Anti-noise method, system, medium, device and program based on adaptive threshold

Abstract

The present invention relates to the technical field of system identification, and provides an anti-noise method, system, medium, device and program based on adaptive threshold. The anti-noise method based on adaptive threshold includes: superimposing a remote input signal with interference noise to obtain a desired signal; decomposing the desired signal through an analysis filter to obtain a sub-band desired signal; decomposing the remote input signal through an analysis filter to obtain a sub-band correlation signal, passing the sub-band correlation signal through an adaptive filter to obtain a sub-band echo estimation signal; subtracting the sub-band echo estimation signal from the sub-band desired signal to obtain a sub-band error signal; calculating the threshold parameter of Tukey's biweight function at the current moment based on the sub-band error signal; determining the target function based on this; and obtaining an output signal based on the target function. The present invention can obtain a faster convergence speed and steady-state error in an impulse noise environment.

Description

Technical Field

[0001] The present invention relates to the field of system identification technology, and in particular to an anti-noise method, system, medium, device and program based on adaptive threshold. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Impulse noise has always been a severe challenge in the field of system identification. Impulse noise has a small probability of occurrence and a short duration, but its realization amplitude is large, resulting in drastic fluctuations in the algorithm convergence performance. Such noise scenarios are often encountered in practical applications such as echo cancellation, underwater acoustics, audio processing, communications, and time series prediction. Therefore, the problem of sparse system identification in an impulse noise environment has always been one of the hot research topics.

[0004] Adaptive filters are widely used in the fields of system identification, channel equalization, active noise control and echo cancellation. The basic principle of adaptive filter system identification technology is to establish a speech model of the input signal based on the correlation between the input signal and the unknown system, estimate the unknown system through the residual between it and the unknown system, and continuously modify the coefficients of the filter so that the estimated value is closer to the parameters of the unknown system. The amplitude of the impulse noise will be superimposed on the output of the unknown system, so that the coefficients of the adaptive filter are updated in the direction of a larger error. Therefore, how to improve and study the adaptive filter algorithm with excellent anti-noise performance is one of the key issues in the field of system identification.

[0005] In the current application of sparse system identification, the more mature method is to use the normalized least mean square (NLMS) algorithm, which is one of the most popular adaptive algorithms due to its robustness to input signal power and low computational complexity. However, when the input signal is highly correlated (such as autoregressive and speech signals), the convergence speed is slow. Summary of the invention

[0006] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides an anti-noise method, system, medium, device and program based on adaptive threshold. The present invention can obtain faster convergence speed and steady-state error in a pulse noise environment, compensate for the divergence problem of traditional methods under pulse noise conditions, and has better robustness.

[0007] In order to achieve the above object, the present invention adopts the following technical solution:

[0008] A first aspect of the present invention provides an anti-noise method based on an adaptive threshold.

[0009] An anti-noise method based on adaptive threshold, comprising:

[0010] The remote input signal acquired at the current moment is superimposed with the interference noise to obtain the desired signal; the desired signal is decomposed by an analysis filter to obtain a sub-band desired signal;

[0011] Decomposing the acquired far-end input signal at the current moment through an analysis filter to obtain a sub-band correlation signal, and passing the sub-band correlation signal through an adaptive filter to obtain a sub-band echo estimation signal;

[0012] Subtracting the sub-band echo estimation signal from the sub-band desired signal to obtain a sub-band error signal;

[0013] Based on the subband error signal, the threshold parameter of Tukey's biweight function at the current moment is calculated; based on the subband error signal and the threshold parameter of Tukey's biweight function at the current moment, the target function is determined; based on the target function, the output signal is obtained; the above process is repeated to process the far-end input signal at the next moment.

[0014] Furthermore, the threshold parameter of the Tukey's biweight function at the current moment is expressed by the following formula:

[0015]

[0016] in, represents the threshold parameter of Tukey's biweight function, represents the subband error signal, is a constant less than or equal to the set value.

[0017] Furthermore, the objective function is expressed by the following formula:

[0018]

[0019] in, represents the objective function, represents the threshold parameter of Tukey's biweight function, represents the sub-band error signal.

[0020] Furthermore, after obtaining the objective function, the method further includes: determining a corresponding piecewise function according to the objective function; embedding the piecewise function into a normalized subband filtering algorithm to obtain a tap weight coefficient vector of an adaptive filter at the next moment;

[0021] The piecewise function is expressed by the following formula:

[0022]

[0023] The tap weight coefficient vector of the adaptive filter at the next moment is expressed by the following formula:

[0024]

[0025] in, represents a piecewise function, represents the threshold parameter of Tukey's biweight function, represents the subband error signal, Indicates the next moment k +1 is the tap weight coefficient vector of the adaptive filter, Indicates the current time k The tap weight coefficient vector of the adaptive filter, is the step size parameter, is a constant less than or equal to the set value. .

