Method and system for compensating for frequency response of microphone

Abstract

The invention relates to the technical field of audio processing, in particular to a method and a system for compensating frequency response of a microphone. The method comprises the following steps: obtaining microphone output signal data and multi-dimensional environment data, and carrying out frequency response multi-level feature integration to obtain coupling frequency response data; frequency response-environment parameter correlation analysis is carried out based on the coupling frequency response data, and a frequency characteristic matrix is obtained; acquiring environmental noise monitoring data, and performing frequency band environmental noise calibration to obtain a noise calibration matrix; performing Monte Carlo multi-dimensional parameter optimization based on the frequency characteristic matrix and the noise calibration matrix so as to obtain an optimization compensation strategy; acquiring real-time microphone output signal data, and performing dynamic compensation feedback to obtain dynamic compensation feedback data; and performing user personalized compensation strategy selection according to the dynamic compensation feedback data to obtain a user microphone personalized compensation strategy. According to the invention, the accuracy and quality of audio processing can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of audio processing, and in particular to a method and system for compensating the frequency response of a microphone. Background Art

[0002] With the development of audio technology, microphones, as key devices for audio collection, are widely used in various audio application scenarios, including speech recognition, communication, acoustic measurement, recording and other fields. However, the frequency response characteristics of microphones directly affect the accuracy and quality of the sound they collect. In practical applications, microphones often have uneven frequency responses, which can cause audio distortion in recording or speech recognition and affect subsequent processing results. Software compensation methods achieve frequency response compensation by digitally processing the audio signal output by the microphone. However, software compensation methods also have certain limitations. Software compensation often relies on a priori microphone frequency response models and algorithms, which requires accurate calibration and modeling before compensation. The frequency response characteristics of microphones of different brands or models can vary greatly. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a method and system for compensating the frequency response of a microphone to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for compensating the frequency response of a microphone comprises the following steps: Step S1: Acquire microphone output signal data and multi-dimensional environmental data, and perform frequency response curve conversion on the microphone output signal data to obtain a microphone frequency response curve; perform multi-level frequency response feature integration based on the microphone frequency response curve and the multi-dimensional environmental data to obtain coupled frequency response data; Step S2: performing frequency band frequency principal component feature screening based on the coupled frequency response data, thereby obtaining frequency band frequency principal component feature data, and performing frequency response-environmental parameter correlation analysis based on the frequency band frequency principal component feature data, thereby obtaining a frequency feature matrix; Step S3: Acquire environmental noise monitoring data, and map the environmental noise monitoring data to a frequency characteristic matrix, thereby obtaining a noise signal mapping matrix; perform frequency band environmental noise calibration on the noise signal mapping matrix, thereby obtaining a noise calibration matrix; Step S4: performing an adaptive dynamic compensation strategy analysis based on the frequency characteristic matrix and the noise calibration matrix to obtain an initial adaptive dynamic compensation strategy; performing Monte Carlo multidimensional parameter optimization on the initial adaptive dynamic compensation strategy according to the coupled frequency response data to obtain an optimized compensation strategy, and transmitting the optimized compensation strategy to the microphone device controller to execute the optimized compensation strategy; Step S5: acquiring real-time microphone output signal data, and evaluating the compensation effect of the real-time microphone output signal data and the optimized compensation strategy, thereby obtaining real-time audio compensation score data; performing dynamic compensation feedback on the optimized compensation strategy based on the real-time audio compensation score data, thereby obtaining dynamic compensation feedback data; Step S6: Model a global compensation model based on dynamic compensation feedback data to obtain a microphone global compensation model; select a user personalized compensation strategy based on the microphone global compensation model to obtain a user microphone personalized compensation strategy, and transmit it to the microphone device controller to execute the user microphone personalized compensation strategy.

[0005] Optionally, step S1 specifically includes: Step S11: Acquire microphone output signal data and multi-dimensional environmental data, and perform data preprocessing on the microphone output signal data and the multi-dimensional environmental data respectively, so as to obtain microphone output signal data to be analyzed and multi-dimensional environmental data to be analyzed; Step S12: performing Fourier transform on the microphone output signal data to be analyzed to obtain the microphone output signal spectrum, and performing frequency domain data standardization on the microphone output signal spectrum to obtain the microphone output signal standardized spectrum; Step S13: integrating the frequency response characteristics of the standardized frequency spectrum of the microphone output signal to obtain microphone frequency response characteristic data, and performing frequency response curve conversion based on the microphone frequency response characteristic data to obtain a microphone frequency response curve; Step S14: performing multi-dimensional environmental data fusion on the multi-dimensional environmental data to be analyzed, thereby obtaining environmental fusion data to be analyzed; Step S15: performing multi-level feature integration of frequency response according to the microphone frequency response curve and the environmental fusion data to be analyzed, thereby obtaining coupled frequency response data.

[0006] Optionally, step S15 is specifically: Step S151: performing frequency feature alignment on the microphone frequency response curve and the environment fusion data to be analyzed, so as to obtain a frequency-aligned response curve and frequency-aligned environment fusion data; Step S152: performing correlation analysis on the frequency alignment response curve and the frequency alignment environment fusion data to obtain microphone frequency-environment correlation data, and performing feature selection on the frequency alignment environment fusion data according to the microphone frequency-environment correlation data to obtain frequency-correlated environment data; Step S153: performing multi-dimensional feature vector splicing on the frequency alignment response curve and the frequency-related environmental data, so as to obtain frequency response-environmental feature composite data; Step S154: performing environmental impact multi-frequency response hierarchical analysis based on the frequency response-environmental feature composite data, thereby obtaining frequency response hierarchical environmental correlation data; Step S155: performing frequency response hierarchical relationship mapping coupling on the frequency response-environmental feature composite data based on the frequency response hierarchical environment association data, thereby obtaining coupled frequency response data.

[0007] Optionally, step S2 specifically includes: Step S21: dividing the coupling frequency response data into frequency bands, thereby obtaining frequency band-divided coupling frequency response data; Step S22: performing frequency band principal component characteristic analysis on the frequency band division coupled frequency response data, thereby obtaining frequency band principal component characteristic data; Step S23: calculating the frequency band frequency response influence factor based on the frequency band principal component characteristic data, thereby obtaining the frequency band frequency response influence factor; Step S24: quantifying the environmental parameter impact according to the frequency band frequency response impact factor, thereby obtaining environmental parameter impact degree data; Step S25: Perform frequency response-environment parameter association on the environmental parameter influence data and the frequency band principal component characteristic data, so as to obtain a frequency response-environment association matrix, and construct a frequency characteristic matrix according to the frequency response-environment association matrix.

[0008] Optionally, step S3 specifically includes: Step S31: Acquire environmental noise monitoring data, and perform data preprocessing on the environmental noise monitoring data, so as to obtain environmental noise monitoring data to be analyzed; Step S32: performing Fourier transform of the environmental noise monitoring data to be analyzed, thereby obtaining an environmental noise signal spectrum, and performing frequency segment energy distribution statistics of the environmental noise signal spectrum, thereby obtaining a frequency segment energy distribution diagram of the noise signal; Step S33: integrating the frequency distribution of the noise signal according to the frequency segment energy distribution diagram of the noise signal, thereby obtaining a frequency distribution matrix of the noise signal; Step S34: performing time series mapping on the noise signal frequency distribution matrix and the frequency feature matrix to obtain an initial signal mapping matrix, and aligning the frequency axis features of the initial signal mapping matrix to obtain a noise signal mapping matrix; Step S35: performing frequency band environmental noise calibration on the noise signal mapping matrix, thereby obtaining a noise calibration matrix.

[0009] Optionally, step S35 is specifically: Step S351: dividing the noise signal mapping matrix into frequency bands, thereby obtaining a frequency band spectrum of the noise signal; Step S352: performing time series correlation of environmental condition-frequency band noise signal according to the noise signal frequency distribution matrix and the frequency band spectrum of the noise signal, thereby obtaining environmental condition-frequency band noise signal change data; Step S353: calculating the environmental noise impact factor based on the environmental condition-frequency band noise signal change data, thereby obtaining the environmental noise impact factor; Step S354: calculating the frequency band calibration coefficient of the frequency band spectrum of the noise signal according to the environmental noise impact factor, thereby obtaining the frequency band calibration coefficient; Step S355: performing noise signal frequency band calibration on the noise signal mapping matrix according to the frequency band calibration coefficient, thereby obtaining a noise calibration matrix.

[0010] Optionally, step S4 is specifically: Step S41: performing feature comparison on the frequency feature matrix and the noise calibration matrix to obtain frequency response difference data, and integrating the noise impact factor according to the frequency response difference data to obtain the noise impact factor; Step S42: constructing an initial noise compensation model based on the frequency response difference data and the noise impact factor; Step S43: designing a compensation strategy through an initial noise compensation model in combination with microphone frequency response characteristic data, frequency band calibration coefficients, and environmental fusion data to be analyzed, thereby obtaining an initial adaptive dynamic compensation strategy; Step S44: setting compensation parameters according to the coupling frequency response data to obtain a compensation parameter set, and performing Monte Carlo random sampling on the compensation parameter set to obtain a random compensation parameter set; Step S45: performing signal compensation simulation on the random compensation parameter set according to the initial adaptive dynamic compensation strategy to obtain signal compensation simulation data, and performing compensation signal error evaluation on the signal compensation simulation data to obtain compensation signal error data; Step S46: selecting a compensation parameter group for the random compensation parameter set based on the compensation signal error data, thereby obtaining an optimal compensation parameter group; Step S47: Optimizing the compensation strategy multi-dimensional parameters of the initial adaptive dynamic compensation strategy according to the optimal compensation parameter group, thereby obtaining an optimized compensation strategy, and transmitting the optimized compensation strategy to the microphone device controller to execute the optimized compensation strategy.

