Balance adjustment method, system, device and medium for mixing console volume controller

Abstract

The present invention relates to the technical field of mixing consoles, and discloses a method, system, device and medium for equalizing and adjusting the volume controller of a mixing console. The method comprises: processing an audio signal at an input end of the mixing console to obtain an initial system parameter matrix; performing probability density function calculation on the initial system parameter matrix to obtain an audio state data matrix and a noise characteristic data matrix; performing first-order volume control calculation based on the audio state data matrix to obtain a volume control data matrix; performing second-order frequency equalization calculation based on the volume control data matrix to obtain a frequency equalization control data matrix; performing iterative optimization calculation on control parameters to obtain an optimal control parameter matrix; performing dual-channel parallel calculation on the transfer function of the volume control channel and the transfer function of the frequency equalization channel to obtain an audio signal at the output end of the mixing console. The present invention realizes independent adjustment of volume control and frequency equalization, and ensures high quality of audio output.

Description

Technical Field

[0001] The present invention relates to the technical field of mixing consoles, and in particular to a method, system, device and medium for adjusting the balance of a volume controller of a mixing console. Background Art

[0002] In recent years, with the widespread application of professional audio equipment, the volume control and frequency equalization performance of the mixer as the core audio processing equipment directly affect the sound quality of the entire audio system. The traditional mixer volume controller mainly adopts the proportional integral differential (PID) control strategy. In practical applications, this method has a contradiction between response speed and sound distortion, and it is difficult to take into account both the fast response and stability of the system.

[0003] In the actual audio processing process, environmental noise interference and the uncertainty of audio signals pose great challenges to the volume control system. Existing control methods often adopt fixed parameter control strategies, which cannot effectively cope with complex and changeable audio environments, especially in scenarios with strong noise interference, where the system's anti-interference ability is particularly insufficient. In addition, the parameter setting process of the mixing console volume controller usually relies on manual experience, which is not only time-consuming and inefficient, but also difficult to ensure the accuracy and repeatability of parameter optimization. Especially in multi-band equalization adjustment, traditional methods are difficult to achieve precise control of different frequency bands, which can easily cause an imbalance in the frequency response and affect the overall sound quality. Summary of the invention

[0004] The present invention provides a method, system, device and medium for equalizing and adjusting the volume controller of a mixing console. The present invention realizes independent adjustment of volume control and frequency equalization, so that the system can perform precise control on different frequency bands, thereby ensuring high quality of audio output.

[0005] In a first aspect, the present invention provides a method for adjusting a volume controller of a mixing console, the method comprising:

[0006] The audio signal at the input of the mixer is sampled, and the environmental noise data is collected through the noise sensor to obtain the initial system parameter matrix;

[0007] Performing probability density function calculation on the initial system parameter matrix to obtain an audio state data matrix and a noise characteristic data matrix;

[0008] Performing a first-order volume control calculation based on the audio state data matrix to obtain a volume control data matrix;

[0009] Perform a second-order frequency equalization calculation according to the volume control data matrix to obtain a frequency equalization control data matrix;

[0010] Based on the volume control data matrix and the frequency equalization control data matrix, iterative optimization calculation is performed on the control parameters to obtain an optimal control parameter matrix;

[0011] According to the optimal control parameter matrix, dual-channel parallel calculations are performed on the transfer function of the volume control channel and the transfer function of the frequency equalization channel to obtain an audio signal at the output end of the mixing console.

[0012] In a second aspect, the present invention provides a balanced adjustment system for a mixing console volume controller, the balanced adjustment system for the mixing console volume controller comprising:

[0013] The sampling module is used to sample the audio signal at the input end of the mixer and collect environmental noise data through the noise sensor to obtain the initial system parameter matrix;

[0014] A state calculation module, used for performing probability density function calculation on the initial system parameter matrix to obtain an audio state data matrix and a noise characteristic data matrix;

[0015] A control calculation module, used for performing a first-order volume control calculation based on the audio state data matrix to obtain a volume control data matrix;

[0016] A balance calculation module, used for performing a second-order frequency balance calculation according to the volume control data matrix to obtain a frequency balance control data matrix;

[0017] An iterative calculation module, used for iteratively optimizing the control parameters based on the volume control data matrix and the frequency equalization control data matrix to obtain an optimal control parameter matrix;

[0018] The parallel calculation module is used to perform dual-channel parallel calculation on the transfer function of the volume control channel and the transfer function of the frequency equalization channel according to the optimal control parameter matrix to obtain the audio signal at the output end of the mixer.

[0019] A third aspect of the present invention provides a balancing adjustment device for a mixing console volume controller, comprising: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor calls the instructions in the memory so that the balancing adjustment device for the mixing console volume controller executes the above-mentioned balancing adjustment method for the mixing console volume controller.

[0020] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned method for equalizing the volume controller of a mixing console.

[0021] In the technical solution provided by the present invention, the contradiction between response speed and sound distortion is solved by replacing the traditional PID control with an anti-disturbance control strategy, and the control accuracy and stability of the system are significantly improved; the audio signal state and noise parameters are jointly estimated, and the real-time and accurate estimation of unknown audio signals and environmental noise is realized, and the anti-interference ability of the system is enhanced; the dual controller structure of the first-order volume ADRC controller and the second-order frequency equalization ADRC controller is adopted, and the coordinated optimization of volume control and frequency equalization is realized, and the overall control performance of the system is improved; the parameters of the ADRC controller are automatically adjusted based on the particle swarm optimization algorithm, and the shortcomings of the traditional manual adjustment of parameters are overcome, which is complex and time-consuming, and the efficiency and accuracy of parameter optimization are improved; the independent adjustment of volume control and frequency equalization is realized through the dual-channel parallel control structure, so that the system can perform accurate control for different frequency bands and ensure the high quality of audio output; the variational reasoning and re-parameterization technology are adopted to improve the accuracy of noise parameter estimation and enhance the adaptability of the system in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0023] Figure 1 A schematic flow chart of a method for adjusting the volume of a mixing console provided in an embodiment of the present application;

[0024] Figure 2 A schematic block diagram of the structure of a balance adjustment system for a mixing console volume controller provided in an embodiment of the present application;

[0025] Figure 3 A schematic block diagram of the structure of a balance adjustment device for a mixing console volume controller provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change based on actual conditions.

[0028] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0029] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0030] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0031] See also Figure 1 , Figure 1 A flow chart of a method for adjusting the balance of a mixing console volume controller provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the equalization adjustment method of the mixing console volume controller provided in the embodiment of the present application includes steps S100 to S600.

[0032] Step S100, sampling the audio signal at the input end of the mixer, and collecting environmental noise data through a noise sensor to obtain an initial system parameter matrix;

[0033] It is understandable that the execution subject of the present invention may be a balance adjustment system of a mixer volume controller, or a terminal or a server, which is not limited here. The embodiment of the present invention is described by taking a server as an execution subject as an example.

