Acoustic signal processing method, system, equipment and medium

Abstract

The invention provides a sound signal processing method, which comprises the following steps of: acquiring an original signal, calculating a short-time energy value of the original signal based on a short-time energy method, and acquiring a short-time energy diagram; constructing an arc tangent function contig sparse variation model, and performing sparse representation on the original signal; on the basis of the short-time energy diagram, regularization parameters of the arc-tangent function contig sparse variation model and multiplication factors of the arc-tangent function are determined; carrying out optimization solution on the sparse representation of the original signal based on an arc tangent function contig sparse variation model; and obtaining a noise reduction signal according to a solving result. Different short-time energy values of useful signals and background noise are considered, and a short-time energy method and an arc-tangent function contiguous group sparse variation method are combined, so that the problem that useful information cannot be effectively reserved due to over-regularization during noise reduction of the arc-tangent function contiguous group sparse variation method is solved, and the noise reduction efficiency is improved. The influence of background noise on the quality of the target signal is weakened, and the signal-to-noise ratio of the target signal is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of acoustic signal processing, and in particular to an acoustic signal processing method, system, device and medium. Background Art

[0002] In modern military scenarios, the threat of snipers and the tactical role of helicopters are becoming increasingly prominent. In order to effectively respond, it is crucial to quickly and accurately locate the sniper's position and monitor the helicopter's activities. The gunfire locating system captures the muzzle wave and ballistic wave generated by the gunshot and uses the time difference of the signal to determine the sniper's position; while for the acoustic monitoring of helicopters, it is necessary to analyze and identify the unique noise characteristics of helicopters in order to track their flight trajectory and provide support for tactical decisions.

[0003] Since the muzzle wave in the gunshot signal has lower intensity and frequency, and appears after the ballistic wave, it may be masked by the interference of the ballistic wave reflection wave and the larger noise. At the same time, the noise generated by the helicopter during flight is characterized by high intensity and complex frequency, which may seriously interfere with the acoustic detection system's recognition and positioning of the target sound. Therefore, it is necessary to properly preprocess the gunshot and helicopter signals to ensure that they can be accurately detected and analyzed.

[0004] In the related technologies, extensive and in-depth research has been conducted on signal noise reduction methods. Common methods such as wavelet transform, singular value decomposition and empirical mode decomposition are based on a certain mathematical model to remove noise and have been widely used in the noise reduction processing of nonlinear and non-stationary signals. However, these methods are highly dependent on the selection of parameters in terms of noise reduction effects. Inappropriate parameter selection may cause signal distortion and distortion, which in turn affects the system's recognition and positioning of the target sound. Summary of the invention

[0005] The present invention provides an acoustic signal processing method, system, device and medium, which solve the problem of signal aberration and distortion caused by the related acoustic signal processing methods depending on parameter selection.

[0006] In order to achieve the above objectives, this application adopts the following technical solutions:

[0007] In a first aspect, a method for processing an acoustic signal is provided, comprising:

[0008] Acquire an original signal, calculate a short-time energy value of the original signal based on a short-time energy method, and obtain a short-time energy graph according to the short-time energy value;

[0009] Constructing an inverse tangent function overlapping group sparse variational model to perform sparse representation on the original signal;

[0010] Based on the short-time energy graph, determining a regularization parameter of the inverse tangent function overlay sparse variational model and a multiplication factor of the inverse tangent function;

[0011] Optimizing and solving the sparse representation of the original signal based on the inverse tangent function overlapping group sparse variational model;

[0012] A noise reduction signal is obtained according to the solution result.

[0013] In a first possible implementation manner of the first aspect, constructing an inverse tangent function overlapping group sparse variational model to perform a sparse representation on the original signal includes:

[0014] Determine a second-order difference matrix, and determine a second-order variation matrix according to the second-order difference matrix;

[0015] Overlapping group sparse variation and inverse tangent function terms are introduced as penalty terms to construct the objective function model.

