtain an initial filter coefficient partial derivative function:
wherein,a coefficient partial derivative function of an initial filter;
and performing partial derivative on the Lagrange multiplier through the Lagrange function to obtain a Lagrange multiplier partial derivative function:
wherein,is a Lagrange multiplier partial derivative function;
and (3) adding the Lagrange multiplier partial derivative function with 0 in iteration convergence to the initial filter coefficient partial derivative function with 0 to obtain a Lagrange multiplier:
the lagrangian function is solved using lagrangian multipliers.
In some embodiments, determining the iterative gradient of the filter from the lagrangian function includes:
and performing bias guide on the initial filter coefficient through a Lagrangian function to obtain an iterative gradient:
wherein,is the iteration gradient of the nth iteration.
In some embodiments, determining the iteration step of the filter from the error loss function includes:
if the error loss function after the iteration is greater than or equal to a minimum iteration error of a times or the maximum iteration times of b times, using a preset initial step length as an iteration step length;
if the error loss function after the iteration is smaller than the minimum iteration error of a times or the maximum iteration times of which the iteration times are larger than b times, multiplying the initial step length by a preset reduction constant to obtain an iteration step length;
wherein, the value of a is more than 1, and the value of b is less than 1.
In some embodiments, performing a block-wise iterative adjustment of the filter coefficients based on the iterative gradient and the iterative step size includes:
subtracting the product of the iteration gradient and the iteration step length from the current filter coefficient to obtain an iterative filter coefficient:
wherein W (n+1) is the filter coefficient of the n+1th iteration, W (n) is the coefficient of the filter of the n-th iteration, μ is the iteration step,is an iterative gradient.
In some embodiments, before filtering the audio sensing signal by the filter with the constant power as a constraint, the method further includes:
transmitting an audio actual signal at the optical fiber sensing monitoring section, and tracking the polarization state change of the received signal at the receiving end to obtain an audio sensing signal;
adjusting the magnitude of the audio sensing signal so that the magnitude of the audio sensing signal is consistent with the magnitude of the audio actual signal
In a second aspect, embodiments of the present application provide an audio sensing signal optimization device based on a constant power constraint, where the audio sensing signal optimization device based on the constant power constraint includes:
the filtering module is used for circularly filtering the audio sensing signal by taking constant power as constraint through the filter to obtain a filtered audio sensing signal;
a calculation module for calculating an error loss function of the filter based on the audio actual signal and the filtered audio sensing signal;
the adjusting module is used for performing block type iterative adjustment on the filter coefficient according to the error loss function until the error loss function converges;
and the processing module is used for processing the audio sensing signal by using the filter after the error loss function is converged.
The embodiment of the application provides an audio sensing signal optimizing method and device based on constant power constraint, which are used for circularly filtering an audio sensing signal by using a filter with constant power as constraint to obtain a filtered audio sensing signal; calculating an error loss function of the filter based on the audio actual signal and the filtered audio sensing signal; performing block-type iterative adjustment on the filter coefficient according to the error loss function until the error loss function converges; the filter after the error loss function is converged is used for processing the audio sensing signal, so that the iterative solution of the filter coefficient is realized on the basis of constant power, the amplitude recovery of outliers in the audio sensing signal can be ensured as much as possible during filtering, the noise of the audio sensing signal after filtering can be effectively reduced, and the accuracy and the effectiveness of optical fiber sensing are improved.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of an audio sensing signal optimization method based on constant power constraint;
FIG. 2 is a schematic flow chart of another embodiment of an audio sensing signal optimization method based on constant power constraint;
fig. 3 is a schematic functional block diagram of an embodiment of an audio sensing signal optimizing apparatus based on constant power constraint.
Detailed Description
In order to make the present application solution better understood by those skilled in the art, the following description will clearly and completely describe the technical solution in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
In a first aspect, embodiments of the present application provide an audio sensing signal optimization method based on constant power constraints.
In an embodiment, referring to fig. 1, fig. 1 is a flowchart of a first embodiment of an audio sensing signal optimization method based on constant power constraint. As shown in fig. 1, the overall concept of the audio sensing signal optimization method based on the constant power constraint includes:
and step S101, filtering the audio sensing signal by taking constant power as constraint through a filter in a circulating way to obtain a filtered audio sensing signal.
Step S102, calculating an error loss function of the filter based on the audio actual signal and the filtered audio sensing signal.