[0026] Furthermore, the expected signal is decomposed through an analysis filter to obtain a sub-band expected signal; the method includes: decomposing the expected signal through an analysis filter to obtain a sub-band related signal; extracting the sub-band related signal, reducing the sampling sequence, and obtaining the sub-band expected signal.

[0027] Furthermore, the sub-band related signal is passed through an adaptive filter to obtain a sub-band echo estimation signal; the method includes: passing the sub-band related signal through an adaptive filter to obtain a sub-band echo estimation related signal; downsampling the sub-band echo estimation related signal to obtain a sub-band echo estimation signal.

[0028] A second aspect of the present invention provides an adaptive threshold based anti-noise system.

[0029] An anti-noise system based on adaptive threshold, comprising:

[0030] The first processing module is configured to: superimpose the acquired remote input signal at the current moment with the interference noise to obtain a desired signal; decompose the desired signal through an analysis filter to obtain a sub-band desired signal;

[0031] The second processing module is configured to: decompose the acquired far-end input signal at the current moment through an analysis filter to obtain a sub-band related signal, and pass the sub-band related signal through an adaptive filter to obtain a sub-band echo estimation signal;

[0032] A third processing module is configured to: subtract the sub-band echo estimation signal from the sub-band desired signal to obtain a sub-band error signal;

[0033] The fourth processing module is configured to: calculate the threshold parameter of Tukey's biweight function at the current moment based on the subband error signal; determine the target function based on the subband error signal and the threshold parameter of Tukey's biweight function at the current moment; obtain the output signal based on the target function; repeat the above process to process the far-end input signal at the next moment.

[0034] A third aspect of the present invention provides a computer-readable storage medium.

[0035] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the anti-noise method based on adaptive threshold as described in the first aspect above.

[0036] A fourth aspect of the present invention provides a computer device.

[0037] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the anti-noise method based on adaptive threshold as described in the first aspect above are implemented.

[0038] A fifth aspect of the present invention provides a computer program product or a computer program.

[0039] The present invention provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in the anti-noise method based on the adaptive threshold as described in the first aspect above.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] The present invention uses the current moment to calculate the noise signal mixed in the input signal in the algorithm. k The subband residual signal , calculate the current time k The threshold parameter of Turkey's biweight function b , and update the current time k The adaptive filter tap weight coefficient vector Embedding Turkey's biweight function into NSAF algorithm not only maintains the performance of NSAF algorithm under highly correlated input signal conditions, but also reduces the sensitivity of NSAF algorithm to impulse noise and enhances the anti-noise ability of NSAF algorithm.b Update the formula and continuously adjust the threshold b , improving the flexibility of the NSAF algorithm, can better balance the contradiction between convergence speed and steady-state convergence error. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0043] Figure 1 is a flow chart of an anti-noise method based on an adaptive threshold shown in the present invention;

[0044] Figure 2 It is an MSD curve diagram of sparse system identification under colored input and Gaussian noise conditions by the LMS algorithm, LMP algorithm, NSAF algorithm and the method of the present invention shown in the present invention;

[0045] Figure 3 It is an MSD curve diagram of sparse system identification by LMS algorithm, LMP algorithm, NSAF algorithm and the method of the present invention after adding impulse noise;

[0046] Figure 4 It is an MSD curve diagram of sparse system identification by the LMS algorithm, the LMP algorithm, the NSAF algorithm and the method of the present invention under the condition that the input signal shown in the present invention is a real speech input;

[0047] Figure 5 4 is a structural diagram of an anti-noise system based on adaptive threshold shown in the present invention. DETAILED DESCRIPTION

[0048] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0049] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0050] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0051] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the methods and systems according to various embodiments of the present disclosure. It should be noted that each box in the flowchart or block diagram can represent a module, a program segment, or a part of a code, and the module, program segment, or a part of a code may include one or more executable instructions for implementing the logical functions specified in each embodiment. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the flowchart and / or block diagram, and the combination of boxes in the flowchart and / or block diagram can be implemented using a dedicated hardware-based system that performs a specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0052] Embodiment 1

