Active noise control method and system based on hyperbolic tangent-logarithmic hyperbolic cosine

Abstract

This invention discloses an active noise control method and system based on hyperbolic tangent-logarithmic hyperbolic cosine, belonging to the field of active noise control technology. A primary noise signal is picked up by a reference microphone, and the weight coefficient vector of an adaptive filter is initialized. The noise input vector is filtered to generate an anti-noise signal, which is then played through a secondary speaker. A residual signal is acquired using an error microphone. Based on a robust cost function containing a scale factor and exponential parameters, the residual signal undergoes a nonlinear transformation to obtain a nonlinear error signal. The weight coefficient vector is updated using the nonlinear error signal, iteratively completing the noise control. The system includes a reference microphone, an adaptive filter, a secondary speaker, an error microphone, a secondary path estimation module, and a weight update module. This invention is highly adaptable, computationally efficient, easy to implement in hardware, and can flexibly handle complex noise environments.

Description

Technical Field

[0001] This invention relates to the field of active noise control technology, specifically to an active noise control method and system based on hyperbolic tangent-logarithmic hyperbolic cosine. Background Technology

[0002] In the field of noise pollution control, active noise control technology, with its advantages of strong targeting and significant noise reduction effect, has become one of the core means to solve low- and mid-frequency noise problems. Its core logic is to use adaptive signal processing technology to track the acoustic characteristics of primary noise in real time, and then use an adaptive filter to generate an anti-noise signal with opposite phase and matched amplitude. After being radiated by the acoustic output device, this signal forms destructive interference with the primary noise in the target area, thereby canceling out the noise energy. The Filter-x Least Mean Square (FxLMS) algorithm, as a fundamental and classic algorithm in this field, is widely used in scenarios with relatively simple noise environment requirements, such as consumer electronics and automotive engineering, due to its simple structural design and low computational complexity. It compensates for the influence of the acoustic transmission path through a secondary path estimation stage and updates the filter parameters by combining the mean square error minimization criterion, achieving a relatively ideal noise reduction effect.

[0003] However, real-world engineering environments often exhibit complex non-Gaussian noise characteristics, posing a significant challenge to the adaptability of traditional filtering-x least mean square algorithms. Noise generated by mechanical shocks in industrial production and impact vibrations during construction contains numerous instantaneous, high-amplitude spike pulse components. This type of impact noise directly undermines the algorithm's Gaussian distribution assumption, leading to severe deviations in gradient estimation and consequently causing abnormal fluctuations in the adaptive filter's weight coefficients. Simultaneously, pulse interference introduced by factors such as sensor malfunctions and electromagnetic interference can also disrupt the algorithm's normal convergence process, ultimately causing a sharp decline in the system's noise reduction performance and even leading to uncontrolled divergence.

[0004] To address the stability issues of active noise control under impact noise, Chinese patent CN117831498A (An Active Noise Control Method Based on Euclidean Direction Search of Generalized Entropy) suffers from poor stability and high computational complexity under strong impact noise. It generates a noise input vector and a weight vector, filters the input vector to obtain an anti-noise signal, collects the residual signal, calculates its generalized entropy, and optimizes the weight vector update strategy based on the generalized entropy. Noise control is achieved iteratively, with the core being the precise control of weight vector updates based on generalized entropy, thus improving system stability under impact noise. However, this patent relies on the complex computational logic of generalized entropy, increasing the system's computational overhead. Furthermore, the weight vector update uses an Euclidean direction search strategy, whose convergence rate is significantly affected by the search step size, making it difficult to simultaneously meet the dual requirements of strong impact noise suppression and rapid convergence. Additionally, it lacks a dedicated processing mechanism for the nonlinear characteristics of the error signal, limiting its adaptability to impact noise of varying intensities.

[0005] Therefore, it is urgent to address the core technical pain points of existing algorithms in complex non-Gaussian noise environments, such as insufficient adaptability, poor convergence stability, and cumbersome parameter adjustment. It is necessary to effectively suppress the interference of large-amplitude impact noise, ensure rapid convergence in small error scenarios, simplify parameter adjustment logic, reduce computational complexity, and provide efficient and practical noise reduction technology for complex noise scenarios such as industrial production and construction. Summary of the Invention

[0006] To address the aforementioned technical problems, this application discloses an active noise control method and system based on hyperbolic tangent-logarithmic hyperbolic cosine; the active noise control method based on hyperbolic tangent-logarithmic hyperbolic cosine specifically includes:

[0007] Obtain the primary noise signal generated by the noise source at discrete time nodes, and construct the noise input vector of the filter;

[0008] The filter's weight coefficient vector is initialized, and the noise input vector is filtered based on the weight coefficient vector to generate an anti-noise output signal, which is then played through a secondary speaker.

[0009] The residual signal at the noise cancellation point is acquired by an error microphone. The residual signal is then subjected to a nonlinear transformation based on a preset hyperbolic tangent-log hyperbolic cosine robust cost function to obtain a nonlinear error signal. The robust cost function is composed of a hyperbolic tangent function and a log hyperbolic cosine function and includes two adjustable parameters: a scaling factor and an exponential parameter.

[0010] The weight coefficient vector is updated using the nonlinear error signal to obtain the weight coefficient vector for the next discrete time node;

[0011] Repeat the above steps iteratively until active noise control is completed.