[0016] Based on the first possible implementation manner of the first aspect, in a second possible implementation manner of the first aspect, the short-time energy is calculated by the following formula:

[0017]

[0018] In the formula, En is the short-time energy of the signal, N is the window length, and x i is the signal sampling value within the window length N;

[0019] The original signal is expressed as:

[0020] y=x+h,

[0021] In the formula, x is the useful signal; h is the background noise;

[0022] The second-order difference matrix is:

[0023]

[0024] The objective function is:

[0025]

[0026] Where λ is the regularization parameter, K is the number of amplitude points in the original signal,

[0027] φ(||[D2x] n,K ‖2; a) is the penalty term, a is the multiplication factor of the inverse tangent function;

[0028] The inverse tangent function is:

[0029]

[0030] Wherein, ν is the value of each amplitude point of the original signal after the overlapping group sparse variation processing.

[0031] Based on the second possible implementation manner of the first aspect, in a third possible implementation manner of the first aspect, optimizing and solving the sparse representation of the original signal based on an inverse tangent function overlapping group sparse variational model includes:

[0032] Construct an optimization function based on the optimization-minimization algorithm and gradually approach the optimal solution through iterative updates;

[0033] The optimization function is constructed by the following formula:

[0034]

[0035] Expands to:

[0036]

[0037] in,

[0038] Transformed into linear algebra form:

[0039]

[0040] At the same time, the matrix inversion theorem is used to transform and obtain the signal representation after noise reduction:

[0041]

[0042] Based on the third possible implementation manner of the first aspect, in a fourth possible implementation manner of the first aspect, the acoustic signal processing method further includes:

[0043] It is determined whether it is an optimal sparse representation according to the iterative stop condition. If so, a noise reduction signal is obtained according to the optimal sparse representation. If not, the iterative update is continued.

[0044] Based on the fourth possible implementation manner of the first aspect, in a fifth possible implementation manner of the first aspect, the iteration stop condition is a preset number of iterations.

[0045] In a second aspect, a sound signal processing system is provided, comprising:

[0046] A short-time energy module is used to obtain an original signal, calculate a short-time energy value of the original signal based on a short-time energy method, and obtain a short-time energy graph according to the short-time energy value;

[0047] An inverse tangent function overlapping group sparse variation module, used for constructing an inverse tangent function overlapping group sparse variation model, and performing sparse representation on the original signal;

[0048] A parameter determination module, used for determining a regularization parameter of the inverse tangent function overlay sparse variational model and a multiplication factor of the inverse tangent function based on the short-time energy graph;

[0049] A noise reduction module, used for optimizing and solving the sparse representation of the original signal based on the inverse tangent function overlapping group sparse variational model;

[0050] A noise reduction signal is obtained according to the solution result.

[0051] In a first possible implementation manner of the second aspect, the inverse tangent function overlay sparse variation module is specifically used to:

[0052] Determine a second-order difference matrix, and determine a second-order variation matrix according to the second-order difference matrix;

[0053] Overlapping group sparse variation and inverse tangent function terms are introduced as penalty terms to construct the objective function model.

[0054] According to a third aspect, an electronic device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the acoustic signal processing method according to the first aspect when executed by the processor.

[0055] In a fourth aspect, a readable storage medium is provided, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the acoustic signal processing method described in the first aspect are implemented.

[0056] The present application performs short-time energy analysis on the target data to distinguish between useful signals and background noise; based on the identification of useful signals and background noise in the short-time energy graph, the target signal is processed by using the inverse tangent function overlapping group sparse variational method, combined with appropriate parameter selection, which can effectively weaken the impact of background noise on the quality of the target signal, thereby improving the signal-to-noise ratio of the target sound signal. The advantage of the present application is that by considering the different short-time energy values ​​of the useful signal and the background noise, the short-time energy method is combined with the inverse tangent function overlapping group sparse variational method, which solves the problem that the useful information cannot be effectively retained due to over-regularization when the inverse tangent function overlapping group sparse variational method is used for denoising, weakens the impact of background noise on the quality of the target signal, and improves the signal-to-noise ratio of the target signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A schematic flow chart of an acoustic signal processing method provided in an embodiment of the present application;