[0011] Optionally, step S5 specifically includes: Step S51: acquiring real-time microphone output signal data, and performing data preprocessing on the real-time microphone output signal data, thereby obtaining real-time microphone output signal data to be analyzed; Step S52: extracting audio signal frequency features from the real-time microphone output signal data to be analyzed, thereby obtaining a real-time audio signal spectrum; Step S53: performing compensation strategy backtracking on the real-time audio signal spectrum according to the optimized compensation strategy, thereby obtaining the real-time uncompensated audio signal spectrum; Step S54: performing compensation effect evaluation based on the real-time audio signal spectrum and the real-time uncompensated audio signal spectrum to obtain audio compensation effect evaluation data, and weighting the evaluation results of the audio compensation effect evaluation data to obtain real-time audio compensation score data; Step S55: classifying the real-time audio compensation score data according to a preset score threshold, and if the real-time audio compensation score data is greater than or equal to the score threshold, sending an instruction to the microphone device controller to continue to execute the optimization compensation strategy; if the real-time audio compensation score data is less than the score threshold, marking the corresponding optimization compensation strategy as an abnormal compensation strategy, and sending an instruction to the microphone device controller to stop executing the optimization compensation strategy; Step S56: adjusting the abnormal compensation strategy in real time according to the real-time audio compensation score data, thereby obtaining dynamic compensation feedback data.

[0012] Optionally, step S6 specifically includes: Step S61: performing principal component feature selection on the dynamic compensation feedback data to obtain dynamic compensation feature data; Step S62: Building a neural network global compensation model according to the dynamic compensation feature data, thereby obtaining a microphone global compensation model; Step S63: acquiring user microphone interaction data, and performing user audio preference analysis on the user microphone interaction data, thereby obtaining user audio preference data; Step S64: selecting a user personalized compensation strategy for the user audio preference data through the microphone global compensation model, thereby obtaining a user microphone personalized compensation strategy, and transmitting it to the microphone device controller to execute the user microphone personalized compensation strategy.

[0013] The present invention significantly improves the accuracy and quality of microphone audio acquisition and output through dynamic compensation and personalized optimization of microphone frequency response and environmental noise. By obtaining microphone output signal data and multi-dimensional environmental data, and performing frequency response curve conversion and feature integration, the frequency response characteristics of the microphone in a specific environment can be effectively described. Combining environmental influences with the frequency response characteristics of the microphone itself provides a comprehensive feature data basis for subsequent compensation. Next, based on the screening of frequency band principal component characteristics and the correlation analysis of frequency response-environmental parameters, it is helpful to extract the key features that affect the frequency response, and combine these features with environmental factors, thereby laying the foundation for a more accurate compensation strategy. By monitoring and calibrating environmental noise, not only the noise interference is effectively quantified, but also the noise impact can be adaptively adjusted under different environmental conditions, thereby optimizing the compensation model and ensuring the efficiency and accuracy of the compensation effect. The optimization of the dynamic compensation strategy adopts Monte Carlo multi-dimensional parameter optimization to ensure the adaptability and stability of the compensation strategy in different environments and noise conditions. Through adaptive dynamic compensation and model modeling based on real-time feedback data, the compensation strategy can be adjusted in time according to actual feedback, thereby ensuring that the microphone can provide an ideal audio acquisition effect in various environments. In particular, through the real-time audio compensation scoring mechanism, the effectiveness of the optimized compensation strategy can be dynamically evaluated, and the compensation strategy can be further adjusted based on real-time feedback to ensure that each compensation meets the user's audio preferences as much as possible. The core advantage of this process is that through real-time monitoring and adjustment, the microphone can adapt to complex and changing audio environments, thereby significantly improving the accuracy and quality of audio processing. By analyzing user preference data and combining it with a global compensation model, it is possible to provide personalized compensation strategies for different users based on their audio needs. The application of this personalized compensation strategy greatly improves the user experience, allowing the microphone to not only compensate when the ambient noise is large, but also adjust its frequency response characteristics according to the preferences of different users to provide the best audio output effect. Through this series of beneficial technical steps, the goal of providing accurate and high-quality audio acquisition and processing in different environments and user needs is finally achieved.

[0014] Optionally, the present specification also provides a system for compensating the frequency response of a microphone, which is used to execute the method for compensating the frequency response of a microphone as described above. The system for compensating the frequency response of a microphone includes: A frequency response coupling module is used to obtain microphone output signal data and multi-dimensional environmental data, and perform frequency response curve conversion on the microphone output signal data to obtain a microphone frequency response curve; perform multi-level feature integration of frequency response according to the microphone frequency response curve and multi-dimensional environmental data to obtain coupled frequency response data; A principal component feature screening module is used to screen the frequency band frequency principal component features based on the coupled frequency response data, thereby obtaining the frequency band frequency principal component feature data, and to perform frequency response-environmental parameter correlation analysis based on the frequency band frequency principal component feature data, thereby obtaining a frequency feature matrix; The environmental noise calibration module is used to obtain environmental noise monitoring data and map the environmental noise monitoring data to a frequency characteristic matrix to obtain a noise signal mapping matrix; perform frequency band environmental noise calibration on the noise signal mapping matrix to obtain a noise calibration matrix; A compensation strategy generation module is used to perform adaptive dynamic compensation strategy analysis based on a frequency characteristic matrix and a noise calibration matrix to obtain an initial adaptive dynamic compensation strategy; perform Monte Carlo multi-dimensional parameter optimization on the initial adaptive dynamic compensation strategy according to coupled frequency response data to obtain an optimized compensation strategy, and transmit the optimized compensation strategy to a microphone device controller to execute the optimized compensation strategy; A compensation strategy feedback module is used to obtain real-time microphone output signal data, and evaluate the compensation effect of the real-time microphone output signal data and the optimized compensation strategy, so as to obtain real-time audio compensation score data; based on the real-time audio compensation score data, dynamic compensation feedback is performed on the optimized compensation strategy, so as to obtain dynamic compensation feedback data; The personalized compensation strategy selection module is used to model a global compensation model based on dynamic compensation feedback data to obtain a microphone global compensation model; select a user personalized compensation strategy based on the microphone global compensation model to obtain a user microphone personalized compensation strategy, and transmit it to the microphone device controller to execute the user microphone personalized compensation strategy.

[0015] The present invention is a system for compensating the frequency response of a microphone. The system can implement any one of the methods for compensating the frequency response of a microphone of the present invention, and is used to combine the operations between various modules with a medium for signal transmission to complete the method for compensating the frequency response of a microphone. The modules within the system cooperate with each other, thereby improving the accuracy and quality of audio processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings: Figure 1 A schematic flow chart of the steps of a method for compensating the frequency response of a microphone according to the present invention; Figure 2 Detailed step flow diagram of step S1 in the present invention; Figure 3 Detailed step flow diagram of step S2 in the present invention; The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0017] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0018] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0019] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0020] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for compensating the frequency response of a microphone, the method comprising the following steps: Step S1: Acquire microphone output signal data and multi-dimensional environmental data, and perform frequency response curve conversion on the microphone output signal data to obtain a microphone frequency response curve; perform multi-level frequency response feature integration based on the microphone frequency response curve and the multi-dimensional environmental data to obtain coupled frequency response data; In this embodiment, the ambient audio signal is collected by a microphone device. The built-in analog-to-digital converter (ADC) of the microphone converts the audio signal into a digital signal, and uses the fast Fourier transform (FFT) to perform frequency domain analysis on it to obtain a frequency response curve. This curve shows the response of the microphone to signals of different frequencies. On this basis, the built-in sensors of the microphone further collect multi-dimensional data in the environment, including temperature, humidity, air pressure, and background noise, etc. These data are acquired in real time through a variety of sensors. Subsequently, the microphone frequency response curve and environmental data are fed into a deep neural network model for multi-level feature integration to obtain coupled frequency response data. This data combines the combined effects of audio signals and environmental factors for the design of subsequent compensation strategies.

[0021] Step S2: performing frequency band frequency principal component feature screening based on the coupled frequency response data, thereby obtaining frequency band frequency principal component feature data, and performing frequency response-environmental parameter correlation analysis based on the frequency band frequency principal component feature data, thereby obtaining a frequency feature matrix; In this embodiment, based on the coupled frequency response data, the principal component analysis (PCA) algorithm is used to reduce the dimension of the data and extract the frequency band frequency principal component features. These features represent the most important change dimensions of the frequency response data. Then, using these frequency band frequency principal component feature data, the correlation between the frequency response and environmental parameters (such as temperature, humidity, noise, etc.) is analyzed through machine learning methods (such as random forests or support vector machines) to identify the law of frequency response changes under different environmental conditions. In this way, the obtained frequency feature matrix can accurately reflect the impact of environmental factors on the frequency response of the audio signal, providing reliable theoretical support for the next step of noise calibration and dynamic compensation.

[0022] Step S3: Acquire environmental noise monitoring data, and map the environmental noise monitoring data to a frequency characteristic matrix, thereby obtaining a noise signal mapping matrix; perform frequency band environmental noise calibration on the noise signal mapping matrix, thereby obtaining a noise calibration matrix; In this embodiment, the environmental noise data is collected in real time by the built-in environmental noise monitoring device (such as an environmental noise sensor) of the microphone. The collected data is converted into a frequency domain signal by FFT to obtain the noise intensity of each frequency band. Then, the environmental noise data is mapped into the frequency characteristic matrix using the obtained frequency characteristic matrix to form a noise signal mapping matrix. In order to eliminate the interference of environmental noise on the output signal of the microphone, a frequency band-based noise calibration algorithm (such as a Kalman filter) is used to calibrate the noise signal mapping matrix. Through noise calibration, a noise calibration matrix is ​​obtained, which can effectively reduce the impact of noise on the audio signal and lay the foundation for the optimization of subsequent compensation strategies.