[0034] Specifically, the audio signal at the input end of the mixer is sampled in the time domain, and the input audio signal is sampled at a fixed sampling rate through a high-precision ADC module to obtain a continuous audio sampling data sequence. The audio sampling data sequence is subjected to spectrum decomposition and bandwidth analysis. The time domain signal is converted to the frequency domain through the fast Fourier transform technology, and the spectrum distribution of the audio signal is extracted. On the basis of spectrum decomposition, the bandwidth characteristics of the audio signal are analyzed, including calculating the maximum amplitude and the minimum amplitude in the spectrum to obtain the amplitude range data, and at the same time, the frequency range data is determined by locating the highest frequency and the lowest frequency in the spectrum. These data together describe the energy distribution and frequency characteristics of the audio signal. At the same time, the environmental noise data is collected through the noise sensor and input into the signal conditioning circuit for processing. In the signal conditioning stage, the noise signal is amplified and filtered. The amplification process ensures the effective perception of weak noise signals, and the filtering process can eliminate high-frequency or low-frequency interference, so that the processed noise data sequence more accurately reflects the characteristics of the environmental background noise. Energy accumulation calculation and mean operation are performed on the noise processing data sequence. The energy accumulation calculation reflects the total energy level of the noise signal by means of square integration, while the mean operation describes the average strength of the noise signal. The calculation results form the environmental noise level data. Based on the amplitude range data and the frequency range data, the parameters of the volume control part are initialized. By designing a state observer in the volume ADRC (active disturbance rejection control) controller, its observation bandwidth is dynamically adjusted according to the characteristics of the audio signal. The amplitude range data and the frequency range data are used as inputs, and the bandwidth parameters of the volume observer are determined by the calculation method of gain matrix decomposition, and the first compensation coefficient is calculated in combination with the system response characteristics to construct the initial parameter group of the volume control. At the same time, the initial parameter group of the frequency equalization control part is designed using the environmental noise level data. In the ADRC controller of frequency equalization, the state observer is also used, and the nonlinear mapping relationship of its compensation coefficient needs to be dynamically adjusted according to the noise level. By mapping and calculating the noise level data, the bandwidth parameters and the second compensation coefficient of the frequency observer are obtained, and the initial parameter group of the frequency control is completed. In order to evaluate the input and output response characteristics of the mixer system, a unit step signal test is performed. By inputting a unit step signal into the system and observing the dynamic changes of its output response, the static gain coefficient and dynamic response parameters of the system are obtained to describe the steady-state and transient performance of the system during signal amplification and adjustment. The audio sampling data sequence, noise processing data sequence, volume control initial parameter group, frequency control initial parameter group and system static gain coefficient obtained above are integrated to construct the initial system parameter matrix.

[0035] Step S200, performing probability density function calculation on the initial system parameter matrix to obtain an audio state data matrix and a noise characteristic data matrix;

[0036] Specifically, the audio signal data and the environmental noise data in the initial system parameter matrix are jointly modeled with probability. By constructing a joint distribution model, the statistical correlation between the audio signal and the environmental noise is characterized to form an audio signal state vector and a noise parameter vector. The audio signal state vector is transformed into a normal distribution to ensure that the data meets the assumptions of the conditional probability calculation, and the conditional probability distribution matrix of the audio signal is obtained, which reflects the possibility distribution of the audio signal under different states. At the same time, the noise parameter vector is processed by a prior distribution modeling method. It is assumed that the noise parameters obey a known distribution, such as a Gaussian distribution or a Laplace distribution, and its distribution parameters are adjusted based on the actual measured data to obtain the prior probability distribution matrix of the noise parameters to describe the characteristic distribution of the environmental noise. The conditional probability distribution matrix of the audio signal and the prior probability distribution matrix of the noise parameters are input into the variational inference unit, and the logarithmic marginal likelihood function is calculated by variational inference. This function can measure the explanatory power of the joint distribution model and provide an objective function for optimization. In variational inference, the logarithmic marginal likelihood function is component-decomposed to decompose it into conjugate terms and non-conjugate terms. By extracting the conjugate term coefficient matrix and the non-conjugate term coefficient matrix, the parts of the calculation that are easy to directly optimize and the parts that require more complex processing are separated. For the extracted conjugate term coefficient matrix, the maximum a posteriori probability calculation method is directly applied to optimize the audio signal state vector and generate the audio state data matrix. For the non-conjugate term coefficient matrix, the random gradient optimization method is used for processing. In the random gradient optimization, the gradient approximation is generated by the sampling method, and the noise parameter vector is updated in combination with the reparameterization technology. The reparameterization technology can convert the optimization of the non-conjugate term into a differentiable form, thereby maintaining high efficiency and stability during the optimization process. Through this step, a noise feature data matrix is ​​generated to describe the dynamic characteristics of the environmental noise.

[0037] Step S300, performing first-order volume control calculation based on the audio state data matrix to obtain a volume control data matrix;

[0038] Specifically, the audio state data matrix is ​​input into the tracking differentiator. The tracking differentiator suppresses the high-frequency noise and mutation components in the signal, calculates the transition process of the audio signal sequence, and obtains a smooth target signal. At the same time, the speed factor of the smooth target signal is calculated, and the state equation of the tracking differentiator is constructed in combination with the dynamic characteristics of the signal change. In the state equation, the derivative of the tracking signal is obtained by numerical calculation, which helps to describe the dynamic change of the audio signal. Based on the derivative of the tracking signal, the state vector and disturbance vector of the first-order extended state observer are recursively calculated. The first-order extended state observer can capture the changes of the system state and external disturbances in real time. The recursive calculation uses the state and disturbance information of the previous moment to derive the state observation data at the current moment through the extended state equation, which contains the current state of the audio signal and its dynamic change trend. The observation error of the state observation data is calculated. The observation error is obtained by comparing the actual observation state with the ideal state target. Combined with the bandwidth parameter of the volume observer, the error is used as input to perform nonlinear function operation to obtain the compensation control amount. The design of nonlinear function operation can enhance the sensitivity of the controller to the dynamic changes of the system and improve its adaptability to external disturbances. Based on the compensation control quantity, the discrete state equation of the extended state observer is numerically solved to obtain the output data of the observer. The error of the observer output data and the smooth target signal is calculated, and the nonlinear state error feedback control law is constructed in combination with the compensation coefficient. Through the feedback control law, the observation error is combined with the compensation coefficient to generate the final volume control quantity. The volume control quantity is input into the control function calculation unit, and the coefficient matrix operation is performed on the nonlinear error feedback control quantity to generate preliminary volume control data. The control function calculation unit adjusts and amplifies the feedback control quantity according to the specific requirements of volume control using the optimized coefficient matrix to ensure that the generated control data meets the actual requirements of volume regulation. The state matrix splicing operation is performed on the volume control data to integrate the control data at different time points or frequency bands into a complete volume control data matrix. The matrix is ​​normalized in combination with the control gain coefficient to optimize the numerical range of the control data to adapt it to the subsequent system input requirements.

[0039] Step S400, performing a second-order frequency equalization calculation according to the volume control data matrix to obtain a frequency equalization control data matrix;

[0040] Specifically, the volume control data matrix is ​​input into the second-order tracking differentiator, and the state of the audio signal is recursively calculated by the tracking differentiator to obtain the state variable of the tracking differentiator, which effectively captures the change trend of the audio signal in the frequency dimension. Based on the state variable of the tracking differentiator, the tracking differentiator equation is solved to obtain the target tracking state sequence, which reflects the frequency change trajectory of the audio signal under ideal conditions. The initial value configuration of the third-order state variable of the extended state observer of the second-order frequency equalization ADRC controller is performed to initialize the state of the observer. When configuring the initial value, the volume control data and the target tracking state sequence are combined to ensure that the initial state matrix of the extended state observer can accurately reflect the dynamic characteristics of the audio signal. After completing the initial value configuration, the state equation of the extended state observer is recursively calculated based on the initial state matrix to generate state observation data. Error analysis is performed on the state observation data and the target tracking state sequence. By calculating the difference between the observation data and the target state, the deviation of the signal in the frequency equalization control is quantified. Based on this error data, a nonlinear state error feedback control law is constructed to generate the frequency compensation control quantity. The nonlinear feedback control law realizes dynamic adjustment of signals in different frequency bands through nonlinear transformation of errors, and can enhance the sensitivity of the system to frequency changes and improve the accuracy of frequency equalization control. The compensation coefficients β11 and β12 in the second-order frequency equalization ADRC controller are compensated and calculated by using the frequency compensation control amount to obtain the frequency equalization control parameters. The optimization adjustment of the compensation coefficient enables the controller to achieve more accurate gain adjustment in different frequency ranges and improve the adaptability of the system in frequency equalization. The frequency equalization control parameters are input into the frequency equalization control unit, in which independent gain control operations are performed on audio signals in different frequency bands. By setting the gain for each frequency band in the spectrum, the energy distribution of the audio signal can be effectively balanced to ensure the clarity and adaptability of the output signal. After completing the independent gain control, the frequency equalization control data is matrix transformed, and the data structure is optimized by matrix transformation, so that the control data can be used for subsequent processing more efficiently. At the same time, combined with the gain coefficient of the controller, the transformed control data is normalized to ensure that the numerical range of the control data is within the standardized range to prevent calculation instability caused by excessive or too small values. Finally, the frequency equalization control data matrix is ​​obtained.