[0012] Preferably, the step of acquiring the primary noise signal generated by the noise source at discrete time nodes and constructing the noise input vector of the filter includes:

[0013] Discrete time nodes are picked up using a reference microphone. Primary noise value Select discrete time nodes and before At that moment Primary noise value Construct the noise input vector , superscript For transpose operation, The number of taps in the filter is denoted as .

[0014] Preferably, the initialization of the filter's weight coefficient vector includes:

[0015] Extracting discrete time nodes and before At that moment Individual weight coefficient Construct the weight coefficient vector , ;

[0016] When discrete time node At that time, the weight coefficient vector Set all elements to 0.

[0017] Preferably, filtering the noise input vector based on the weight coefficient vector to generate an anti-noise output signal includes:

[0018] Calculate the weight coefficient vector With the noise input vector The inner product is then compared with the impulse response of the secondary path. Perform convolution operation to obtain the anti-noise output signal. , ,symbol This is a convolution operation.

[0019] Preferably, the expression for the hyperbolic tangent-logarithmic hyperbolic cosine robust cost function is:

[0020]

[0021] in, The residual signal, The scaling factor is used to control the saturation threshold of the cost function. The exponent parameter is used to control the order of sensitivity to error signals.

[0022] Preferably, the residual signal is subjected to a nonlinear transformation based on a preset hyperbolic tangent-logarithmic hyperbolic cosine robust cost function to obtain a nonlinear error signal, including:

[0023] Based on the robust cost function, the residual signal The nonlinear error signal is derived by taking the derivative and using the gradient descent method. Its expression is: ,in, For hyperbolic secant operation, It is the square term of the hyperbolic secant function.

[0024] Preferably, updating the weight coefficient vector using the nonlinear error signal to obtain the weight coefficient vector for the next discrete time node includes:

[0025] The nonlinear error signal Compared with the reference signal vector filtered by the secondary path estimation model Multiply, then combine the product with the step factor. After multiplication, the weight coefficient vector at the current discrete time node. By superimposing these values, we obtain the weight coefficient vector for the next discrete time node. , .

[0026] Preferably, the parameters It is a positive real number. The robust cost function described above exhibits approximately linear saturation characteristics with respect to errors. It is less sensitive to small errors and can be adjusted Control the severity of the penalty for small errors.

[0027] Preferably, the parameters The value of determines the saturation level of the cost function. The larger the value, the more sensitive it is to small errors and the stronger its ability to suppress large errors. It can be adaptively adjusted according to the statistical characteristics of noise.

[0028] The active noise control system based on hyperbolic tangent-logarithmic hyperbolic cosine includes a reference microphone, an adaptive filter, a secondary speaker, an error microphone, a secondary path estimation module, and a weight update module.

[0029] The reference microphone is used to pick up the primary noise signal generated by the noise source and to construct the noise input vector of the adaptive filter;

[0030] The adaptive filter has a built-in 128th-order FIR filter unit, which is used to initialize the weight coefficient vector and filter the noise input vector based on the weight coefficient vector to generate an anti-noise output signal.

[0031] The secondary loudspeaker is used to play the anti-noise output signal, which forms a destructive interference with the primary noise;

[0032] The error microphone is used to collect the residual signal at the noise cancellation point and transmit it to the weight update module;

[0033] The secondary path estimation module is used to filter the noise input vector to obtain a reference signal vector and transmit it to the weight update module.

[0034] The weight update module incorporates the hyperbolic tangent-logarithmic hyperbolic cosine robust cost function, which is used to perform nonlinear transformation on the residual signal to obtain a nonlinear error signal, and to iteratively update the weight coefficient vector of the adaptive filter based on the nonlinear error signal and the reference signal vector.

[0035] Compared with the prior art, the technical solution of this application has the following technical effects:

[0036] This invention's active noise control method employs a robust cost function combining hyperbolic tangent and logarithmic hyperbolic cosine to achieve refined nonlinear transformation of the residual signal. This cost function incorporates a dual-parameter control system with a scaling factor and an exponential parameter, allowing for flexible parameter adjustments based on the noise characteristics of the actual acoustic scenario. This precisely matches the dynamic changes of noise of different intensities and types, ensuring the active noise control system remains adaptable in various complex environments and enabling targeted processing of noise signals.

[0037] The control method of this invention exhibits excellent shock resistance. When encountering large-amplitude shock noise, the hyperbolic tangent term of the cost function enters a saturated state, while the logarithmic hyperbolic cosine term shows a steady growth characteristic. The synergistic effect of these two terms causes the gradient of the cost function to gradually decay as the magnitude of the residual signal increases. This gradient change characteristic effectively constrains the update amplitude of the filter weight coefficients, avoids abnormal fluctuations in the weight coefficients, ensures that the adaptive filter always operates in a stable state, and maintains the continuous convergence characteristics of the system.

[0038] The hyperbolic tangent-logarithmic hyperbolic cosine robust cost function used in this invention is a globally continuous and differentiable smooth function, perfectly matching the weight coefficient update logic of the gradient descent method. By adjusting the exponential parameter, the penalty strength of the cost function in small error scenarios can be precisely controlled. When the system enters the low-noise residual stage, the convergence accuracy can be optimized, allowing the system to maintain robustness while possessing efficient convergence response capabilities, thus improving the overall performance of noise control.