[0023] Step S4: performing an adaptive dynamic compensation strategy analysis based on the frequency characteristic matrix and the noise calibration matrix to obtain an initial adaptive dynamic compensation strategy; performing Monte Carlo multidimensional parameter optimization on the initial adaptive dynamic compensation strategy according to the coupled frequency response data to obtain an optimized compensation strategy, and transmitting the optimized compensation strategy to the microphone device controller to execute the optimized compensation strategy; In this embodiment, based on the obtained frequency characteristic matrix and noise calibration matrix, an adaptive filtering algorithm is used for analysis to generate an initial adaptive dynamic compensation strategy. This strategy automatically adjusts the microphone gain, frequency response and noise suppression parameters mainly according to environmental parameters and frequency characteristics to optimize the audio quality. In order to further improve the compensation effect, the Monte Carlo simulation method is used to perform multi-dimensional optimization of the various parameters of the initial compensation strategy, simulate the compensation effect under different environments, and adjust the compensation strategy. After optimization, the obtained compensation strategy is sent to the microphone device controller via wireless transmission and executed in the controller. The optimized compensation strategy can adjust the working state of the microphone according to the real-time environmental conditions to ensure that the audio signal is always clear and stable.

[0024] Step S5: acquiring real-time microphone output signal data, and evaluating the compensation effect of the real-time microphone output signal data and the optimized compensation strategy, thereby obtaining real-time audio compensation score data; performing dynamic compensation feedback on the optimized compensation strategy based on the real-time audio compensation score data, thereby obtaining dynamic compensation feedback data; In this embodiment, the obtained microphone output signal data is analyzed in the frequency domain by a digital signal processing (DSP) module, and combined with the optimized compensation strategy to evaluate the compensation effect. The compensation effect is evaluated by calculating the error between the optimized audio signal and the uncompensated audio signal to obtain real-time audio compensation scoring data. The scoring data is analyzed by a machine learning algorithm (such as a neural network) to provide real-time feedback on the effectiveness of the optimized compensation strategy. If the scoring data is lower than a preset threshold, the system will automatically adjust the compensation strategy to optimize the signal processing process. The dynamic compensation feedback data further adjusts the compensation strategy through an adaptive algorithm to ensure that the compensation effect of the real-time audio signal is continuously improved.

[0025] Step S6: Model a global compensation model based on dynamic compensation feedback data to obtain a microphone global compensation model; select a user personalized compensation strategy based on the microphone global compensation model to obtain a user microphone personalized compensation strategy, and transmit it to the microphone device controller to execute the user microphone personalized compensation strategy.

[0026] In this embodiment, a global compensation model is constructed based on dynamic compensation feedback data. The model uses regression analysis methods to comprehensively consider frequency characteristics, environmental factors, noise conditions and real-time compensation feedback data to generate a global compensation model. Through this model, personalized compensation strategies can be automatically selected according to different user needs. Personalized compensation strategies are adjusted according to the specific needs of users (such as preferences for audio in a specific frequency band, different usage environments), such as enhancing high-frequency parts and reducing low-frequency noise. Finally, the personalized compensation strategy is transmitted to the microphone device controller to ensure that the device executes the compensation strategy that best suits the user, thereby providing the best audio experience.

[0027] Optionally, step S1 specifically includes: Step S11: Acquire microphone output signal data and multi-dimensional environmental data, and perform data preprocessing on the microphone output signal data and the multi-dimensional environmental data respectively, so as to obtain microphone output signal data to be analyzed and multi-dimensional environmental data to be analyzed; In this embodiment, audio output signal data is obtained from the microphone device, and the data is collected and converted into a digital signal by an analog-to-digital converter (ADC). At the same time, environmental data is collected in real time through multiple environmental sensors built into the microphone device (such as temperature and humidity sensors, air pressure sensors, noise sensors, etc.). These sensor data include multi-dimensional data such as ambient temperature, humidity, and noise level. Next, these two types of data are preprocessed: the microphone output signal data is denoised and filtered to remove high-frequency noise and unnecessary interference to ensure the validity of the signal; and the multi-dimensional environmental data is interpolated to fill in missing values, and unit conversion and standardization are performed to ensure that each environmental parameter has the same dimension and consistency. The preprocessed data is marked as microphone output signal data to be analyzed and environmental data to be analyzed, respectively, and is ready to enter the subsequent analysis process.

[0028] Step S12: performing Fourier transform on the microphone output signal data to be analyzed to obtain the microphone output signal spectrum, and performing frequency domain data standardization on the microphone output signal spectrum to obtain the microphone output signal standardized spectrum; In this embodiment, the microphone output signal data to be analyzed is subjected to a Fourier transform (FFT) to convert the signal from the time domain to the frequency domain. The Fourier transform can decompose the time domain waveform of the audio signal into different frequency components to obtain the spectrum of the microphone output signal. After obtaining the spectrum data, the spectrum data is further standardized. Standardization includes normalizing the spectrum data to ensure that the signal strength within the frequency range is on a uniform scale and to remove the amplitude differences caused by different devices or recording environments, thereby improving the stability and consistency of data processing. After standardization, the spectrum data obtained is the standardized spectrum of the microphone output signal for subsequent feature extraction and analysis.

[0029] Step S13: integrating the frequency response characteristics of the standardized frequency spectrum of the microphone output signal to obtain microphone frequency response characteristic data, and performing frequency response curve conversion based on the microphone frequency response characteristic data to obtain a microphone frequency response curve; In this embodiment, based on the obtained standardized spectrum of the microphone output signal, it is analyzed by integrating the frequency response characteristics. By weighted synthesis of signals in different frequency bands, frequency response characteristic data representing the microphone is extracted. At this time, the responses of different frequency bands are integrated according to the design characteristics of the microphone (such as directivity, frequency response curve, etc.), so that the response characteristics of the microphone under different environmental conditions can be reflected. Next, the frequency response curve is converted based on the extracted frequency response characteristic data. The conversion process uses a mathematical model (such as linear regression or neural network) to generate a frequency response curve of the microphone based on the frequency response characteristic data. The frequency response curve describes the sensitivity of the microphone to signals of different frequencies, providing a basis for subsequent optimization and compensation strategies.

[0030] Step S14: performing multi-dimensional environmental data fusion on the multi-dimensional environmental data to be analyzed, thereby obtaining environmental fusion data to be analyzed; In this embodiment, the multi-dimensional environmental data to be analyzed is fused. First, the environmental data (such as temperature, humidity, air pressure, noise, etc.) collected by multiple sensors are fused by weighted averaging or principal component analysis (PCA). In this way, data from different sources and different types can be integrated into a unified environmental fusion data. Specifically, if the environmental noise data has a high correlation, it can be weighted fused with the temperature and humidity data to reduce the volatility caused by a single data source. At the same time, the environmental data fusion method also takes into account the accuracy and reliability of each sensor to ensure the representativeness and accuracy of the final fused data. The fused data is marked as environmental fusion data to be analyzed, which provides a basis for subsequent feature integration.

[0031] Step S15: performing multi-level feature integration of frequency response according to the microphone frequency response curve and the environmental fusion data to be analyzed, thereby obtaining coupled frequency response data.

[0032] In this embodiment, a multi-level feature integration of the frequency response is performed based on the microphone frequency response curve and the environmental fusion data to be analyzed. The representative features are extracted by combining the frequency response characteristics of the microphone and the environmental data through a multi-level feature fusion method. This process uses an integrated learning algorithm, such as random forest or XGBoost, to fuse the frequency response characteristics of the microphone and environmental parameters to capture the complex relationship between the audio signal and environmental factors. After integration, the coupled frequency response data obtained contains the comprehensive response characteristics of the microphone under different environmental conditions, providing data support for subsequent compensation and optimization processing.

[0033] Optionally, step S15 is specifically: Step S151: performing frequency feature alignment on the microphone frequency response curve and the environment fusion data to be analyzed, so as to obtain a frequency-aligned response curve and frequency-aligned environment fusion data; In this embodiment, the frequency response curve of the microphone is a frequency domain signal obtained by Fourier transform and filtering. Relevant parameters such as temperature, humidity, noise level, etc. are extracted from the multi-dimensional environmental data collected by the environmental sensor and mapped to the frequency range corresponding to the microphone frequency response curve. Through the alignment method of the time axis and the frequency axis (such as interpolation or time-frequency transformation), the frequency characteristics of these two types of data are aligned to ensure that each environmental data point corresponds to a specific frequency band. After alignment, the generated frequency alignment response curve and frequency alignment environment fusion data will provide a unified frequency reference for subsequent analysis.

[0034] Step S152: performing correlation analysis on the frequency alignment response curve and the frequency alignment environment fusion data to obtain microphone frequency-environment correlation data, and performing feature selection on the frequency alignment environment fusion data according to the microphone frequency-environment correlation data to obtain frequency-correlated environment data; In this embodiment, by calculating the correlation between the frequency alignment response curve and the frequency alignment environment fusion data, statistical methods such as the Pearson correlation coefficient or mutual information are used to evaluate the impact of environmental factors (such as temperature and humidity changes, background noise, etc.) on the microphone frequency response. For example, when the background noise is large, the high-frequency response of the microphone may be suppressed, while the low-frequency response changes less. Based on the calculated correlation data, select environmental data features that have a greater impact on the microphone frequency response, such as selecting only temperature and humidity as influencing factors, and remove factors that contribute less to the frequency response. The frequency-related environmental data finally obtained will include environmental factors that are closely related to the frequency response.