[0058] Figure 2 A schematic flow chart of another acoustic signal processing method provided in an embodiment of the present application;

[0059] Figure 3 A time domain signal waveform diagram of a single gunshot of background noise provided in an embodiment of the present application;

[0060] Figure 4 A time-frequency diagram of a single gunshot signal containing background noise provided in an embodiment of the present application;

[0061] Figure 5 A short-time energy diagram of a single gunshot signal under background noise provided by an embodiment of the present application;

[0062] Figure 6 A time domain signal diagram of a single gunshot with noise reduction provided in an embodiment of the present application;

[0063] Figure 7 A local diagram of the muzzle wave of a single gunshot with reduced noise provided by an embodiment of the present application;

[0064] Figure 8 A time-frequency diagram of a single gunshot with noise reduction provided in an embodiment of the present application;

[0065] Fig. 9 A waveform diagram of a helicopter time domain signal containing background noise provided in an embodiment of the present application;

[0066] Fig.10 A time-frequency diagram of a helicopter sound signal containing background noise provided in an embodiment of the present application;

[0067] Fig.11 A short-time energy diagram of a helicopter signal under background noise provided by an embodiment of the present application;

[0068] Fig.12 A helicopter time domain signal with noise reduction provided in an embodiment of the present application;

[0069] Fig.13 A time-frequency diagram of a helicopter with reduced noise provided in an embodiment of the present application;

[0070] Fig.14 A structural block diagram of an acoustic signal processing system provided in an embodiment of the present application;

[0071] Fig.15 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0072] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined purpose, the technical solutions in the embodiments of the present application are clearly described. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present application.

[0073] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0074] The description of the method flow in the specification of the present application and the steps of the flowchart in the drawings of the present specification do not have to be strictly executed according to the step numbers, and the method steps can be executed in a different order. Moreover, some steps can be omitted, multiple steps can be combined into one step, and / or one step can be decomposed into multiple steps.

[0075] The following is a detailed description of the acoustic signal processing method, system, device and medium provided in the embodiments of the present application in conjunction with the accompanying drawings and preferred embodiments.

[0076] First, the application scenario of the acoustic signal processing method of the embodiment of the present application is described in detail.

[0077] Preprocessing of gunshots and helicopter signals. In the related technologies: Sallai et al. detected ballistic waves and muzzle waves by taking advantage of the different waveform characteristics of ballistic waves and muzzle waves in the time domain. However, this detection method requires that the interference in the signal be kept at a low level to ensure the accuracy of the detection result; Clavel et al. used linear prediction cepstral coefficients to characterize the sound signal, and used Gaussian mixture model to detect and identify gunshots in a noisy environment. However, the research was mainly limited to Gaussian white noise and did not consider other types of noise; Mkinen et al. obtained gunshot signals through two sensors and proposed a ballistic wave based on the frequency and peak value of transient impact. Determination criteria, but this method has high requirements for the consistency of the detection environment; Jiang Ting tried to use the weighted least squares method to optimize the accuracy of gunfire positioning under noisy conditions, but it is worth noting that the least squares method is mainly suitable for removing DC components and trend terms, and it still has limitations in processing complex noise environments; Wu Chengfeng used a cyclic Wiener filter and combined it with the envelope spectrum based on Hilber transform to extract the helicopter signal submerged in the background noise, thereby improving the detection effect and detection distance of the helicopter; Wang Yong et al. used the Mel spectrum feature extraction method through appropriate parameter settings to improve the recognition ability of helicopter sound signals when the signal-to-noise ratio is low.