And step S103, performing block-type iterative adjustment on the filter coefficient according to the error loss function until the error loss function converges.
Step S104, the audio sensing signal is processed by using the filter after the error loss function is converged.
It is worth to say that in the process of solving the filter coefficient in this embodiment, the power before and after the audio signal with limited constraint length is filtered is kept to be constant, so that the amplitude recovery of outliers in the audio sensing signal can be ensured as much as possible, and the audio trend of the amplitude variation before and after the outliers is kept, so that the filter can better estimate the audio sensing signal, the noise of the audio sensing signal after the filtering is reduced, and the accuracy and reliability of optical fiber sensing are improved.
In some embodiments, before the audio sensing signal is filtered by the filter with constant power as a constraint, the method further includes: and transmitting an audio actual signal in the optical fiber sensing monitoring section, and obtaining an audio sensing signal according to the optical polarization state of the optical signal of the receiving end.
By way of example, the present embodiment realizes that an audio actual signal is transmitted in an optical fiber sensing monitoring section by playing audio in the optical fiber sensing monitoring section, then tracks the change of the optical polarization state in the optical fiber based on a polarization fast tracking algorithm, recovers an audio sensing signal from a transmission matrix according to the change of the optical polarization state, and performs channel estimation design filter based on the audio actual signal and the audio sensing signal.
It should be noted that, a pair of signal lights with orthogonal polarization states can normally recover 4 paths of sensing signals, and in a channel using higher order polarization multiplexing, there are more polarization states, a transmission matrix is more complex, and the recovered sensing signals are more. In the embodiment, the audio sensing signal and the audio actual signal are correlated with each other to select one path of audio sensing signal with the strongest correlation to perform sensing channel estimation.
In this embodiment, the sending of the audio actual signal at the optical fiber sensing monitoring section is:
D＝[d(1)，d(2)，d(3)…d(N)] T
the audio sensing signals selected according to the maximum correlation coefficient criterion are:
S＝[s(1)，s(2)，s(3)…s(N)] T
wherein N is the length of the audio actual signal and the audio sensing signal.
Preferably, after the audio sensing signal is obtained, the magnitude of the audio sensing signal needs to be adjusted so that the magnitude of the audio sensing signal and the magnitude of the audio actual signal are identical. Adjusting the magnitude of the audio sensing signal includes:
wherein i=1, 2,3 … N, the resulting adjusted audio sensing signal is:
wherein,is the adjusted audio sensing signal.
It should be understood that, since the magnitudes of the audio sensing signal and the audio actual signal calculated by using the polarization fast tracking algorithm may be inconsistent, the audio actual signal and the audio sensing signal with inconsistent magnitudes may cause a large error when performing initial iteration, and thus cause difficulty in adaptive filtering convergence, so that the magnitudes of the audio sensing signal and the audio actual signal are adjusted to be consistent, and the influence of the magnitude of the signal is eliminated, thereby helping the filter to converge fast.
Further, filtering the audio sensing signal by the filter with constant power as constraint to obtain a filtered audio sensing signal, including:
and carrying out bidirectional prediction on the audio sensing signal based on the length of the filter to obtain an audio sensing signal to be processed:
wherein,and inputting an audio sensing signal to be processed for a filter of the ith filtered audio sensing signal, wherein L is the length of the filter.
Inputting the audio sensing signal to be processed into a filter, and filtering the audio sensing signal to be processed by the filter with constant power as constraint:
W(n)＝[w n (1)，w n (2)，w n (3)…w n (L)] T
obtaining a filtered audio sensing signal:
wherein,m is the length of the audio sensing signal block, y n (i) For the ith filtered audio sensor signal in the nth iteration, W (n) is the filter coefficient at the nth filtering, Y (n) is the nth filtered audio sensor signal block, pn is constant power>d (i) is the audio actual signal corresponding to the i-th point.
It should be noted that, in this embodiment, the power before and after the training data filtering of the limited length is constrained to be constant in the process of iterative solution, so that the iteration can be performed on the basis of constant power. On the premise of constant power, the power error of the outlier is larger than that of other points, so that the amplitude recovery of the outlier can be ensured as much as possible, the audio trend of amplitude change before and after the outlier is reserved, and the sensing signal can be estimated better. And the change trend of the audio sensing signal can be better reserved by adopting the design of the self-adaptive filter of the bidirectional prediction, so that the noise of the filtered audio sensing signal is reduced.