[0041] Step S500: Based on the volume control data matrix and the frequency equalization control data matrix, iterative optimization calculation is performed on the control parameters to obtain an optimal control parameter matrix;

[0042] Specifically, the volume control data matrix and the frequency equalization control data matrix are parameter-encoded. The control parameters in the two matrices are expanded into a multidimensional vector, and the elements of each vector represent the corresponding control parameter values. By combining the vectors, the initial particle swarm position matrix is ​​constructed, and each row represents the current position of a particle, that is, a candidate parameter combination. The dimension of the position matrix depends on the number of control parameters and the size of the particle swarm, ensuring that the search space covers a wide enough range. The velocity vector is initialized for each particle in the initial particle swarm position matrix. The velocity vector determines the direction and amplitude of the particle's movement in the parameter space. By setting a random initial velocity, combining the inertia weight and the acceleration constant, the initial motion parameters of the particle swarm are obtained. The inertia weight is used to balance the exploration and development capabilities of the particles, while the acceleration constant controls the tracking speed of the particles to the individual historical optimal position and the global optimal position. The integrated time-weighted absolute error (ITAE) index is calculated based on the initial motion parameters of the particle swarm as the calculation basis of the fitness function. The ITAE index can comprehensively reflect the performance of volume control and frequency equalization control in terms of dynamic response, steady-state accuracy, etc. For each particle, the ITAE value under the corresponding parameter combination is calculated as the fitness value of the particle. After the fitness function value is calculated, the fitness values ​​of all particles are sorted, and the particle position with the best fitness value is extracted as the current global optimal position. After determining the optimization target position data, the speed and position of the particle are updated. According to the speed update equation of particle swarm optimization, the new speed vector of each particle is calculated by combining the inertia weight, the individual historical optimal position and the global optimal position information. The design of the speed update equation ensures that the particles can gradually approach the global optimal position while retaining a certain degree of randomness to avoid falling into the local optimum. Using the updated speed vector, the new position data of each particle is calculated by the position update equation. These new position data represent the next search direction of the particle in the parameter space. After the position update is completed, the convergence of the new particle position data is judged. The particle position change is calculated and compared with the preset threshold. When the position change is less than the threshold, it means that the particle swarm has basically converged. At this time, the current global optimal position is extracted as the optimal control parameter vector. The optimal control parameter vector contains all the optimal parameter values ​​of volume control and frequency equalization control, which can significantly improve the overall performance of the mixer volume controller. The optimal control parameter vector is reorganized into blocks according to the volume control parameters and the frequency equalization control parameters. The optimal control parameter matrix is ​​generated by rearranging the elements of the parameter vector and converting it into the same structure as the original control data matrix.

[0043] Step S600: According to the optimal control parameter matrix, dual-channel parallel calculations are performed on the transfer function of the volume control channel and the transfer function of the frequency equalization channel to obtain an audio signal at the output end of the mixer.

[0044] Specifically, the optimal control parameter matrix is ​​separated according to the volume control parameters and the frequency equalization control parameters, and the transfer function coefficients for volume control and the transfer function coefficients for frequency equalization are extracted. The volume transfer function coefficients describe the gain and dynamic response characteristics, while the frequency equalization transfer function coefficients are for independent adjustment of signals in different frequency bands. After completing the parameter separation, the volume transfer function coefficients are Laplace transformed to construct the system response function of the volume control channel. The Laplace transform maps the dynamic behavior in the time domain to the frequency domain. Through this transformation, a mathematical model describing the dynamic characteristics of volume control is established, which is usually expressed in the form of a first-order or second-order transfer function. At the same time, for the frequency equalization transfer function coefficients, they are grouped and processed according to the frequency band, and the coefficients of different frequency bands are independently classified. The transfer function coefficients of each frequency band are Laplace transformed respectively to generate a multi-band response function of the frequency equalization channel. The response function of each frequency band describes the gain and phase characteristics of the signal in the frequency band in the equalization adjustment. Based on the system response function of the volume control channel and the multi-band response function of the frequency equalization channel, the system total transfer function is constructed to describe the transmission law of the audio signal in the entire mixer system. The total transfer function is in the form of a multi-input multi-output model, and the coupled control of volume and frequency equalization is realized through mathematical combination in the frequency domain. The total transfer function of the system is discretized. The total transfer function in the continuous domain is converted into a discrete domain function by using the bilinear transformation method. The bilinear transformation maps the continuous signal characteristics to the digital signal processing framework by the method of pre-distortion frequency, ensuring the fidelity of the frequency response during the discretization process. The form of the discrete domain transfer function is suitable for the implementation architecture with the digital signal processor as the core. The discrete domain transfer function is converted into a differential equation for actual calculation implementation. The differential equation directly acts on the sampled digital signal, and generates the volume control output and the frequency equalization control output through recursive operation. In the parallel computing framework, the signals of the two output channels are calculated separately and then superimposed to generate a complete audio output sequence. The superposition calculation makes full use of the complementarity of volume control and frequency equalization, which can not only improve the dynamic range of the signal, but also optimize the spectrum distribution. The audio output sequence is subjected to amplitude normalization operation to adjust the signal amplitude range to a standardized level to avoid distortion problems caused by excessive or weak signals. In the normalization process, gain compensation is performed in combination with the system static gain coefficient to optimize the amplitude characteristics of the signal to adapt it to the output standard of the mixer. The normalized audio data is converted into an analog signal through a digital-to-analog converter and output to the analog output end of the mixer to obtain the audio signal at the output end of the mixer.

[0045] In the embodiment of the present invention, by adopting an anti-disturbance control strategy to replace the traditional PID control, the contradiction between response speed and sound distortion is solved, and the control accuracy and stability of the system are significantly improved; the audio signal state and noise parameters are jointly estimated, and the real-time and accurate estimation of unknown audio signals and environmental noise is realized, and the anti-interference ability of the system is enhanced; the dual controller structure of the first-order volume ADRC controller and the second-order frequency equalization ADRC controller is adopted to realize the coordinated optimization of volume control and frequency equalization, and improve the overall control performance of the system; the parameters of the ADRC controller are automatically adjusted based on the particle swarm optimization algorithm, which overcomes the shortcomings of the traditional manual adjustment of parameters, which is complex and time-consuming, and improves the efficiency and accuracy of parameter optimization; through the dual-channel parallel control structure, the independent adjustment of volume control and frequency equalization is realized, so that the system can perform precise control for different frequency bands and ensure the high quality of audio output; the variational reasoning and re-parameterization technology are adopted to improve the accuracy of noise parameter estimation and enhance the adaptability of the system in complex environments.