[0078] Regarding signal denoising methods, extensive and in-depth research has also been conducted in related technologies. Common methods such as wavelet transform, singular value decomposition and empirical mode decomposition have been widely used in the denoising of nonlinear and non-stationary signals. However, these methods are highly dependent on the selection of parameters in terms of denoising effects, which may cause signal distortion and distortion. For example: if the parameter threshold is set too low, it may lead to overfitting, that is, the noise is overly suppressed, and the useful information in the signal is also removed, resulting in signal distortion; conversely, if the parameter threshold is set too high, it may lead to underfitting, that is, the noise is not fully removed, and the signal is still affected by the noise; inappropriate parameter selection may change the basic characteristics of the signal, such as the amplitude and frequency components of the signal. For gunfire signals, if the noise reduction method used over-smoothes the signal, the subtle differences between the muzzle wave and the ballistic wave may be smoothed out, making it impossible to correctly distinguish the two signal characteristics, which in turn affects the precise calculation of the sniper's position; for helicopter noise, the helicopter noise spectrum is complex. If the noise reduction method cannot adapt well to this complexity, some important frequency components in the signal may be mistakenly regarded as noise and removed, thereby affecting the positioning and tracking of the helicopter.

[0079] In contrast, the signal sparse denoising method is favored for its simple calculation and efficient algorithm iteration. Through sparse approximation, this method can accurately express the important information in the target signal. Wang Chaoge et al. combined the non-convex variable function overlapping group sparse with the VMD algorithm and applied it to the early fault feature extraction of planetary gearboxes, highlighting the fault impact characteristics of gears; Chen et al. incorporated the non-convex variable function into the objective function of the overlapping group sparse method, and successfully applied it to the fields of speech signal denoising and image processing, achieving remarkable results.

[0080] Based on this, the present application provides a method for processing acoustic signals, which is based on short-time energy and inverse tangent function overlapping group sparse variational denoising, and combines the short-time energy algorithm with the inverse tangent function overlapping group sparse variational method to process noisy target data, thereby improving the signal-to-noise ratio of the target acoustic signal.

[0081] This application considers the denoising and detection of different acoustic signals, collects different types of acoustic signals through a sensor array, and then performs sparse variational denoising of the collected data using an inverse tangent function overlapping group. When performing denoising, it is necessary to clarify the muzzle wave and ballistic wave in the gunshot signal and retain the line spectrum information in the helicopter signal. Considering that the short-time energy value of the background noise is small, while the short-time energy of the useful signal is large, a method based on the short-time energy graph is used to calculate the short-time energy values ​​of the gunshot and helicopter signals under the background noise. On this basis, the sparse variational denoising method using an inverse tangent function overlapping group is used for denoising, thereby improving the processing capability of the target signal under different background noise environments. By combining the short-time energy algorithm and the sparse variational algorithm using an inverse tangent function overlapping group, this method is suitable for denoising gunshots and helicopter signals under different background noise conditions.

[0082] See also Figure 1-2 , the present application embodiment provides a method for processing an acoustic signal, such as Figure 1-2 As shown, the acoustic signal processing method of the embodiment of the present application includes the following steps:

[0083] Step S1, obtaining an original signal, calculating a short-time energy value of the original signal based on a short-time energy method, and obtaining a short-time energy graph according to the short-time energy value.

[0084] Among them, the short-time energy method is a signal processing technology. It first divides the signal into multiple frames by a method similar to windowing, and then calculates the energy of each frame as the short-time energy of the frame. Next, the window is gradually moved with a certain step size, and the short-time energy of each new frame is continued to be calculated. In this method, the energy calculation formula is expressed as:

[0085]

[0086] In the formula, En is the short-time energy of the signal, N is the window length, and xi is the signal sampling value within the window length N. The short-time energy En of the signal is calculated by calculating the signal sampling value x within the window length N. i In this process, each x i Represents the sampled value of the signal at a specific moment.

[0087] This step divides the collected original signal into multiple frames and calculates the short-time energy of each frame according to the above formula. Then, based on these short-time energy values, the signal is processed for duration and a short-time energy graph of the collected signal is drawn. The larger value part is determined as the useful signal, and the smaller value part is regarded as background noise.

[0088] Step S2, constructing an inverse tangent function overlapping group sparse variational model to perform sparse representation on the original signal.

[0089] The purpose of this step is to extract the key features of the signal through sparse methods, thereby effectively removing noise interference and maintaining the integrity and accuracy of the signal.