[0035] Step S153: performing multi-dimensional feature vector splicing on the frequency alignment response curve and the frequency-related environmental data, so as to obtain frequency response-environmental feature composite data; In this embodiment, the frequency alignment response curve and the frequency-related environmental data are spliced ​​into feature vectors. The specific operation is to combine the response data (such as amplitude, phase, etc.) at each frequency point with the corresponding environmental data (such as temperature, humidity, noise level, etc.) to form a composite feature vector. For example, for a specific frequency band (such as 1kHz to 3kHz), the microphone response data of the frequency band and the temperature and humidity data of the frequency band period are spliced ​​into a new feature vector. This process can be achieved by point-by-point splicing, or by weighted splicing of different frequency bands to form composite data containing environmental factors and frequency response information. These composite data will provide rich information for the next step of environmental impact analysis.

[0036] Step S154: performing environmental impact multi-frequency response hierarchical analysis based on the frequency response-environmental feature composite data, thereby obtaining frequency response hierarchical environmental correlation data; In this embodiment, a multi-frequency response hierarchical analysis method (such as hierarchical cluster analysis, principal component analysis, etc.) is used to process the frequency response-environmental feature composite data. Specifically, the frequency response data and environmental data are hierarchically divided according to different frequency bands, and the response characteristics of each frequency band under different environmental conditions are analyzed. For example, the response of the low frequency band (20Hz to 200Hz) may have a greater relationship with humidity changes, while the mid-frequency band (200Hz to 2kHz) may be more affected by temperature changes. By performing a hierarchical analysis on these frequency bands, it is possible to identify which frequency bands are more significantly affected in different environments, thereby obtaining frequency response hierarchical environmental correlation data. These data can help further understand the specific correlation between microphone responses in different frequency bands and environmental factors.

[0037] Step S155: performing frequency response hierarchical relationship mapping coupling on the frequency response-environmental feature composite data based on the frequency response hierarchical environment association data, thereby obtaining coupled frequency response data.

[0038] In this embodiment, frequency response hierarchical relationship mapping coupling is performed based on the obtained frequency response hierarchical environment association data. Specifically, by constructing a mapping model, the hierarchical association relationship between the frequency response characteristics and the environmental factors is mapped into a unified coupling model. The mapping model adopts methods such as regression model, support vector machine (SVM) or neural network. Through this model, the environmental factors of each data point in the frequency response-environmental feature composite data are mapped to the corresponding frequency response data, thereby obtaining coupled frequency response data. At this time, the coupled frequency response data includes the influence of environmental factors on the frequency response of each frequency band, and can effectively reflect how environmental changes affect the frequency response performance of the microphone.

[0039] Optionally, step S2 specifically includes: Step S21: dividing the coupling frequency response data into frequency bands, thereby obtaining frequency band-divided coupling frequency response data; In this embodiment, the complete frequency response information is extracted from the coupled frequency response data, and then these data are divided into different frequency bands according to the required frequency band division standard (such as low frequency band: 20Hz-200Hz, medium frequency band: 200Hz-2kHz, high frequency band: 2kHz-20kHz). The response data in each frequency band is processed separately to ensure that the characteristics of each frequency band can be analyzed independently. For example, the low frequency band may be mainly affected by temperature and humidity, while the high frequency band is more affected by air pressure and noise. Through this frequency band division, each frequency band can be subsequently analyzed separately to provide data support for the analysis of the main component characteristics of the frequency band.

[0040] Step S22: performing frequency band principal component characteristic analysis on the frequency band division coupled frequency response data, thereby obtaining frequency band principal component characteristic data; In this embodiment, the principal component analysis (PCA) technique is applied to the coupled frequency response data of each frequency band to extract the principal component characteristic data. In this process, the covariance matrix calculation and eigenvalue decomposition are performed for the response data of the low frequency band, the medium frequency band, and the high frequency band, respectively, to obtain the principal component characteristics of each frequency band. Through PCA analysis, the characteristic dimensions that have the greatest impact on the frequency response change can be identified. For example, the principal component of a certain frequency band may reveal that the frequency band is particularly sensitive to changes in humidity, while another frequency band may be more affected by temperature changes. The principal component characteristic data of each frequency band will provide core information for the subsequent calculation of the frequency response influencing factor.

[0041] Step S23: calculating the frequency band frequency response influence factor based on the frequency band principal component characteristic data, thereby obtaining the frequency band frequency response influence factor; In this embodiment, based on the frequency band principal component characteristic data, regression analysis or multivariate statistical methods are used to calculate the response influencing factors of each frequency band to environmental factors (such as temperature, humidity, air pressure, etc.). Taking temperature as an example, assuming that the principal component characteristic data of the low-frequency band shows the trend of frequency response changing with temperature, a linear regression model can be used to quantify the impact of temperature change on the frequency band response. Specifically, the environmental parameters can be set as independent variables, the principal component characteristics of the frequency band can be set as dependent variables, and the degree of influence of each environmental parameter on each frequency band can be calculated. For example, the calculated low-frequency temperature influence factor is 0.35, and the humidity influence factor is 0.22, which means that temperature changes have a greater impact on the frequency response of the low-frequency band. In this way, clear influencing factor data can be provided for subsequent quantification of environmental impacts.

[0042] Step S24: quantifying the environmental parameter impact according to the frequency band frequency response impact factor, thereby obtaining environmental parameter impact degree data; In this embodiment, the frequency response influencing factors of the frequency bands obtained by calculation are analyzed to quantify the influence of each environmental parameter on the frequency response of the microphone. For example, the relationship between the frequency response influencing factors of each frequency band and the environmental parameters is quantified through statistical methods (such as standard deviation analysis, regression model, etc.), and the influence of each environmental parameter on the frequency response is obtained. For example, if the humidity change has a greater impact factor on the low frequency band, then the influence of humidity on the low frequency band may reach 60%, while the influence of temperature is 40%. These influence data help to understand the priority of environmental factors and how to optimize the frequency response by adjusting parameters.

[0043] Step S25: Perform frequency response-environment parameter association on the environmental parameter influence data and the frequency band principal component characteristic data, so as to obtain a frequency response-environment association matrix, and construct a frequency characteristic matrix according to the frequency response-environment association matrix.

[0044] In this embodiment, based on the environmental parameter influence data and the frequency band principal component characteristic data, a frequency response-environment association matrix is ​​constructed through correlation analysis or weighted calculation. Each row represents the response of a frequency band, and each column represents an environmental parameter. Through this matrix, it is possible to clearly see how each frequency band is affected by different environmental factors. For example, the low frequency band is very sensitive to humidity and temperature changes, while the high frequency band is more affected by noise. A frequency characteristic matrix is ​​constructed based on the frequency response-environment association matrix, which integrates the relationship between the frequency band and the environmental parameters, and is convenient for subsequent frequency optimization, compensation and other processing. The response of each frequency band is weighted using the association matrix to obtain a frequency characteristic matrix. For example, for a certain frequency band, its frequency characteristics can be expressed as a weighted sum with factors such as ambient temperature and humidity. The specific implementation method can be completed by matrix multiplication.

[0045] Optionally, step S3 specifically includes: Step S31: Acquire environmental noise monitoring data, and perform data preprocessing on the environmental noise monitoring data, so as to obtain environmental noise monitoring data to be analyzed; In this embodiment, the noise signal in the environment is collected in real time by the noise monitoring device built into the microphone, and is usually output in the form of sound intensity, frequency, etc. The acquired data often contains multiple noise sources such as background noise, traffic noise, industrial noise, etc. In the preprocessing stage, these raw data need to be filtered first to remove high-frequency or low-frequency interference noise. Common methods include low-pass filtering, high-pass filtering, band-pass filtering, etc., and the selection of specific filters depends on the frequency range of the noise to be analyzed. For example, in an industrial environment, noise in the range of 50-5000 Hz may be of concern, so a band-pass filter is needed to filter out irrelevant frequency bands. Preprocessing may also include operations such as denoising, removing outliers, and supplementing missing data to ensure that the data to be analyzed is of high quality.

[0046] Step S32: performing Fourier transform of the environmental noise monitoring data to be analyzed, thereby obtaining an environmental noise signal spectrum, and performing frequency segment energy distribution statistics of the environmental noise signal spectrum, thereby obtaining a frequency segment energy distribution diagram of the noise signal; In this embodiment, the environmental noise signal is converted into frequency domain data by Fourier transform. Specifically, firstly, the processed time domain noise signal is subjected to fast Fourier transform (FFT) to convert it from the time domain to the frequency domain to obtain the energy distribution of the noise signal at each frequency. This process can use existing mathematical tools, such as numpy.fft in Python or fft function in MATLAB. The spectrum data obtained after Fourier transform contains the energy of the environmental noise signal in different frequency bands. Then, the energy proportion of each frequency band is counted to form a frequency band energy distribution diagram of the noise signal. For example, the spectrum diagram can show the energy distribution of low frequency (20 Hz to 500 Hz), medium frequency (500 Hz to 2000 Hz) and high frequency (2000 Hz to 10000 Hz) regions. In order to have a clear understanding of the frequency distribution of the noise signal, the spectrum diagram can be divided into several frequency bands and the energy value of each frequency band can be calculated. This graph can help analyze the contribution of specific frequency bands to noise and which frequency bands have a greater impact on the environment.

[0047] Step S33: integrating the frequency distribution of the noise signal according to the frequency segment energy distribution diagram of the noise signal, thereby obtaining a frequency distribution matrix of the noise signal; In this embodiment, by analyzing the energy distribution diagram of the noise signal frequency band, the energy distribution of the frequency band is integrated into a comprehensive noise signal frequency distribution matrix. The specific method is to distribute the energy of each frequency band to the corresponding position in the matrix. For example, the rows of the matrix represent different time periods or noise monitoring positions, and the columns represent different frequency bands. Each element in the matrix represents the noise energy of a certain frequency band at a certain moment (or a certain measurement point). In this way, a multi-dimensional noise frequency distribution matrix can be formed, which can not only reflect the changes in the time series, but also show the noise energy distribution at different positions or under different environmental conditions.