[0046] In a specific embodiment, the process of executing step S100 may specifically include the following steps:

[0047] Performing time domain sampling on the audio signal at the input end of the mixer to obtain an audio sampling data sequence;

[0048] Performing spectrum decomposition and bandwidth analysis on the audio sampling data sequence to obtain amplitude range data including the maximum amplitude and the minimum amplitude and frequency range data including the highest frequency and the lowest frequency;

[0049] The noise data collected by the noise sensor is input into the signal conditioning circuit, the noise signal is amplified and filtered to obtain a noise processing data sequence;

[0050] Perform energy accumulation calculation and mean operation on the noise processing data sequence to obtain the environmental noise level data;

[0051] Based on the amplitude range data and the frequency range data, a gain matrix decomposition operation is performed on the observation bandwidth of the state observer of the volume ADRC controller to obtain an initial volume control parameter group, wherein the initial volume control parameter group includes a volume observer bandwidth and a first compensation coefficient;

[0052] According to the environmental noise level data, a nonlinear mapping operation is performed on the compensation coefficient of the state observer of the frequency equalization ADRC controller to obtain a frequency control initial parameter group, which includes a frequency observer bandwidth and a second compensation coefficient;

[0053] Conduct unit step signal test on the input and output response characteristics of the mixer system to obtain the static gain coefficient and dynamic response parameters of the system;

[0054] The audio sampling data sequence, the noise processing data sequence, the volume control initial parameter group, the frequency control initial parameter group and the system static gain coefficient are constructed into an initial system parameter matrix.

[0055] Specifically, the audio signal at the input of the mixer is sampled in the time domain. By using a high-precision ADC (analog-to-digital converter) with a fixed sampling rate For continuous input signal Discretize to get the time domain audio sampling data sequence The discretization formula is as follows:

[0056] , , ;

[0057] in, is the sampling period, is the index of the sampling point, is the sampled data sequence. Sampling rate The Nyquist sampling theorem should be satisfied, that is, in is the highest frequency of the signal. Perform spectrum decomposition and bandwidth analysis. Convert time domain data into frequency domain representation through fast Fourier transform , the calculation formula is:

[0058] ;

[0059] in, is the number of fast Fourier transform points, is the frequency index, is the complex number representation of the spectrum. Analysis to extract the maximum amplitude of the signal , minimum amplitude , Maximum frequency and minimum frequency These data together constitute the amplitude range data and frequency range data At the same time, the environmental noise signal collected by the noise sensor Input to the signal conditioning circuit for processing. In the conditioning circuit, the noise signal is amplified and filtered. Assume that the amplification factor is , the filter transfer function is , then the conditioned noise signal is:

[0060] ;

[0061] in, is the Laplace transform of the noise signal, Designed as a low-pass or band-pass filter, it is used to eliminate high-frequency or non-target frequency band noise. The conditioned noise signal is sampled to obtain a discrete noise processing data sequence . For the noise processing data sequence Perform energy accumulation calculation and average operation to obtain environmental noise level data The energy accumulation formula is:

[0062] ;

[0063] The formula for the mean operation is:

[0064] ;

[0065] in, is the total energy of the noise signal, is the average intensity of the noise, reflecting the overall level of environmental noise. Based on the amplitude range data and frequency range data, the gain matrix decomposition operation is performed on the state observer of the volume ADRC controller. Assume that the target gain matrix of the volume control is , and its calculation formula is:

[0066] ;

[0067] in, is the gain adjustment coefficient. The observation bandwidth of the state observer according to Perform the calculation:

[0068] ;

[0069] represents the target response time constant, and the obtained and the first compensation coefficient The initial volume control parameter group is formed. According to the environmental noise level data , nonlinear mapping operation is performed on the compensation coefficient of the frequency equalization ADRC controller. Assume that the compensation function of the frequency equalization is ,but:

[0070] ;

[0071] in, is the frequency compensation reference value, is the mapping adjustment coefficient. The bandwidth of the frequency equalization observer is calculated and compensation coefficient , get the initial frequency control parameter group. Test the input and output response characteristics of the mixer system with a unit step signal. Input a unit step signal , record the output of the system By measuring the steady-state gain of the system and dynamic response time , describing the static gain coefficient and dynamic characteristics of the system:

[0072] ;

[0073] in, represents the time constant, the time from 0 to 63.2% of the steady-state value;

[0074] The audio sample data sequence , noise processing data series , Volume control initial parameter group , Frequency control initial parameter group and the system static gain factor Integrate into the initial system parameter matrix :

[0075] ;

[0076] Through this matrix, audio signals, environmental noise and control parameters are integrated.

[0077] In a specific embodiment, the process of executing step S200 may specifically include the following steps:

[0078] Perform joint probability modeling on the audio signal data and the environmental noise data in the initial system parameter matrix to obtain an audio signal state vector and a noise parameter vector;

[0079] Performing a normal distribution transformation on the state vector of the audio signal to obtain a conditional probability distribution matrix of the audio signal, and performing a prior distribution modeling on the noise parameter vector to obtain a prior probability distribution matrix of the noise parameter;

[0080] Input the conditional probability distribution matrix and the prior probability distribution matrix into the variational inference unit, calculate the logarithmic marginal likelihood function, obtain the variational optimization function, and perform component decomposition operation on the variational optimization function to extract the conjugate term coefficient matrix and the non-conjugate term coefficient matrix;

[0081] Based on the conjugate term coefficient matrix, the maximum a posteriori probability calculation is performed on the audio signal state vector to obtain the audio state data matrix, and the random gradient optimization calculation is performed on the non-conjugate term coefficient matrix. The noise parameter vector is updated in combination with the reparameterization technology to obtain the noise feature data matrix.

[0082] Specifically, extract the audio signal data from the initial system parameter matrix and environmental noise data . Assume the audio signal data matrix is , where each is the sampling value of the audio signal at different times, with a total of sampling points; the environmental noise data matrix is , each is the noise value of the corresponding sampling point. These data are regarded as random variables in probability modeling, and their joint probability density function is defined as:

[0083] ;

[0084] in is the conditional probability distribution of the audio signal, which represents the distribution of the audio signal when the noise data is known; is the prior probability distribution of the noise data, and are the parameter vectors of the audio signal and noise model respectively. Through this joint modeling, the audio signal state vector is constructed and the noise parameter vector . For the audio signal state vector Perform normal distribution transformation. Assume that the audio signal obeys normal distribution under noise conditions ,in is the mean, is the variance, then the conditional probability density function of the audio signal is:

[0085] ;

[0086] Combine the density values ​​of all conditional probability distributions to construct the conditional probability distribution matrix of the audio signal:

[0087] ;

[0088] At the same time, the noise parameter vector is modeled with a prior distribution, assuming that the noise follows a mean of , the variance is Normal distribution , then the prior probability density function is:

[0089] ;

[0090] Combine all prior probabilities into the prior probability distribution matrix of the noise parameters:

[0091] ;

[0092] The conditional probability distribution matrix of the audio signal and the prior probability distribution matrix of the noise parameters Input to the variational inference unit to calculate the log marginal likelihood function

[0093] ;

[0094] Since it is relatively complex to directly calculate the log-marginal likelihood function, we use variational optimization techniques to decompose it into conjugate terms and non-conjugate terms. Conjugate terms refer to terms that can be directly optimized by analytical methods, while non-conjugate terms need to be calculated by numerical optimization. The variational optimization function is decomposed into components to extract the conjugate coefficient matrix. and the non-conjugate coefficient matrix . Based on the conjugate coefficient matrix , the maximum a posteriori probability (MAP) is calculated for the audio signal state vector. The formula for MAP calculation is:

[0095] ;

[0096] By maximizing the posterior probability, we get the audio state data matrix , whose elements are the optimal estimates of the audio signal under different noise conditions. For the non-conjugate coefficient matrix , and the stochastic gradient optimization method is used to optimize it. Stochastic gradient optimization calculates the gradient of each sample and gradually updates the noise parameter vector , the formula is:

[0097] ;

[0098] in is the learning rate, is the gradient of the logarithmic marginal likelihood function with respect to the noise parameter. In order to improve the stability of the optimization process, the reparameterization technique is introduced to transform the noise parameter vector Represented as a differentiable function ,in is the standard normal noise, is a trainable parameter. The formula of the reparameterization technique is:

[0099] ;

[0100] The noise parameter vector is updated by reparameterization technology to obtain the final noise feature data matrix , which contains the optimized noise parameter values ​​under different noise conditions.