[0090] In the field of signal processing, the received sound signal, that is, the original signal, is usually represented as two parts:

[0091] y=x+h

[0092] Among them, x is the useful signal; h is the background noise, that is, the useless part that interferes with the quality of the sound signal.

[0093] In the process of signal reconstruction, since the performance of the first-order variational method is not ideal, this application proposes an improved solution, which uses a second-order variational matrix to replace the first-order variational matrix. At the same time, considering that the amplitude points of the acoustic signal have obvious clustering characteristics, this application introduces a variational term with overlapping group sparsity as a regularization term to characterize the second-order variational form of the signal. Through this method, the noise-free signal can be estimated more effectively, thereby improving the performance of signal reconstruction.

[0094] Specifically, step S2 includes the following steps:

[0095] Step S201, determine a second-order difference matrix, and determine a second-order variation matrix according to the second-order difference matrix.

[0096] In order to implement the overlapping group sparse regularization of the second-order variation, the second-order difference matrix D2 of the signal x is defined, and its specific expression is given as:

[0097]

[0098] The second-order variation matrix of the collected original signal is determined according to formula D2.

[0099] Step S202, introducing overlapping group sparse variation and arctangent function terms as penalty terms to construct an objective function model.

[0100] The minimum objective function to be optimized can be expressed as:

[0101]

[0102] Wherein, λ is a regularization parameter; the overlapping group sparse variation refers to processing K amplitude points in the original signal (where K represents the size of the overlapping group sparse group) using the following formula, and its specific expansion form is as follows:

[0103]

[0104] In the regularization process, the penalty term can take the form of a convex variable function such as the L1 norm, or a non-convex variable function. In order to reduce the influence of the regularization parameter on the quality of the denoised signal, a non-convex variable function is selected as the penalty term of the overlapping group sparse variation, which can enhance the sparsity of the signal. Considering that the inverse tangent function has significant concavity, this application selects it as a non-convex variable function.

[0105] Specifically, the minimum objective function to be optimized can be further expressed as:

[0106]

[0107] In the formula, λ is the regularization parameter and a is the multiplication factor of the inverse tangent function. At this time, the penalty term means that after the overlapping group sparse variation of the signal is calculated at each amplitude point, the non-convex variable function will be further applied for processing. The objective function is designed to consist of two parts: the first part The purpose of using the norm to implement constraints is to ensure that the denoised signal is as close to the original signal as possible; and the second part φ(||[D2x] n,K ||2; a) As a penalty term, its purpose is to promote the sparsity of the signal.

[0108] According to the objective function formula and the principle of the inverse tangent function overlapping group sparse variation, the penalty term of the target signal to be analyzed is determined, and the overlapping group sparse variation and the inverse tangent function terms are added to construct the objective function model.

[0109] The inverse tangent function is as follows:

[0110]

[0111] Where ν is the value of each amplitude point of the original signal after overlapping group sparse variation processing.

[0112] The optimization process of the objective function is transformed into a convex optimization problem for easy solution. Based on the optimization solution of the objective function model, the optimal sparse representation of the signal can be obtained.

[0113] Step S3, based on the short-time energy graph, determining the regularization parameter of the inverse tangent function overlay sparse variational model and the multiplication factor of the inverse tangent function.

[0114] According to the analysis results in the short-time energy graph, the regularization parameter λ in the penalty term of the inverse tangent function overlapping group sparse variation method and the multiplication factor a in the inverse tangent function are determined under the premise of ensuring the quality of the signal to be analyzed.

[0115] Step S4, optimizing and solving the sparse representation of the original signal based on the inverse tangent function overlapping group sparse variational model.

[0116] To facilitate the solution, this application uses the optimization-minimization algorithm to transform the optimization process of the objective function into a convex optimization problem. By constructing a simple optimization function and gradually approaching the optimal solution through iterative updates, the final optimization result is obtained.