[0048] Step S34: performing time series mapping on the noise signal frequency distribution matrix and the frequency feature matrix to obtain an initial signal mapping matrix, and aligning the frequency axis features of the initial signal mapping matrix to obtain a noise signal mapping matrix; In this embodiment, the noise signal frequency distribution matrix and the frequency characteristic matrix are mapped in time series. Time series mapping refers to aligning the time series data of the noise frequency distribution matrix with the data of the frequency characteristic matrix in time. The purpose of this is to associate the noise data from different time periods with the corresponding frequency response characteristics. For example, it is assumed that the frequency characteristic matrix reflects the response of the microphone to the ambient noise in different frequency bands, and the noise signal frequency distribution matrix contains the noise energy distribution data of each frequency band at a certain moment. During mapping, the noise signal spectrum at each moment is mapped to the corresponding frequency band data of the frequency characteristic matrix to form a preliminary signal mapping matrix. Next, the frequency axis feature alignment is performed to ensure the alignment of the frequency response data and the noise data. Even if the frequency range of the noise spectrum does not completely overlap with the frequency band of the microphone frequency response, it can be aligned by interpolation, weighting or standardization to match them on the frequency axis.

[0049] Step S35: performing frequency band environmental noise calibration on the noise signal mapping matrix, thereby obtaining a noise calibration matrix.

[0050] In this embodiment, the noise signal mapping matrix will be calibrated so that the frequency response of the noise signal is consistent with the noise level in the real environment. The purpose of calibration is to adjust the values ​​in the noise signal mapping matrix so that it reflects the actual noise situation in the environment, rather than just the original measurement data. The calibration method may include using a known standard noise source to adjust the value of the mapping matrix. For example, in a laboratory, a white noise source of known intensity and frequency may be used to calibrate the measured noise data. By comparing with the standard noise source, each element in the noise signal mapping matrix is ​​corrected to make it closer to the actual noise level in the environment. The influence of factors such as the temperature and humidity of the environment may also be considered, and the noise signal may be further corrected by establishing a compensation model. The noise calibration matrix finally obtained can more accurately reflect the frequency characteristics of the environmental noise and provide a basis for subsequent noise suppression or optimization.

[0051] Optionally, step S35 is specifically: Step S351: dividing the noise signal mapping matrix into frequency bands, thereby obtaining a frequency band spectrum of the noise signal; In this embodiment, the frequency data in the noise signal mapping matrix first needs to be divided into frequency bands in order to better analyze the noise characteristics of different frequency bands. According to the frequency range of the ambient noise signal and the preset frequency band division strategy, the entire frequency range is divided into multiple sub-frequency bands. For example, the range of 20 Hz to 1000 Hz may be divided into a low frequency band (20 Hz-250 Hz), a medium-low frequency band (250 Hz-500 Hz), a medium frequency band (500 Hz-750 Hz) and a high frequency band (750 Hz-1000Hz). Each frequency band contains different frequency information, which can be used for a more detailed analysis of noise signals in different frequency bands. The frequency data of each frequency band is extracted from the noise signal mapping matrix, and the corresponding noise signal frequency band spectrum is generated. Each frequency band spectrum will reflect the noise intensity distribution in the frequency band. For spectrum analysis, the noise signal can be converted into the frequency domain using a fast Fourier transform (FFT), and then the energy distribution in each frequency band is statistically analyzed to obtain the frequency band spectrum. For complex environments, the frequency band division needs to be dynamically adjusted to cope with changes in different noise sources.

[0052] Step S352: performing time series correlation of environmental condition-frequency band noise signal according to the noise signal frequency distribution matrix and the frequency band spectrum of the noise signal, thereby obtaining environmental condition-frequency band noise signal change data; In this embodiment, the time series data in the frequency distribution matrix of the noise signal is combined with the frequency band spectrum, and the frequency characteristics of the noise signal are organized in chronological order. For example, assuming that the noise monitoring obtains spectrum data in different time periods, these spectrum data need to be matched and analyzed with the environmental conditions (such as temperature, humidity, wind speed, etc.) at that time. By calculating the changes in the noise frequency band at different time points and combining the environmental factors (such as temperature changes, wind speed, etc.) for time series correlation, the change trend of the noise signal under different environmental conditions can be revealed. The time series correlation process can use a sliding window analysis method to select the frequency band data within a certain time period, and perform correlation analysis in combination with the environmental conditions at that time, so as to obtain the environmental condition-frequency band noise signal change data. This data reflects the impact of environmental changes on noise signals of different frequency bands, as well as the changing characteristics of noise signals under different environmental conditions.

[0053] Step S353: calculating the environmental noise impact factor based on the environmental condition-frequency band noise signal change data, thereby obtaining the environmental noise impact factor; In this embodiment, the environmental noise impact factor is calculated based on the environmental condition-frequency band noise signal change data. The impact factor describes the degree of influence of environmental conditions on noise signals of different frequency bands. The relationship between environmental conditions (such as temperature, humidity, air pressure, etc.) and noise signal changes can be subjected to regression analysis or correlation analysis. Through statistical analysis, the influence coefficient of environmental factor changes on noise signal frequency band changes is calculated. For example, when the temperature rises, the low-frequency noise signal will increase due to the change in air propagation speed, and when the humidity is high, the absorption of medium and high frequency noise will be enhanced. In this case, a quantitative relationship model between environmental conditions and noise signal frequency bands can be established using methods such as multivariate linear regression models or support vector machines (SVM). Ultimately, these relationships are converted into an environmental noise impact factor, which represents the intensity of the impact of environmental factors on noise in a specific frequency band.

[0054] Step S354: calculating the frequency band calibration coefficient of the frequency band spectrum of the noise signal according to the environmental noise impact factor, thereby obtaining the frequency band calibration coefficient; In this embodiment, the frequency band spectrum of the noise signal is calibrated based on the environmental noise impact factor. According to the calculated environmental noise impact factor, the calibration factor of each frequency band is first determined. These factors indicate how the noise signal of each frequency band should be adjusted under different environmental conditions. For example, a low frequency band is strongly affected by temperature changes and needs to be multiplied by a larger calibration factor, while another high frequency band is less affected by humidity changes and has a relatively small calibration factor. The calibration coefficient can be calculated by weighted averaging, least squares method or regression analysis based on neural networks. The specific approach is: for each frequency band, the relationship between the environmental noise impact factor and the frequency band noise signal is used to estimate the calibration factor, and this process requires composite calculation of multiple environmental variables. For example, within the temperature change range, the calibration factor can represent the increment of the noise intensity of a certain frequency band when the temperature rises, and the change in humidity corresponds to another type of calibration factor.

[0055] Step S355: performing noise signal frequency band calibration on the noise signal mapping matrix according to the frequency band calibration coefficient, thereby obtaining a noise calibration matrix.

[0056] In this embodiment, the noise signal mapping matrix is ​​calibrated using the obtained frequency band calibration coefficient. Specifically, the noise signal of each frequency band is multiplied by the corresponding frequency band calibration coefficient, thereby correcting the amplitude of the noise signal to more accurately reflect the impact of environmental conditions on the noise signal. Assuming that the noise signal mapping matrix contains noise data at each time and frequency band, the noise value at each time point and frequency band can be adjusted by applying the calibration coefficient to the data of each frequency band. For example, if the calibration coefficient of a certain frequency band is 1.2, indicating that the noise signal strength of the frequency band should be increased by 20%, the corresponding value of the frequency band in the noise signal mapping matrix is ​​multiplied by 1.2. In this way, the influence of the noise signal under different environmental conditions is effectively compensated and corrected, and finally a noise calibration matrix is ​​obtained. This calibration matrix can be used for subsequent noise optimization, compensation and other processing to ensure the accurate expression and adjustment of environmental noise.

[0057] Optionally, step S4 is specifically: Step S41: performing feature comparison on the frequency feature matrix and the noise calibration matrix to obtain frequency response difference data, and integrating the noise impact factor according to the frequency response difference data to obtain the noise impact factor; In this embodiment, the data in the frequency characteristic matrix and the noise calibration matrix are compared, especially the characteristic value of each frequency band and the noise signal strength after calibration. The goal of the comparison is to identify the difference in frequency response, that is, to compare the changes in the noise signal in different frequency bands. Based on the frequency band spectrum, the difference data between the same frequency band in the frequency characteristic matrix and the noise calibration matrix is ​​calculated to form frequency response difference data. For example, if a certain frequency band shows a stronger response in the frequency characteristic matrix and a weaker response in the noise calibration matrix, then the difference data of the frequency band reflects the impact of the ambient noise on the frequency band. After obtaining the frequency response difference data, the noise impact factor is integrated based on these data. The noise impact factor is a quantitative description of how ambient noise affects the frequency response of the microphone device. It is usually necessary to perform weighted averaging or other statistical methods on the difference data of multiple frequency bands to generate a comprehensive noise impact factor. This factor can be used for subsequent noise compensation model construction.

[0058] Step S42: constructing an initial noise compensation model based on the frequency response difference data and the noise impact factor; In this embodiment, an initial noise compensation model is constructed based on the calculation results of the frequency response difference data and the noise impact factor. The goal of this step is to establish a mathematical model that can automatically adjust the response characteristics of the microphone according to environmental conditions and frequency band noise differences. Common models can use regression analysis, neural networks or machine learning algorithms. For example, first determine the key frequency bands in the frequency response difference data, and then weight these frequency bands according to the noise impact factor to construct a preliminary noise compensation strategy. Through experiments, it is found that the noise impact in the low frequency band is greater, while the high frequency band is relatively smaller. The nonlinear relationship between these frequency bands can be learned by constructing a polynomial regression model or a model based on deep learning, so as to effectively predict the impact of noise on the microphone response and compensate for it. The preliminary compensation model also needs to be verified and adjusted through experimental data.