[0039] The core operations of the control method of this invention only include hyperbolic tangent, hyperbolic secant, logarithmic and fundamental powers, and fractional operations. The computational logic is simple and the hardware execution efficiency is high, which can meet the computational requirements of real-time noise control. The algorithm architecture of this method has good compatibility and can be directly extended to multi-channel active noise control systems without large-scale reconstruction. It takes into account both the convenience of engineering implementation and the wide range of application scenarios, making it easy to implement in various noise control scenarios.

[0040] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the preferred embodiments of this application are described in detail below with reference to the accompanying drawings.

[0041] The above and other objects, advantages and features of this application will become more apparent to those skilled in the art from the following detailed description of specific embodiments in conjunction with the accompanying drawings. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In all drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0043] Based on the description of the figures and their corresponding technical content in the document, the titles of the figures are as follows:

[0044] Figure 1 A schematic diagram of the core steps of an active noise control method based on hyperbolic tangent-logarithmic hyperbolic cosine.

[0045] Figure 2 A detailed flowchart illustrating the process of using the hyperbolic tangent-logarithmic hyperbolic cosine robust cost function to process residual signals;

[0046] Figure 3 : An architecture diagram of an active noise control system based on hyperbolic tangent-logarithmic hyperbolic cosine, which includes multiple modules working collaboratively;

[0047] Figure 4 Schematic diagram of time-domain waveforms of three types of α-stable distribution impulse noise with α values ​​of 1.3, 1.5, and 1.7;

[0048] Figure 5The figure shows a comparison of the average noise residual ratio of the method of this invention and two comparative algorithms for processing impact noise of different intensities. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. In the following description, specific details such as specific configurations and components are provided merely to help fully understand the embodiments of this application. Therefore, those skilled in the art should understand that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. In addition, for clarity and brevity, descriptions of known functions and structures are omitted in the embodiments.

[0050] It should be understood that the phrase "an embodiment" or "this embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "an embodiment" or "this embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments.

[0051] Furthermore, reference numerals and / or letters may be repeated in different examples within this application. Such repetition is for the purpose of simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or settings discussed.

[0052] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist simultaneously. The term " / and" in this article describes another type of relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " in this article generally indicates that the related objects before and after it are in an "or" relationship.

[0053] In this article, the term "at least one" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, "at least one of A and B" can mean: A exists alone, A and B exist simultaneously, or B exists alone.

[0054] It should also be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion.

[0055] Example 1

[0056] This embodiment mainly describes an active noise control method based on hyperbolic tangent-logarithmic hyperbolic cosine, such as... Figure 1 As shown, it specifically includes:

[0057] Obtain the primary noise signal generated by the noise source at discrete time nodes, and construct the noise input vector of the filter;

[0058] The filter's weight coefficient vector is initialized, and the noise input vector is filtered based on the weight coefficient vector to generate an anti-noise output signal, which is then played through a secondary speaker.

[0059] The residual signal at the noise cancellation point is acquired by an error microphone. The residual signal is then subjected to a nonlinear transformation based on a preset hyperbolic tangent-log hyperbolic cosine robust cost function to obtain a nonlinear error signal. The robust cost function is composed of a hyperbolic tangent function and a log hyperbolic cosine function and includes two adjustable parameters: a scaling factor and an exponential parameter.

[0060] The weight coefficient vector is updated using the nonlinear error signal to obtain the weight coefficient vector for the next discrete time node;

[0061] Repeat the above steps iteratively until active noise control is completed.

[0062] Furthermore, the primary noise signal generated by the noise source at discrete time nodes is acquired, the noise input vector of the filter is constructed, and the acoustic signal radiated by the noise source is acquired in real time through a reference microphone to accurately capture the discrete time nodes. Corresponding primary noise value ,in A positive integer representing the discretized time unit, used to distinguish signal data at different times. (The current discrete time node is used as the reference.) Based on this, trace back the consecutive previous... At that moment, extract the The primary noise value at each time point, and the primary noise value at the current time. The common components include A primary noise sequence of elements, specifically, is... ,in The fixed number of taps for the filter, and This tap configuration is compatible with the 128th-order FIR filter unit structure of the adaptive filter. Following the chronological order, the above... The initial noise values ​​are constructed into a noise input vector in column vector form. Its formula is superscript in the formula This represents the transpose operation of a vector, which converts a one-dimensional row sequence into a column vector, ensuring that the vector dimension matches the computational requirements of the subsequent filter weight coefficient vector, and providing a standardized input signal carrier for filtering processing.

[0063] Further, the weight coefficient vector of the filter is initialized, and the noise input vector is filtered based on the weight coefficient vector to generate an anti-noise output signal, which is then played through a secondary speaker, including:

[0064] Initialization of the filter weight coefficient vector: For a 128th-order FIR adaptive filter, extract its values ​​at discrete time nodes. The weight coefficients below ,in The number of filter taps and the noise input vector. The dimensions remain consistent, forming a structure containing A sequence of weight coefficients for each element. Construct a weight coefficient vector. Its formula is This column vector structure is adapted to noise input vectors in column vector form. The inner product operation rules. Initialization is performed on the weight coefficient vector, and when the discrete-time nodes satisfy... When the condition is met, it indicates that the filter is in the initial startup phase, at which point the weight coefficient vector is... All elements are set to 0 to complete the initial state configuration of the filter, avoiding interference from the random values ​​of the weight coefficients in the initial stage, and ensuring the operational stability of the filter in the early stage of iteration.