Further, calculating an error loss function of the filter based on the audio actual signal and the filtered audio sensor signal, comprising: calculating an error loss function of the filter based on a minimum mean square error of the audio actual signal and the filtered audio sensing signal:
J(n)＝E((y n (i)-d(i)) 2 )
where J (n) is the error loss function of the nth iteration.
Further, the problem of constant audio power filtering is a constrained optimization problem, so the present embodiment uses the lagrangian algorithm to build the lagrangian function based on the error loss function:
wherein J is lagrange (n) is a Lagrangian function and λ is a Lagrangian multiplier.
And performing partial derivative on the initial filter coefficient through the Lagrangian function to obtain an initial filter coefficient partial derivative function:
wherein,a coefficient partial derivative function of an initial filter;
and performing partial derivative on the Lagrange multiplier through the Lagrange function to obtain a Lagrange multiplier partial derivative function:
wherein,is a Lagrange multiplier partial derivative function;
and (3) adding the Lagrange multiplier partial derivative function with 0 in iteration convergence to the initial filter coefficient partial derivative function with 0 to obtain a Lagrange multiplier:
the lagrangian function is solved using lagrangian multipliers.
Then, after the Lagrangian function is established, the initial filter coefficients are biased through the Lagrangian function, and an iterative gradient is obtained:
wherein,is the iteration gradient of the nth iteration.
Then determining the iteration step length of the filter according to the error loss function, wherein the method specifically comprises the following steps:
if the error loss function after the iteration is greater than or equal to a minimum iteration error of a times or the maximum iteration times of b times, using a preset initial step length as an iteration step length. And if the error loss function after the iteration is smaller than the minimum iteration error of a times or the maximum iteration times of which the iteration times are larger than b times, multiplying the initial step length by a preset reduction constant to obtain an iteration step length, wherein the value of a is larger than 1, and the value of b is smaller than 1.
Exemplary, in this embodiment, a has a value of 2 and b has a value ofThe iteration step is calculated as:
wherein μ is the iteration step size, μ 0 For the initial step size, gen is the number of iterations,Gen max for maximum iteration number, J min For the minimum iteration error, m is a reduction constant, and the value of m can be selected according to the requirement. It is worth noting that in setting μ 0 When the value of the code is not too large, the code needs to meet the limiting condition, and too large selection can cause the failure of convergence, and the code can be determined according toThe size of (3) is taken as a value.
By means of the method, the data can be found out to be near the possible optimal point as soon as possible in the early stage of iteration, the data can be optimized more accurately in the later stage of iteration, and meanwhile the situation that the data falls into local optimal can be avoided.
Further, performing block-type iterative adjustment on the filter coefficient based on the iterative gradient and the iterative step length, including:
subtracting the product of the iteration gradient and the iteration step length from the current filter coefficient to obtain an iterative filter coefficient:
wherein W (n+1) is the filter coefficient of the n+1th iteration, W (n) is the coefficient of the filter of the n-th iteration, μ is the iteration step,is an iterative gradient.
It should be noted that, in the present embodiment, the gradient descent method is used to design the filter, where the gradient is obtained by deriving the filter coefficient through the lagrangian function, so that the calculation expectation can be avoided, and the expectation can be approximately replaced by the average value, so that the iterative formula of the adaptive filtering can be obtained. In addition, the filter coefficient of the LMS algorithm used in the embodiment is not updated point by point, but is updated in the whole test data block, so that the influence of individual points on the filter can be avoided.
And circularly executing the steps, wherein the obtained W (n) meeting the iteration convergence requirement is the coefficient of the estimated signal filter and is used for processing the subsequent audio sensing data to obtain the filtered audio sensing data.
In some embodiments, the concept of power constancy can be extended to other adaptive filtering algorithms, such as wiener filtering and kalman filtering, so as to have faster iteration speed and filtering accuracy.