[0053] like Figure 1 As shown, this embodiment provides an anti-noise method based on adaptive thresholds. This embodiment uses the method applied to a server as an example. It can be understood that the method can also be applied to a terminal, and can also be applied to a terminal, a server, and a system, and is implemented through the interaction between the terminal and the server. The server can be an independent physical server, or a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communications, middleware services, domain name services, security services CDN, and big data and artificial intelligence platforms. The terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited to this. The terminal and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in this application. In this embodiment, the method includes the following steps:

[0054] The remote input signal acquired at the current moment is superimposed with the interference noise to obtain the desired signal; the desired signal is decomposed by an analysis filter to obtain a sub-band desired signal;

[0055] Decomposing the acquired far-end input signal at the current moment through an analysis filter to obtain a sub-band correlation signal, and passing the sub-band correlation signal through an adaptive filter to obtain a sub-band echo estimation signal;

[0056] Subtracting the sub-band echo estimation signal from the sub-band desired signal to obtain a sub-band error signal;

[0057] Based on the subband error signal, the threshold parameter of Tukey's biweight function at the current moment is calculated; based on the subband error signal and the threshold parameter of Tukey's biweight function at the current moment, the target function is determined; based on the target function, the output signal is obtained; the above process is repeated to process the far-end input signal at the next moment.

[0058] The present embodiment is described in detail below:

[0059] Step 1: Signal sampling

[0060] Sample the remote signal from the remote end to get the current time n The discrete value of the far-end signal , the current moment n arrive n - L +1 discrete value of the far-end signal at a certain moment , forming the current moment n The adaptive filter input vector , , superscript T represents the transpose operation, L is the tap length of the adaptive filter, and its value is 512.

[0061] Step 2: Subband desired signal decomposition

[0062] Step 2-1: Decomposition of the desired signal

[0063] By analyzing the filter , , decomposition of the expected signal , and get the sub-band correlation signal ,in is the number of subbands, and its value is 4.

[0064] Step 2-2: Downsampling of the desired signal

[0065] For each subband i , are strictly decimated to reduce the sampling rate sequence and obtain the sub-band desired signal ,in ,variable n represents the original sequence, k The variable represents the extracted sequence.

[0066] Step 3: Subband echo estimation signal decomposition

[0067] Step 3-1: Input signal decomposition

[0068] By analyzing the filter , , decompose the input signal , and get the sub-band correlation signal ;

[0069] Step 3-2: Downsampling of echo estimation signal

[0070] Subband Correlation Signal Generate sub-band echo estimation correlation signal through adaptive filter , and after downsampling, the subband echo estimation signal is obtained .

[0071] Step 4: Subband echo signal estimation

[0072] Will After analysis filter After segmentation, the sub-band correlation signal is obtained , and input into the weight vector as Adaptive filter to obtain the sub-band echo estimation signal ,Right now ,in .

[0073] Step 5: Echo signal cancellation

[0074] Remote input signal Through unknown sparse system and interference noise After superposition, the expected signal is generated , and then the expected signal By analyzing the filter Then perform segmentation and extraction to obtain the sub-band desired signal , k represents the extracted sequence. The same remote input signal First pass the analysis filter Then the sub-band correlation signal is obtained by segmentation and through the adaptive filter and generate sub-band echo estimation signal after strict extraction , and the sub-band desired signal Subtract to get the subband error signal Then sent back to the remote end.

[0075] Step 6: Filter tap weight vector update

[0076] Step 6-1: Calculation of threshold parameters of Tukey's biweight function

[0077] According to the current time k The decimated subband error signal , calculate the current time kTukey's biweight function threshold parameter , , It is a constant less than or equal to the set value, and the set value can be 0.01.

[0078] Step 6-2: Calculation of Tukey's biweight estimate

[0079] The objective function of Tukey's biweight M estimate is: ;

[0080] The corresponding piecewise function is ;

[0081] When the impact noise produces too large an error, if it exceeds the range of the threshold b, the result of the piecewise function is 0, and the filter update formula will not be performed, which can effectively avoid the impact of the impact noise on the filter parameter update.

[0082] Step 6-3: Weight vector update

[0083] Tukey's biweight M estimation function is embedded into the normalized subband filtering algorithm to obtain the next moment n +1 adaptive filter tap weight coefficient vector , ,in is the step size parameter, It is a constant less than or equal to the set value, and the set value can be 0.01.

[0084] Step 7: Repeat

[0085] make n=n +1, repeat steps 1 to 6 until the call ends.

[0086] Simulation experiment

[0087] In order to verify the effectiveness of the method of the present invention, experiments were carried out and the performance was compared with the LMS algorithm, LMP algorithm and NSAF algorithm.