[0065] The filtering process generates an anti-noise output signal. Based on the linear filtering logic of the FIR adaptive filter, the discrete time nodes are first processed. The weight coefficient column vector With noise input column vector Performing the inner product operation involves multiplying each element of the weight coefficient vector by the corresponding element of the noise input vector, and then summing the results to obtain the filtered base signal. ,in weight coefficient vector The transpose of the vector is used to convert the column vector into a row vector, ensuring dimension matching for the inner product operation. The filtered base signal obtained from the inner product operation is then compared with the impulse response of the secondary path. Perform a convolution operation; the convolution operator is... This convolution operation is used to compensate for the influence of the acoustic transmission path from the filter output to the secondary speaker on the signal, including characteristics such as signal attenuation and time delay, so that the generated signal can match the actual acoustic propagation law. Through the above convolution operation, the discrete time node is finally obtained. The anti-noise output signal is output to the secondary speaker. Its formula is After the anti-noise output signal is generated, it is converted into an acoustic signal by a secondary loudspeaker and radiated outward, providing an acoustic basis for the destructive interference with the primary noise in the target area.

[0066] Furthermore, residual signals at noise cancellation points are acquired using an error microphone. Based on a preset hyperbolic tangent-logarithmic hyperbolic cosine robust cost function, a nonlinear transformation is performed on the residual signals to obtain a nonlinear error signal, including:

[0067] For residual signal acquisition, an error microphone is deployed in the target noise cancellation area, which is the core region where primary noise and anti-noise undergo destructive interference. The error microphone is used to monitor the residual state of the acoustic signal in this region in real time. The residual acoustic signal in the target region is converted into an electrical signal using the error microphone, yielding discrete-time nodes. The residual signal below This signal is in scalar form and directly reflects the cancellation effect between anti-noise and primary noise at the current moment. It is the core feedback basis for subsequent filter weight coefficient updates, and its amplitude is negatively correlated with the noise cancellation effect.

[0068] The preset hyperbolic tangent-logarithmic hyperbolic cosine robust cost function is a smooth function specifically designed for nonlinear processing of error signals, and its formula is as follows: In the formula Discrete time nodes The residual signal below, The absolute value of the residual signal is used to ensure the non-negativity of the error term; Let be the scaling factor, and let its values ​​satisfy . The core function of the constraint condition is to control the saturation threshold of the cost function. The larger the value of , the more sensitive the cost function is to small errors and the stronger its suppression effect on large errors; For the exponential parameter, its value satisfies The constraints, and It can be a positive integer or a positive real number, used to control the order of sensitivity of the cost function to the error signal. The cost function exhibits an approximately linear saturation characteristic with respect to error. It is less sensitive to small errors and can be adjusted The penalty intensity for small errors is controlled. This cost function achieves nonlinear compression of the error signal by combining the hyperbolic tangent function and the log-hyperbolic cosine function, while maintaining the continuous differentiability of the function.

[0069] The nonlinear error signal is obtained based on the nonlinear transformation of the robust cost function. Based on the optimization principle of gradient descent, the hyperbolic tangent-log-hyperbolic cosine robust cost function is applied to the residual signal. The first derivative is calculated to obtain the gradient information of the cost function. This gradient information directly determines the update direction and magnitude of the weight coefficient vector. Using this gradient information, the discrete time nodes are derived. Nonlinear error signal Its formula is: In the formula The hyperbolic secant operation is a derivative operation of the hyperbolic tangent function, satisfying the following conditions: , Let the square term of the hyperbolic secant function take values ​​that vary with the independent variable. The increase of has an exponential decay trend, which can effectively suppress the influence of large-value residual signals on the gradient; The absolute value of the residual signal The power term is used to adjust the intensity of the nonlinear transformation under different error amplitudes; finally, the hyperbolic secant square term, the power term, and the residual signal are combined. By multiplying them sequentially, the nonlinear error signal is obtained. The signal is in scalar form, which realizes the nonlinear transformation of the residual signal, thus preserving the effective feedback information of the small error signal and suppressing the adverse effects of large-amplitude impact noise.

[0070] Furthermore, the weight coefficient vector is updated using the nonlinear error signal to obtain the weight coefficient vector for the next discrete time node, and a reference signal vector is constructed. This vector represents discrete-time nodes. The noise input vector via secondary path estimation model Filtered signal. Secondary path estimation model. This is an estimation model of the acoustic path transfer function of the secondary speaker-noise cancellation point-error microphone stage. This model is used to estimate the noise input vector. Filtering is performed to simulate the transmission process of the anti-noise signal in the secondary path, generating a reference signal vector that matches the actual signal characteristics after transmission. This vector is related to the noise input vector. These are column vectors of the same dimension, used to compensate for the impact of secondary paths on weight coefficient updates, avoiding update biases caused by acoustic path characteristics. A step size factor is selected. This factor is an adjustable positive parameter, and its value directly determines the update rate of the weight coefficient vector. A larger value results in faster weight coefficient updates but affects system stability; a smaller value results in better system stability but slower convergence. The step size factor can be adjusted according to the characteristics of the actual noise environment. The value of . The discrete time nodes. Nonlinear error signal With reference signal vector Perform scalar multiplication to obtain the basic value of the update increment in vector form, and then multiply this basic value by the step size factor. Perform scalar multiplication to obtain the final weight coefficient vector update increment. Discrete time nodes The weight coefficient vector below Perform vector addition with the above update increment to obtain the next discrete time node. The weight coefficient vector below Its formula is The update formula is in vector operation form. Each element in the weight coefficient vector is adjusted according to the corresponding update increment to ensure that the weight coefficient vector can adaptively track changes in noise characteristics.