[0117] Therefore, the following formula can be obtained:

[0118]

[0119] The above formula is expanded into:

[0120]

[0121] in, In order to improve the solution efficiency, the above formula is converted into linear algebra form:

[0122]

[0123] At the same time, the matrix inversion theorem is used to transform and obtain the signal representation x after noise reduction:

[0124]

[0125] By solving the above equation within the set number of iterations i, the optimal solution is gradually approached through the iterative update method, and finally the optimization result is obtained, so that the arctangent overlapping group sparse variational denoising of different types of acoustic signals can be realized.

[0126] Step S5, judging whether it is the optimal sparse representation according to the iteration stop condition, if so, obtaining the noise reduction signal according to the optimal sparse representation, if not, continuing the iterative update.

[0127] Specifically, the iteration stop condition may be a preset number of iterations, and whether the preset number of iterations is met is judged; according to the iteration stop condition, once the stop condition is met, useful gunshots and helicopter signals can be separated from background noise.

[0128] Exemplarily, the application process of the present application in a typical single gunshot signal is described in detail below.

[0129] First, the microphone array of the car platform was used to record the gunshot signal of the shooting range on site, and the single gunshot signal of the car platform when it was parked and at a certain speed was obtained, with a sampling frequency of 80kHz. Figure 3-4 , Figure 3 This is the time domain signal waveform of a single gunshot with background noise in the parking state. Figure 4 is the time-frequency diagram of a single gunshot signal containing background noise; Figure 3 , Figure 4 It can be seen that the time domain waveform of a single gunshot contains a lot of noise, which affects the discrimination of the gunshot signal, and the frequency band energy in the time-frequency diagram is evenly distributed due to the influence of background noise.

[0130] Secondly, the window width is set to 2 and the step length is set to 1, and the single gunshot signal is processed using the short-time energy method. Figure 5 The short-time energy diagram of a single gunshot signal under background noise is given. Analysis shows that compared with the short-time energy value of the background noise, the amplitude levels of the muzzle wave and ballistic wave in the single gunshot signal are higher. The short-time energy diagram can effectively distinguish the background noise and useful signal in the single gunshot signal.

[0131] After distinguishing the background noise and the useful signal, the appropriate regularization parameter and multiplication factor are determined according to the amplitude levels of the muzzle wave and the ballistic wave to prevent the over-regularization phenomenon of the inverse tangent function overlapping group sparse variational method.

[0132] Finally, the optimization-minimization method is used to determine the optimal convex function for the single gunshot signal and solve the denoised signal.

[0133] Within the specified number of iterations, the signal equation to be solved is calculated and the Figure 6-Figure 7 The de-noised single gunshot time domain signal and the local image near the muzzle wave are shown. Figure 8 As shown in Figure 6-7, it can be seen that the use of short-time energy and arc tangent function overlapping group sparse variational method can effectively retain the muzzle wave and ballistic wave, while improving the signal-to-noise ratio of the gunshot time domain signal. Figure 8 Analysis shows that because the background noise is suppressed, the frequency band energy is only concentrated near the ballistic wave and muzzle wave.

[0134] By way of example, the typical application process of the present application in improving the signal-to-noise ratio of a helicopter signal is described in detail below.

[0135] First, in the field flight test, the microphone array was used to collect helicopter flight data on site. When collecting helicopter flight data, the background contained wind sound and human voices. The helicopter initially took off from a relatively close position, gradually flew far away, and then returned to its original position at a constant speed. The helicopter sound signal at a certain speed was obtained, with a sampling frequency of 81.92kHz. Fig. 9 The time domain signal waveform of the helicopter containing background noise is given. Fig.10 is the time-frequency diagram of the helicopter sound signal containing background noise. Fig. 9 , Fig.10 It can be seen that the amplitude of the time domain signal is related to the distance the helicopter flies. When the helicopter flies farther, but the time domain signal still has a large amplitude, it is caused by the background noise interference at the test site. There is background noise in the time-frequency diagram, and the line spectrum information is interfered.