[0059] Step S43: designing a compensation strategy through an initial noise compensation model in combination with microphone frequency response characteristic data, frequency band calibration coefficients, and environmental fusion data to be analyzed, thereby obtaining an initial adaptive dynamic compensation strategy; In this embodiment, a preliminary compensation strategy is designed by combining the initial noise compensation model with the frequency response characteristic data of the microphone, the frequency band calibration coefficient, and the environmental fusion data to be analyzed. The microphone frequency response characteristic data provides the response capability of the microphone in different frequency bands, while the frequency band calibration coefficient helps to adjust the signal strength of each frequency band. Environmental fusion data (such as temperature, humidity, etc.) provide dynamic adjustment conditions for the compensation strategy. Combined with these data, dynamic calculations are performed through the initial compensation model to obtain an adaptive compensation strategy. For example, in a high temperature or high humidity environment, the high-frequency response of the microphone will be suppressed, so the compensation strategy will enhance the gain of the high-frequency band. At low temperatures, the response of the low-frequency band is stronger, and the compensation strategy will reduce the low-frequency gain. Through this strategy, the microphone can optimize its frequency response under various environmental conditions.

[0060] Step S44: setting compensation parameters according to the coupling frequency response data to obtain a compensation parameter set, and performing Monte Carlo random sampling on the compensation parameter set to obtain a random compensation parameter set; In this embodiment, the compensation parameter set needs to be set according to the coupling frequency response data. The coupling frequency response data reflects the actual impact of environmental conditions on the frequency response of the microphone, including the interaction between the device and environmental factors. By analyzing these data, the initial compensation parameters in the compensation parameter set, such as gain, attenuation coefficient, etc., are determined. The Monte Carlo method is used to randomly sample the compensation parameter set. Monte Carlo sampling is a statistical method used to randomly extract a set of parameters in a given parameter space and evaluate their effects. For example, different combinations of gain values ​​and attenuation coefficients are used to simulate the response of the microphone in different environments. Through multiple random samplings, a series of potential compensation parameter combinations are generated, which represent the best possible compensation strategies under different conditions.

[0061] Step S45: performing signal compensation simulation on the random compensation parameter set according to the initial adaptive dynamic compensation strategy to obtain signal compensation simulation data, and performing compensation signal error evaluation on the signal compensation simulation data to obtain compensation signal error data; In this embodiment, according to the initial adaptive dynamic compensation strategy, a signal compensation simulation is performed on the random compensation parameter set. By applying different combinations of compensation parameters, the output signals of the microphone in different environments are simulated, and the compensated signals are calculated. Specifically, each set of compensation parameters is used to generate a noise signal in a simulated environment, and the noise signal is corrected by the compensation model. During the simulation process, an error will occur between the compensated signal and the real signal. To this end, it is necessary to evaluate the error of the compensated signal for each simulation, using an error analysis method such as mean square error (MSE) or signal distortion evaluation. By comparing the difference between the compensated signal and the ideal signal, the compensated signal error data is obtained. These data will be used for subsequent optimization of the compensation parameters.

[0062] Step S46: selecting a compensation parameter group for the random compensation parameter set based on the compensation signal error data, thereby obtaining an optimal compensation parameter group; In this embodiment, the best compensation parameter group is selected according to the compensation signal error data. By analyzing the compensation signal error data, the compensation parameter combination with the smallest error is identified, and the optimal parameter set is selected. A sorting and screening algorithm can be used to select the compensation parameter combination with the smallest error. For example, an error threshold can be set, and the compensation parameter combination below the threshold is considered to be the optimal compensation group. At the same time, an optimization algorithm, such as a genetic algorithm, particle swarm optimization (PSO), etc., is used to further screen the compensation parameter set to improve the compensation effect. Finally, the compensation parameter group that best suits the current environmental conditions is selected.

[0063] Step S47: Optimizing the compensation strategy multi-dimensional parameters of the initial adaptive dynamic compensation strategy according to the optimal compensation parameter group, thereby obtaining an optimized compensation strategy, and transmitting the optimized compensation strategy to the microphone device controller to execute the optimized compensation strategy.

[0064] In this embodiment, based on the optimal compensation parameter group, the initial adaptive dynamic compensation strategy is optimized in multiple dimensions. By further optimizing the compensation parameters, such as adjusting the gain, frequency range, etc., a more accurate compensation strategy is finally obtained. This optimized compensation strategy should adapt to various environmental changes and be able to be adjusted in real time. The optimized compensation strategy is finally transmitted to the microphone device controller for actual execution. In specific implementation, the compensation strategy can be transmitted to the controller via wireless communication or wired connection, and the compensation strategy can be applied in real time when the device is running to ensure that the microphone always maintains the best audio capture effect in different environments.

[0065] Optionally, step S5 specifically includes: Step S51: acquiring real-time microphone output signal data, and performing data preprocessing on the real-time microphone output signal data, thereby obtaining real-time microphone output signal data to be analyzed; In this embodiment, the microphone device will continuously capture the sound in the environment and convert it into an electrical signal. These signals need to be sampled, usually at a certain sampling rate (such as 44.1kHz or 48kHz). During the preprocessing process, the audio signal needs to be denoised, DC offset removed, gain adjusted, and other operations. Specifically, a low-pass filter can be applied to remove high-frequency noise, a mean filter can be used to remove larger mutation signals, and even a time domain or frequency domain denoising algorithm (such as Wiener filtering) can be applied to improve the quality of the signal and remove unnecessary background noise. The preprocessed signal can be used for subsequent frequency analysis and compensation.

[0066] Step S52: extracting audio signal frequency features from the real-time microphone output signal data to be analyzed, thereby obtaining a real-time audio signal spectrum; In this embodiment, frequency feature extraction is performed on the processed microphone output signal. The audio signal in the time domain is converted to the frequency domain by fast Fourier transform (FFT). In specific implementation, the preprocessed signal is first divided into multiple short-time frames, each frame signal is usually tens of milliseconds to hundreds of milliseconds long, and a window function (such as a Hamming window) is applied to reduce spectrum leakage. Then, FFT is applied to each frame to extract the frequency components of each frequency band to obtain spectrum data. For example, the FFT result of a certain frame may show the amplitude of frequency components such as 1kHz, 2kHz and 4kHz. Through these spectrum data, the frequency distribution of the current microphone signal can be evaluated, providing a basis for subsequent compensation and optimization.

[0067] Step S53: performing compensation strategy backtracking on the real-time audio signal spectrum according to the optimized compensation strategy, thereby obtaining the real-time uncompensated audio signal spectrum; In this embodiment, the original spectrum of the real-time audio signal is evaluated before the optimization compensation strategy is retroactively applied. The optimization compensation strategy usually compensates by means of gain adjustment, attenuation, frequency correction, etc. of a specific frequency band. In order to evaluate the effect of the compensation, it is necessary to trace back to the original uncompensated audio signal spectrum. By comparing the real-time audio signal spectrum, the state of the audio signal before compensation is identified. For example, if the goal of the optimization compensation strategy is to improve the low-frequency band response, the retrospective process will show that the low-frequency band gain is insufficient before compensation. This retrospective step provides a comparison basis for subsequent effect evaluation.

[0068] Step S54: performing compensation effect evaluation based on the real-time audio signal spectrum and the real-time uncompensated audio signal spectrum to obtain audio compensation effect evaluation data, and weighting the evaluation results of the audio compensation effect evaluation data to obtain real-time audio compensation score data; In this embodiment, the effect of the compensation strategy is evaluated by comparing the difference between the real-time audio signal spectrum and the uncompensated spectrum. Specifically, indicators such as frequency response error, signal enhancement, and signal distortion are used for quantification. For example, if the compensation strategy is effective, the gain of the low-frequency and mid-frequency bands should be improved, and the spectrum should present a closer to ideal form. The evaluation data can be obtained by calculating the mean square error (MSE) or signal enhancement ratio between the real-time spectrum and the uncompensated spectrum. In addition, the evaluation results of different frequency bands can be weighted according to the importance or goals of different frequency bands. For example, for speech signals, certain key frequency bands in the frequency range (such as mid-frequency bands) may be given higher weights, and finally comprehensive real-time audio compensation score data is obtained.

[0069] Step S55: classifying the real-time audio compensation score data according to a preset score threshold, and if the real-time audio compensation score data is greater than or equal to the score threshold, sending an instruction to the microphone device controller to continue to execute the optimization compensation strategy; if the real-time audio compensation score data is less than the score threshold, marking the corresponding optimization compensation strategy as an abnormal compensation strategy, and sending an instruction to the microphone device controller to stop executing the optimization compensation strategy; In this embodiment, by comparing the real-time audio compensation scoring data with the preset scoring threshold, it is determined whether to continue to execute the current optimization compensation strategy. If the real-time compensation scoring data is greater than or equal to the scoring threshold, indicating that the current compensation effect has reached the expected level, the compensation strategy will continue to be maintained or optimized. For example, if the scoring threshold is set to 80 points, and the real-time audio compensation scoring data is 85 points, it means that the compensation strategy is effective, and the controller can continue to execute the optimization compensation strategy. On the contrary, if the compensation scoring data is lower than the scoring threshold, it means that the compensation strategy has not achieved the expected effect, which may cause the audio quality to deteriorate. The strategy will be marked as abnormal, and an instruction to stop executing the strategy will be sent to the microphone controller. In this way, it is ensured that only effective compensation strategies are applied to avoid invalid or erroneous compensation strategies affecting microphone performance.