[0071] In obtaining discrete time nodes weight coefficient vector Then, update the counts of the discrete time nodes to... Using the updated time node as the new current moment, the steps of constructing the noise input vector, initializing the weight coefficient vector and filtering to generate the anti-noise output signal, acquiring the residual signal and generating the nonlinear error signal, and updating the weight coefficient vector are repeatedly executed to form a closed-loop iterative operation process. During the iteration process, as the time node continues to advance, the filter's weight coefficient vector... Continuously based on nonlinear error signals Perform adaptive updates and noise reduction output signal. The system also dynamically adjusts to ensure that the anti-noise response can match the acoustic characteristics changes of the primary noise in real time, achieving continuous noise suppression. Iterative calculations continue to execute until preset noise control termination conditions are met, including the completion of the noise control target task, the residual signal amplitude at the noise cancellation point reaching a preset stable range, and the receipt of an external noise control stop command. At this point, the entire iterative process terminates, completing all operations of active noise control.

[0072] This implementation achieves refined nonlinear transformation of the residual signal through a robust cost function combining hyperbolic tangent and logarithmic hyperbolic cosine and a dual-parameter control system. It can effectively suppress abnormal fluctuations in weight coefficients caused by large-amplitude impact noise by leveraging the gradient decay characteristics of the cost function, ensuring the continuous and stable convergence of the adaptive filter, and can also flexibly adjust parameters to adapt to different noise scenarios.

[0073] Example 2 describes in detail an active noise control system based on hyperbolic tangent-logarithmic hyperbolic cosine, such as... Figure 2 As shown, it includes a reference microphone, an adaptive filter, a secondary speaker, an error microphone, a secondary path estimation module, and a weight update module;

[0074] A reference microphone is used to pick up the primary noise signal generated by the noise source and to construct the noise input vector of the adaptive filter;

[0075] The adaptive filter has a built-in 128th-order FIR filter unit, which is used to initialize the weight coefficient vector and filter the noise input vector based on the weight coefficient vector to generate an anti-noise output signal.

[0076] The secondary speaker is used to play the anti-noise output signal, which forms a destructive interference with the primary noise;

[0077] The error microphone is used to collect the residual signal at the noise cancellation point and transmit it to the weight update module;

[0078] The secondary path estimation module is used to filter the noise input vector to obtain a reference signal vector and transmit it to the weight update module.

[0079] The weight update module incorporates a hyperbolic tangent-logarithmic hyperbolic cosine robust cost function, which is used to perform nonlinear transformation on the residual signal to obtain a nonlinear error signal, and to iteratively update the weight coefficient vector of the adaptive filter based on the nonlinear error signal and the reference signal vector.

[0080] Furthermore, the reference microphone accurately captures the primary noise acoustic signals generated by the noise source at consecutive discrete time points and converts them into standardized electrical signals. At discrete time points... The reference microphone picks up the primary noise value at the current moment. At the same time, automatic caching before Primary noise values ​​at consecutive time points ( (consistent with the number of taps in the adaptive filter), forming a system containing... The noise value sequence of each element is used. Based on this sequence, the reference microphone performs a column vector construction operation in chronological order to generate the noise input vector required by the adaptive filter. This ensures that the vector dimension matches the computational requirements of the adaptive filter, providing a standardized input carrier for subsequent filtering processing.

[0081] Furthermore, the adaptive filter incorporates a fixed-configuration 128th-order FIR (Finite Length Unit Impulse Response) filter unit, the structure of which determines the number of taps in the filter. It adapts to the convolution operation requirements of noisy signals at multiple time points. Its core operation process consists of two steps: weight coefficient vector initialization and filtered signal generation.

[0082] Weight coefficient vector initialization: The filter extracts the current discrete time node. of Each weight coefficient is used to construct a weight coefficient vector. When discrete time nodes At that time, the filter is in the initial startup phase, and the weight coefficient vector... All elements are set to 0 to avoid computational interference in the initial state and ensure system startup stability.

[0083] Filtering to generate anti-noise signals: Based on the FIR filtering principle, the filter first adjusts the weight coefficient vector... With noise input vector Perform inner product operation to obtain the filtered base signal; then multiply this base signal with the impulse response of the secondary path. The convolution operation is performed to compensate for the influence of the acoustic transmission path from the filter to the secondary speaker on the signal, ultimately generating an anti-noise output signal. This signal is a prototype of an acoustic signal with amplitude matching and phase opposite to the primary noise, providing a basis for destructive interference.

[0084] Furthermore, the secondary loudspeaker, acting as the acoustic signal output terminal, receives the anti-noise output electrical signal transmitted by the adaptive filter and efficiently converts it into an acoustic signal for external playback. Its core function is to precisely release the standardized anti-noise signal into the noise control area, allowing the anti-noise and primary noise to meet in space and form destructive interference. Through the equal amplitude and opposite phase characteristics of the anti-noise and primary noise, the acoustic energy of the primary noise is canceled out, thereby reducing the overall noise intensity in the noise control area. The loudspeaker's output power and acoustic response characteristics are optimized to ensure that the propagation range and amplitude stability of the anti-noise signal are matched with the distribution characteristics of the primary noise, guaranteeing the uniformity and reliability of the destructive interference effect.