[0101] In a specific embodiment, the process of executing step S300 may specifically include the following steps:

[0102] Input the audio state data matrix into the tracking differentiator, calculate the transition process of the audio signal sequence to obtain a smooth target signal, calculate the speed factor of the smooth target signal, construct the tracking differentiator state equation, and obtain the tracking signal derivative;

[0103] Based on the tracking signal derivative, the state vector and disturbance vector of the first-order extended state observer are recursively calculated to obtain the state observation data;

[0104] The observation error of the state observation data is calculated, and the nonlinear function operation is performed in combination with the bandwidth of the volume observer to obtain the compensation control quantity, and the discrete equation of the extended state observer is solved based on the compensation control quantity to obtain the observer output data;

[0105] The error between the observer output data and the smooth target signal is calculated, and the nonlinear state error feedback control law is constructed in combination with the compensation coefficient to obtain the volume control value.

[0106] The volume control amount is input into the control function calculation unit, the coefficient matrix operation is performed on the nonlinear error feedback control amount to obtain volume control data, and the state matrix splicing operation is performed on the volume control data, and normalization processing is performed in combination with the control gain coefficient to obtain the volume control data matrix.

[0107] Specifically, the audio state data matrix Input to the tracking differentiator to smooth and calculate the transition process of the audio signal sequence. Suppose the audio signal sequence is , filtering and smoothing it by tracking the differentiator model to generate a smooth target signal The state equation of the tracking differentiator is expressed as:

[0108] ;

[0109] in, represents the smooth target signal, represents the derivative of the signal (i.e., the speed factor), and It is a parameter used to control the response speed and stability of the tracking differentiator. It is recursively calculated in the discrete domain by numerical integration methods (such as the Euler method or the fourth-order Runge-Kutta method). and ,in is a discrete time step. Smooth target signal and speed factor The output of the tracking differentiator is used as the input to construct a first-order extended state observer. The purpose of the first-order extended state observer is to calculate the state vector of the system. and external disturbances For real-time estimation, the state equation is:

[0110] ;

[0111] in, is the state observation value, Its derivative is is the gain parameter of the observer. Through recursive operation, the state vector and disturbance estimate of the previous moment are used to calculate the state observation data of the current moment . Calculate the observation error of the state observation data and define the error as . Combined with the bandwidth of the volume observer , design nonlinear function Used to generate compensation control quantity:

[0112] ;

[0113] in, Represents a saturation function, which is used to limit the amplitude of the compensation control amount. Combined with the compensation control amount, the discrete state equation of the extended state observer is solved again to generate a new observer output data ,in:

[0114] ;

[0115] Output data to the observer and smooth target signal Calculate the error again . Combined with the compensation factor , construct the nonlinear state error feedback control law:

[0116] ;

[0117] in, is the volume control amount, which represents the real-time adjustment result of the system on the input signal. Input to the control function calculation unit, perform coefficient matrix operation on the nonlinear error feedback control quantity to generate volume control data. Assume that the coefficient matrix of the control function is , the generation formula of control data is:

[0118] ;

[0119] in, is the control gain coefficient, Represents the volume control data vector at the current moment. Perform state matrix concatenation operations and combine it with the control data at other times to form a volume control data matrix . Combined with normalization processing, the amplitude range of all data in the matrix is ​​unified to ensure numerical stability and system consistency. The normalization formula is:

[0120] ;

[0121] in, represents the element in the i-th row and j-th column of the normalized volume control data matrix, represents the element in the i-th row and j-th column of the original volume control data matrix, represents the minimum value in the matrix C, Represents the maximum value in the matrix C; m and n represent the number of rows and columns of the matrix respectively.

[0122] In a specific embodiment, the process of executing step S400 may specifically include the following steps:

[0123] The volume control data matrix is ​​input into the second-order tracking differentiator, and the state recursive calculation is performed on the audio signal to obtain the state variable of the tracking differentiator;

[0124] Based on the tracking differentiator state variables, the tracking differentiator equation is solved to obtain the target tracking state sequence;

[0125] Perform initial configuration of the third-order state variables of the extended state observer of the second-order frequency equalization ADRC controller to obtain the initial state matrix of the extended state observer, and perform recursive calculation on the state equation based on the initial state matrix of the extended state observer to obtain state observation data;

[0126] Perform error analysis on state observation data and target tracking state sequence, build nonlinear state error feedback control law, and obtain frequency compensation control quantity;

[0127] Based on the frequency compensation control amount, the compensation coefficients β11 and β12 of the second-order frequency equalization ADRC controller are compensated and calculated to obtain the frequency equalization control parameters;

[0128] The frequency equalization control parameters are input into the frequency equalization control unit, and independent gain control operations are performed on audio signals of different frequency bands to obtain frequency equalization control data. The frequency equalization control data is subjected to matrix transformation operations, and normalized in combination with the controller gain coefficient to obtain a frequency equalization control data matrix.

[0129] Specifically, the volume control data matrix Input the second-order tracking differentiator to perform state recursive calculation on the audio signal. Assume that each column of the volume control data matrix represents the volume control amount at a time point. , decompose it into a continuous signal The core of the second-order tracking differentiator is to simultaneously track the value of the signal and its rate of change. Its state equation is expressed as:

[0130] ;

[0131] in, represents the estimated value of the tracking signal, represents the derivative (speed) of a signal; and It is the gain parameter of the tracking differentiator, which is used to adjust the tracking speed and smoothness of the signal. Through the discretization method (such as Euler method), the state recursion formula is written as:

[0132] ;

[0133] ;

[0134] in, is the sampling period, is a discrete time step The input volume control amount under recursive calculation output and are the value and derivative of the tracking signal, respectively, which constitute the state variables of the tracking differentiator. Based on the above state variables, the equations of the tracking differentiator are solved to generate the target tracking state sequence. Target state sequence It reflects the ideal state of volume control after smoothing and dynamic adjustment. The calculation formula is:

[0135] ;

[0136] in, is a tuning parameter used to balance the weight of the signal value and the dynamic change rate. The target tracking state sequence is used as input to configure the initial value of the third-order state variable of the extended state observer of the second-order frequency equalization ADRC controller. Assume that the state vector of the extended state observer is , its initial state matrix is ​​defined as:

[0137] ;

[0138] in, represents an estimate of the signal value, represents the first-order derivative estimate of the signal, Represents the external disturbance estimate of the expansion. After the initial value configuration, the recursive calculation is performed according to the state equation of the expanded state observer, and the state equation is:

[0139] ;

[0140] After discretization, the recursive formula is:

[0141] ;

[0142] ;

[0143] ;