According to the audio sensing signal optimization method based on constant power constraint, constraint conditions are added to the LMS algorithm, namely, the power before and after the audio sensing signal with limited length is constrained to be constant in the process of iteratively solving the filter, so that the filter can be solved iteratively on the basis of constant power. On the premise of constant power, the power error of the outlier is larger than that of other points, so that the amplitude recovery of the outlier can be ensured as much as possible, the audio trend of amplitude change before and after the outlier is reserved, and the audio sensing signal can be estimated better. And the traditional LMS algorithm is improved into a block type LMS algorithm, the filter coefficients are not updated point by point, but are updated through the whole test block, so that the calculation of power can be ensured to be in the same filter coefficient, the filter coefficients are updated after the next block operation, and the block type LMS algorithm is more stable than the traditional LMS algorithm. Meanwhile, the bidirectional prediction is utilized to better reserve the change trend of the audio sensing signal, reduce the noise of the recovered audio sensing signal, and the adaptive filtering can be faster and more accurately carried out by utilizing the variable iteration step length, so that the accuracy and the reliability of optical fiber sensing are improved.
According to the embodiment, the audio optical fiber sensing research is performed by quickly tracking the change of the polarization state of the signal light, meanwhile, the audio sensing signal can be recovered by using a simple and effective algorithm, the audio sensing is realized, a new application scene integrating sensing and communication is provided, the sound change in a specific environment can be monitored, even simple voice sensing communication can be performed, and resources are not consumed in addition.
In a second aspect, embodiments of the present application further provide an audio sensing signal optimization apparatus based on a constant power constraint.
In an embodiment, referring to fig. 3, fig. 3 is a schematic functional block diagram of an embodiment of an audio sensing signal optimizing apparatus based on constant power constraint in the present application. As shown in fig. 3, the audio sensing signal optimizing apparatus based on the constant power constraint includes:
the filtering module is used for circularly filtering the audio sensing signal by taking constant power as constraint through the filter to obtain a filtered audio sensing signal;
a calculation module for calculating an error loss function of the filter based on the audio actual signal and the filtered audio sensing signal;
the adjusting module is used for performing block type iterative adjustment on the filter coefficient according to the error loss function until the error loss function converges;
and the processing module is used for processing the audio sensing signal by using the filter after the error loss function is converged.
Further, in an embodiment, the filtering module is further configured to:
and carrying out bidirectional prediction on the audio sensing signal based on the length of the filter to obtain an audio sensing signal to be processed:
wherein,inputting an audio sensing signal to be processed for a filter of the ith filtered audio sensing signal, wherein L is the length of the filter;
inputting the audio sensing signal to be processed into a filter, and filtering the audio sensing signal to be processed by the filter with constant power as constraint:
W(n)＝[w n (1)，w n (2)，w n (3)…w n (L)] T
obtaining a filtered audio sensing signal:
wherein,m is the length of the audio sensing signal block, y n (i) For the ith filtered audio sensor signal in the nth iteration, W (n) is the filter coefficient at the nth filtering, Y (n) is the nth filtered audio sensor signal block, pn is constant power>d (i) is the audio actual signal corresponding to the i-th point.
Further, in an embodiment, the audio sensing signal optimizing device based on the constant power constraint further includes a new module, and the calculating module is configured to:
calculating an error loss function of the filter based on a minimum mean square error of the audio actual signal and the filtered audio sensing signal:
J(n)＝E((y n (i)-d(i)) 2 )
where J (n) is the error loss function of the nth iteration.
Further, in an embodiment, the adjusting module is further configured to:
the lagrangian function is established using the lagrangian algorithm based on the error loss function:
wherein J is lagrange (n) is a Lagrangian function, λ is the Lagrangian multiplier;
determining an iterative gradient of the filter according to the Lagrangian function;
determining the iteration step length of the filter according to the error loss function;
and performing block-type iterative adjustment on the filter coefficients based on the iterative gradient and the iterative step length until the error loss function converges.
Further, in an embodiment, the adjusting module is further configured to:
and performing partial derivative on the initial filter coefficient through the Lagrangian function to obtain an initial filter coefficient partial derivative function:
wherein,a coefficient partial derivative function of an initial filter;
and performing partial derivative on the Lagrange multiplier through the Lagrange function to obtain a Lagrange multiplier partial derivative function:
wherein,is a Lagrange multiplier partial derivative function;
and (3) setting the Lagrange multiplier partial derivative function at the iteration convergence time to be 0 and setting the Lagrange multiplier partial derivative function to be 0 in the initial filter coefficient partial derivative function to obtain the Lagrange multiplier:
the lagrangian function is solved using lagrangian multipliers.
Further, in an embodiment, the adjusting module is further configured to:
and performing bias guide on the initial filter coefficient through a Lagrangian function to obtain an iterative gradient:
wherein,is the iteration gradient of the nth iteration.