[0085] Furthermore, an error microphone is deployed in the target area for noise cancellation (i.e., the core location where de-interference occurs) to monitor the residual acoustic signal after de-interference in real time and convert it into a residual signal in the form of an electrical signal. The residual signal directly characterizes the noise control effect at the current moment and is the core feedback basis for the system's adaptive adjustment. When the residual signal amplitude is large, it indicates that the current anti-noise signal and the primary noise are not well matched, requiring optimization through subsequent weight coefficient updates. When the residual signal amplitude is small and tends to be stable, it indicates that the system has achieved the ideal noise control effect. The error microphone has high sensitivity and low noise characteristics, enabling it to accurately capture weak residual noise signals, ensuring the authenticity and accuracy of the feedback signal, and providing reliable input data for the weight update module.

[0086] Furthermore, the secondary path estimation module processes the noise input vector. Perform filtering to generate a reference signal vector. Essentially, it estimates and compensates for the transfer function of the secondary acoustic path, from the loudspeaker to the noise cancellation point to the error microphone. The module has a built-in secondary path estimation model. This model models the acoustic characteristics of the secondary acoustic path (such as signal attenuation, time delay, frequency response, etc.) and the noise input vector. Perform filtering operations to simulate the transmission process of the anti-noise signal in the secondary path, and generate a reference signal vector that matches the characteristics of the signal after actual transmission. This vector is used in subsequent weight coefficient update calculations, which can compensate for the influence of secondary paths on the signal, avoid weight coefficient update deviations caused by acoustic path characteristics, and ensure the convergence stability and control accuracy of the system.

[0087] Furthermore, the weight update module incorporates a hyperbolic tangent-logarithmic hyperbolic cosine robust cost function. Through nonlinear transformation and iterative updating of weight coefficients, it enables the system to adaptively adapt to noisy environments. The specific calculation process is as follows:

[0088] Robust cost function configuration: The module's built-in hyperbolic tangent-log-hyperbolic cosine robust cost function is composed of a hyperbolic tangent function and a log-hyperbolic cosine function, and includes a scaling factor. and exponential parameters Two adjustable parameters can be flexibly configured according to noise statistical characteristics.

[0089] Nonlinear error signal generation: Based on the gradient descent optimization principle, the module optimizes the robust cost function with respect to the residual signal. By taking the first derivative, the nonlinear error signal is derived. Through this nonlinear transformation, the influence of large-amplitude residual signals is effectively suppressed, while the feedback effect of small-error signals is preserved.

[0090] Iterative update of weight coefficient vector: The module updates the nonlinear error signal Reference signal vector generated by the secondary path estimation module Multiply, then add the step factor. (Adjustable parameter, adjusting update rate) multiply to obtain the weight coefficient update increment; this increment is then multiplied by the weight coefficient vector at the current time. Superimpose to generate the weight coefficient vector for the next discrete time node. The signal is then fed back to the adaptive filter to update the weighting coefficients. Through continuous iteration of this process, the anti-noise signal is constantly optimized, ensuring that the system maintains good noise control performance even in complex noise environments.

[0091] This embodiment system ensures the stability and convergence of the adaptive filter weight coefficient update. It adapts to different noise scenarios through flexible parameter configuration and improves the accuracy of weight coefficient update by leveraging the signal compensation function of the secondary path estimation module. It can be extended to multi-channel scenarios and achieves a synergistic improvement in robustness, adaptability and engineering practicality in complex noise environments, significantly optimizing the active noise control effect.

[0092] Based on Embodiment 1 or 2, this embodiment designs a multi-scenario comparative simulation experiment to verify the effectiveness and superiority of this application. The experiment compares the following algorithms: Filter-x Least Mean Square (FxLMS), Robust FxLMS based on Huber function (RFxLMS, ​​corresponding to reference 1 Nithin V. George and Ganapati Panda. A robust filtered-s LMS algorithm for nonlinear active noise control[J]. Applied Acoustics, vol. 73,pp: 836–841, 2012), and FxEHSAF based on exponential hyperbolic secant (corresponding to reference 2 Tanveer Alam Khan and Somanath Pradhan. Robust exponential hyperbolic secant algorithm for active control against impulsive noise environments[J]. IEEE Signal Processing Letters, vol. 33, 2026). The evaluation is carried out from two core dimensions: noise suppression effect and convergence speed. All experiments are performed on the same hardware simulation platform and software environment.

[0093] The acoustic path modeling in the experiment was implemented using high-order FIR filters. Both the main and secondary paths used filters of order 256 to accurately simulate the time delay, attenuation, and frequency response characteristics of signal transmission in a real acoustic environment. The active noise controller employed a 128th-order FIR adaptive filter with L=128 taps, consistent with the dimensions of the weighting coefficient vector and the noise input vector. The sampling frequency was set to 16kHz, with each experimental data set consisting of 10,000 discrete time points. Each experimental scenario was independently repeated 30 times, and the final result was averaged to eliminate the influence of random errors. The core parameters involved in the experiment were uniformly configured as follows: step size factor. =0.0001, the scaling factor λ=0.3 and the exponent parameter p=1.0 of the method in this application; the parameters of the comparison algorithms are all set according to the optimal configuration of their original literature, of which the threshold parameter of the RFxLMS algorithm is set to 1.5 and the kernel bandwidth parameter of the FxEHSAF algorithm is set to 0.8.