[0070] Step S56: adjusting the abnormal compensation strategy in real time according to the real-time audio compensation score data, thereby obtaining dynamic compensation feedback data.

[0071] In this embodiment, if an abnormal compensation strategy is detected, the strategy is adjusted to improve the compensation effect by analyzing the real-time audio compensation score data. For example, if the score data is too low, it may be that the compensation gain of a certain frequency band is too strong or too weak, resulting in audio distortion or noise enhancement. At this time, the strategy can be corrected by adjusting the frequency band gain, attenuation or other compensation parameters. During the dynamic adjustment process, it is necessary to analyze the frequency characteristics of the microphone response and the current environmental factors (such as temperature and humidity changes, etc.) in real time, and adjust the compensation parameters according to the feedback. The adjusted strategy includes strengthening the compensation of certain frequency bands, reducing the gain of other frequency bands, or adjusting the bandwidth of the filter. These adjustments will be fed back to the compensation strategy in real time, and through continuous optimization, the quality of the audio signal is ensured to be continuously improved.

[0072] Optionally, step S6 specifically includes: Step S61: performing principal component feature selection on the dynamic compensation feedback data to obtain dynamic compensation feature data; In this embodiment, the dynamic compensation feedback data is subjected to principal component analysis (PCA) for feature selection. Principal component analysis is a commonly used dimensionality reduction technique that aims to extract the most representative features from high-dimensional data. In actual operation, the dynamic compensation feedback data contains information such as compensation errors and noise signal changes in multiple frequency bands, and these data have high dimensions. Through PCA, the main change directions in the data can be identified, and these principal components can be selected as new feature representations. For example, assuming that the dynamic compensation feedback data contains signal characteristics of 10 frequency bands, through PCA analysis, 3 main components may be extracted, representing the core information of signal changes. These principal components will become the basis for subsequent modeling and analysis, helping to reduce computational complexity and improve model efficiency.

[0073] Step S62: Building a neural network global compensation model according to the dynamic compensation feature data, thereby obtaining a microphone global compensation model; In this embodiment, a neural network model is trained using dynamic compensation feature data to establish a global compensation model. First, an appropriate neural network architecture is selected, such as a multi-layer perceptron (MLP) or a convolutional neural network (CNN). The input of the network is the extracted dynamic compensation feature data, and the output is the corresponding compensation parameter or frequency band correction value. The training data set includes different audio environments and compensation feedback data. After multiple trainings, the neural network learns how to output effective compensation parameters based on the input dynamic compensation feature data. For example, it is assumed that through training, the network can learn how to adjust the gain or attenuation of the low frequency band under specific environmental noise, thereby optimizing the sound quality of the microphone. After the model training is completed, a global compensation model for compensating the microphone response is obtained.

[0074] Step S63: acquiring user microphone interaction data, and performing user audio preference analysis on the user microphone interaction data, thereby obtaining user audio preference data; In this embodiment, the user's audio preferences are obtained and understood by analyzing the interaction between the user and the microphone. User interaction data can be obtained through the microphone controller, which may include operation information such as volume adjustment, frequency response adjustment, and gain setting. For example, the user may prefer to increase the gain in the low frequency band, or improve the voice clarity in a specific noise environment. After collecting these data, the user's specific requirements for audio characteristics are obtained by analyzing the user's preference patterns in different environments. For example, if the user frequently adjusts the volume control to a certain value, or modifies the high frequency band response multiple times, the analysis tool can identify that the user prefers clear, high-frequency sound. These data will serve as input for the design of personalized compensation strategies.

[0075] Step S64: selecting a user personalized compensation strategy for the user audio preference data through the microphone global compensation model, thereby obtaining a user microphone personalized compensation strategy, and transmitting it to the microphone device controller to execute the user microphone personalized compensation strategy.

[0076] In this embodiment, a personalized compensation strategy is generated in combination with a global compensation model based on the obtained user audio preference data. The global compensation model has been trained based on different dynamic compensation features and contains general compensation rules. On this basis, the compensation model is personalized using the user's audio preference data. For example, assuming that the user prefers a strong low-frequency response and clear high frequencies, the model can moderately increase the gain of the low-frequency band according to these preferences and optimize the clarity of the high frequencies. By adjusting the parameters of the global compensation model, a compensation strategy that meets the user's personalized needs is obtained. Ultimately, this compensation strategy is transmitted to the microphone device controller, which adjusts the frequency response of the microphone according to the strategy to ensure that the user's audio experience is optimal. For example, if the user prefers voice clarity, the microphone's frequency response from 1kHz to 5kHz can be enhanced to suppress unnecessary background noise, thereby improving the intelligibility and clarity of the voice.

[0077] Optionally, the present specification also provides a system for compensating the frequency response of a microphone, which is used to execute the method for compensating the frequency response of a microphone as described above. The system for compensating the frequency response of a microphone includes: A frequency response coupling module is used to obtain microphone output signal data and multi-dimensional environmental data, and perform frequency response curve conversion on the microphone output signal data to obtain a microphone frequency response curve; perform multi-level feature integration of frequency response according to the microphone frequency response curve and multi-dimensional environmental data to obtain coupled frequency response data; A principal component feature screening module is used to screen the frequency band frequency principal component features based on the coupled frequency response data, thereby obtaining the frequency band frequency principal component feature data, and to perform frequency response-environmental parameter correlation analysis based on the frequency band frequency principal component feature data, thereby obtaining a frequency feature matrix; The environmental noise calibration module is used to obtain environmental noise monitoring data and map the environmental noise monitoring data to a frequency characteristic matrix to obtain a noise signal mapping matrix; perform frequency band environmental noise calibration on the noise signal mapping matrix to obtain a noise calibration matrix; A compensation strategy generation module is used to perform adaptive dynamic compensation strategy analysis based on a frequency characteristic matrix and a noise calibration matrix to obtain an initial adaptive dynamic compensation strategy; perform Monte Carlo multi-dimensional parameter optimization on the initial adaptive dynamic compensation strategy according to coupled frequency response data to obtain an optimized compensation strategy, and transmit the optimized compensation strategy to a microphone device controller to execute the optimized compensation strategy; A compensation strategy feedback module is used to obtain real-time microphone output signal data, and evaluate the compensation effect of the real-time microphone output signal data and the optimized compensation strategy, so as to obtain real-time audio compensation score data; based on the real-time audio compensation score data, dynamic compensation feedback is performed on the optimized compensation strategy, so as to obtain dynamic compensation feedback data; The personalized compensation strategy selection module is used to model a global compensation model based on dynamic compensation feedback data to obtain a microphone global compensation model; select a user personalized compensation strategy based on the microphone global compensation model to obtain a user microphone personalized compensation strategy, and transmit it to the microphone device controller to execute the user microphone personalized compensation strategy.

[0078] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0079] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for compensating the frequency response of a microphone, characterized in that The following steps are involved: Step S1: Acquire microphone output signal data and multi-dimensional environmental data, and perform frequency response curve conversion on the microphone output signal data to obtain a microphone frequency response curve; perform multi-level frequency response feature integration based on the microphone frequency response curve and the multi-dimensional environmental data to obtain coupled frequency response data; Step S2: performing frequency band frequency principal component feature screening based on the coupled frequency response data, thereby obtaining frequency band frequency principal component feature data, and performing frequency response-environmental parameter correlation analysis based on the frequency band frequency principal component feature data, thereby obtaining a frequency feature matrix; Step S3: Acquire environmental noise monitoring data, and map the environmental noise monitoring data to a frequency characteristic matrix, thereby obtaining a noise signal mapping matrix; Perform frequency band environmental noise calibration on the noise signal mapping matrix to obtain a noise calibration matrix; Step S4: performing adaptive dynamic compensation strategy analysis based on the frequency characteristic matrix and the noise calibration matrix, thereby obtaining an initial adaptive dynamic compensation strategy; Performing Monte Carlo multi-dimensional parameter optimization on the initial adaptive dynamic compensation strategy according to the coupling frequency response data to obtain an optimized compensation strategy, and transmitting the optimized compensation strategy to the microphone device controller to execute the optimized compensation strategy; Step S5: acquiring real-time microphone output signal data, and evaluating the compensation effect of the real-time microphone output signal data and the optimized compensation strategy, thereby obtaining real-time audio compensation score data; performing dynamic compensation feedback on the optimized compensation strategy based on the real-time audio compensation score data, thereby obtaining dynamic compensation feedback data; Step S6: building a global compensation model according to the dynamic compensation feedback data, thereby obtaining a microphone global compensation model; A user personalized compensation strategy is selected based on the microphone global compensation model to obtain the user microphone personalized compensation strategy, and the strategy is transmitted to the microphone device controller to execute the user microphone personalized compensation strategy.

2. The method for compensating the frequency response of a microphone according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: Acquire microphone output signal data and multi-dimensional environmental data, and perform data preprocessing on the microphone output signal data and the multi-dimensional environmental data respectively, so as to obtain microphone output signal data to be analyzed and multi-dimensional environmental data to be analyzed; Step S12: performing Fourier transform on the microphone output signal data to be analyzed to obtain the microphone output signal spectrum, and performing frequency domain data standardization on the microphone output signal spectrum to obtain the microphone output signal standardized spectrum; Step S13: integrating the frequency response characteristics of the standardized frequency spectrum of the microphone output signal to obtain microphone frequency response characteristic data, and performing frequency response curve conversion based on the microphone frequency response characteristic data to obtain a microphone frequency response curve; Step S14: performing multi-dimensional environmental data fusion on the multi-dimensional environmental data to be analyzed, thereby obtaining environmental fusion data to be analyzed; Step S15: performing multi-level feature integration of frequency response according to the microphone frequency response curve and the environmental fusion data to be analyzed, thereby obtaining coupled frequency response data.