[0144] in, and is the gain parameter of the observer. and target tracking state sequence Perform error analysis and define the error as:

[0145] ;

[0146] Based on error , construct a nonlinear state error feedback control law to generate the frequency compensation control quantity :

[0147] ;

[0148] in, is the feedback gain coefficient, is the saturation function, is the error threshold, which is used to limit the amplitude of the compensation amount. , the compensation coefficient of the second-order frequency-balanced ADRC controller and The compensation formula is:

[0149] ;

[0150] in, and is the initial compensation coefficient, and is the adjustment factor. The calculated frequency equalization control parameters are input into the frequency equalization control unit to perform independent gain control operations on audio signals in different frequency bands. Assume that the spectrum of the audio signal is , the gain function of frequency equalization is , then the equalized signal is:

[0151] ;

[0152] Among them, the gain function According to the frequency equalization control parameters, the frequency equalization data is adjusted in sections. The frequency equalization data is transformed into a matrix and normalized to generate a frequency equalization control data matrix. The normalization formula is:

[0153] ;

[0154] in, represents the element in the i-th row and j-th column of the normalized frequency equalization control data matrix, represents the element in the i-th row and j-th column of the original frequency-equalized data matrix, Represents the maximum absolute value of all elements in the matrix Y; m and n represent the number of rows and columns of the matrix respectively.

[0155] In a specific embodiment, the process of executing step S500 may specifically include the following steps:

[0156] Perform parameter encoding on the volume control data matrix and the frequency equalization control data matrix to construct the initial particle swarm position matrix;

[0157] Perform velocity vector initialization operation on each particle in the initial particle swarm position matrix, set inertia weight and acceleration constant, and obtain the initial motion parameters of the particle swarm;

[0158] Based on the initial motion parameters of the particle swarm, the integral time weighted absolute error index is calculated to obtain the fitness function value, and the fitness function value is sorted, and the particle position corresponding to the optimal fitness value is set as the global optimal position to obtain the optimized target position data;

[0159] According to the optimized target position data, the particle velocity update equation is solved to obtain the new particle velocity vector, and based on the new particle velocity vector, the particle position update equation is calculated to obtain the new particle position data;

[0160] The convergence of the new particle position data is judged. When the position change is less than the set threshold, the current global optimal position is extracted to obtain the optimal control parameter vector. The optimal control parameter vector is reorganized into blocks according to the volume control parameters and frequency equalization control parameters to obtain the optimal control parameter matrix.

[0161] Specifically, from the volume control data matrix and frequency equalization control data matrix Extract the control parameters and expand them linearly to form a multi-dimensional parameter vector. The dimension is , The dimension is , then the parameter vector is expressed as:

[0162] ;

[0163] in is a comprehensive vector containing volume control parameters and frequency equalization parameters, with a dimension of Based on these parameters, the initial particle swarm position matrix is ​​constructed , where each row represents the position vector of a particle:

[0164] ;

[0165] in is the number of particle swarms, Indicates The initial position of each particle. Perform the initialization operation of the velocity vector on the position of each particle. Suppose the initial velocity vector of each particle is , whose dimensions are the same as the position vector The same, the initialization formula is:

[0166] ;

[0167] in, , indicating that in the range A uniformly distributed random number, Is the maximum speed limit. Set the inertia weight , individual acceleration constant and the population acceleration constant After that, the initial motion parameters of the particle swarm are defined. Based on the initialized particle position and velocity, the fitness value of each particle is calculated. The fitness function uses the integral time weighted absolute error indicator, and its formula is:

[0168] ;

[0169] in, is the actual output of the system, is the target reference signal, For time, is the total running time. In the discrete domain, the fitness value is expressed as:

[0170] ;

[0171] in, It is The fitness value of a particle, It is The system output under the corresponding parameters of each particle. Sort the fitness values ​​of all particles and select the optimal fitness value The corresponding particle position As the current global optimal position. According to the optimization target position data , update the particle's velocity and position. The particle's velocity update equation is:

[0172] ;

[0173] in, is a random number, It is The historical optimal position of a particle, is the global optimal position. The updated speed Used for location updates:

[0174] ;

[0175] After updating the position, the position changes of all particles are judged for convergence. Less than the set threshold When , the particle swarm is considered to have converged, and the current global optimal position is extracted As the optimal control parameter vector. According to the volume control parameters and frequency equalization parameters, the block matrix is ​​reorganized. The reorganized volume control parameter matrix and frequency equalization parameter matrix It is expressed as:

[0176] ;

[0177] ;

[0178] Among them, the reshape() function is used to rearrange a one-dimensional vector into a matrix of specified dimensions. It means taking the first mn elements of the optimal parameter vector, where m and n represent the number of rows and columns of the reorganized matrix respectively. The reshape() function rearranges the elements in a one-dimensional vector into an m×n matrix in column-priority order.

[0179] In a specific embodiment, the process of executing step S600 may specifically include the following steps:

[0180] Separating the optimal control parameter matrix according to the volume control parameters and the frequency equalization control parameters to obtain the volume transfer function coefficient and the frequency equalization transfer function coefficient;

[0181] Perform Laplace transform on the volume transfer function coefficients to construct the system response function of the volume control channel;

[0182] The frequency equalization transfer function coefficients are grouped according to the frequency bands, and the transfer function coefficients of each frequency band are Laplace transformed to obtain the multi-band response function of the frequency equalization channel;

[0183] Based on the system response function of the volume control channel and the multi-band response function of the frequency equalization channel, the total transfer function of the system is constructed;

[0184] Discretize the total transfer function of the system, use the bilinear transformation method to perform z-transformation, and obtain the discrete domain transfer function;

[0185] The discrete domain transfer function is converted into a differential equation, and the volume control output and the frequency equalization control output are calculated in parallel and superimposed to obtain an audio output sequence;

[0186] The audio output sequence is amplitude normalized, and gain compensation is performed in combination with the system static gain coefficient to obtain normalized audio data. The normalized audio data is digital-to-analog converted and output to the analog output terminal of the mixer to obtain the audio signal at the output terminal of the mixer.

[0187] Specifically, from the optimal control parameter matrix In the example, the volume control parameters and the frequency equalization control parameters are separated. Contains the volume control parameter matrix and frequency equalization control parameter matrix , obtained by matrix block operation:

[0188] ;

[0189] in Represents parameters related to volume control, with dimensions of , Represents parameters related to frequency equalization, with dimensions of .right Extract the transfer function coefficients from each column and , construct the Laplace domain system response function of the volume control channel. Its transfer function form is:

[0190] ;

[0191] in, is the transfer function of the volume control channel, describing the input volume signal With output signal relationship; is the Laplace transform variable, representing the complex frequency; , are the numerator coefficients, representing the first-order and zero-order gains of the input signal respectively; , , is the denominator coefficient, which represents the second-order, first-order, and zero-order dynamic characteristics of the system. Group processing by frequency band. Assume that the frequency band is divided into sub-bands, and the transfer function coefficients of each band are and , then the multi-band transfer function of the frequency equalization channel is expressed as:

[0192] ;

[0193] in Indicates frequency bands, is the response function of the corresponding frequency band. The system response function of the volume control channel constructed above is Multi-band response function of the frequency equalization channel , the total transfer function of the system is constructed by weighted superposition:

[0194] ;

[0195] in, is the weighting coefficient of the frequency band, which is used to balance the contribution of each frequency band. After the total transfer function is constructed, it is discretized so that it can be implemented in the digital signal processing system. The continuous domain transfer function is converted into Convert to discrete domain transfer function The bilinear transformation formula is:

[0196] ;

[0197] in is the sampling period, is a discrete domain variable. Replace with In the discrete domain, we get the transfer function:

[0198] ;

[0199] in, and are the discrete coefficients of the numerator and denominator, respectively, is the order of the transfer function. Converted into a difference equation form for recursive calculation of audio signals. The general form of the difference equation is:

[0200] ;

[0201] in is the system output signal, is the input signal. The volume control output and frequency equalization control output are calculated in parallel through differential equations, and the audio output sequence is obtained by superposition. . Perform amplitude normalization on the output sequence, the formula is:

[0202] ;

[0203] The normalized signal is combined with the system static gain coefficient To perform gain compensation:

[0204] ;

[0205] The audio signal after gain compensation It is converted into an analog signal through a digital-to-analog converter (DAC) and output to the analog output of the mixing console to obtain the final audio signal.