[0094] like Figure 4 As shown, the experiment selected three types of α-stable distribution impulse noise of different intensities as primary noise to cover three typical non-Gaussian noise scenarios: light, medium, and heavy. The α values ​​were set to 1.3, 1.5, and 1.7, respectively. The smaller the characteristic index α of the α-stable distribution, the stronger the impulse of the noise and the higher the amplitude and density of the spike pulses. Specifically, α=1.3 corresponds to high-intensity impulse noise, α=1.5 corresponds to medium-intensity impulse noise, and α=1.7 corresponds to low-intensity impulse noise. The time-domain characteristics of the three types of primary noise were obtained through... Figure 4 Visual representation: Time-domain waveform of high-intensity impact noise with α=1.3 Figure 4 a. This waveform contains a large number of dense, high-amplitude spike pulses, with the pulse peak amplitude to background noise amplitude ratio reaching 22:1. The duration of a single pulse is concentrated in 1-3 sampling points, and the pulse interval is randomly distributed between 5-20 sampling points, resulting in severe overall noise fluctuations; the time-domain waveform of moderate-intensity impulse noise with α=1.5 corresponds to... Figure 4 b, compared to Figure 4 a) The amplitude and frequency of the spike pulses are significantly reduced, the ratio of the peak pulse amplitude to the background noise amplitude is approximately 16:1, the pulse duration remains mainly 1-3 sampling points, the pulse interval is extended to 10-30 sampling points, and the waveform fluctuation amplitude is somewhat smoothed out; the time-domain waveform of low-intensity impulse noise with α=1.7 corresponds to Figure 4 c. The amplitude of the spike pulse in the waveform is further reduced, the ratio of the peak pulse amplitude to the background noise amplitude is about 11:1, the frequency of pulse occurrence is greatly reduced, the pulse interval is concentrated between 20-50 sampling points, the overall waveform is closer to Gaussian noise but still contains obvious non-Gaussian pulse components, which is consistent with the actual characteristics of low-intensity impact noise in industrial production.

[0095] like Figure 5As shown, to visually demonstrate the denoising effects of different algorithms, three types of primary noise were processed by the method of this application and the comparison algorithm, respectively. The average noise residual ratio (the ratio of the root mean square value of the filtered residual signal to the root mean square value of the unfiltered primary noise, in dB; the smaller the value, the better the denoising effect) was obtained. Figure 5 The processing results for high-intensity impact noise with α=1.3 correspond to Figure 5 Figure a shows the graph, where the horizontal axis represents discrete time points and the vertical axis represents the average noise residual ratio. It is clearly observed that the proposed method converges rapidly in the early stages of iteration (approximately 1200 time points), achieving a stable average noise residual ratio of -33.2 dB. In contrast, the RFxLMS algorithm requires 3800 iterations to stabilize, with a stable average noise residual ratio of -21.5 dB, and the FxEHSAF algorithm takes approximately 3200 time points to reach a stable average noise residual ratio of -25.1 dB. The proposed method demonstrates significant advantages in both noise reduction depth and convergence speed. The processing results for moderate-intensity impact noise with α=1.5 are also shown. Figure 5 b, The coordinate settings of this graph are... Figure 5 The convergence and stabilization time of the method in this application is further shortened to 1000 time nodes, and the average noise residual ratio after stabilization reaches -36.5dB. The average noise residual ratio after stabilization for the RFxLMS algorithm is -26.3dB, with a convergence and stabilization time of approximately 3000 time nodes, and the average noise residual ratio after stabilization for the FxEHSAF algorithm is -28.7dB, with a convergence and stabilization time of approximately 2500 time nodes. The noise reduction advantage of the method in this application is maintained. The processing results for low-intensity impact noise with α=1.7 correspond to... Figure 5 c. The coordinate settings of this graph are consistent with those described above. The method of this application reaches a stable convergence state after 800 time nodes, with an average noise residual ratio of -39.8dB after stabilization and a steady-state fluctuation amplitude of less than ±0.4dB, demonstrating excellent stability. The average noise residual ratio of the RFxLMS algorithm after stabilization is -31.2dB, and the convergence stabilization time is approximately 2200 time nodes. The average noise residual ratio of the FxEHSAF algorithm after stabilization is -33.5dB, and the convergence stabilization time is approximately 1800 time nodes. The advantages of the convergence speed and noise reduction accuracy of the method of this application in low-intensity impact noise scenarios are further highlighted.

[0096] The simulation results show that, regardless of whether the impact noise environment is high-intensity, medium-intensity, or low-intensity, the method of this application can maintain a faster convergence speed and a lower steady-state noise residue, which fully verifies its robustness and superiority in non-Gaussian impact noise environment. At the same time, the noise residue fluctuation amplitude after stabilization is smaller, indicating that the system has stronger operational stability and can adapt to noise environments with different statistical characteristics, providing a reliable technical solution for active noise control in complex noise scenarios.