Further, in an embodiment, the adjusting module is further configured to:
if the error loss function after the iteration is greater than or equal to a minimum iteration error of a times or the maximum iteration times of b times, using a preset initial step length as an iteration step length;
if the error loss function after the iteration is smaller than the minimum iteration error of a times or the maximum iteration times of which the iteration times are larger than b times, multiplying the initial step length by a preset reduction constant to obtain an iteration step length;
wherein, the value of a is more than 1, and the value of b is less than 1.
Further, in an embodiment, the adjusting module is further configured to:
subtracting the product of the iteration gradient and the iteration step length from the current filter coefficient to obtain an iterative filter coefficient:
wherein W (n+1) is the filter coefficient of the n+1th iteration, W (n) is the coefficient of the filter of the n-th iteration, μ is the iteration step,is an iterative gradient.
Further, in an embodiment, the device is further configured to:
transmitting an audio actual signal at the optical fiber sensing monitoring section, and tracking the polarization state change of the received signal at the receiving end to obtain an audio sensing signal;
the magnitude of the audio sensing signal is adjusted so that the magnitude of the audio sensing signal is consistent with the magnitude of the audio actual signal.
The function implementation of each module in the audio sensing signal optimizing device based on the constant power constraint corresponds to each step in the audio sensing signal optimizing method embodiment based on the constant power constraint, and the function and the implementation process of the function implementation are not described in detail herein.
It should be noted that, the foregoing embodiment numbers are merely for describing the embodiments, and do not represent the advantages and disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising several instructions for causing a terminal device to perform the method described in the various embodiments of the present application.
The terms "comprising" and "having" and any variations thereof in the description and claims of the present application and in the foregoing drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus. The terms "first," "second," and "third," etc. are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order, and are not limited to the fact that "first," "second," and "third" are not identical.
In the description of embodiments of the present application, "exemplary," "such as," or "for example," etc., are used to indicate an example, instance, or illustration. Any embodiment or design described herein as "exemplary," "such as" or "for example" is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "such as" or "for example," etc., is intended to present related concepts in a concrete fashion.
In the description of the embodiments of the present application, unless otherwise indicated, "/" means or, for example, a/B may represent a or B; the text "and/or" is merely an association relation describing the associated object, and indicates that three relations may exist, for example, a and/or B may indicate: the three cases where a exists alone, a and B exist together, and B exists alone, and in addition, in the description of the embodiments of the present application, "plural" means two or more than two.
In some of the processes described in the embodiments of the present application, a plurality of operations or steps occurring in a particular order are included, but it should be understood that these operations or steps may be performed out of the order in which they occur in the embodiments of the present application or in parallel, the sequence numbers of the operations merely serve to distinguish between the various operations, and the sequence numbers themselves do not represent any order of execution. In addition, the processes may include more or fewer operations, and the operations or steps may be performed in sequence or in parallel, and the operations or steps may be combined.
The foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the claims, and all equivalent structures or equivalent processes using the descriptions and drawings of the present application, or direct or indirect application in other related technical fields are included in the scope of the claims of the present application.
Claims (10)
1. The audio sensing signal optimization method based on the constant power constraint is characterized by comprising the following steps of:
circularly filtering the audio sensing signal by using the filter with constant power as constraint to obtain a filtered audio sensing signal;
calculating an error loss function of the filter based on the audio actual signal and the filtered audio sensing signal;
performing block-type iterative adjustment on the filter coefficient according to the error loss function until the error loss function converges;
and processing the audio sensing signal by using a filter after the error loss function is converged.
2. The method for optimizing an audio sensor signal based on constant power constraint according to claim 1, wherein filtering the audio sensor signal with a filter under constant power constraint to obtain a filtered audio sensor signal comprises:
and carrying out bidirectional prediction on the audio sensing signal based on the length of the filter to obtain an audio sensing signal to be processed:
wherein,inputting an audio sensing signal to be processed for a filter of the ith filtered audio sensing signal, wherein L is the length of the filter;
inputting the audio sensing signal to be processed into a filter, and filtering the audio sensing signal to be processed by the filter with constant power as constraint:
W(n)＝[w n (1)，w n (2)，w n (3)…w n (L)] T
obtaining a filtered audio sensing signal:
wherein,m is the length of the audio sensing signal block, y n (i) For the ith filtered audio sensor signal in the nth iteration, W (n) is the filter coefficient at the nth filtering, Y (n) is the nth filtered audio sensor signal block, pn is constant power>d (i) is the audio actual signal corresponding to the i-th point.