[0206] See also Figure 2 , Figure 2 The structure schematic block diagram of the equalization adjustment system 200 of the mixing console volume controller provided in the embodiment of the present application is as follows: Figure 2 As shown, the equalization adjustment system 200 of the mixing console volume controller includes:

[0207] The sampling module 210 is used to sample the audio signal at the input end of the mixer and collect environmental noise data through the noise sensor to obtain an initial system parameter matrix;

[0208] A state calculation module 220 is used to perform probability density function calculation on the initial system parameter matrix to obtain an audio state data matrix and a noise characteristic data matrix;

[0209] A control calculation module 230, used for performing a first-order volume control calculation based on the audio state data matrix to obtain a volume control data matrix;

[0210] The equalization calculation module 240 is used to perform a second-order frequency equalization calculation according to the volume control data matrix to obtain a frequency equalization control data matrix;

[0211] Iterative calculation module 250, used for iteratively optimizing the control parameters based on the volume control data matrix and the frequency equalization control data matrix to obtain an optimal control parameter matrix;

[0212] The parallel calculation module 260 is used to perform dual-channel parallel calculations on the transfer function of the volume control channel and the transfer function of the frequency equalization channel according to the optimal control parameter matrix to obtain the audio signal at the output end of the mixer.

[0213] Through the coordinated cooperation of the above-mentioned components, the contradiction between response speed and sound distortion is solved by adopting the anti-disturbance control strategy instead of the traditional PID control, and the control accuracy and stability of the system are significantly improved; the joint estimation of the audio signal state and noise parameters is carried out to realize the real-time and accurate estimation of unknown audio signals and environmental noise, and enhance the anti-interference ability of the system; the dual controller structure of the first-order volume ADRC controller and the second-order frequency equalization ADRC controller is adopted to realize the coordinated optimization of volume control and frequency equalization, and improve the overall control performance of the system; the parameters of the ADRC controller are automatically adjusted based on the particle swarm optimization algorithm, which overcomes the shortcomings of the traditional manual adjustment of parameters, which is complex and time-consuming, and improves the efficiency and accuracy of parameter optimization; the independent adjustment of volume control and frequency equalization is realized through the dual-channel parallel control structure, so that the system can accurately control different frequency bands and ensure the high quality of audio output; the variational reasoning and re-parameterization technology are adopted to improve the accuracy of noise parameter estimation and enhance the adaptability of the system in complex environments.

[0214] See also Figure 3 , Figure 3 The present invention is a schematic block diagram of the structure of an equalizer adjustment device 300 for a mixing console volume controller provided in an embodiment of the present application. The equalizer adjustment device 300 for a mixing console volume controller includes a processor 301 and a memory 302. The processor 301 and the memory 302 are connected via a system bus 303, wherein the memory 302 may include a non-volatile storage medium and an internal memory.

[0215] The non-volatile storage medium can store a computer program. The computer program includes program instructions, and when the program instructions are executed by the processor 301, the processor 301 can execute any of the above-mentioned equalization adjustment methods for the volume controller of a mixing console.

[0216] The processor 301 is used to provide computing and control capabilities to support the operation of the equalization adjustment device 300 of the entire mixing console volume controller.

[0217] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor 301, the processor 301 can execute any of the above-mentioned equalization adjustment methods for the volume controller of a mixing console.

[0218] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present application, and does not constitute a limitation on the equalizer adjustment device 300 of the mixing console volume controller involved in the scheme of the present application. The specific equalizer adjustment device 300 of the mixing console volume controller may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0219] It should be understood that the processor 301 may be a central processing unit (CPU), and the processor 301 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0220] It should be noted that, technical personnel in the relevant field can clearly understand that, for the convenience and simplicity of description, the specific working process of the equalization adjustment device 300 of the mixing console volume controller described above can refer to the corresponding process of the equalization adjustment method of the aforementioned mixing console volume controller, and will not be repeated here.

[0221] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by one or more processors, the one or more processors implement the equalization adjustment method of the mixing console volume controller provided in the embodiment of the present application.

[0222] The computer-readable storage medium may be an internal storage unit of the equalization adjustment device 300 of the mixer volume controller of the aforementioned embodiment, such as a hard disk or memory of the equalization adjustment device 300 of the mixer volume controller. The computer-readable storage medium may also be an external storage device of the equalization adjustment device 300 of the mixer volume controller, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped with the equalization adjustment device 300 of the mixer volume controller.

[0223] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0224] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0225] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for equalizing a volume controller of a mixing console, characterized in that: include: The audio signal at the input of the mixer is sampled, and the environmental noise data is collected through the noise sensor to obtain the initial system parameter matrix; Performing probability density function calculation on the initial system parameter matrix to obtain an audio state data matrix and a noise characteristic data matrix; Performing a first-order volume control calculation based on the audio state data matrix to obtain a volume control data matrix; Perform a second-order frequency equalization calculation according to the volume control data matrix to obtain a frequency equalization control data matrix; Based on the volume control data matrix and the frequency equalization control data matrix, the control parameters are iteratively optimized to obtain the optimal control parameter matrix; specifically, the method comprises: performing parameter encoding on the volume control data matrix and the frequency equalization control data matrix to construct an initial particle swarm position matrix; performing velocity vector initialization operation on each particle in the initial particle swarm position matrix, and setting inertia weight and acceleration constant to obtain the initial motion parameters of the particle swarm; based on the initial motion parameters of the particle swarm, calculating the integral time weighted absolute error index to obtain the fitness function value, and performing sorting operation on the fitness function value to select the most optimal particle swarm position matrix; The particle position corresponding to the best fitness value is set as the global optimal position to obtain the optimized target position data; according to the optimized target position data, the particle velocity update equation is solved to obtain the new particle velocity vector, and based on the new particle velocity vector, the particle position update equation is calculated to obtain new particle position data; the new particle position data is judged for convergence, and when the position change is less than the set threshold, the current global optimal position is extracted to obtain the optimal control parameter vector, and the optimal control parameter vector is reorganized into blocks according to the volume control parameter and the frequency equalization control parameter to obtain the optimal control parameter matrix; According to the optimal control parameter matrix, dual-channel parallel calculations are performed on the transfer function of the volume control channel and the transfer function of the frequency equalization channel to obtain an audio signal at the output end of the mixing console.