[0136] Secondly, the helicopter sound signal is processed using the short-time energy method. Fig.11 The short-time energy diagram of the helicopter signal under background noise shows that the short-time energy value of the background noise is low. After distinguishing the background noise and the useful signal, in order to ensure that the inverse tangent function overlapping group sparse variational method will not produce deviations due to over-regularization, appropriate regularization parameters and multiplication factors are selected.

[0137] Finally, the optimization-minimization method is used to determine the optimal convex function of the helicopter acoustic signal, and then the noise reduction signal is solved.

[0138] Within the specified number of iterations, the signal equation to be solved is calculated and the Fig.12 The noise-reduced helicopter time domain signal is shown. Fig.13 The helicopter time-frequency diagram of the noise reduction is shown. Fig.12 , Fig.13 It can be seen that the signal-to-noise ratio of the helicopter signal after denoising is significantly higher than that of the original time domain waveform, and the line spectrum information in the time-frequency diagram after denoising is highlighted.

[0139] In summary, the present application processes the target sound data received by the sensor array under background noise, considers the retention of muzzle waves, ballistic waves in the gunfire signal, and line spectrum information in the helicopter signal, performs short-time energy analysis on the target data, and realizes the distinction between useful signals and background noise; based on the identification of useful signals and background noise of the short-time energy graph, the target signal is processed by using the inverse tangent function overlapping group sparse variation method, combined with appropriate parameter selection, which can effectively weaken the influence of background noise on the quality of the target signal, thereby improving the signal-to-noise ratio of the target sound signal. Its advantage is that by considering the different short-time energy values ​​of the useful signal and the background noise, the short-time energy method is combined with the inverse tangent function overlapping group sparse variation method, which solves the problem that the useful information cannot be effectively retained due to over-regularization when the inverse tangent function overlapping group sparse variation method is used for denoising, weakens the influence of background noise on the quality of the target signal, and improves the signal-to-noise ratio of the target signal.

[0140] See also Fig.14 , corresponding to the above-mentioned acoustic signal processing method embodiment, the embodiment of the present application provides an acoustic signal processing system, the acoustic signal processing system comprising:

[0141] The short-time energy module 1001 is used to obtain an original signal, calculate a short-time energy value of the original signal based on a short-time energy method, and obtain a short-time energy graph according to the short-time energy value;

[0142] The inverse tangent function contig sparse variation module 1002 is used to construct an inverse tangent function contig sparse variation model to perform sparse representation on the original signal;

[0143] A parameter determination module 1003 is used to determine the regularization parameter of the inverse tangent function overlay sparse variational model and the multiplication factor of the inverse tangent function based on the short-time energy map;

[0144] A noise reduction module 1004 is used to optimize and solve the sparse representation of the original signal based on the inverse tangent function overlapping group sparse variational model;

[0145] A noise reduction signal is obtained according to the solution result.

[0146] Furthermore, the inverse tangent function overlapping group sparse variation module is specifically used for:

[0147] Determine a second-order difference matrix, and determine a second-order variation matrix according to the second-order difference matrix;

[0148] Overlapping group sparse variation and inverse tangent function terms are introduced as penalty terms to construct the objective function model.

[0149] The above-mentioned acoustic signal processing system implements the steps of the above-mentioned acoustic signal processing method embodiment and each process of the embodiment, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0150] See also Fig.15 Corresponding to the above-mentioned embodiment of the acoustic signal processing method, an embodiment of the present application provides an electronic device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps of the above-mentioned embodiment of the acoustic signal processing method and the various processes of the embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0151] The memory 1009 can be used to store software programs and various data. The memory 1009 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instructions required for at least one function (such as a sound playback function, an image playback function, etc.), etc. In addition, the memory 1009 may include a volatile memory or a non-volatile memory, or the memory 1009 may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDRSDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM) and a direct memory bus random access memory (DRRAM). The memory 1009 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0152] The processor 1010 may include one or more processing units; optionally, the processor 1010 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It is understandable that the modem processor may not be integrated into the processor 1010.