3. The method for optimizing an audio sensor signal based on constant power constraints of claim 2, wherein calculating an error loss function of the filter based on the audio actual signal and the filtered audio sensor signal comprises:
calculating an error loss function of the filter based on a minimum mean square error of the audio actual signal and the filtered audio sensing signal:
J(n)＝E((y n (i)-d(i)) 2 )
where J (n) is the error loss function of the nth iteration.
4. A method of optimizing an audio sensor signal based on constant power constraints as claimed in claim 3, wherein the iterative block-wise adjustment of the filter coefficients according to the error loss function until the error loss function converges comprises:
the lagrangian function is established using the lagrangian algorithm based on the error loss function:
wherein J is lagrange (n) is a Lagrangian function, λ is the Lagrangian multiplier;
determining an iterative gradient of the filter according to the Lagrangian function;
determining the iteration step length of the filter according to the error loss function;
and performing block-type iterative adjustment on the filter coefficients based on the iterative gradient and the iterative step length until the error loss function converges.
5. The method for optimizing an audio sensor signal based on constant power constraints of claim 4, wherein the lagrangian function is established using a lagrangian algorithm based on the error loss function, further comprising:
and performing partial derivative on the initial filter coefficient through the Lagrangian function to obtain an initial filter coefficient partial derivative function:
wherein,a coefficient partial derivative function of an initial filter;
and performing partial derivative on the Lagrange multiplier through the Lagrange function to obtain a Lagrange multiplier partial derivative function:
wherein,is a Lagrange multiplier partial derivative function;
and (3) adding the Lagrange multiplier partial derivative function with 0 in iteration convergence to the initial filter coefficient partial derivative function with 0 to obtain a Lagrange multiplier:
the lagrangian function is solved using lagrangian multipliers.
6. The method for optimizing an audio sensor signal based on constant power constraints of claim 4, wherein determining the iterative gradient of the filter based on the lagrangian function comprises:
and performing bias guide on the initial filter coefficient through a Lagrangian function to obtain an iterative gradient:
wherein,is the iteration gradient of the nth iteration.
7. The method for optimizing an audio sensor signal based on constant power constraints of claim 6, wherein determining the iteration step of the filter based on the error loss function comprises:
if the error loss function after the iteration is greater than or equal to a minimum iteration error of a times or the maximum iteration times of b times, using a preset initial step length as an iteration step length;
if the error loss function after the iteration is smaller than the minimum iteration error of a times or the maximum iteration times of which the iteration times are larger than b times, multiplying the initial step length by a preset reduction constant to obtain an iteration step length;
wherein, the value of a is more than 1, and the value of b is less than 1.
8. The method for optimizing an audio sensor signal based on constant power constraints of claim 7, wherein the block-wise iterative adjustment of the filter coefficients based on the iteration gradient and the iteration step comprises:
subtracting the product of the iteration gradient and the iteration step length from the current filter coefficient to obtain an iterative filter coefficient:
wherein W (n+1) is the filter coefficient of the n+1th iteration, W (n) is the coefficient of the filter of the n-th iteration, μ is the iteration step,is an iterative gradient.
9. The method for optimizing an audio sensor signal based on constant power constraints of claim 1, further comprising, prior to filtering the audio sensor signal with the constant power constraint by a filter to obtain a filtered audio sensor signal:
transmitting an audio actual signal at the optical fiber sensing monitoring section, and tracking the polarization state change of the received signal at the receiving end to obtain an audio sensing signal;
the magnitude of the audio sensing signal is adjusted so that the magnitude of the audio sensing signal is consistent with the magnitude of the audio actual signal.
10. An audio sensing signal optimizing device based on constant power constraint, characterized in that the audio sensing signal optimizing device based on constant power constraint comprises:
the filtering module is used for circularly filtering the audio sensing signal by taking constant power as constraint through the filter to obtain a filtered audio sensing signal;
a calculation module for calculating an error loss function of the filter based on the audio actual signal and the filtered audio sensing signal;
the adjusting module is used for performing block type iterative adjustment on the filter coefficient according to the error loss function until the error loss function converges;
and the processing module is used for processing the audio sensing signal by using the filter after the error loss function is converged.
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