[0088] Adaptive filter tap length in simulation experiment L The input signal at the far end is a first-order autoregressive (AR(1)) signal. The AR(1) input signal is generated by a first-order system. The specific values ​​of the parameters of each algorithm in the experiment are shown in Table 1, Table 2, and Table 3:

[0089] Table 1 Figure 1 Parameter value

[0090]

[0091] Table 2 Figure 2 Parameter value

[0092]

[0093] Table 3 Figure 3 Parameter value

[0094]

[0095] The simulation results are obtained by averaging 100 independent runs.

[0096] Figure 2 It is an MSD curve diagram of sparse system identification under colored input and Gaussian noise conditions by the LMS algorithm, LMP algorithm, NSAF algorithm and the method of the present invention shown in the present invention; Figure 3 It is an MSD curve diagram of sparse system identification by LMS algorithm, LMP algorithm, NSAF algorithm and the method of the present invention after adding impulse noise; Figure 4 It is an MSD curve diagram of sparse system identification using the LMS algorithm, LMP algorithm, NSAF algorithm and the method of the present invention under the condition that the input signal is a real speech input.

[0097] from Figure 2 It can be seen that in a Gaussian environment, under the condition of almost the same steady-state error, the convergence speed of the present invention is slightly lower than that of the NSAF algorithm.

[0098] from Figure 3 It can be seen that when the algorithm works in a noisy environment, the LMS algorithm and the NSAF algorithm diverge because they do not have the ability to resist noise. The present invention has a steady-state convergence error of about -25 dB, and the LMP algorithm has a steady-state convergence error of about -20 dB.

[0099] exist Figure 4 When the input signal is changed to real speech input and doped with impulse noise, the invention has the fastest convergence speed and the lowest steady-state MSD, which is about -12dB. The MSDs of the LMS algorithm, LMP algorithm and NSAF algorithm are about -5dB, -7dB and -9dB respectively.

[0100] Embodiment 2

[0101] like Figure 5 As shown, this embodiment provides an anti-noise system based on an adaptive threshold, including:

[0102] The first processing module is configured to: superimpose the acquired remote input signal at the current moment with the interference noise to obtain a desired signal; decompose the desired signal through an analysis filter to obtain a sub-band desired signal;

[0103] The second processing module is configured to: decompose the acquired far-end input signal at the current moment through an analysis filter to obtain a sub-band related signal, and pass the sub-band related signal through an adaptive filter to obtain a sub-band echo estimation signal;

[0104] A third processing module is configured to: subtract the sub-band echo estimation signal from the sub-band desired signal to obtain a sub-band error signal;

[0105] The fourth processing module is configured to: calculate the threshold parameter of Tukey's biweight function at the current moment based on the subband error signal; determine the target function based on the subband error signal and the threshold parameter of Tukey's biweight function at the current moment; obtain the output signal based on the target function; repeat the above process to process the far-end input signal at the next moment.

[0106] In some embodiments, the threshold parameter of the Tukey's biweight function at the current moment is expressed by the following formula:

[0107]

[0108] in, represents the threshold parameter of Tukey's biweight function, represents the subband error signal, is a constant less than or equal to the set value.

[0109] In some embodiments, the objective function is expressed by the following formula:

[0110]

[0111] in, represents the objective function, represents the threshold parameter of Tukey's biweight function, represents the sub-band error signal.

[0112] In some embodiments, after obtaining the objective function, the method further includes: determining a corresponding piecewise function according to the objective function; embedding the piecewise function into a normalized subband filtering algorithm to obtain a tap weight coefficient vector of an adaptive filter at the next moment;

[0113] The piecewise function is expressed by the following formula:

[0114]

[0115] The tap weight coefficient vector of the adaptive filter at the next moment is expressed by the following formula:

[0116]

[0117] in, represents a piecewise function, represents the threshold parameter of Tukey's biweight function, represents the subband error signal, Indicates the next moment k +1 is the tap weight coefficient vector of the adaptive filter, Indicates the current time k The tap weight coefficient vector of the adaptive filter, is the step size parameter, is a constant less than or equal to the set value. .

[0118] In some embodiments, the first processing module is further configured to: decompose the desired signal through an analysis filter to obtain a sub-band related signal; extract the sub-band related signal, reduce the sampling sequence, and obtain the sub-band desired signal.

[0119] In some embodiments, the second processing module is further configured to: pass the sub-band correlation signal through an adaptive filter to obtain a sub-band echo estimation correlation signal; and downsample the sub-band echo estimation correlation signal to obtain a sub-band echo estimation signal.