[0153] Corresponding to the above-mentioned embodiment of the sound signal processing method, the embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by the processor, the steps of the above-mentioned embodiment of the sound signal processing method and the various processes of the embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0154] The processor is the processor in the electronic device described in the above embodiment of the present application. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.

[0155] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0156] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0157] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A method for processing an acoustic signal, characterized in that: include: Acquire an original signal, calculate a short-time energy value of the original signal based on a short-time energy method, and obtain a short-time energy graph according to the short-time energy value; Constructing an inverse tangent function overlapping group sparse variational model to perform sparse representation on the original signal; Based on the short-time energy graph, determining a regularization parameter of the inverse tangent function overlay sparse variational model and a multiplication factor of the inverse tangent function; Optimizing and solving the sparse representation of the original signal based on the inverse tangent function overlapping group sparse variational model; A noise reduction signal is obtained according to the solution result.

2. The acoustic signal processing method according to claim 1, characterized in that: The step of constructing an inverse tangent function overlapping group sparse variational model to perform sparse representation on the original signal includes: Determine a second-order difference matrix, and determine a second-order variation matrix according to the second-order difference matrix; Overlapping group sparse variation and inverse tangent function terms are introduced as penalty terms to construct the objective function model.

3. The acoustic signal processing method according to claim 2, characterized in that: The short-time energy is calculated by the following formula: In the formula, En is the short-time energy of the signal, N is the window length, and x i is the signal sampling value within the window length N; The original signal is expressed as: y=x+h, In the formula, x is the useful signal; h is the background noise; The second-order difference matrix is: The objective function is: Where λ is the regularization parameter, K is the number of amplitude points in the original signal, φ(||[D2x] n,K ||2; a) is the penalty term, a is the multiplication factor of the inverse tangent function; The inverse tangent function is: Wherein, ν is the value of each amplitude point of the original signal after the overlapping group sparse variation processing.

4. The acoustic signal processing method according to claim 3, characterized in that: The sparse representation of the original signal is optimized based on the inverse tangent function overlapping group sparse variational model, including: Construct an optimization function based on the optimization-minimization algorithm and gradually approach the optimal solution through iterative updates; The optimization function is constructed by the following formula: Expands to: in, Transformed into linear algebra form: At the same time, the matrix inversion theorem is used to transform and obtain the signal representation after noise reduction:

5. The acoustic signal processing method according to claim 4, characterized in that: The acoustic signal processing method further comprises: It is determined whether it is an optimal sparse representation according to the iterative stop condition. If so, a noise reduction signal is obtained according to the optimal sparse representation. If not, the iterative update is continued.

6. The acoustic signal processing method according to claim 5, characterized in that: The iteration stopping condition is a preset number of iterations.

7. An acoustic signal processing system, characterized in that: include: A short-time energy module is used to obtain an original signal, calculate a short-time energy value of the original signal based on a short-time energy method, and obtain a short-time energy graph according to the short-time energy value; An inverse tangent function overlapping group sparse variation module, used for constructing an inverse tangent function overlapping group sparse variation model, and performing sparse representation on the original signal; A parameter determination module, used for determining a regularization parameter of the inverse tangent function overlay sparse variational model and a multiplication factor of the inverse tangent function based on the short-time energy graph; A noise reduction module, used for optimizing and solving the sparse representation of the original signal based on the inverse tangent function overlapping group sparse variational model; A noise reduction signal is obtained according to the solution result.

8. The acoustic signal processing system according to claim 7, characterized in that: The inverse tangent function overlapping group sparse variation module is specifically used for: Determine a second-order difference matrix, and determine a second-order variation matrix according to the second-order difference matrix; Overlapping group sparse variation and inverse tangent function terms are introduced as penalty terms to construct the objective function model.

9. An electronic device, characterized in that: The electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the acoustic signal processing method according to any one of claims 1 to 6 when executed by the processor.

10. A readable storage medium, characterized in that: The readable storage medium stores a program or an instruction, and when the program or the instruction is executed by the processor, the steps of the acoustic signal processing method according to any one of claims 1 to 6 are implemented.

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