3. The method for compensating the frequency response of a microphone according to claim 2, characterized in that: Step S15 is specifically as follows: Step S151: performing frequency feature alignment on the microphone frequency response curve and the environment fusion data to be analyzed, so as to obtain a frequency-aligned response curve and frequency-aligned environment fusion data; Step S152: performing correlation analysis on the frequency alignment response curve and the frequency alignment environment fusion data to obtain microphone frequency-environment correlation data, and performing feature selection on the frequency alignment environment fusion data according to the microphone frequency-environment correlation data to obtain frequency-correlated environment data; Step S153: performing multi-dimensional feature vector splicing on the frequency alignment response curve and the frequency-related environmental data, so as to obtain frequency response-environmental feature composite data; Step S154: performing environmental impact multi-frequency response hierarchical analysis based on the frequency response-environmental feature composite data, thereby obtaining frequency response hierarchical environmental correlation data; Step S155: performing frequency response hierarchical relationship mapping coupling on the frequency response-environmental feature composite data based on the frequency response hierarchical environment association data, thereby obtaining coupled frequency response data.

4. The method for compensating the frequency response of a microphone according to claim 1, characterized in that: Step S2 is specifically as follows: Step S21: dividing the coupling frequency response data into frequency bands, thereby obtaining frequency band-divided coupling frequency response data; Step S22: performing frequency band principal component characteristic analysis on the frequency band division coupled frequency response data, thereby obtaining frequency band principal component characteristic data; Step S23: calculating the frequency band frequency response influence factor based on the frequency band principal component characteristic data, thereby obtaining the frequency band frequency response influence factor; Step S24: quantifying the environmental parameter impact according to the frequency band frequency response impact factor, thereby obtaining environmental parameter impact degree data; Step S25: Perform frequency response-environment parameter association on the environmental parameter influence data and the frequency band principal component characteristic data, so as to obtain a frequency response-environment association matrix, and construct a frequency characteristic matrix according to the frequency response-environment association matrix.

5. The method for compensating the frequency response of a microphone according to claim 1, characterized in that: Step S3 is specifically as follows: Step S31: Acquire environmental noise monitoring data, and perform data preprocessing on the environmental noise monitoring data, so as to obtain environmental noise monitoring data to be analyzed; Step S32: performing Fourier transform of the environmental noise monitoring data to be analyzed, thereby obtaining an environmental noise signal spectrum, and performing frequency segment energy distribution statistics of the environmental noise signal spectrum, thereby obtaining a frequency segment energy distribution diagram of the noise signal; Step S33: integrating the frequency distribution of the noise signal according to the frequency segment energy distribution diagram of the noise signal, thereby obtaining a frequency distribution matrix of the noise signal; Step S34: performing time series mapping on the noise signal frequency distribution matrix and the frequency feature matrix to obtain an initial signal mapping matrix, and aligning the frequency axis features of the initial signal mapping matrix to obtain a noise signal mapping matrix; Step S35: performing frequency band environmental noise calibration on the noise signal mapping matrix, thereby obtaining a noise calibration matrix.

6. The method for compensating the frequency response of a microphone according to claim 5, characterized in that: Step S35 is specifically as follows: Step S351: dividing the noise signal mapping matrix into frequency bands, thereby obtaining a frequency band spectrum of the noise signal; Step S352: performing time series correlation of environmental condition-frequency band noise signal according to the noise signal frequency distribution matrix and the frequency band spectrum of the noise signal, thereby obtaining environmental condition-frequency band noise signal change data; Step S353: calculating the environmental noise impact factor based on the environmental condition-frequency band noise signal change data, thereby obtaining the environmental noise impact factor; Step S354: calculating the frequency band calibration coefficient of the frequency band spectrum of the noise signal according to the environmental noise impact factor, thereby obtaining the frequency band calibration coefficient; Step S355: performing noise signal frequency band calibration on the noise signal mapping matrix according to the frequency band calibration coefficient, thereby obtaining a noise calibration matrix.

7. The method for compensating the frequency response of a microphone according to claim 1, characterized in that: Step S4 is specifically as follows: Step S41: performing feature comparison on the frequency feature matrix and the noise calibration matrix to obtain frequency response difference data, and integrating the noise impact factor according to the frequency response difference data to obtain the noise impact factor; Step S42: constructing an initial noise compensation model based on the frequency response difference data and the noise impact factor; Step S43: designing a compensation strategy through an initial noise compensation model in combination with microphone frequency response characteristic data, frequency band calibration coefficients, and environmental fusion data to be analyzed, thereby obtaining an initial adaptive dynamic compensation strategy; Step S44: setting compensation parameters according to the coupling frequency response data to obtain a compensation parameter set, and performing Monte Carlo random sampling on the compensation parameter set to obtain a random compensation parameter set; Step S45: performing signal compensation simulation on the random compensation parameter set according to the initial adaptive dynamic compensation strategy to obtain signal compensation simulation data, and performing compensation signal error evaluation on the signal compensation simulation data to obtain compensation signal error data; Step S46: selecting a compensation parameter group for the random compensation parameter set based on the compensation signal error data, thereby obtaining an optimal compensation parameter group; Step S47: Optimizing the compensation strategy multi-dimensional parameters of the initial adaptive dynamic compensation strategy according to the optimal compensation parameter group, thereby obtaining an optimized compensation strategy, and transmitting the optimized compensation strategy to the microphone device controller to execute the optimized compensation strategy.

8. The method for compensating the frequency response of a microphone according to claim 1, characterized in that: Step S5 is specifically as follows: Step S51: acquiring real-time microphone output signal data, and performing data preprocessing on the real-time microphone output signal data, thereby obtaining real-time microphone output signal data to be analyzed; Step S52: extracting audio signal frequency features from the real-time microphone output signal data to be analyzed, thereby obtaining a real-time audio signal spectrum; Step S53: performing compensation strategy backtracking on the real-time audio signal spectrum according to the optimized compensation strategy, thereby obtaining the real-time uncompensated audio signal spectrum; Step S54: performing compensation effect evaluation based on the real-time audio signal spectrum and the real-time uncompensated audio signal spectrum to obtain audio compensation effect evaluation data, and weighting the evaluation results of the audio compensation effect evaluation data to obtain real-time audio compensation score data; Step S55: classifying the real-time audio compensation score data according to a preset score threshold, and if the real-time audio compensation score data is greater than or equal to the score threshold, sending an instruction to the microphone device controller to continue to execute the optimization compensation strategy; if the real-time audio compensation score data is less than the score threshold, marking the corresponding optimization compensation strategy as an abnormal compensation strategy, and sending an instruction to the microphone device controller to stop executing the optimization compensation strategy; Step S56: adjusting the abnormal compensation strategy in real time according to the real-time audio compensation score data, thereby obtaining dynamic compensation feedback data.

9. The method for compensating the frequency response of a microphone according to claim 1, characterized in that: Step S6 is specifically as follows: Step S61: performing principal component feature selection on the dynamic compensation feedback data to obtain dynamic compensation feature data; Step S62: Building a neural network global compensation model according to the dynamic compensation feature data, thereby obtaining a microphone global compensation model; Step S63: acquiring user microphone interaction data, and performing user audio preference analysis on the user microphone interaction data, thereby obtaining user audio preference data; Step S64: selecting a user personalized compensation strategy for the user audio preference data through the microphone global compensation model, thereby obtaining a user microphone personalized compensation strategy, and transmitting it to the microphone device controller to execute the user microphone personalized compensation strategy.

10. A system for compensating the frequency response of a microphone, characterized in that For executing the method for compensating the frequency response of a microphone as claimed in claim 1, the system for compensating the frequency response of a microphone comprises: A frequency response coupling module is used to obtain microphone output signal data and multi-dimensional environmental data, and perform frequency response curve conversion on the microphone output signal data to obtain a microphone frequency response curve; perform multi-level feature integration of frequency response according to the microphone frequency response curve and multi-dimensional environmental data to obtain coupled frequency response data; A principal component feature screening module is used to screen the frequency band frequency principal component features based on the coupled frequency response data, thereby obtaining the frequency band frequency principal component feature data, and to perform frequency response-environmental parameter correlation analysis based on the frequency band frequency principal component feature data, thereby obtaining a frequency feature matrix; The environmental noise calibration module is used to obtain environmental noise monitoring data and map the environmental noise monitoring data to a frequency characteristic matrix to obtain a noise signal mapping matrix; perform frequency band environmental noise calibration on the noise signal mapping matrix to obtain a noise calibration matrix; A compensation strategy generation module is used to perform adaptive dynamic compensation strategy analysis based on a frequency characteristic matrix and a noise calibration matrix to obtain an initial adaptive dynamic compensation strategy; perform Monte Carlo multi-dimensional parameter optimization on the initial adaptive dynamic compensation strategy according to coupled frequency response data to obtain an optimized compensation strategy, and transmit the optimized compensation strategy to a microphone device controller to execute the optimized compensation strategy; A compensation strategy feedback module is used to obtain real-time microphone output signal data, and evaluate the compensation effect of the real-time microphone output signal data and the optimized compensation strategy, so as to obtain real-time audio compensation score data; based on the real-time audio compensation score data, dynamic compensation feedback is performed on the optimized compensation strategy, so as to obtain dynamic compensation feedback data; The personalized compensation strategy selection module is used to model a global compensation model based on dynamic compensation feedback data to obtain a microphone global compensation model; select a user personalized compensation strategy based on the microphone global compensation model to obtain a user microphone personalized compensation strategy, and transmit it to the microphone device controller to execute the user microphone personalized compensation strategy.

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