[0097] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any changes, modifications, substitutions, integrations, and parameter changes made to these embodiments within the spirit and principles of the present invention, without departing from the principles and spirit of the present invention, through conventional substitutions or to achieve the same function, fall within the scope of protection of the present invention.

Claims

1. A method of active noise control based on hyperbolic tangent- hyperbolic cosine- logarithm, characterized by, include: Obtain the primary noise signal generated by the noise source at discrete time nodes, and construct the noise input vector of the filter; The filter's weight coefficient vector is initialized, and the noise input vector is filtered based on the weight coefficient vector to generate an anti-noise output signal, which is then played through a secondary speaker. The residual signal of the noise cancellation point is collected by the error microphone. The residual signal is nonlinearly transformed based on the preset hyperbolic tangent-log hyperbolic cosine robust cost function to obtain the nonlinear error signal. The robust cost function is composed of a combination of hyperbolic tangent function and log hyperbolic cosine function and includes two adjustable parameters: scale factor and exponential parameter. The weight coefficient vector is updated using the nonlinear error signal to obtain the weight coefficient vector for the next discrete time node; Repeat the above steps iteratively until active noise control is complete.

2. The hyperbolic tangent-hyperbolic cosine logarithmic sine-based active noise control method according to claim 1, characterized by, The step of acquiring the primary noise signal generated by the noise source at discrete time nodes and constructing the noise input vector of the filter includes: Discrete time nodes are picked up using a reference microphone. Primary noise value Select discrete time nodes and before At that moment Primary noise value Construct the noise input vector , superscript For transpose operation, The number of taps in the filter is denoted as .

3. The hyperbolic tangent-hyperbolic cosine logarithmic sine-based active noise control method according to claim 1, characterized by, The initialization of the filter's weight coefficient vector includes: At discrete time nodes , extract a current time's weight coefficients , construct the weight coefficient vector , ; When discrete time nodes At that time, the weight coefficient vector Set all elements to 0.

4. The active noise control method based on hyperbolic tangent-logarithmic hyperbolic cosine as described in claim 3, characterized in that, The noise input vector is filtered based on the weight coefficient vector to generate an anti-noise output signal, including: Calculate the weight coefficient vector With the noise input vector The inner product is then compared with the impulse response of the secondary path. Perform convolution operation to obtain the anti-noise output signal. , ,symbol This is a convolution operation.

5. The active noise control method based on hyperbolic tangent-logarithmic hyperbolic cosine as described in claim 1, characterized in that, The expression for the hyperbolic tangent-log hyperbolic cosine robust cost function is as follows: in, The residual signal, The scaling factor is used to control the saturation threshold of the cost function. The exponent parameter is used to control the order of sensitivity to error signals.

6. The active noise control method based on hyperbolic tangent-logarithmic hyperbolic cosine as described in claim 5, characterized in that, The residual signal is nonlinearly transformed based on a preset hyperbolic tangent-logarithmic hyperbolic cosine robust cost function to obtain a nonlinear error signal, including: Based on the robust cost function, the residual signal The nonlinear error signal is derived by taking the derivative and using the gradient descent method. Its expression is: ,in, For hyperbolic secant operation, It is the square term of the hyperbolic secant function.

7. The active noise control method based on hyperbolic tangent-logarithmic hyperbolic cosine as described in claim 6, characterized in that, The weight coefficient vector is updated using the nonlinear error signal to obtain the weight coefficient vector for the next discrete time node, including: The nonlinear error signal Compared with the reference signal vector filtered by the secondary path estimation model Multiply, then multiply the product by the step factor. After multiplication, the weight coefficient vector at the current discrete time node is obtained. By superimposing these values, we obtain the weight coefficient vector for the next discrete time node. , .

8. The active noise control method based on hyperbolic tangent-logarithmic hyperbolic cosine as described in claim 6, characterized in that, The parameters It is a positive real number. The robust cost function described above exhibits approximately linear saturation characteristics with respect to errors. It is less sensitive to small errors and can be adjusted Control the severity of the penalty for small errors.

9. The active noise control method based on hyperbolic tangent-logarithmic hyperbolic cosine as described in claim 8, characterized in that, The parameters The value of determines the saturation level of the cost function. The larger the value, the more sensitive it is to small errors and the stronger its ability to suppress large errors. It can be adaptively adjusted according to the statistical characteristics of noise.

10. An active noise control system based on hyperbolic tangent-logarithmic hyperbolic cosine, characterized in that, It includes a reference microphone, an adaptive filter, a secondary speaker, an error microphone, a secondary path estimation module, and a weight update module; The reference microphone is used to pick up the primary noise signal generated by the noise source and to construct the noise input vector of the adaptive filter; The adaptive filter has a built-in 128th-order FIR filter unit, which is used to initialize the weight coefficient vector and filter the noise input vector based on the weight coefficient vector to generate an anti-noise output signal. The secondary loudspeaker is used to play the anti-noise output signal, which forms a destructive interference with the primary noise; The error microphone is used to collect the residual signal at the noise cancellation point and transmit it to the weight update module; The secondary path estimation module is used to filter the noise input vector to obtain a reference signal vector and transmit it to the weight update module. The weight update module incorporates the hyperbolic tangent-logarithmic hyperbolic cosine robust cost function, which is used to perform nonlinear transformation on the residual signal to obtain a nonlinear error signal, and to iteratively update the weight coefficient vector of the adaptive filter based on the nonlinear error signal and the reference signal vector.

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