[0120] Embodiment 3

[0121] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps in the anti-noise method based on adaptive threshold as described in the first embodiment are implemented.

[0122] Embodiment 4

[0123] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the anti-noise method based on adaptive threshold as described in the first embodiment are implemented.

[0124] Embodiment 5

[0125] This embodiment provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the steps in the method for identifying moldy tobacco leaves described in the first embodiment.

[0126] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0127] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0128] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0129] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0130] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0131] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. The anti-noise method based on adaptive threshold is characterized by: include: The remote input signal acquired at the current moment is superimposed with the interference noise to obtain the desired signal; the desired signal is decomposed by an analysis filter to obtain a sub-band desired signal; Decomposing the acquired far-end input signal at the current moment through an analysis filter to obtain a sub-band correlation signal, and passing the sub-band correlation signal through an adaptive filter to obtain a sub-band echo estimation signal; Subtracting the sub-band echo estimation signal from the sub-band desired signal to obtain a sub-band error signal; Based on the subband error signal, calculate the threshold parameter of Tukey's biweight function at the current moment; Determine the target function based on the subband error signal and the threshold parameter of Tukey's biweight function at the current moment; obtain the output signal based on the target function; repeat the above process to process the far-end input signal at the next moment; The objective function is expressed by the following formula: in, represents the objective function, represents the threshold parameter of Tukey's biweight function, represents a sub-band error signal; The current Tukey's biweight function threshold parameter is expressed by the following formula: in, represents the threshold parameter of Tukey's biweight function, represents the subband error signal, is a constant less than or equal to the set value.

2. The anti-noise method based on adaptive threshold according to claim 1, characterized in that: After obtaining the objective function, the method further includes: determining a corresponding piecewise function according to the objective function; embedding the piecewise function into a normalized subband filtering algorithm to obtain a tap weight coefficient vector of an adaptive filter at the next moment; The piecewise function is expressed by the following formula: The tap weight coefficient vector of the adaptive filter at the next moment is expressed by the following formula: in, represents a piecewise function, represents the threshold parameter of Tukey's biweight function, represents the subband error signal, Indicates the next moment k +1 is the tap weight coefficient vector of the adaptive filter, Indicates the current time k The tap weight coefficient vector of the adaptive filter, is the step size parameter, is a constant less than or equal to the set value. .

3. The anti-noise method based on adaptive threshold according to claim 1, characterized in that: The method comprises: decomposing the expected signal through the analysis filter to obtain the sub-band expected signal; extracting the sub-band related signal, reducing the sampling sequence, and obtaining the sub-band expected signal.

4. The anti-noise method based on adaptive threshold according to claim 1, characterized in that: The sub-band related signal is passed through an adaptive filter to obtain a sub-band echo estimation signal; the method comprises: passing the sub-band related signal through an adaptive filter to obtain a sub-band echo estimation related signal; down-sampling the sub-band echo estimation related signal to obtain a sub-band echo estimation signal. 5.Anti-noise system based on adaptive threshold, characterized in that: include: The first processing module is configured to: superimpose the acquired remote input signal at the current moment with the interference noise to obtain a desired signal; decompose the desired signal through an analysis filter to obtain a sub-band desired signal; The second processing module is configured to: decompose the acquired far-end input signal at the current moment through an analysis filter to obtain a sub-band related signal, and pass the sub-band related signal through an adaptive filter to obtain a sub-band echo estimation signal; A third processing module is configured to: subtract the sub-band echo estimation signal from the sub-band desired signal to obtain a sub-band error signal; A fourth processing module is configured to: calculate a threshold parameter of Tukey's biweight function at a current moment based on the subband error signal; Determine the target function based on the subband error signal and the threshold parameter of Tukey's biweight function at the current moment; obtain the output signal based on the target function; repeat the above process to process the far-end input signal at the next moment; The objective function is expressed by the following formula: in, represents the objective function, represents the threshold parameter of Tukey's biweight function, represents a sub-band error signal; The current Tukey's biweight function threshold parameter is expressed by the following formula: in, represents the threshold parameter of Tukey's biweight function, represents the subband error signal, is a constant less than or equal to the set value.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the anti-noise method based on adaptive threshold as described in any one of claims 1 to 4 are implemented.

7. A computer device 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 program, the steps in the anti-noise method based on adaptive threshold as described in any one of claims 1 to 4 are implemented.

8. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps in the anti-noise method based on adaptive threshold are implemented as described in any one of claims 1 to 4.

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