2. The method for equalizing the volume controller of a mixing console according to claim 1, characterized in that: The audio signal at the input end of the mixer is sampled, and environmental noise data is collected through a noise sensor to obtain an initial system parameter matrix, including: Performing time domain sampling on the audio signal at the input end of the mixer to obtain an audio sampling data sequence; Performing spectrum decomposition and bandwidth analysis on the audio sampling data sequence to obtain amplitude range data including a maximum amplitude and a minimum amplitude and frequency range data including a maximum frequency and a minimum frequency; Inputting the noise data collected by the noise sensor into a signal conditioning circuit, amplifying and filtering the noise signal, and obtaining a noise processing data sequence; Performing energy accumulation calculation and mean operation on the noise processed data sequence to obtain environmental noise level data; Based on the amplitude range data and the frequency range data, a gain matrix decomposition operation is performed on the observation bandwidth of the state observer of the volume ADRC controller to obtain an initial volume control parameter group, wherein the initial volume control parameter group includes a volume observer bandwidth and a first compensation coefficient; According to the environmental noise level data, a nonlinear mapping operation is performed on the compensation coefficient of the state observer of the frequency equalization ADRC controller to obtain a frequency control initial parameter group, wherein the frequency control initial parameter group includes a frequency observer bandwidth and a second compensation coefficient; Conduct unit step signal test on the input and output response characteristics of the mixer system to obtain the static gain coefficient and dynamic response parameters of the system; The audio sampling data sequence, the noise processing data sequence, the volume control initial parameter group, the frequency control initial parameter group and the system static gain coefficient are constructed into an initial system parameter matrix.

3. The method for equalizing the volume controller of a mixing console according to claim 2, characterized in that: The method of performing probability density function calculation on the initial system parameter matrix to obtain an audio state data matrix and a noise characteristic data matrix comprises: Performing joint probability modeling on the audio signal data and the environmental noise data in the initial system parameter matrix to obtain an audio signal state vector and a noise parameter vector; Performing a normal distribution transformation on the audio signal state vector to obtain a conditional probability distribution matrix of the audio signal, and performing a priori distribution modeling on the noise parameter vector to obtain a priori probability distribution matrix of the noise parameter; Inputting the conditional probability distribution matrix and the prior probability distribution matrix into a variational reasoning unit, calculating the logarithmic marginal likelihood function to obtain a variational optimization function, and performing component decomposition operation on the variational optimization function to extract a conjugate term coefficient matrix and a non-conjugate term coefficient matrix; Based on the conjugate term coefficient matrix, the maximum a posteriori probability calculation is performed on the audio signal state vector to obtain an audio state data matrix, and the random gradient optimization calculation is performed on the non-conjugate term coefficient matrix, and the noise parameter vector is updated in combination with the reparameterization technology to obtain a noise feature data matrix.

4. The method for equalizing the volume controller of a mixing console according to claim 3, characterized in that: The first-order volume control calculation is performed based on the audio state data matrix to obtain a volume control data matrix, including: Inputting the audio state data matrix into a tracking differentiator, performing transition process calculation on the audio signal sequence to obtain a smooth target signal, performing speed factor calculation on the smooth target signal, constructing a tracking differentiator state equation, and obtaining a tracking signal derivative; Based on the tracking signal derivative, recursively operate the state vector and the disturbance vector of the first-order extended state observer to obtain state observation data; Calculating the observation error of the state observation data, and performing nonlinear function operation in combination with the bandwidth of the volume observer to obtain a compensation control amount, and solving the extended state observer discrete equation based on the compensation control amount to obtain observer output data; Performing error calculation on the observer output data and the smooth target signal, and constructing a nonlinear state error feedback control law in combination with a compensation coefficient to obtain a volume control amount; The volume control amount is input into the control function calculation unit, a coefficient matrix operation is performed on the nonlinear error feedback control amount to obtain volume control data, a state matrix splicing operation is performed on the volume control data, and normalization processing is performed in combination with the control gain coefficient to obtain a volume control data matrix.

5. The method for equalizing the volume controller of a mixing console according to claim 4, characterized in that: The second-order frequency equalization calculation is performed according to the volume control data matrix to obtain a frequency equalization control data matrix, including: Inputting the volume control data matrix into a second-order tracking differentiator, performing state recursive calculation on the audio signal, and obtaining a tracking differentiator state variable; Solving the tracking differentiator equation based on the tracking differentiator state variable to obtain a target tracking state sequence; Performing initial value configuration of the third-order state variable of the extended state observer of the second-order frequency equalization ADRC controller to obtain the initial state matrix of the extended state observer, and recursively calculating the state equation according to the initial state matrix of the extended state observer to obtain state observation data; Performing error analysis on the state observation data and the target tracking state sequence, constructing a nonlinear state error feedback control law, and obtaining a frequency compensation control amount; Based on the frequency compensation control amount, compensation calculation is performed on compensation coefficients β11 and β12 of the second-order frequency equalization ADRC controller to obtain frequency equalization control parameters; The frequency equalization control parameters are input into a frequency equalization control unit, and independent gain control operations are performed on audio signals of different frequency bands to obtain frequency equalization control data. Matrix transformation operations are performed on the frequency equalization control data, and normalization processing is performed in combination with the controller gain coefficient to obtain a frequency equalization control data matrix.

6. The method for equalizing the volume controller of a mixing console according to claim 1, characterized in that: The method of performing dual-channel parallel calculation on the transfer function of the volume control channel and the transfer function of the frequency equalization channel according to the optimal control parameter matrix to obtain the audio signal at the output end of the mixing console includes: Separating the optimal control parameter matrix according to volume control parameters and frequency equalization control parameters to obtain volume transfer function coefficients and frequency equalization transfer function coefficients; Performing Laplace transform on the volume transfer function coefficients to construct a system response function of the volume control channel; The frequency equalization transfer function coefficients are grouped according to frequency bands, and the transfer function coefficients of each frequency band are Laplace transformed to obtain a multi-band response function of a frequency equalization channel; Constructing a system total transfer function based on the system response function of the volume control channel and the multi-band response function of the frequency equalization channel; The total transfer function of the system is discretized and z-transformed by using a bilinear transformation method to obtain a discrete domain transfer function; The discrete domain transfer function is converted into a differential equation, and the volume control output and the frequency equalization control output are parallel superimposed and calculated to obtain an audio output sequence; The audio output sequence is amplitude normalized, gain compensated in combination with the system static gain coefficient to obtain normalized audio data, and the normalized audio data is digital-to-analog converted and output to the analog output end of the mixing console to obtain an audio signal at the output end of the mixing console.

7. A balanced adjustment system for a mixing console volume controller, characterized in that: A method for performing equalization adjustment of a mixing console volume controller according to any one of claims 1 to 6, comprising: The sampling module is used to sample the audio signal at the input end of the mixer and collect environmental noise data through the noise sensor to obtain the initial system parameter matrix; A state calculation module, used for performing probability density function calculation on the initial system parameter matrix to obtain an audio state data matrix and a noise characteristic data matrix; A control calculation module, used for performing a first-order volume control calculation based on the audio state data matrix to obtain a volume control data matrix; A balance calculation module, used for performing a second-order frequency balance calculation according to the volume control data matrix to obtain a frequency balance control data matrix; An iterative calculation module, used for iteratively optimizing the control parameters based on the volume control data matrix and the frequency equalization control data matrix to obtain an optimal control parameter matrix; The parallel calculation module is used to perform dual-channel parallel calculation on the transfer function of the volume control channel and the transfer function of the frequency equalization channel according to the optimal control parameter matrix to obtain the audio signal at the output end of the mixer.

8. A device for equalizing and adjusting the volume controller of a mixing console, characterized in that: The equalization adjustment device of the mixing console volume controller comprises: a memory and at least one processor, wherein the memory stores instructions; The at least one processor calls the instructions in the memory to enable the equalization adjustment device of the mixing console volume controller to execute the equalization adjustment method of the mixing console volume controller according to any one of claims 1-6.

9. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the equalization adjustment method of the mixing console volume controller according to any one of claims 1 to 6 is implemented.

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